Merge pull request #281 from MervinPraison/develop

Update praisonaiagents to version 0.0.31 and enhance agent functionality

Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.31 gunicorn markdown
+RUN pip install flask praisonai==2.0.39 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

agents/.cursorrules

@@ -0,0 +1,8 @@
+1. Make minimal code changes as possible
+2. if you are creating function, make sure to name it in more easy, user friendly , optimised manner. 
+3. Never copy the exact code if presented with a code from other location or file or codebase, just make it optimised
+4. Always Follow the principle LESS IS MORE
+5. Make it minimal change as possible
+6. Firstly try not to make any modification to the existing code as possible
+7. Only modify the existing code if its highly required, without that if it cant be done, then add new code section. 
+8. If you are adding new code, make sure to add it in a way that it can be easily integrated with the existing codebase. 

agents/.gitignore

@@ -83,4 +83,5 @@ dist
 build
 *.mp4 
 *.png 
-graph.py
+graph.py
+chroma_db/

agents/.praisonignore

@@ -0,0 +1,32 @@
+.cursorrules
+.gitignore
+.praisoncontext
+.praisonignore
+.windsurfrules
+async_example.py
+async_example_full.py
+async_example_full_multigroups.py
+auto_agents_example.py
+code_agents_example.py
+example_callback.py
+example_custom_tools.py
+example_sequential.py
+image.jpg
+langchain_example.py
+langchain_logs.py
+long_term.db
+mini_agents_example.py
+multimodal.py
+praisonaiagents/tools/*
+praisonaiagents/process/*
+pyproject.toml
+short_term.db
+structured_agents_example.py
+structured_response_example.py
+test.py
+tools_example.py
+uv.lock
+workflow_example_basic.py
+workflow_example_detailed.py
+__pycache__
+__pycache__/*

agents/.windsurfrules

@@ -0,0 +1,8 @@
+1. Make minimal code changes as possible
+2. if you are creating function, make sure to name it in more easy, user friendly , optimised manner. 
+3. Never copy the exact code if presented with a code from other location or file or codebase, just make it optimised
+4. Always Follow the principle LESS IS MORE
+5. Make it minimal change as possible
+6. Firstly try not to make any modification to the existing code as possible
+7. Only modify the existing code if its highly required, without that if it cant be done, then add new code section. 
+8. If you are adding new code, make sure to add it in a way that it can be easily integrated with the existing codebase. 

agents/auto_agents_example.py

@@ -0,0 +1,11 @@
+from praisonaiagents import AutoAgents
+from praisonaiagents.tools import duckduckgo
+
+agents = AutoAgents(
+    instructions="Search for information about AI Agents",
+    tools=[duckduckgo],
+    process="sequential",
+    verbose=True
+)
+
+agents.start()

agents/code_agents_example.py

@@ -0,0 +1,50 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import json
+from e2b_code_interpreter import Sandbox
+
+def code_interpreter(code: str):
+    """
+    A function to demonstrate running Python code dynamically using e2b_code_interpreter.
+    """
+    print(f"\n{'='*50}\n> Running following AI-generated code:\n{code}\n{'='*50}")
+    exec_result = Sandbox().run_code(code)
+    if exec_result.error:
+        print("[Code Interpreter error]", exec_result.error)
+        return {"error": str(exec_result.error)}
+    else:
+        results = []
+        for result in exec_result.results:
+            if hasattr(result, '__iter__'):
+                results.extend(list(result))
+            else:
+                results.append(str(result))
+        logs = {"stdout": list(exec_result.logs.stdout), "stderr": list(exec_result.logs.stderr)}
+        return json.dumps({"results": results, "logs": logs})
+
+code_agent = Agent(
+    name="code_agent",
+    llm="gpt-4o-mini",
+    backstory="Expert in writing Python scripts",
+    self_reflect=False
+)
+execution_agent = Agent(
+    name="execution_agent",
+    llm="gpt-4o-mini",
+    backstory="Expert in executing Python scripts",
+    self_reflect=False,
+    tools=[code_interpreter]
+)
+
+code_agent_task = Task(
+    description="Write a simple Python script to print 'Hello, World!'",
+    expected_output="A Python script that prints 'Hello, World!'",
+    agent=code_agent
+)
+execution_agent_task = Task(
+    description="Execute the Python script",
+    expected_output="The output of the Python script",
+    agent=execution_agent
+)
+
+agents = PraisonAIAgents(agents=[code_agent, execution_agent], tasks=[code_agent_task, execution_agent_task])
+agents.start()

agents/example2.py

@@ -1,74 +0,0 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents
-from typing import List, Dict
-from duckduckgo_search import DDGS
-
-# 1. Tool
-def internet_search_tool(query: str) -> List[Dict]:
-    """
-    Perform a search using DuckDuckGo.
-
-    Args:
-        query (str): The search query.
-
-    Returns:
-        list: A list of search result titles, URLs, and snippets.
-    """
-    try:
-        results = []
-        ddgs = DDGS()
-        for result in ddgs.text(keywords=query, max_results=10):
-            results.append({
-                "title": result.get("title", ""),
-                "url": result.get("href", ""),
-                "snippet": result.get("body", "")
-            })
-        return results
-
-    except Exception as e:
-        print(f"Error during DuckDuckGo search: {e}")
-        return []
-    
-# 2. Agent
-data_agent = Agent(
-    name="DataCollector",
-    role="Search Specialist",
-    goal="Perform internet searches to collect relevant information.",
-    backstory="Expert in finding and organising internet data.",
-    tools=[internet_search_tool],
-    self_reflect=False
-)
-
-# 3. Tasks
-collect_task = Task(
-    description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
-    expected_output="List of search results with titles, URLs, and snippets.",
-    agent=data_agent,
-    name="collect_data",
-    is_start=True,
-    next_tasks=["validate_data"]
-)
-
-validate_task = Task(
-    description="""Validate the collected data. Check if:
-    1. At least 5 results are returned.
-    2. Each result contains a title and a URL.
-    Return validation_result as 'valid' or 'invalid' only no other text.""",
-    expected_output="Validation result indicating if data is valid or invalid.",
-    agent=data_agent,
-    name="validate_data",
-    task_type="decision",
-    condition={
-        "valid": [],  # End the workflow on valid data
-        "invalid": ["collect_data"]  # Retry data collection on invalid data
-    },
-)
-
-# 4. Workflow
-agents = PraisonAIAgents(
-    agents=[data_agent],
-    tasks=[collect_task, validate_task],
-    verbose=1,
-    process="workflow"
-)
-
-agents.start()

agents/example_custom_tools.py

@@ -0,0 +1,151 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from typing import List, Dict, Union
+from duckduckgo_search import DDGS
+from langchain_community.tools import YouTubeSearchTool
+from langchain_community.utilities import WikipediaAPIWrapper
+
+# 1. Tool
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform a search using DuckDuckGo.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        list: A list of search result titles, URLs, and snippets.
+    """
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=10):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+
+    except Exception as e:
+        print(f"Error during DuckDuckGo search: {e}")
+        return []
+    
+def youtube_search_tool(query: str, inspect: bool = False, max_results: int = 2):
+    """
+    Provide a custom wrapper around the YouTubeSearchTool.
+    
+    Args:
+        query (str): The search query for YouTube.
+        inspect (bool): If True, returns tool inspection info instead of search results.
+        max_results (int): Maximum number of results to return (default: 2).
+    Returns:
+        Union[List[str], dict]: List of YouTube video URLs or tool inspection info.
+    """
+    yt = YouTubeSearchTool()
+    
+    if inspect:
+        inspection_info = {
+            "type": type(yt),
+            "attributes": [attr for attr in dir(yt) if not attr.startswith('_')],
+            "methods": {
+                "run": getattr(yt, 'run', None),
+                "arun": getattr(yt, 'arun', None)
+            },
+            "properties": {
+                "name": getattr(yt, 'name', 'youtube_search'),
+                "description": getattr(yt, 'description', 'Search YouTube videos'),
+                "return_direct": getattr(yt, 'return_direct', False)
+            }
+        }
+        return inspection_info
+    
+    # Format query with max_results
+    formatted_query = f"{query}, {max_results}"
+    return yt.run(formatted_query)
+
+def wikipedia_search_tool(query: str, inspect: bool = False, max_chars: int = 4000, top_k: int = 3):
+    """
+    Provide a custom wrapper around langchain_community's WikipediaAPIWrapper.
+
+    Args:
+        query (str): A search query for Wikipedia.
+        inspect (bool): If True, returns tool inspection info instead of search results.
+        max_chars (int): Maximum characters to return (default: 4000).
+        top_k (int): Number of top results to consider (default: 3).
+    Returns:
+        Union[str, dict]: Summary from Wikipedia or tool inspection info if inspect=True.
+    """
+    w = WikipediaAPIWrapper(
+        top_k_results=top_k,
+        doc_content_chars_max=max_chars,
+        lang='en'
+    )
+    
+    if inspect:
+        inspection_info = {
+            "type": type(w),
+            "attributes": [attr for attr in dir(w) if not attr.startswith('_')],
+            "methods": {
+                "run": getattr(w, 'run', None),
+                "arun": getattr(w, 'arun', None)
+            },
+            "properties": {
+                "name": "wikipedia",
+                "description": "Search and get summaries from Wikipedia",
+                "top_k": w.top_k_results,
+                "lang": w.lang,
+                "max_chars": w.doc_content_chars_max
+            }
+        }
+        return inspection_info
+    
+    try:
+        result = w.run(query)
+        return result
+    except Exception as e:
+        return f"Error searching Wikipedia: {str(e)}"
+
+# 2. Agent
+data_agent = Agent(
+    name="DataCollector",
+    role="Search Specialist",
+    goal="Perform internet searches to collect relevant information.",
+    backstory="Expert in finding and organizing internet data from multiple sources.",
+    tools=[internet_search_tool, youtube_search_tool, wikipedia_search_tool],
+    self_reflect=False
+)
+
+# 3. Tasks
+collect_task = Task(
+    description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
+    expected_output="List of search results with titles, URLs, and snippets.",
+    agent=data_agent,
+    name="collect_data",
+    is_start=True,
+    next_tasks=["validate_data"]
+)
+
+validate_task = Task(
+    description="""Validate the collected data. Check if:
+    1. At least 5 results are returned.
+    2. Each result contains a title and a URL.
+    Return validation_result as 'valid' or 'invalid' only no other text.""",
+    expected_output="Validation result indicating if data is valid or invalid.",
+    agent=data_agent,
+    name="validate_data",
+    task_type="decision",
+    condition={
+        "valid": [],  # End the workflow on valid data
+        "invalid": ["collect_data"]  # Retry data collection on invalid data
+    },
+)
+
+# 4. Workflow
+agents = PraisonAIAgents(
+    agents=[data_agent],
+    tasks=[collect_task, validate_task],
+    verbose=1,
+    process="workflow"
+)
+
+agents.start()

agents/langchain_example.py

@@ -0,0 +1,30 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from langchain_community.tools import YouTubeSearchTool
+from langchain_community.utilities import WikipediaAPIWrapper
+
+# Create an agent with both tools
+agent = Agent(
+    name="SearchAgent",
+    role="Research Assistant",
+    goal="Search for information from multiple sources",
+    backstory="I am an AI assistant that can search YouTube and Wikipedia.",
+    tools=[YouTubeSearchTool, WikipediaAPIWrapper],
+    self_reflect=False
+)
+
+# Create tasks to demonstrate both tools
+task = Task(
+    name="search_task",
+    description="Search for information about 'AI advancements' on both YouTube and Wikipedia",
+    expected_output="Combined information from YouTube videos and Wikipedia articles",
+    agent=agent
+)
+
+# Create and start the workflow
+agents = PraisonAIAgents(
+    agents=[agent],
+    tasks=[task],
+    verbose=True
+)
+
+agents.start()

agents/langchain_logs.py

@@ -0,0 +1,73 @@
+from langchain_community.tools import YouTubeSearchTool
+from langchain_community.utilities import WikipediaAPIWrapper
+
+def test_tools():
+    """Test both YouTube and Wikipedia tools"""
+    
+    print("\n=== Testing YouTube Search ===")
+    # Test YouTube
+    yt = YouTubeSearchTool()
+    
+    # Test basic search
+    print("\nBasic YouTube Search:")
+    query = "AI advancements 2024"
+    print(f"Query: {query}")
+    result = yt.run(query)
+    print(f"Results: {result}")
+    
+    # Test with max results
+    print("\nYouTube Search with max results:")
+    query = "AI advancements 2024, 3"  # Format: query, max_results
+    print(f"Query: {query}")
+    result = yt.run(query)
+    print(f"Results: {result}")
+    
+    print("\n=== Testing Wikipedia Search ===")
+    # Test Wikipedia
+    wiki = WikipediaAPIWrapper(
+        top_k_results=2,
+        doc_content_chars_max=500
+    )
+    
+    # Test basic search
+    print("\nBasic Wikipedia Search:")
+    query = "Artificial Intelligence"
+    print(f"Query: {query}")
+    result = wiki.run(query)
+    print(f"Results: {result[:200]}..." if result else "No result")
+    
+    # Test with different parameters
+    print("\nWikipedia Search with different language:")
+    wiki_fr = WikipediaAPIWrapper(
+        lang='fr',
+        top_k_results=1,
+        doc_content_chars_max=300
+    )
+    result = wiki_fr.run("Intelligence Artificielle")
+    print(f"French Results: {result[:200]}..." if result else "No result")
+
+def print_tool_info():
+    """Print detailed information about the tools"""
+    
+    print("\n=== Tool Information ===")
+    
+    # YouTube Tool Info
+    print("\nYouTube Tool:")
+    yt = YouTubeSearchTool()
+    print("Type:", type(yt))
+    print("Name:", getattr(yt, 'name', None))
+    print("Description:", getattr(yt, 'description', None))
+    print("Arguments:", getattr(yt, 'args', None))
+    
+    # Wikipedia Tool Info
+    print("\nWikipedia Tool:")
+    wiki = WikipediaAPIWrapper()
+    print("Type:", type(wiki))
+    print("Available Settings:")
+    print("- Language:", wiki.lang)
+    print("- Top K Results:", wiki.top_k_results)
+    print("- Max Characters:", wiki.doc_content_chars_max)
+
+if __name__ == "__main__":
+    print_tool_info()
+    test_tools() 

agents/memory_example.py

@@ -0,0 +1,183 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import logging
+import os
+
+def main():
+    # Initialize memory config
+    memory_config = {
+        "provider": "rag",
+        "use_embedding": True,
+        "storage": {
+            "type": "sqlite",
+            "path": "./.praison/memory.db"
+        },
+        "rag_db_path": "./.praison/chroma_db"
+    }
+    
+    # Test facts
+    fact1 = "The capital city of Jujuha is Hahanu and its population is 102300"
+    fact2 = "Three main ingredients in a classic proloder are eggs, sugar, and flour"
+    fact3 = "The year the first Josinga was released is 2007"
+    
+    # # Check if database exists
+    # if os.path.exists("./memory.db"):
+    #     logger.info("Found existing memory database")
+    # else:
+    #     logger.info("Creating new memory database")
+    
+    # Create task config (without memory config since it's moved to PraisonAIAgents)
+    task_config = {}
+
+    # Create agents with different roles
+    researcher = Agent(
+        role="Research Analyst",
+        goal="Research and document key information about topics",
+        backstory="Expert at analyzing and storing information in memory",
+        llm="gpt-4o-mini"
+    )
+    
+    retriever = Agent(
+        role="Information Retriever",
+        goal="Retrieve and verify stored information from memory",
+        backstory="Specialist in searching and validating information from memory",
+        llm="gpt-4o-mini"
+    )
+
+    # Task 1: Process the facts
+    store_task = Task(
+        description=f"""
+        Process and analyze this information:
+        1. {fact1}
+        2. {fact2}
+        3. {fact3}
+
+        Provide a clear summary of each fact.
+        """,
+        expected_output="""
+        Clear statements summarizing each fact.
+        Example format:
+        1. [Summary of fact 1]
+        2. [Summary of fact 2]
+        3. [Summary of fact 3]
+        """,
+        agent=researcher
+    )
+
+    # Task 2: Write essay about AI
+    verify_task = Task(
+        description="""
+        write few points about AI
+        """,
+        expected_output="Points about AI",
+        agent=retriever
+    )
+
+    # Task 3: Query memory
+    query_task = Task(
+        description="""
+        Using ONLY information found in memory:
+        1. What is stored in memory about Hahanu?
+        2. What ingredients for proloder are recorded in memory?
+        3. What year is stored in memory for the Josinga release?
+
+        For each answer, cite the memory record you found.
+        """,
+        expected_output="Answers based solely on memory records with citations",
+        agent=retriever
+    )
+
+    # Task 4: Query both short-term and long-term memory
+    query_both_task = Task(
+        description="""
+        Using ONLY information found in memory:
+        1. What is stored in both short-term and long-term memory about Jujuha?
+        2. What ingredients for proloder are recorded in both short-term and long-term memory?
+        3. What year is stored in both short-term and long-term memory for the Josinga release?
+
+        For each answer, cite the memory record you found.
+        """,
+        expected_output="Answers based solely on memory records with citations",
+        agent=retriever
+    )
+
+    # Initialize PraisonAIAgents with memory configuration
+    agents = PraisonAIAgents(
+        agents=[researcher, retriever],
+        tasks=[store_task, verify_task, query_task, query_both_task],
+        verbose=True,  # Use same verbose level as memory
+        memory=True,
+        embedder={
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small"
+            }
+        }
+    )
+
+    # agents = PraisonAIAgents(
+    #     agents=[researcher, retriever],
+    #     tasks=[store_task, verify_task, query_task, query_both_task],
+    #     verbose=True,  # Use same verbose level as memory
+    #     memory=True
+    # )
+    
+    # Execute tasks
+    print("\nExecuting Memory Test Tasks...")
+    print("-" * 50)
+    agents.start()
+    
+    # Use shared memory for final checks
+    memory = agents.shared_memory
+    
+    # Test memory retrieval with different quality thresholds
+    if memory:
+        print("\nFinal Memory Check:")
+        print("-" * 50)
+        
+        queries = ["Jujuha", "proloder", "Josinga"]
+        for query in queries:
+            print(f"\nSearching memory for: {query}")
+            
+            # Search in both short-term and long-term memory
+            print("\nShort-term memory results:")
+            stm_results = memory.search_short_term(query)
+            if stm_results:
+                for item in stm_results:
+                    print(f"Content: {item.get('content', '')[:200]}")
+                    if 'meta' in item:
+                        print(f"Metadata: {item['meta']}")
+                    print("-" * 20)
+            else:
+                print("No results found in short-term memory")
+
+            print("\nLong-term memory results:")
+            ltm_results = memory.search_long_term(query)
+            if ltm_results:
+                for item in ltm_results:
+                    print(f"Content: {item.get('text', '')[:200]}")
+                    if 'metadata' in item:
+                        print(f"Metadata: {item['metadata']}")
+                    print("-" * 20)
+            else:
+                print("No results found in long-term memory")
+                
+            # Also check ChromaDB if using RAG
+            if memory.use_rag and hasattr(memory, "chroma_col"):
+                print("\nChromaDB results:")
+                try:
+                    all_items = memory.chroma_col.get()
+                    print(f"Found {len(all_items['ids'])} items in ChromaDB")
+                    for i in range(len(all_items['ids'])):
+                        print(f"ID: {all_items['ids'][i]}")
+                        print(f"Content: {all_items['documents'][i][:200]}")
+                        print(f"Metadata: {all_items['metadatas'][i]}")
+                        print("-" * 20)
+                except Exception as e:
+                    print(f"Error querying ChromaDB: {e}")
+            
+            print("-" * 30)
+    else:
+        print("\nNo memory available for final checks")
+
+if __name__ == "__main__":
+    main()

agents/memory_simple.py

@@ -0,0 +1,63 @@
+from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import duckduckgo
+
+# Test facts
+fact1 = "The capital city of Jujuha is Hahanu and its population is 102300"
+fact2 = "Three main ingredients in a classic proloder are eggs, sugar, and flour"
+fact3 = "The year the first Josinga was released is 2007"
+
+fact_agent = Agent(
+    name="Fact Agent",
+    instructions="You are a fact agent, you store and retrieve facts in memory",
+    llm="gpt-4o-mini"
+)
+
+research_agent = Agent(
+    name="Research Agent",
+    instructions="You are a research analyst, you research and document key points about topics",
+    llm="gpt-4o-mini"
+)
+
+blog_agent = Agent(
+    name="Blog Agent",
+    instructions="You are a blog writer, you write a blog post about the research",
+    llm="gpt-4o-mini"
+)
+
+fact_task = Task(
+    description="Store the following facts in memory: " + fact1 + ", " + fact2 + ", " + fact3,
+    agent=fact_agent
+)
+
+research_task = Task(
+    description="Research and document 2 key points about AI",
+    agent=research_agent
+)
+
+research_task2 = Task(
+    description="Research and document 2 key points about AI in healthcare",
+    agent=research_agent
+)
+
+research_task3 = Task(
+    description="Research and document 2 key points about AI in education",
+    agent=research_agent
+)
+
+research_task4 = Task(
+    description="Research and document 2 key points about AI in finance",
+    agent=research_agent
+)
+
+blog_task = Task(
+    description="Write a blog post about Jujuha",
+    agent=blog_agent
+)
+
+agents = PraisonAIAgents(
+    agents=[fact_agent, research_agent, blog_agent],
+    tasks=[fact_task, research_task, research_task2, research_task3, research_task4, blog_task],
+    memory=True
+)   
+
+agents.start()

agents/praisonaiagents/__init__.py

@@ -6,6 +6,8 @@ from .agent.agent import Agent
 from .agents.agents import PraisonAIAgents
 from .task.task import Task
 from .tools.tools import Tools
+from .agents.autoagents import AutoAgents
+from .memory.memory import Memory
 from .main import (
     TaskOutput,
     ReflectionOutput,
@@ -33,6 +35,8 @@ __all__ = [
     'Task',
     'TaskOutput',
     'ReflectionOutput',
+    'AutoAgents',
+    'Memory',
     'display_interaction',
     'display_self_reflection',
     'display_instruction',

agents/praisonaiagents/agent/agent.py

@@ -18,6 +18,7 @@ from ..main import (
     client,
     error_logs
 )
+import inspect
 
 if TYPE_CHECKING:
     from ..task.task import Task
@@ -67,9 +68,24 @@ class Agent:
             return None
 
         import inspect
+        # Langchain tools
+        if inspect.isclass(func) and hasattr(func, 'run'):
+            original_func = func
+            func = func.run
+            function_name = original_func.__name__
+
         sig = inspect.signature(func)
         logging.debug(f"Function signature: {sig}")
         
+        # Skip self, *args, **kwargs, so they don't get passed in arguments
+        parameters_list = []
+        for name, param in sig.parameters.items():
+            if name == "self":
+                continue
+            if param.kind in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD):
+                continue
+            parameters_list.append((name, param))
+
         parameters = {
             "type": "object",
             "properties": {},
@@ -95,7 +111,7 @@ class Agent:
         
         logging.debug(f"Parameter descriptions: {param_descriptions}")
 
-        for name, param in sig.parameters.items():
+        for name, param in parameters_list:
             param_type = "string"  # Default type
             if param.annotation != inspect.Parameter.empty:
                 if param.annotation == int:
@@ -146,7 +162,7 @@ class Agent:
         max_iter: int = 20,
         max_rpm: Optional[int] = None,
         max_execution_time: Optional[int] = None,
-        memory: bool = True,
+        memory: Optional[Any] = None,
         verbose: bool = True,
         allow_delegation: bool = False,
         step_callback: Optional[Any] = None,
@@ -162,7 +178,7 @@ class Agent:
         knowledge_sources: Optional[List[Any]] = None,
         use_system_prompt: Optional[bool] = True,
         markdown: bool = True,
-        self_reflect: Optional[bool] = None,
+        self_reflect: bool = False,
         max_reflect: int = 3,
         min_reflect: int = 1,
         reflect_llm: Optional[str] = None
@@ -249,25 +265,31 @@ Your Goal: {self.goal}
         # Try to find the function in the agent's tools list first
         func = None
         for tool in self.tools:
-            if callable(tool) and getattr(tool, '__name__', '') == function_name:
+            if (callable(tool) and getattr(tool, '__name__', '') == function_name) or \
+               (inspect.isclass(tool) and tool.__name__ == function_name):
                 func = tool
                 break
         
-        logging.debug(f"Looking for {function_name} in agent tools: {func is not None}")
-        
-        # If not found in tools, try globals and main
-        if not func:
+        if func is None:
+            # If not found in tools, try globals and main
             func = globals().get(function_name)
-            logging.debug(f"Looking for {function_name} in globals: {func is not None}")
-            
             if not func:
                 import __main__
                 func = getattr(__main__, function_name, None)
-                logging.debug(f"Looking for {function_name} in __main__: {func is not None}")
 
-        if func and callable(func):
+        if func:
             try:
-                return func(**arguments)
+                # If it's a class with run method, instantiate and call run
+                if inspect.isclass(func) and hasattr(func, 'run'):
+                    instance = func()
+                    # Extract only the parameters that run() expects
+                    run_params = {k: v for k, v in arguments.items() 
+                                if k in inspect.signature(instance.run).parameters 
+                                and k != 'self'}
+                    return instance.run(**run_params)
+                # Otherwise treat as regular function
+                elif callable(func):
+                    return func(**arguments)
             except Exception as e:
                 error_msg = str(e)
                 logging.error(f"Error executing tool {function_name}: {error_msg}")
@@ -359,7 +381,7 @@ Your Goal: {self.goal}
                     display_generating("", start_time),
                     console=self.console,
                     refresh_per_second=4,
-                    transient=False,  # Changed to False to preserve output
+                    transient=True,  # Changed to False to preserve output
                     vertical_overflow="ellipsis",
                     auto_refresh=True
                 ) as live:
@@ -394,7 +416,7 @@ Your Goal: {self.goal}
             display_error(f"Error in chat completion: {e}")
             return None
 
-    def chat(self, prompt, temperature=0.2, tools=None, output_json=None):
+    def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
         if self.use_system_prompt:
             system_prompt = f"""{self.backstory}\n
 Your Role: {self.role}\n
@@ -402,6 +424,8 @@ Your Goal: {self.goal}
             """
             if output_json:
                 system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_json.schema_json()}"
+            elif output_pydantic:
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_pydantic.schema_json()}"
         else:
             system_prompt = None
 
@@ -410,9 +434,9 @@ Your Goal: {self.goal}
             messages.append({"role": "system", "content": system_prompt})
         messages.extend(self.chat_history)
 
-        # Modify prompt if output_json is specified
+        # Modify prompt if output_json or output_pydantic is specified
         original_prompt = prompt
-        if output_json:
+        if output_json or output_pydantic:
             if isinstance(prompt, str):
                 prompt += "\nReturn ONLY a valid JSON object. No other text or explanation."
             elif isinstance(prompt, list):
@@ -442,7 +466,15 @@ Your Goal: {self.goal}
                         display_text = next((item["text"] for item in prompt if item["type"] == "text"), "")
                     
                     if display_text and str(display_text).strip():
-                        display_instruction(f"Agent {self.name} is processing prompt: {display_text}", console=self.console)
+                        # Pass agent information to display_instruction
+                        agent_tools = [t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools]
+                        display_instruction(
+                            f"Agent {self.name} is processing prompt: {display_text}", 
+                            console=self.console,
+                            agent_name=self.name,
+                            agent_role=self.role,
+                            agent_tools=agent_tools
+                        )
 
                 response = self._chat_completion(messages, temperature=temperature, tools=tools if tools else None)
                 if not response:
@@ -487,29 +519,21 @@ Your Goal: {self.goal}
                         return None
                     response_text = response.choices[0].message.content.strip()
 
-                # Handle output_json if specified
-                if output_json:
-                    try:
-                        # Clean the response text to get only JSON
-                        cleaned_json = self.clean_json_output(response_text)
-                        # Parse into Pydantic model
-                        parsed_model = output_json.model_validate_json(cleaned_json)
-                        # Add to chat history and return
-                        self.chat_history.append({"role": "user", "content": original_prompt})
-                        self.chat_history.append({"role": "assistant", "content": response_text})
-                        if self.verbose:
-                            display_interaction(original_prompt, response_text, markdown=self.markdown, 
-                                             generation_time=time.time() - start_time, console=self.console)
-                        return parsed_model
-                    except Exception as e:
-                        display_error(f"Failed to parse response as {output_json.__name__}: {e}")
-                        return None
+                # Handle output_json or output_pydantic if specified
+                if output_json or output_pydantic:
+                    # Add to chat history and return raw response
+                    self.chat_history.append({"role": "user", "content": original_prompt})
+                    self.chat_history.append({"role": "assistant", "content": response_text})
+                    if self.verbose:
+                        display_interaction(original_prompt, response_text, markdown=self.markdown, 
+                                         generation_time=time.time() - start_time, console=self.console)
+                    return response_text
 
                 if not self.self_reflect:
                     self.chat_history.append({"role": "user", "content": original_prompt})
                     self.chat_history.append({"role": "assistant", "content": response_text})
                     if self.verbose:
-                        logging.info(f"Agent {self.name} final response: {response_text}")
+                        logging.debug(f"Agent {self.name} final response: {response_text}")
                     display_interaction(original_prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
                     return response_text
 
@@ -585,19 +609,21 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             cleaned = cleaned[:-3].strip()
         return cleaned 
 
-    async def achat(self, prompt, temperature=0.2, tools=None, output_json=None):
+    async def achat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
         """Async version of chat method"""
         try:
             # Build system prompt
             system_prompt = self.system_prompt
             if output_json:
                 system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_json.schema_json()}"
+            elif output_pydantic:
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_pydantic.schema_json()}"
 
             # Build messages
             if isinstance(prompt, str):
                 messages = [
                     {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": prompt + ("\nReturn ONLY a valid JSON object. No other text or explanation." if output_json else "")}
+                    {"role": "user", "content": prompt + ("\nReturn ONLY a valid JSON object. No other text or explanation." if (output_json or output_pydantic) else "")}
                 ]
             else:
                 # For multimodal prompts
@@ -605,13 +631,29 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                     {"role": "system", "content": system_prompt},
                     {"role": "user", "content": prompt}
                 ]
-                if output_json:
+                if output_json or output_pydantic:
                     # Add JSON instruction to text content
                     for item in messages[-1]["content"]:
                         if item["type"] == "text":
                             item["text"] += "\nReturn ONLY a valid JSON object. No other text or explanation."
                             break
 
+            # Display instruction with agent info if verbose
+            if self.verbose:
+                display_text = prompt
+                if isinstance(prompt, list):
+                    display_text = next((item["text"] for item in prompt if item["type"] == "text"), "")
+                
+                if display_text and str(display_text).strip():
+                    agent_tools = [t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools]
+                    await adisplay_instruction(
+                        f"Agent {self.name} is processing prompt: {display_text}",
+                        console=self.console,
+                        agent_name=self.name,
+                        agent_role=self.role,
+                        agent_tools=agent_tools
+                    )
+
             # Format tools if provided
             formatted_tools = []
             if tools:
@@ -639,22 +681,15 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                     tools=formatted_tools
                 )
                 return await self._achat_completion(response, tools)
-            elif output_json:
+            elif output_json or output_pydantic:
                 response = await async_client.chat.completions.create(
                     model=self.llm,
                     messages=messages,
                     temperature=temperature,
                     response_format={"type": "json_object"}
                 )
-                result = response.choices[0].message.content
-                # Clean and parse the JSON response
-                cleaned_json = self.clean_json_output(result)
-                try:
-                    parsed = json.loads(cleaned_json)
-                    return output_json(**parsed)
-                except Exception as e:
-                    display_error(f"Error parsing JSON response: {e}")
-                    return None
+                # Return the raw response
+                return response.choices[0].message.content
             else:
                 response = await async_client.chat.completions.create(
                     model=self.llm,
@@ -722,4 +757,8 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             return None
         except Exception as e:
             display_error(f"Error in _achat_completion: {e}")
-            return None 
+            return None 
+
+    def run(self):
+        """Alias for start() method"""
+        return self.start() 

agents/praisonaiagents/agents/__init__.py

@@ -1,4 +1,5 @@
 """Agents module for managing multiple AI agents"""
 from .agents import PraisonAIAgents
+from .autoagents import AutoAgents
 
-__all__ = ['PraisonAIAgents'] 
+__all__ = ['PraisonAIAgents', 'AutoAgents'] 

agents/praisonaiagents/agents/agents.py

@@ -13,6 +13,9 @@ from ..task.task import Task
 from ..process.process import Process, LoopItems
 import asyncio
 
+# Set up logger
+logger = logging.getLogger(__name__)
+
 def encode_file_to_base64(file_path: str) -> str:
     """Base64-encode a file."""
     import base64
@@ -41,7 +44,7 @@ def process_video(video_path: str, seconds_per_frame=2):
     return base64_frames
 
 class PraisonAIAgents:
-    def __init__(self, agents, tasks=None, verbose=0, completion_checker=None, max_retries=5, process="sequential", manager_llm=None):
+    def __init__(self, agents, tasks=None, verbose=0, completion_checker=None, max_retries=5, process="sequential", manager_llm=None, memory=False, memory_config=None, embedder=None):
         if not agents:
             raise ValueError("At least one agent must be provided")
             
@@ -57,8 +60,21 @@ class PraisonAIAgents:
         
         # Check for manager_llm in environment variable if not provided
         self.manager_llm = manager_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        
+        # Set logger level based on verbose
+        if verbose >= 5:
+            logger.setLevel(logging.INFO)
+        else:
+            logger.setLevel(logging.WARNING)
+            
+        # Also set third-party loggers to WARNING
+        logging.getLogger('chromadb').setLevel(logging.WARNING)
+        logging.getLogger('openai').setLevel(logging.WARNING)
+        logging.getLogger('httpx').setLevel(logging.WARNING)
+        logging.getLogger('httpcore').setLevel(logging.WARNING)
+
         if self.verbose:
-            logging.info(f"Using model {self.manager_llm} for manager")
+            logger.info(f"Using model {self.manager_llm} for manager")
         
         # If no tasks provided, generate them from agents
         if tasks is None:
@@ -66,12 +82,12 @@ class PraisonAIAgents:
             for agent in self.agents:
                 task = agent.generate_task()
                 tasks.append(task)
-            logging.info(f"Auto-generated {len(tasks)} tasks from agents")
+            logger.info(f"Auto-generated {len(tasks)} tasks from agents")
         else:
             # Validate tasks for backward compatibility
             if not tasks:
                 raise ValueError("If tasks are provided, at least one task must be present")
-            logging.info(f"Using {len(tasks)} provided tasks")
+            logger.info(f"Using {len(tasks)} provided tasks")
         
         # Add tasks and set their status
         for task in tasks:
@@ -87,9 +103,65 @@ class PraisonAIAgents:
                 if tasks[i + 1].context is None:
                     tasks[i + 1].context = []
                 tasks[i + 1].context.append(tasks[i])
-            logging.info("Set up sequential flow with automatic context passing")
+            logger.info("Set up sequential flow with automatic context passing")
         
         self._state = {}  # Add state storage at PraisonAIAgents level
+        
+        # Initialize memory system
+        self.shared_memory = None
+        if memory:
+            try:
+                from ..memory.memory import Memory
+                
+                # Get memory config from parameter or first task
+                mem_cfg = memory_config
+                if not mem_cfg:
+                    mem_cfg = next((t.config.get('memory_config') for t in tasks if hasattr(t, 'config') and t.config), None)
+                    
+                # Set default memory config if none provided
+                if not mem_cfg:
+                    mem_cfg = {
+                        "provider": "rag",
+                        "use_embedding": True,
+                        "storage": {
+                            "type": "sqlite",
+                            "path": "./.praison/memory.db"
+                        },
+                        "rag_db_path": "./.praison/chroma_db"
+                    }
+                
+                # Add embedder config if provided
+                if embedder:
+                    if isinstance(embedder, dict):
+                        mem_cfg = mem_cfg or {}
+                        mem_cfg["embedder"] = embedder
+                    else:
+                        # Handle direct embedder function
+                        mem_cfg = mem_cfg or {}
+                        mem_cfg["embedder_function"] = embedder
+
+                if mem_cfg:
+                    # Pass verbose level to Memory
+                    self.shared_memory = Memory(config=mem_cfg, verbose=verbose)
+                    if verbose >= 5:
+                        logger.info("Initialized shared memory for PraisonAIAgents")
+                    
+                    # Distribute memory to tasks
+                    for task in tasks:
+                        if not task.memory:
+                            task.memory = self.shared_memory
+                            if verbose >= 5:
+                                logger.info(f"Assigned shared memory to task {task.id}")
+                            
+            except Exception as e:
+                logger.error(f"Failed to initialize shared memory: {e}")
+            
+        # Update tasks with shared memory
+        if self.shared_memory:
+            for task in tasks:
+                if not task.memory:
+                    task.memory = self.shared_memory
+                    logger.info(f"Assigned shared memory to task {task.id}")
 
     def add_task(self, task):
         task_id = self.task_id_counter
@@ -142,7 +214,7 @@ class PraisonAIAgents:
         task_prompt = f"""
 You need to do the following task: {task.description}.
 Expected Output: {task.expected_output}.
-        """
+"""
         if task.context:
             context_results = ""
             for context_task in task.context:
@@ -157,8 +229,8 @@ Here are the results of previous tasks that might be useful:\n
         task_prompt += "Please provide only the final result of your work. Do not add any conversation or extra explanation."
 
         if self.verbose >= 2:
-            logging.info(f"Executing task {task_id}: {task.description} using {executor_agent.name}")
-        logging.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
+            logger.info(f"Executing task {task_id}: {task.description} using {executor_agent.name}")
+        logger.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
 
         if task.images:
             def _get_multimodal_message(text_prompt, images):
@@ -195,10 +267,17 @@ Here are the results of previous tasks that might be useful:\n
 
             agent_output = await executor_agent.achat(
                 _get_multimodal_message(task_prompt, task.images),
-                tools=task.tools
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
             )
         else:
-            agent_output = await executor_agent.achat(task_prompt, tools=task.tools)
+            agent_output = await executor_agent.achat(
+                task_prompt,
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
+            )
 
         if agent_output:
             task_output = TaskOutput(
@@ -216,8 +295,8 @@ Here are the results of previous tasks that might be useful:\n
                     task_output.json_dict = parsed
                     task_output.output_format = "JSON"
                 except:
-                    logging.warning(f"Warning: Could not parse output of task {task_id} as JSON")
-                    logging.debug(f"Output that failed JSON parsing: {agent_output}")
+                    logger.warning(f"Warning: Could not parse output of task {task_id} as JSON")
+                    logger.debug(f"Output that failed JSON parsing: {agent_output}")
 
             if task.output_pydantic:
                 cleaned = self.clean_json_output(agent_output)
@@ -227,8 +306,8 @@ Here are the results of previous tasks that might be useful:\n
                     task_output.pydantic = pyd_obj
                     task_output.output_format = "Pydantic"
                 except:
-                    logging.warning(f"Warning: Could not parse output of task {task_id} as Pydantic Model")
-                    logging.debug(f"Output that failed Pydantic parsing: {agent_output}")
+                    logger.warning(f"Warning: Could not parse output of task {task_id} as Pydantic Model")
+                    logger.debug(f"Output that failed Pydantic parsing: {agent_output}")
 
             task.result = task_output
             return task_output
@@ -243,37 +322,53 @@ Here are the results of previous tasks that might be useful:\n
             return
         task = self.tasks[task_id]
         if task.status == "completed":
-            logging.info(f"Task with ID {task_id} is already completed")
+            logger.info(f"Task with ID {task_id} is already completed")
             return
 
         retries = 0
         while task.status != "completed" and retries < self.max_retries:
-            logging.debug(f"Attempt {retries+1} for task {task_id}")
+            logger.debug(f"Attempt {retries+1} for task {task_id}")
             if task.status in ["not started", "in progress"]:
                 task_output = await self.aexecute_task(task_id)
                 if task_output and self.completion_checker(task, task_output.raw):
                     task.status = "completed"
-                    if task.callback:
+                    # Run execute_callback for memory operations
+                    try:
                         await task.execute_callback(task_output)
+                    except Exception as e:
+                        logger.error(f"Error executing memory callback for task {task_id}: {e}")
+                        logger.exception(e)
+                    
+                    # Run task callback if exists
+                    if task.callback:
+                        try:
+                            if asyncio.iscoroutinefunction(task.callback):
+                                await task.callback(task_output)
+                            else:
+                                task.callback(task_output)
+                        except Exception as e:
+                            logger.error(f"Error executing task callback for task {task_id}: {e}")
+                            logger.exception(e)
+                            
                     self.save_output_to_file(task, task_output)
                     if self.verbose >= 1:
-                        logging.info(f"Task {task_id} completed successfully.")
+                        logger.info(f"Task {task_id} completed successfully.")
                 else:
                     task.status = "in progress"
                     if self.verbose >= 1:
-                        logging.info(f"Task {task_id} not completed, retrying")
+                        logger.info(f"Task {task_id} not completed, retrying")
                     await asyncio.sleep(1)
                     retries += 1
             else:
                 if task.status == "failed":
-                    logging.info("Task is failed, resetting to in-progress for another try...")
+                    logger.info("Task is failed, resetting to in-progress for another try...")
                     task.status = "in progress"
                 else:
-                    logging.info("Invalid Task status")
+                    logger.info("Invalid Task status")
                     break
 
         if retries == self.max_retries and task.status != "completed":
-            logging.info(f"Task {task_id} failed after {self.max_retries} retries.")
+            logger.info(f"Task {task_id} failed after {self.max_retries} retries.")
 
     async def arun_all_tasks(self):
         """Async version of run_all_tasks method"""
@@ -321,7 +416,7 @@ Here are the results of previous tasks that might be useful:\n
                 with open(task.output_file, "w") as f:
                     f.write(str(task_output))
                 if self.verbose >= 1:
-                    logging.info(f"Task output saved to {task.output_file}")
+                    logger.info(f"Task output saved to {task.output_file}")
             except Exception as e:
                 display_error(f"Error saving task output to file: {e}")
 
@@ -332,6 +427,15 @@ Here are the results of previous tasks that might be useful:\n
             return
         task = self.tasks[task_id]
         
+        logger.info(f"Starting execution of task {task_id}")
+        logger.info(f"Task config: {task.config}")
+        
+        # Initialize memory before task execution
+        if not task.memory:
+            task.memory = task.initialize_memory()
+        
+        logger.info(f"Task memory status: {'Initialized' if task.memory else 'Not initialized'}")
+        
         # Only import multimodal dependencies if task has images
         if task.images and task.status == "not started":
             try:
@@ -364,11 +468,20 @@ Expected Output: {task.expected_output}.
 Here are the results of previous tasks that might be useful:\n
 {context_results}
 """
+        # Add memory context if available
+        if task.memory:
+            try:
+                memory_context = task.memory.build_context_for_task(task.description)
+                if memory_context:
+                    task_prompt += f"\n\nRelevant memory context:\n{memory_context}"
+            except Exception as e:
+                logger.error(f"Error getting memory context: {e}")
+
         task_prompt += "Please provide only the final result of your work. Do not add any conversation or extra explanation."
 
         if self.verbose >= 2:
-            logging.info(f"Executing task {task_id}: {task.description} using {executor_agent.name}")
-        logging.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
+            logger.info(f"Executing task {task_id}: {task.description} using {executor_agent.name}")
+        logger.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
 
         if task.images:
             def _get_multimodal_message(text_prompt, images):
@@ -405,12 +518,30 @@ Here are the results of previous tasks that might be useful:\n
 
             agent_output = executor_agent.chat(
                 _get_multimodal_message(task_prompt, task.images),
-                tools=task.tools
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
             )
         else:
-            agent_output = executor_agent.chat(task_prompt, tools=task.tools)
+            agent_output = executor_agent.chat(
+                task_prompt,
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
+            )
 
         if agent_output:
+            # Store the response in memory
+            if task.memory:
+                try:
+                    task.store_in_memory(
+                        content=agent_output,
+                        agent_name=executor_agent.name,
+                        task_id=task_id
+                    )
+                except Exception as e:
+                    logger.error(f"Failed to store agent output in memory: {e}")
+
             task_output = TaskOutput(
                 description=task.description,
                 summary=task.description[:10],
@@ -426,8 +557,8 @@ Here are the results of previous tasks that might be useful:\n
                     task_output.json_dict = parsed
                     task_output.output_format = "JSON"
                 except:
-                    logging.warning(f"Warning: Could not parse output of task {task_id} as JSON")
-                    logging.debug(f"Output that failed JSON parsing: {agent_output}")
+                    logger.warning(f"Warning: Could not parse output of task {task_id} as JSON")
+                    logger.debug(f"Output that failed JSON parsing: {agent_output}")
 
             if task.output_pydantic:
                 cleaned = self.clean_json_output(agent_output)
@@ -437,8 +568,8 @@ Here are the results of previous tasks that might be useful:\n
                     task_output.pydantic = pyd_obj
                     task_output.output_format = "Pydantic"
                 except:
-                    logging.warning(f"Warning: Could not parse output of task {task_id} as Pydantic Model")
-                    logging.debug(f"Output that failed Pydantic parsing: {agent_output}")
+                    logger.warning(f"Warning: Could not parse output of task {task_id} as Pydantic Model")
+                    logger.debug(f"Output that failed Pydantic parsing: {agent_output}")
 
             task.result = task_output
             return task_output
@@ -453,37 +584,54 @@ Here are the results of previous tasks that might be useful:\n
             return
         task = self.tasks[task_id]
         if task.status == "completed":
-            logging.info(f"Task with ID {task_id} is already completed")
+            logger.info(f"Task with ID {task_id} is already completed")
             return
 
         retries = 0
         while task.status != "completed" and retries < self.max_retries:
-            logging.debug(f"Attempt {retries+1} for task {task_id}")
+            logger.debug(f"Attempt {retries+1} for task {task_id}")
             if task.status in ["not started", "in progress"]:
                 task_output = self.execute_task(task_id)
                 if task_output and self.completion_checker(task, task_output.raw):
                     task.status = "completed"
+                    # Run execute_callback for memory operations
+                    try:
+                        loop = asyncio.get_event_loop()
+                        loop.run_until_complete(task.execute_callback(task_output))
+                    except Exception as e:
+                        logger.error(f"Error executing memory callback for task {task_id}: {e}")
+                        logger.exception(e)
+                    
+                    # Run task callback if exists
                     if task.callback:
-                        task.callback(task_output)
+                        try:
+                            if asyncio.iscoroutinefunction(task.callback):
+                                loop.run_until_complete(task.callback(task_output))
+                            else:
+                                task.callback(task_output)
+                        except Exception as e:
+                            logger.error(f"Error executing task callback for task {task_id}: {e}")
+                            logger.exception(e)
+                            
                     self.save_output_to_file(task, task_output)
                     if self.verbose >= 1:
-                        logging.info(f"Task {task_id} completed successfully.")
+                        logger.info(f"Task {task_id} completed successfully.")
                 else:
                     task.status = "in progress"
                     if self.verbose >= 1:
-                        logging.info(f"Task {task_id} not completed, retrying")
+                        logger.info(f"Task {task_id} not completed, retrying")
                     time.sleep(1)
                     retries += 1
             else:
                 if task.status == "failed":
-                    logging.info("Task is failed, resetting to in-progress for another try...")
+                    logger.info("Task is failed, resetting to in-progress for another try...")
                     task.status = "in progress"
                 else:
-                    logging.info("Invalid Task status")
+                    logger.info("Invalid Task status")
                     break
 
         if retries == self.max_retries and task.status != "completed":
-            logging.info(f"Task {task_id} failed after {self.max_retries} retries.")
+            logger.info(f"Task {task_id} failed after {self.max_retries} retries.")
 
     def run_all_tasks(self):
         """Synchronous version of run_all_tasks method"""

agents/praisonaiagents/agents/autoagents.py

@@ -0,0 +1,335 @@
+"""
+AutoAgents - A class for automatically creating and managing AI agents and tasks.
+
+This class provides a simplified interface for creating and running AI agents with tasks.
+It automatically handles agent creation, task setup, and execution flow.
+"""
+
+from .agents import PraisonAIAgents
+from ..agent.agent import Agent
+from ..task.task import Task
+from typing import List, Any, Optional, Dict, Union
+import logging
+import os
+from pydantic import BaseModel, Field
+from ..main import display_instruction, display_tool_call, display_interaction, client
+
+# Define Pydantic models for structured output
+class TaskConfig(BaseModel):
+    name: str
+    description: str
+    expected_output: str
+    tools: List[str]
+
+class AgentConfig(BaseModel):
+    name: str
+    role: str
+    goal: str
+    backstory: str
+    tools: List[str]
+    tasks: List[TaskConfig]
+
+class AutoAgentsConfig(BaseModel):
+    main_instruction: str
+    process_type: str
+    agents: List[AgentConfig]
+
+class AutoAgents(PraisonAIAgents):
+    def __init__(
+        self,
+        instructions: str,
+        tools: Optional[List[Any]] = None,
+        verbose: bool = False,
+        process: str = "sequential",
+        manager_llm: Optional[str] = None,
+        max_retries: int = 5,
+        completion_checker: Optional[Any] = None,
+        allow_code_execution: bool = False,
+        memory: bool = True,
+        markdown: bool = True,
+        self_reflect: bool = False,
+        max_reflect: int = 3,
+        min_reflect: int = 1,
+        llm: Optional[str] = None,
+        function_calling_llm: Optional[str] = None,
+        respect_context_window: bool = True,
+        code_execution_mode: str = "safe",
+        embedder_config: Optional[Dict[str, Any]] = None,
+        knowledge_sources: Optional[List[Any]] = None,
+        use_system_prompt: bool = True,
+        cache: bool = True,
+        allow_delegation: bool = False,
+        step_callback: Optional[Any] = None,
+        system_template: Optional[str] = None,
+        prompt_template: Optional[str] = None,
+        response_template: Optional[str] = None,
+        max_rpm: Optional[int] = None,
+        max_execution_time: Optional[int] = None,
+        max_iter: int = 20,
+        reflect_llm: Optional[str] = None,
+        base_url: Optional[str] = None,
+        api_key: Optional[str] = None,
+        max_agents: int = 3  # New parameter for maximum number of agents
+    ):
+        """Initialize AutoAgents with configuration for automatic agent and task creation."""
+        if max_agents < 1:
+            raise ValueError("max_agents must be at least 1")
+        if max_agents > 10:
+            raise ValueError("max_agents cannot exceed 10")
+            
+        # Store all configuration parameters first
+        self.instructions = instructions
+        self.tools = tools or []
+        self.verbose = verbose  # Store verbose parameter
+        self.max_agents = max_agents  # Store max_agents parameter
+        self.allow_code_execution = allow_code_execution
+        self.memory = memory
+        self.markdown = markdown
+        self.self_reflect = self_reflect
+        self.max_reflect = max_reflect
+        self.min_reflect = min_reflect
+        self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        self.function_calling_llm = function_calling_llm
+        self.respect_context_window = respect_context_window
+        self.code_execution_mode = code_execution_mode
+        self.embedder_config = embedder_config
+        self.knowledge_sources = knowledge_sources
+        self.use_system_prompt = use_system_prompt
+        self.cache = cache
+        self.allow_delegation = allow_delegation
+        self.step_callback = step_callback
+        self.system_template = system_template
+        self.prompt_template = prompt_template
+        self.response_template = response_template
+        self.max_rpm = max_rpm
+        self.max_execution_time = max_execution_time
+        self.max_iter = max_iter
+        self.reflect_llm = reflect_llm
+        
+        # Display initial instruction
+        if self.verbose:
+            display_instruction(f"🎯 Main Task: {self.instructions}")
+            display_instruction(f"📊 Maximum Agents: {self.max_agents}")
+            if self.tools:
+                tool_names = [t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools]
+                display_tool_call(f"🛠️  Available Tools: {', '.join(tool_names)}")
+        
+        # Generate agents and tasks configuration
+        config = self._generate_config()
+        agents, tasks = self._create_agents_and_tasks(config)
+        
+        # Display agents and their tasks
+        if self.verbose:
+            self._display_agents_and_tasks(agents, tasks)
+        
+        # Initialize parent class with generated agents and tasks
+        super().__init__(
+            agents=agents,
+            tasks=tasks,
+            verbose=verbose,
+            completion_checker=completion_checker,
+            max_retries=max_retries,
+            process=process,
+            manager_llm=manager_llm
+        )
+
+    def _display_agents_and_tasks(self, agents: List[Agent], tasks: List[Task]):
+        """Display the created agents and their assigned tasks"""
+        display_instruction("\n🤖 Generated Agents and Tasks:")
+        
+        # Create a mapping of agents to their tasks
+        agent_tasks = {}
+        for task in tasks:
+            if task.agent not in agent_tasks:
+                agent_tasks[task.agent] = []
+            agent_tasks[task.agent].append(task)
+        
+        # Display each agent and their tasks
+        for agent in agents:
+            agent_tools = [t.__name__ if hasattr(t, '__name__') else str(t) for t in agent.tools]
+            display_interaction(
+                f"\n👤 Agent: {agent.name}",
+                f"""Role: {agent.role}
+Goal: {agent.goal}
+Tools: {', '.join(agent_tools)}"""
+            )
+            
+            # Display tasks for this agent
+            if agent in agent_tasks:
+                for i, task in enumerate(agent_tasks[agent], 1):
+                    task_tools = [t.__name__ if hasattr(t, '__name__') else str(t) for t in task.tools]
+                    display_instruction(
+                        f"""  📋 Task {i}: {task.name}
+     Description: {task.description}
+     Expected Output: {task.expected_output}
+     Tools: {', '.join(task_tools)}"""
+                    )
+
+    def _get_available_tools(self) -> List[str]:
+        """Get list of available tools"""
+        if not self.tools:
+            return []
+        return [t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools]
+
+    def _get_tool_by_name(self, tool_name: str) -> Optional[Any]:
+        """Get tool object by its name"""
+        for tool in self.tools:
+            if (hasattr(tool, '__name__') and tool.__name__ == tool_name) or str(tool) == tool_name:
+                return tool
+        return None
+
+    def _assign_tools_to_agent(self, agent_config: AgentConfig) -> List[Any]:
+        """
+        Assign appropriate tools to an agent based on its role and tasks.
+        
+        Args:
+            agent_config: The agent configuration containing role and required tools
+            
+        Returns:
+            List of tool objects assigned to this agent
+        """
+        assigned_tools = []
+        tool_names = set(agent_config.tools)
+        
+        # Also look at task requirements
+        for task in agent_config.tasks:
+            tool_names.update(task.tools)
+        
+        # Assign tools that match the requirements
+        for tool_name in tool_names:
+            tool = self._get_tool_by_name(tool_name)
+            if tool:
+                assigned_tools.append(tool)
+        
+        # If no specific tools matched but we have tools available,
+        # assign all tools to ensure functionality
+        if not assigned_tools and self.tools:
+            assigned_tools = self.tools
+            
+        return assigned_tools
+
+    def _generate_config(self) -> AutoAgentsConfig:
+        """Generate the configuration for agents and tasks"""
+        prompt = f"""
+Generate a configuration for AI agents to accomplish this task: "{self.instructions}"
+
+The configuration should include:
+1. A main instruction that clearly states the overall goal
+2. A process type (sequential, workflow, or hierarchical)
+3. A list of maximum {self.max_agents} agents (no more, no less), each with:
+   - Name, role, goal, and backstory
+   - List of required tools from: {self._get_available_tools()}
+   - Only add tools that are needed for the agent to perform its task
+   - Only one task per agent. Add more than one task if absolutely necessary.
+   - List of specific tasks they need to perform
+   - Whether they should self-reflect or allow delegation
+
+Requirements:
+1. Each agent should have clear, focused responsibilities
+2. Tasks should be broken down into manageable steps
+3. Tool selection should be appropriate for each task
+4. The process type should match the task requirements
+5. Generate maximum {self.max_agents} agents to handle this task efficiently
+
+Return the configuration in a structured JSON format matching the AutoAgentsConfig schema.
+"""
+        
+        try:
+            response = client.beta.chat.completions.parse(
+                model=self.llm,
+                response_format=AutoAgentsConfig,
+                messages=[
+                    {"role": "system", "content": "You are a helpful assistant designed to generate AI agent configurations."},
+                    {"role": "user", "content": prompt}
+                ]
+            )
+            
+            # Ensure we have exactly max_agents number of agents
+            if len(response.choices[0].message.parsed.agents) > self.max_agents:
+                response.choices[0].message.parsed.agents = response.choices[0].message.parsed.agents[:self.max_agents]
+            elif len(response.choices[0].message.parsed.agents) < self.max_agents:
+                logging.warning(f"Generated {len(response.choices[0].message.parsed.agents)} agents, expected {self.max_agents}")
+            
+            return response.choices[0].message.parsed
+        except Exception as e:
+            logging.error(f"Error generating configuration: {e}")
+            raise
+
+    def _create_agents_and_tasks(self, config: AutoAgentsConfig) -> tuple[List[Agent], List[Task]]:
+        """Create agents and tasks from configuration"""
+        agents = []
+        tasks = []
+        
+        for agent_config in config.agents:
+            # Get appropriate tools for this agent
+            agent_tools = self._assign_tools_to_agent(agent_config)
+            
+            # Create the agent with all parameters
+            agent = Agent(
+                name=agent_config.name,
+                role=agent_config.role,
+                goal=agent_config.goal,
+                backstory=agent_config.backstory,
+                tools=agent_tools,  # Use assigned tools
+                verbose=self.verbose >= 1,
+                allow_code_execution=self.allow_code_execution,
+                memory=self.memory,
+                markdown=self.markdown,
+                self_reflect=self.self_reflect,
+                max_reflect=self.max_reflect,
+                min_reflect=self.min_reflect,
+                llm=self.llm,
+                function_calling_llm=self.function_calling_llm,
+                respect_context_window=self.respect_context_window,
+                code_execution_mode=self.code_execution_mode,
+                embedder_config=self.embedder_config,
+                knowledge_sources=self.knowledge_sources,
+                use_system_prompt=self.use_system_prompt,
+                cache=self.cache,
+                allow_delegation=self.allow_delegation,
+                step_callback=self.step_callback,
+                system_template=self.system_template,
+                prompt_template=self.prompt_template,
+                response_template=self.response_template,
+                max_rpm=self.max_rpm,
+                max_execution_time=self.max_execution_time,
+                max_iter=self.max_iter,
+                reflect_llm=self.reflect_llm
+            )
+            agents.append(agent)
+            
+            # Create tasks for this agent
+            for task_config in agent_config.tasks:
+                # Get task-specific tools
+                task_tools = [self._get_tool_by_name(t) for t in task_config.tools]
+                task_tools = [t for t in task_tools if t]  # Remove None values
+                
+                # If no specific tools matched, use agent's tools
+                if not task_tools:
+                    task_tools = agent_tools
+                
+                task = Task(
+                    name=task_config.name,
+                    description=task_config.description,
+                    expected_output=task_config.expected_output,
+                    agent=agent,
+                    tools=task_tools  # Use task-specific tools
+                )
+                tasks.append(task)
+        
+        return agents, tasks
+
+    async def astart(self):
+        """
+        Async version of start() method.
+        Creates tasks based on the instructions, then starts execution.
+        Returns the task status and results dictionary.
+        """
+        return await super().astart()
+
+    def start(self):
+        """
+        Creates tasks based on the instructions, then starts execution.
+        Returns the task status and results dictionary.
+        """
+        return super().start() 

agents/praisonaiagents/main.py

@@ -116,10 +116,10 @@ def display_interaction(message, response, markdown=True, generation_time=None,
         console.print(Text(f"Response generated in {generation_time:.1f}s", style="dim"))
 
     if markdown:
-        console.print(Panel.fit(Markdown(message), title="Message", border_style="cyan"))
+        console.print(Panel.fit(Markdown(message), title="Task", border_style="cyan"))
         console.print(Panel.fit(Markdown(response), title="Response", border_style="cyan"))
     else:
-        console.print(Panel.fit(Text(message, style="bold green"), title="Message", border_style="cyan"))
+        console.print(Panel.fit(Text(message, style="bold green"), title="Task", border_style="cyan"))
         console.print(Panel.fit(Text(response, style="bold blue"), title="Response", border_style="cyan"))
 
 def display_self_reflection(message: str, console=None):
@@ -135,7 +135,7 @@ def display_self_reflection(message: str, console=None):
     
     console.print(Panel.fit(Text(message, style="bold yellow"), title="Self Reflection", border_style="magenta"))
 
-def display_instruction(message: str, console=None):
+def display_instruction(message: str, console=None, agent_name: str = None, agent_role: str = None, agent_tools: List[str] = None):
     if not message or not message.strip():
         return
     if console is None:
@@ -146,7 +146,19 @@ def display_instruction(message: str, console=None):
     if 'instruction' in sync_display_callbacks:
         sync_display_callbacks['instruction'](message=message)
     
-    console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
+    # Display agent info if available
+    if agent_name:
+        agent_info = f"[bold #FF9B9B]👤 Agent:[/] [#FFE5E5]{agent_name}[/]"
+        if agent_role:
+            agent_info += f"\n[bold #B4B4B3]Role:[/] [#FFE5E5]{agent_role}[/]"
+        if agent_tools:
+            tools_str = ", ".join(f"[italic #B4D4FF]{tool}[/]" for tool in agent_tools)
+            agent_info += f"\n[bold #86A789]Tools:[/] {tools_str}"
+        console.print(Panel(agent_info, border_style="#D2E3C8", title="[bold]Agent Info[/]", title_align="left", padding=(1, 2)))
+    
+    # Only print if log level is DEBUG
+    if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+        console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
 
 def display_tool_call(message: str, console=None):
     if not message or not message.strip():
@@ -222,10 +234,10 @@ async def adisplay_interaction(message, response, markdown=True, generation_time
         console.print(Text(f"Response generated in {generation_time:.1f}s", style="dim"))
 
     if markdown:
-        console.print(Panel.fit(Markdown(message), title="Message", border_style="cyan"))
+        console.print(Panel.fit(Markdown(message), title="Task", border_style="cyan"))
         console.print(Panel.fit(Markdown(response), title="Response", border_style="cyan"))
     else:
-        console.print(Panel.fit(Text(message, style="bold green"), title="Message", border_style="cyan"))
+        console.print(Panel.fit(Text(message, style="bold green"), title="Task", border_style="cyan"))
         console.print(Panel.fit(Text(response, style="bold blue"), title="Response", border_style="cyan"))
 
 async def adisplay_self_reflection(message: str, console=None):
@@ -241,7 +253,7 @@ async def adisplay_self_reflection(message: str, console=None):
     
     console.print(Panel.fit(Text(message, style="bold yellow"), title="Self Reflection", border_style="magenta"))
 
-async def adisplay_instruction(message: str, console=None):
+async def adisplay_instruction(message: str, console=None, agent_name: str = None, agent_role: str = None, agent_tools: List[str] = None):
     """Async version of display_instruction."""
     if not message or not message.strip():
         return
@@ -252,7 +264,19 @@ async def adisplay_instruction(message: str, console=None):
     if 'instruction' in async_display_callbacks:
         await async_display_callbacks['instruction'](message=message)
     
-    console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
+    # Display agent info if available
+    if agent_name:
+        agent_info = f"[bold #FF9B9B]👤 Agent:[/] [#FFE5E5]{agent_name}[/]"
+        if agent_role:
+            agent_info += f"\n[bold #B4B4B3]Role:[/] [#FFE5E5]{agent_role}[/]"
+        if agent_tools:
+            tools_str = ", ".join(f"[italic #B4D4FF]{tool}[/]" for tool in agent_tools)
+            agent_info += f"\n[bold #86A789]Tools:[/] {tools_str}"
+        console.print(Panel(agent_info, border_style="#D2E3C8", title="[bold]Agent Info[/]", title_align="left", padding=(1, 2)))
+    
+    # Only print if log level is DEBUG
+    if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+        console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
 
 async def adisplay_tool_call(message: str, console=None):
     """Async version of display_tool_call."""

agents/praisonaiagents/memory/memory.py

@@ -0,0 +1,931 @@
+import os
+import sqlite3
+import json
+import time
+import shutil
+from typing import Any, Dict, List, Optional, Union, Literal
+import logging
+
+# Set up logger
+logger = logging.getLogger(__name__)
+
+try:
+    import chromadb
+    from chromadb.config import Settings as ChromaSettings
+    CHROMADB_AVAILABLE = True
+except ImportError:
+    CHROMADB_AVAILABLE = False
+    logger.warning("To use memory features, please run: pip install \"praisonaiagents[memory]\"")
+
+try:
+    import mem0
+    MEM0_AVAILABLE = True
+except ImportError:
+    MEM0_AVAILABLE = False
+
+try:
+    import openai
+    OPENAI_AVAILABLE = True
+except ImportError:
+    OPENAI_AVAILABLE = False
+
+
+
+
+class Memory:
+    """
+    A single-file memory manager covering:
+    - Short-term memory (STM) for ephemeral context
+    - Long-term memory (LTM) for persistent knowledge
+    - Entity memory (structured data about named entities)
+    - User memory (preferences/history for each user)
+    - Quality score logic for deciding which data to store in LTM
+    - Context building from multiple memory sources
+
+    Config example:
+    {
+      "provider": "rag" or "mem0" or "none",
+      "use_embedding": True,
+      "short_db": "short_term.db",
+      "long_db": "long_term.db",
+      "rag_db_path": "rag_db",   # optional path for local embedding store
+      "config": {
+        "api_key": "...",       # if mem0 usage
+        "org_id": "...",
+        "project_id": "...",
+        ...
+      }
+    }
+    """
+
+    def __init__(self, config: Dict[str, Any], verbose: int = 0):
+        self.cfg = config or {}
+        self.verbose = verbose
+        
+        # Set logger level based on verbose
+        if verbose >= 5:
+            logger.setLevel(logging.INFO)
+        else:
+            logger.setLevel(logging.WARNING)
+            
+        # Also set ChromaDB and OpenAI client loggers to WARNING
+        logging.getLogger('chromadb').setLevel(logging.WARNING)
+        logging.getLogger('openai').setLevel(logging.WARNING)
+        logging.getLogger('httpx').setLevel(logging.WARNING)
+        logging.getLogger('httpcore').setLevel(logging.WARNING)
+        logging.getLogger('chromadb.segment.impl.vector.local_persistent_hnsw').setLevel(logging.ERROR)
+            
+        self.provider = self.cfg.get("provider", "rag")
+        self.use_mem0 = (self.provider.lower() == "mem0") and MEM0_AVAILABLE
+        self.use_rag = (self.provider.lower() == "rag") and CHROMADB_AVAILABLE and self.cfg.get("use_embedding", False)
+
+        # Create .praison directory if it doesn't exist
+        os.makedirs(".praison", exist_ok=True)
+
+        # Short-term DB
+        self.short_db = self.cfg.get("short_db", ".praison/short_term.db")
+        self._init_stm()
+
+        # Long-term DB
+        self.long_db = self.cfg.get("long_db", ".praison/long_term.db")
+        self._init_ltm()
+
+        # Conditionally init Mem0 or local RAG
+        if self.use_mem0:
+            self._init_mem0()
+        elif self.use_rag:
+            self._init_chroma()
+
+    def _log_verbose(self, msg: str, level: int = logging.INFO):
+        """Only log if verbose >= 5"""
+        if self.verbose >= 5:
+            logger.log(level, msg)
+
+    # -------------------------------------------------------------------------
+    #                          Initialization
+    # -------------------------------------------------------------------------
+    def _init_stm(self):
+        """Creates or verifies short-term memory table."""
+        os.makedirs(os.path.dirname(self.short_db) or ".", exist_ok=True)
+        conn = sqlite3.connect(self.short_db)
+        c = conn.cursor()
+        c.execute("""
+        CREATE TABLE IF NOT EXISTS short_mem (
+            id TEXT PRIMARY KEY,
+            content TEXT,
+            meta TEXT,
+            created_at REAL
+        )
+        """)
+        conn.commit()
+        conn.close()
+
+    def _init_ltm(self):
+        """Creates or verifies long-term memory table."""
+        os.makedirs(os.path.dirname(self.long_db) or ".", exist_ok=True)
+        conn = sqlite3.connect(self.long_db)
+        c = conn.cursor()
+        c.execute("""
+        CREATE TABLE IF NOT EXISTS long_mem (
+            id TEXT PRIMARY KEY,
+            content TEXT,
+            meta TEXT,
+            created_at REAL
+        )
+        """)
+        conn.commit()
+        conn.close()
+
+    def _init_mem0(self):
+        """Initialize Mem0 client for agent or user memory."""
+        from mem0 import MemoryClient
+        mem_cfg = self.cfg.get("config", {})
+        api_key = mem_cfg.get("api_key", os.getenv("MEM0_API_KEY"))
+        org_id = mem_cfg.get("org_id")
+        proj_id = mem_cfg.get("project_id")
+        if org_id and proj_id:
+            self.mem0_client = MemoryClient(api_key=api_key, org_id=org_id, project_id=proj_id)
+        else:
+            self.mem0_client = MemoryClient(api_key=api_key)
+
+    def _init_chroma(self):
+        """Initialize a local Chroma client for embedding-based search."""
+        try:
+            # Create directory if it doesn't exist
+            rag_path = self.cfg.get("rag_db_path", "chroma_db")
+            os.makedirs(rag_path, exist_ok=True)
+
+            # Initialize ChromaDB with persistent storage
+            self.chroma_client = chromadb.PersistentClient(
+                path=rag_path,
+                settings=ChromaSettings(
+                    anonymized_telemetry=False,
+                    allow_reset=True
+                )
+            )
+
+            collection_name = "memory_store"
+            try:
+                self.chroma_col = self.chroma_client.get_collection(name=collection_name)
+                self._log_verbose("Using existing ChromaDB collection")
+            except Exception as e:
+                self._log_verbose(f"Collection '{collection_name}' not found. Creating new collection. Error: {e}")
+                self.chroma_col = self.chroma_client.create_collection(
+                    name=collection_name,
+                    metadata={"hnsw:space": "cosine"}
+                )
+                self._log_verbose("Created new ChromaDB collection")
+
+        except Exception as e:
+            self._log_verbose(f"Failed to initialize ChromaDB: {e}", logging.ERROR)
+            self.use_rag = False
+
+    # -------------------------------------------------------------------------
+    #                      Basic Quality Score Computation
+    # -------------------------------------------------------------------------
+    def compute_quality_score(
+        self,
+        completeness: float,
+        relevance: float,
+        clarity: float,
+        accuracy: float,
+        weights: Dict[str, float] = None
+    ) -> float:
+        """
+        Combine multiple sub-metrics into one final score, as an example.
+
+        Args:
+            completeness (float): 0-1
+            relevance (float): 0-1
+            clarity (float): 0-1
+            accuracy (float): 0-1
+            weights (Dict[str, float]): optional weighting like {"completeness": 0.25, "relevance": 0.3, ...}
+
+        Returns:
+            float: Weighted average 0-1
+        """
+        if not weights:
+            weights = {
+                "completeness": 0.25,
+                "relevance": 0.25,
+                "clarity": 0.25,
+                "accuracy": 0.25
+            }
+        total = (completeness * weights["completeness"]
+                 + relevance   * weights["relevance"]
+                 + clarity     * weights["clarity"]
+                 + accuracy    * weights["accuracy"]
+                )
+        return round(total, 3)  # e.g. round to 3 decimal places
+
+    # -------------------------------------------------------------------------
+    #                           Short-Term Methods
+    # -------------------------------------------------------------------------
+    def store_short_term(
+        self,
+        text: str,
+        metadata: Dict[str, Any] = None,
+        completeness: float = None,
+        relevance: float = None,
+        clarity: float = None,
+        accuracy: float = None,
+        weights: Dict[str, float] = None,
+        evaluator_quality: float = None
+    ):
+        """Store in short-term memory with optional quality metrics"""
+        logger.info(f"Storing in short-term memory: {text[:100]}...")
+        logger.info(f"Metadata: {metadata}")
+        
+        metadata = self._process_quality_metrics(
+            metadata, completeness, relevance, clarity, 
+            accuracy, weights, evaluator_quality
+        )
+        logger.info(f"Processed metadata: {metadata}")
+        
+        # Existing store logic
+        try:
+            conn = sqlite3.connect(self.short_db)
+            ident = str(time.time_ns())
+            conn.execute(
+                "INSERT INTO short_mem (id, content, meta, created_at) VALUES (?,?,?,?)",
+                (ident, text, json.dumps(metadata), time.time())
+            )
+            conn.commit()
+            conn.close()
+            logger.info(f"Successfully stored in short-term memory with ID: {ident}")
+        except Exception as e:
+            logger.error(f"Failed to store in short-term memory: {e}")
+            raise
+
+    def search_short_term(
+        self, 
+        query: str, 
+        limit: int = 5,
+        min_quality: float = 0.0,
+        relevance_cutoff: float = 0.0
+    ) -> List[Dict[str, Any]]:
+        """Search short-term memory with optional quality filter"""
+        self._log_verbose(f"Searching short memory for: {query}")
+        
+        if self.use_mem0 and hasattr(self, "mem0_client"):
+            results = self.mem0_client.search(query=query, limit=limit)
+            filtered = [r for r in results if r.get("score", 1.0) >= relevance_cutoff]
+            return filtered
+            
+        elif self.use_rag and hasattr(self, "chroma_col"):
+            try:
+                from openai import OpenAI
+                client = OpenAI()
+                
+                response = client.embeddings.create(
+                    input=query,
+                    model="text-embedding-3-small"
+                )
+                query_embedding = response.data[0].embedding
+                
+                resp = self.chroma_col.query(
+                    query_embeddings=[query_embedding],
+                    n_results=limit
+                )
+                
+                results = []
+                if resp["ids"]:
+                    for i in range(len(resp["ids"][0])):
+                        metadata = resp["metadatas"][0][i] if "metadatas" in resp else {}
+                        quality = metadata.get("quality", 0.0)
+                        score = 1.0 - (resp["distances"][0][i] if "distances" in resp else 0.0)
+                        if quality >= min_quality and score >= relevance_cutoff:
+                            results.append({
+                                "id": resp["ids"][0][i],
+                                "text": resp["documents"][0][i],
+                                "metadata": metadata,
+                                "score": score
+                            })
+                return results
+            except Exception as e:
+                self._log_verbose(f"Error searching ChromaDB: {e}", logging.ERROR)
+                return []
+        
+        else:
+            # Local fallback
+            conn = sqlite3.connect(self.short_db)
+            c = conn.cursor()
+            rows = c.execute(
+                "SELECT id, content, meta FROM short_mem WHERE content LIKE ? LIMIT ?",
+                (f"%{query}%", limit)
+            ).fetchall()
+            conn.close()
+
+            results = []
+            for row in rows:
+                meta = json.loads(row[2] or "{}")
+                quality = meta.get("quality", 0.0)
+                if quality >= min_quality:
+                    results.append({
+                        "id": row[0],
+                        "text": row[1],
+                        "metadata": meta
+                    })
+            return results
+
+    def reset_short_term(self):
+        """Completely clears short-term memory."""
+        conn = sqlite3.connect(self.short_db)
+        conn.execute("DELETE FROM short_mem")
+        conn.commit()
+        conn.close()
+
+    # -------------------------------------------------------------------------
+    #                           Long-Term Methods
+    # -------------------------------------------------------------------------
+    def _sanitize_metadata(self, metadata: Dict) -> Dict:
+        """Sanitize metadata for ChromaDB - convert to acceptable types"""
+        sanitized = {}
+        for k, v in metadata.items():
+            if v is None:
+                continue
+            if isinstance(v, (str, int, float, bool)):
+                sanitized[k] = v
+            elif isinstance(v, dict):
+                # Convert dict to string representation
+                sanitized[k] = str(v)
+            else:
+                # Convert other types to string
+                sanitized[k] = str(v)
+        return sanitized
+
+    def store_long_term(
+        self,
+        text: str,
+        metadata: Dict[str, Any] = None,
+        completeness: float = None,
+        relevance: float = None,
+        clarity: float = None,
+        accuracy: float = None,
+        weights: Dict[str, float] = None,
+        evaluator_quality: float = None
+    ):
+        """Store in long-term memory with optional quality metrics"""
+        logger.info(f"Storing in long-term memory: {text[:100]}...")
+        logger.info(f"Initial metadata: {metadata}")
+        
+        # Process metadata
+        metadata = metadata or {}
+        metadata = self._process_quality_metrics(
+            metadata, completeness, relevance, clarity,
+            accuracy, weights, evaluator_quality
+        )
+        logger.info(f"Processed metadata: {metadata}")
+        
+        # Generate unique ID
+        ident = str(time.time_ns())
+        created = time.time()
+
+        # Store in SQLite
+        try:
+            conn = sqlite3.connect(self.long_db)
+            conn.execute(
+                "INSERT INTO long_mem (id, content, meta, created_at) VALUES (?,?,?,?)",
+                (ident, text, json.dumps(metadata), created)
+            )
+            conn.commit()
+            conn.close()
+            logger.info(f"Successfully stored in SQLite with ID: {ident}")
+        except Exception as e:
+            logger.error(f"Error storing in SQLite: {e}")
+            return
+
+        # Store in vector database if enabled
+        if self.use_rag and hasattr(self, "chroma_col"):
+            try:
+                from openai import OpenAI
+                client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))  # Ensure API key is correctly set
+                
+                logger.info("Getting embeddings from OpenAI...")
+                logger.debug(f"Embedding input text: {text}")  # Log the input text
+                
+                response = client.embeddings.create(
+                    input=text,
+                    model="text-embedding-3-small"
+                )
+                embedding = response.data[0].embedding
+                logger.info("Successfully got embeddings")
+                logger.debug(f"Received embedding of length: {len(embedding)}")  # Log embedding details
+                
+                # Sanitize metadata for ChromaDB
+                sanitized_metadata = self._sanitize_metadata(metadata)
+                
+                # Store in ChromaDB with embedding
+                self.chroma_col.add(
+                    documents=[text],
+                    metadatas=[sanitized_metadata],
+                    ids=[ident],
+                    embeddings=[embedding]
+                )
+                logger.info(f"Successfully stored in ChromaDB with ID: {ident}")
+            except Exception as e:
+                logger.error(f"Error storing in ChromaDB: {e}")
+        
+        elif self.use_mem0 and hasattr(self, "mem0_client"):
+            try:
+                self.mem0_client.add(text, metadata=metadata)
+                logger.info("Successfully stored in Mem0")
+            except Exception as e:
+                logger.error(f"Error storing in Mem0: {e}")
+
+
+    def search_long_term(
+        self, 
+        query: str, 
+        limit: int = 5, 
+        relevance_cutoff: float = 0.0,
+        min_quality: float = 0.0
+    ) -> List[Dict[str, Any]]:
+        """Search long-term memory with optional quality filter"""
+        self._log_verbose(f"Searching long memory for: {query}")
+        self._log_verbose(f"Min quality: {min_quality}")
+
+        found = []
+
+        if self.use_mem0 and hasattr(self, "mem0_client"):
+            results = self.mem0_client.search(query=query, limit=limit)
+            # Filter by quality
+            filtered = [r for r in results if r.get("metadata", {}).get("quality", 0.0) >= min_quality]
+            logger.info(f"Found {len(filtered)} results in Mem0")
+            return filtered
+
+        elif self.use_rag and hasattr(self, "chroma_col"):
+            try:
+                from openai import OpenAI
+                client = OpenAI()
+                
+                # Get query embedding
+                response = client.embeddings.create(
+                    input=query,
+                    model="text-embedding-3-small"  # Using consistent model
+                )
+                query_embedding = response.data[0].embedding
+                
+                # Search ChromaDB with embedding
+                resp = self.chroma_col.query(
+                    query_embeddings=[query_embedding],
+                    n_results=limit,
+                    include=["documents", "metadatas", "distances"]
+                )
+                
+                results = []
+                if resp["ids"]:
+                    for i in range(len(resp["ids"][0])):
+                        metadata = resp["metadatas"][0][i] if "metadatas" in resp else {}
+                        text = resp["documents"][0][i]
+                        # Add memory record citation
+                        text = f"{text} (Memory record: {text})"
+                        found.append({
+                            "id": resp["ids"][0][i],
+                            "text": text,
+                            "metadata": metadata,
+                            "score": 1.0 - (resp["distances"][0][i] if "distances" in resp else 0.0)
+                        })
+                logger.info(f"Found {len(found)} results in ChromaDB")
+
+            except Exception as e:
+                self._log_verbose(f"Error searching ChromaDB: {e}", logging.ERROR)
+
+        # Always try SQLite as fallback or additional source
+        conn = sqlite3.connect(self.long_db)
+        c = conn.cursor()
+        rows = c.execute(
+            "SELECT id, content, meta, created_at FROM long_mem WHERE content LIKE ? LIMIT ?",
+            (f"%{query}%", limit)
+        ).fetchall()
+        conn.close()
+
+        for row in rows:
+            meta = json.loads(row[2] or "{}")
+            text = row[1]
+            # Add memory record citation if not already present
+            if "(Memory record:" not in text:
+                text = f"{text} (Memory record: {text})"
+            # Only add if not already found by ChromaDB/Mem0
+            if not any(f["id"] == row[0] for f in found):
+                found.append({
+                    "id": row[0],
+                    "text": text,
+                    "metadata": meta,
+                    "created_at": row[3]
+                })
+        logger.info(f"Found {len(found)} total results after SQLite")
+
+        results = found
+
+        # Filter by quality if needed
+        if min_quality > 0:
+            self._log_verbose(f"Found {len(results)} initial results")
+            results = [
+                r for r in results 
+                if r.get("metadata", {}).get("quality", 0.0) >= min_quality
+            ]
+            self._log_verbose(f"After quality filter: {len(results)} results")
+
+        # Apply relevance cutoff if specified
+        if relevance_cutoff > 0:
+            results = [r for r in results if r.get("score", 1.0) >= relevance_cutoff]
+            logger.info(f"After relevance filter: {len(results)} results")
+        
+        return results[:limit]
+
+    def reset_long_term(self):
+        """Clear local LTM DB, plus Chroma or mem0 if in use."""
+        conn = sqlite3.connect(self.long_db)
+        conn.execute("DELETE FROM long_mem")
+        conn.commit()
+        conn.close()
+
+        if self.use_mem0 and hasattr(self, "mem0_client"):
+            # Mem0 has no universal reset API. Could implement partial or no-op.
+            pass
+        if self.use_rag and hasattr(self, "chroma_client"):
+            self.chroma_client.reset()  # entire DB
+            self._init_chroma()         # re-init fresh
+
+    # -------------------------------------------------------------------------
+    #                       Entity Memory Methods
+    # -------------------------------------------------------------------------
+    def store_entity(self, name: str, type_: str, desc: str, relations: str):
+        """
+        Save entity info in LTM (or mem0/rag). 
+        We'll label the metadata type = entity for easy filtering.
+        """
+        data = f"Entity {name}({type_}): {desc} | relationships: {relations}"
+        self.store_long_term(data, metadata={"category": "entity"})
+
+    def search_entity(self, query: str, limit: int = 5) -> List[Dict[str, Any]]:
+        """
+        Filter to items that have metadata 'category=entity'.
+        """
+        all_hits = self.search_long_term(query, limit=20)  # gather more
+        ents = []
+        for h in all_hits:
+            meta = h.get("metadata") or {}
+            if meta.get("category") == "entity":
+                ents.append(h)
+        return ents[:limit]
+
+    def reset_entity_only(self):
+        """
+        If you only want to drop entity items from LTM, you'd do a custom 
+        delete from local DB where meta LIKE '%category=entity%'. 
+        For brevity, we do a full LTM reset here.
+        """
+        self.reset_long_term()
+
+    # -------------------------------------------------------------------------
+    #                       User Memory Methods
+    # -------------------------------------------------------------------------
+    def store_user_memory(self, user_id: str, text: str, extra: Dict[str, Any] = None):
+        """
+        If mem0 is used, do user-based addition. Otherwise store in LTM with user in metadata.
+        """
+        meta = {"user_id": user_id}
+        if extra:
+            meta.update(extra)
+
+        if self.use_mem0 and hasattr(self, "mem0_client"):
+            self.mem0_client.add(text, user_id=user_id, metadata=meta)
+        else:
+            self.store_long_term(text, metadata=meta)
+
+    def search_user_memory(self, user_id: str, query: str, limit: int = 5) -> List[Dict[str, Any]]:
+        """
+        If mem0 is used, pass user_id in. Otherwise fallback to local filter on user in metadata.
+        """
+        if self.use_mem0 and hasattr(self, "mem0_client"):
+            return self.mem0_client.search(query=query, limit=limit, user_id=user_id)
+        else:
+            hits = self.search_long_term(query, limit=20)
+            filtered = []
+            for h in hits:
+                meta = h.get("metadata", {})
+                if meta.get("user_id") == user_id:
+                    filtered.append(h)
+            return filtered[:limit]
+
+    def reset_user_memory(self):
+        """
+        Clear all user-based info. For simplicity, we do a full LTM reset. 
+        Real usage might filter only metadata "user_id".
+        """
+        self.reset_long_term()
+
+    # -------------------------------------------------------------------------
+    #                 Putting it all Together: Task Finalization
+    # -------------------------------------------------------------------------
+    def finalize_task_output(
+        self,
+        content: str,
+        agent_name: str,
+        quality_score: float,
+        threshold: float = 0.7,
+        metrics: Dict[str, Any] = None,
+        task_id: str = None
+    ):
+        """Store task output in memory with appropriate metadata"""
+        logger.info(f"Finalizing task output: {content[:100]}...")
+        logger.info(f"Agent: {agent_name}, Quality: {quality_score}, Threshold: {threshold}")
+        
+        metadata = {
+            "task_id": task_id,
+            "agent": agent_name,
+            "quality": quality_score,
+            "metrics": metrics,
+            "task_type": "output",
+            "stored_at": time.time()
+        }
+        logger.info(f"Prepared metadata: {metadata}")
+        
+        # Always store in short-term memory
+        try:
+            logger.info("Storing in short-term memory...")
+            self.store_short_term(
+                text=content,
+                metadata=metadata
+            )
+            logger.info("Successfully stored in short-term memory")
+        except Exception as e:
+            logger.error(f"Failed to store in short-term memory: {e}")
+        
+        # Store in long-term memory if quality meets threshold
+        if quality_score >= threshold:
+            try:
+                logger.info(f"Quality score {quality_score} >= {threshold}, storing in long-term memory...")
+                self.store_long_term(
+                    text=content,
+                    metadata=metadata
+                )
+                logger.info("Successfully stored in long-term memory")
+            except Exception as e:
+                logger.error(f"Failed to store in long-term memory: {e}")
+        else:
+            logger.info(f"Quality score {quality_score} < {threshold}, skipping long-term storage")
+
+    # -------------------------------------------------------------------------
+    #                 Building Context (Short, Long, Entities, User)
+    # -------------------------------------------------------------------------
+    def build_context_for_task(
+        self,
+        task_descr: str,
+        user_id: Optional[str] = None,
+        additional: str = "",
+        max_items: int = 3
+    ) -> str:
+        """
+        Merges relevant short-term, long-term, entity, user memories
+        into a single text block with deduplication and clean formatting.
+        """
+        q = (task_descr + " " + additional).strip()
+        lines = []
+        seen_contents = set()  # Track unique contents
+
+        def normalize_content(content: str) -> str:
+            """Normalize content for deduplication"""
+            # Extract just the main content without citations for comparison
+            normalized = content.split("(Memory record:")[0].strip()
+            # Keep more characters to reduce false duplicates
+            normalized = ''.join(c.lower() for c in normalized if not c.isspace())
+            return normalized
+
+        def format_content(content: str, max_len: int = 150) -> str:
+            """Format content with clean truncation at word boundaries"""
+            if not content:
+                return ""
+            
+            # Clean up content by removing extra whitespace and newlines
+            content = ' '.join(content.split())
+            
+            # If content contains a memory citation, preserve it
+            if "(Memory record:" in content:
+                return content  # Keep original citation format
+            
+            # Regular content truncation
+            if len(content) <= max_len:
+                return content
+            
+            truncate_at = content.rfind(' ', 0, max_len - 3)
+            if truncate_at == -1:
+                truncate_at = max_len - 3
+            return content[:truncate_at] + "..."
+
+        def add_section(title: str, hits: List[Any]) -> None:
+            """Add a section of memory hits with deduplication"""
+            if not hits:
+                return
+                
+            formatted_hits = []
+            for h in hits:
+                content = h.get('text', '') if isinstance(h, dict) else str(h)
+                if not content:
+                    continue
+                    
+                # Keep original format if it has a citation
+                if "(Memory record:" in content:
+                    formatted = content
+                else:
+                    formatted = format_content(content)
+                
+                # Only add if we haven't seen this normalized content before
+                normalized = normalize_content(formatted)
+                if normalized not in seen_contents:
+                    seen_contents.add(normalized)
+                    formatted_hits.append(formatted)
+            
+            if formatted_hits:
+                # Add section header
+                if lines:
+                    lines.append("")  # Space before new section
+                lines.append(title)
+                lines.append("=" * len(title))  # Underline the title
+                lines.append("")  # Space after title
+                
+                # Add formatted content with bullet points
+                for content in formatted_hits:
+                    lines.append(f" • {content}")
+
+        # Add each section
+        # First get all results
+        short_term = self.search_short_term(q, limit=max_items)
+        long_term = self.search_long_term(q, limit=max_items)
+        entities = self.search_entity(q, limit=max_items)
+        user_mem = self.search_user_memory(user_id, q, limit=max_items) if user_id else []
+
+        # Add sections in order of priority
+        add_section("Short-term Memory Context", short_term)
+        add_section("Long-term Memory Context", long_term)
+        add_section("Entity Context", entities)
+        if user_id:
+            add_section("User Context", user_mem)
+
+        return "\n".join(lines) if lines else ""
+
+    # -------------------------------------------------------------------------
+    #                      Master Reset (Everything)
+    # -------------------------------------------------------------------------
+    def reset_all(self):
+        """
+        Fully wipes short-term, long-term, and any memory in mem0 or rag.
+        """
+        self.reset_short_term()
+        self.reset_long_term()
+        # Entities & user memory are stored in LTM or mem0, so no separate step needed.
+
+    def _process_quality_metrics(
+        self,
+        metadata: Dict[str, Any],
+        completeness: float = None,
+        relevance: float = None,
+        clarity: float = None, 
+        accuracy: float = None,
+        weights: Dict[str, float] = None,
+        evaluator_quality: float = None
+    ) -> Dict[str, Any]:
+        """Process and store quality metrics in metadata"""
+        metadata = metadata or {}
+        
+        # Handle sub-metrics if provided
+        if None not in [completeness, relevance, clarity, accuracy]:
+            metadata.update({
+                "completeness": completeness,
+                "relevance": relevance,
+                "clarity": clarity,
+                "accuracy": accuracy,
+                "quality": self.compute_quality_score(
+                    completeness, relevance, clarity, accuracy, weights
+                )
+            })
+        # Handle external evaluator quality if provided
+        elif evaluator_quality is not None:
+            metadata["quality"] = evaluator_quality
+        
+        return metadata
+
+    def calculate_quality_metrics(
+        self,
+        output: str,
+        expected_output: str,
+        llm: Optional[str] = None,
+        custom_prompt: Optional[str] = None
+    ) -> Dict[str, float]:
+        """Calculate quality metrics using LLM"""
+        logger.info("Calculating quality metrics for output")
+        logger.info(f"Output: {output[:100]}...")
+        logger.info(f"Expected: {expected_output[:100]}...")
+        
+        # Default evaluation prompt
+        default_prompt = f"""
+        Evaluate the following output against expected output.
+        Score each metric from 0.0 to 1.0:
+        - Completeness: Does it address all requirements?
+        - Relevance: Does it match expected output?
+        - Clarity: Is it clear and well-structured?
+        - Accuracy: Is it factually correct?
+
+        Expected: {expected_output}
+        Actual: {output}
+
+        Return ONLY a JSON with these keys: completeness, relevance, clarity, accuracy
+        Example: {{"completeness": 0.95, "relevance": 0.8, "clarity": 0.9, "accuracy": 0.85}}
+        """
+
+        try:
+            # Use OpenAI client from main.py
+            from ..main import client
+            
+            response = client.chat.completions.create(
+                model=llm or "gpt-4o",
+                messages=[{
+                    "role": "user", 
+                    "content": custom_prompt or default_prompt
+                }],
+                response_format={"type": "json_object"},
+                temperature=0.3
+            )
+            
+            metrics = json.loads(response.choices[0].message.content)
+            
+            # Validate metrics
+            required = ["completeness", "relevance", "clarity", "accuracy"]
+            if not all(k in metrics for k in required):
+                raise ValueError("Missing required metrics in LLM response")
+            
+            logger.info(f"Calculated metrics: {metrics}")
+            return metrics
+            
+        except Exception as e:
+            logger.error(f"Error calculating metrics: {e}")
+            return {
+                "completeness": 0.0,
+                "relevance": 0.0,
+                "clarity": 0.0,
+                "accuracy": 0.0
+            }
+
+    def store_quality(
+        self,
+        text: str,
+        quality_score: float,
+        task_id: Optional[str] = None,
+        iteration: Optional[int] = None,
+        metrics: Optional[Dict[str, float]] = None,
+        memory_type: Literal["short", "long"] = "long"
+    ) -> None:
+        """Store quality metrics in memory"""
+        logger.info(f"Attempting to store in {memory_type} memory: {text[:100]}...")
+        
+        metadata = {
+            "quality": quality_score,
+            "task_id": task_id,
+            "iteration": iteration
+        }
+        
+        if metrics:
+            metadata.update({
+                k: v for k, v in metrics.items()  # Remove metric_ prefix
+            })
+            
+        logger.info(f"With metadata: {metadata}")
+        
+        try:
+            if memory_type == "short":
+                self.store_short_term(text, metadata=metadata)
+                logger.info("Successfully stored in short-term memory")
+            else:
+                self.store_long_term(text, metadata=metadata)
+                logger.info("Successfully stored in long-term memory")
+        except Exception as e:
+            logger.error(f"Failed to store in memory: {e}")
+
+    def search_with_quality(
+        self,
+        query: str,
+        min_quality: float = 0.0,
+        memory_type: Literal["short", "long"] = "long",
+        limit: int = 5
+    ) -> List[Dict[str, Any]]:
+        """Search with quality filter"""
+        logger.info(f"Searching {memory_type} memory for: {query}")
+        logger.info(f"Min quality: {min_quality}")
+        
+        search_func = (
+            self.search_short_term if memory_type == "short" 
+            else self.search_long_term
+        )
+        
+        results = search_func(query, limit=limit)
+        logger.info(f"Found {len(results)} initial results")
+        
+        filtered = [
+            r for r in results 
+            if r.get("metadata", {}).get("quality", 0.0) >= min_quality
+        ]
+        logger.info(f"After quality filter: {len(filtered)} results")
+        
+        return filtered

agents/praisonaiagents/task/task.py

@@ -5,12 +5,17 @@ from pydantic import BaseModel
 from ..main import TaskOutput
 from ..agent.agent import Agent
 import uuid
+import os
+import time
+
+# Set up logger
+logger = logging.getLogger(__name__)
 
 class Task:
     def __init__(
         self,
         description: str,
-        expected_output: str,
+        expected_output: Optional[str] = None,
         agent: Optional[Agent] = None,
         name: Optional[str] = None,
         tools: Optional[List[Any]] = None,
@@ -30,12 +35,14 @@ class Task:
         task_type: str = "task",
         condition: Optional[Dict[str, List[str]]] = None,
         is_start: bool = False,
-        loop_state: Optional[Dict[str, Union[str, int]]] = None
+        loop_state: Optional[Dict[str, Union[str, int]]] = None,
+        memory=None,
+        quality_check=True
     ):
         self.id = str(uuid.uuid4()) if id is None else str(id)
         self.name = name
         self.description = description
-        self.expected_output = expected_output
+        self.expected_output = expected_output if expected_output is not None else "Complete the task successfully"
         self.agent = agent
         self.tools = tools if tools else []
         self.context = context if context else []
@@ -54,6 +61,21 @@ class Task:
         self.condition = condition if condition else {}
         self.is_start = is_start
         self.loop_state = loop_state if loop_state else {}
+        self.memory = memory
+        self.quality_check = quality_check
+
+        # Set logger level based on config verbose level
+        verbose = self.config.get("verbose", 0)
+        if verbose >= 5:
+            logger.setLevel(logging.INFO)
+        else:
+            logger.setLevel(logging.WARNING)
+            
+        # Also set third-party loggers to WARNING
+        logging.getLogger('chromadb').setLevel(logging.WARNING)
+        logging.getLogger('openai').setLevel(logging.WARNING)
+        logging.getLogger('httpx').setLevel(logging.WARNING)
+        logging.getLogger('httpcore').setLevel(logging.WARNING)
 
         if self.output_json and self.output_pydantic:
             raise ValueError("Only one output type can be defined")
@@ -64,11 +86,137 @@ class Task:
     def __str__(self):
         return f"Task(name='{self.name if self.name else 'None'}', description='{self.description}', agent='{self.agent.name if self.agent else 'None'}', status='{self.status}')"
 
+    def initialize_memory(self):
+        """Initialize memory if config exists but memory doesn't"""
+        if not self.memory and self.config.get('memory_config'):
+            try:
+                from ..memory.memory import Memory
+                logger.info(f"Task {self.id}: Initializing memory from config: {self.config['memory_config']}")
+                self.memory = Memory(config=self.config['memory_config'])
+                logger.info(f"Task {self.id}: Memory initialized successfully")
+                
+                # Verify database was created
+                if os.path.exists(self.config['memory_config']['storage']['path']):
+                    logger.info(f"Task {self.id}: Memory database exists after initialization")
+                else:
+                    logger.error(f"Task {self.id}: Failed to create memory database!")
+                return self.memory
+            except Exception as e:
+                logger.error(f"Task {self.id}: Failed to initialize memory: {e}")
+                logger.exception(e)
+        return None
+
+    def store_in_memory(self, content: str, agent_name: str = None, task_id: str = None):
+        """Store content in memory with metadata"""
+        if self.memory:
+            try:
+                logger.info(f"Task {self.id}: Storing content in memory...")
+                self.memory.store_long_term(
+                    text=content,
+                    metadata={
+                        "agent_name": agent_name or "Agent",
+                        "task_id": task_id or self.id,
+                        "timestamp": time.time()
+                    }
+                )
+                logger.info(f"Task {self.id}: Content stored in memory")
+            except Exception as e:
+                logger.error(f"Task {self.id}: Failed to store content in memory: {e}")
+                logger.exception(e)
+
     async def execute_callback(self, task_output: TaskOutput) -> None:
-        """Execute the callback function, handling both sync and async callbacks"""
+        """Execute callback and store quality metrics if enabled"""
+        logger.info(f"Task {self.id}: execute_callback called")
+        logger.info(f"Quality check enabled: {self.quality_check}")
+        
+        # Initialize memory if not already initialized
+        if not self.memory:
+            self.memory = self.initialize_memory()
+        
+        logger.info(f"Memory object exists: {self.memory is not None}")
+        if self.memory:
+            logger.info(f"Memory config: {self.memory.cfg}")
+            # Store task output in memory
+            try:
+                logger.info(f"Task {self.id}: Storing task output in memory...")
+                self.store_in_memory(
+                    content=task_output.raw,
+                    agent_name=self.agent.name if self.agent else "Agent",
+                    task_id=self.id
+                )
+                logger.info(f"Task {self.id}: Task output stored in memory")
+            except Exception as e:
+                logger.error(f"Task {self.id}: Failed to store task output in memory: {e}")
+                logger.exception(e)
+        
+        logger.info(f"Task output: {task_output.raw[:100]}...")
+        
+        if self.quality_check and self.memory:
+            try:
+                logger.info(f"Task {self.id}: Starting memory operations")
+                logger.info(f"Task {self.id}: Calculating quality metrics for output: {task_output.raw[:100]}...")
+                
+                # Get quality metrics from LLM
+                metrics = self.memory.calculate_quality_metrics(
+                    task_output.raw,
+                    self.expected_output
+                )
+                logger.info(f"Task {self.id}: Quality metrics calculated: {metrics}")
+                
+                quality_score = metrics.get("accuracy", 0.0)
+                logger.info(f"Task {self.id}: Quality score: {quality_score}")
+                
+                # Store in both short and long-term memory with higher threshold
+                logger.info(f"Task {self.id}: Finalizing task output in memory...")
+                self.memory.finalize_task_output(
+                    content=task_output.raw,
+                    agent_name=self.agent.name if self.agent else "Agent",
+                    quality_score=quality_score,
+                    threshold=0.7,  # Only high quality outputs in long-term memory
+                    metrics=metrics,
+                    task_id=self.id
+                )
+                logger.info(f"Task {self.id}: Finalized task output in memory")
+                
+                # Store quality metrics separately
+                logger.info(f"Task {self.id}: Storing quality metrics...")
+                self.memory.store_quality(
+                    text=task_output.raw,
+                    quality_score=quality_score,
+                    task_id=self.id,
+                    metrics=metrics
+                )
+                
+                # Store in both short and long-term memory with higher threshold
+                self.memory.finalize_task_output(
+                    content=task_output.raw,
+                    agent_name=self.agent.name if self.agent else "Agent",
+                    quality_score=quality_score,
+                    threshold=0.7  # Only high quality outputs in long-term memory
+                )
+                
+                # Build context for next tasks
+                if self.next_tasks:
+                    logger.info(f"Task {self.id}: Building context for next tasks...")
+                    context = self.memory.build_context_for_task(
+                        task_descr=task_output.raw,
+                        max_items=5
+                    )
+                    logger.info(f"Task {self.id}: Built context for next tasks: {len(context)} items")
+                
+                logger.info(f"Task {self.id}: Memory operations complete")
+            except Exception as e:
+                logger.error(f"Task {self.id}: Failed to process memory operations: {e}")
+                logger.exception(e)  # Print full stack trace
+                # Continue execution even if memory operations fail
+
+        # Execute original callback
         if self.callback:
-            if asyncio.iscoroutinefunction(self.callback):
-                await self.callback(task_output)
-            else:
-                loop = asyncio.get_event_loop()
-                await loop.run_in_executor(None, self.callback, task_output) 
+            try:
+                if asyncio.iscoroutinefunction(self.callback):
+                    await self.callback(task_output)
+                else:
+                    self.callback(task_output)
+            except Exception as e:
+                logger.error(f"Task {self.id}: Failed to execute callback: {e}")
+                logger.exception(e)

agents/praisonaiagents/tools/README.md

@@ -0,0 +1,149 @@
+# PraisonAI Tools Guide
+
+Welcome to the PraisonAI Tools directory! This guide will help you understand how our tools work and how to create new ones, whether you're a beginner or an experienced programmer.
+
+## What is a Tool?
+
+A tool is a piece of code that helps our AI agents perform specific tasks. Think of tools as special abilities that we give to our agents. For example:
+- An internet search tool lets agents search the web
+- A stock market tool lets agents check stock prices
+- A weather tool lets agents check the weather
+
+## Creating New Tools: The Two Approaches
+
+### 1. Function-Based Approach (Simple Tools)
+
+Best for simple tools that do one specific thing. Like a calculator that just adds numbers.
+
+**When to use:**
+- Tool does one simple task
+- Doesn't need to remember information between uses
+- Doesn't need to share information with other tools
+- Quick, one-time operations
+
+**Example:**
+```python
+def internet_search(query: str):
+    # Search the internet and return results
+    return search_results
+```
+
+**Usage:**
+```python
+from praisonaiagents.tools import internet_search
+
+results = internet_search("AI news")
+```
+
+### 2. Class-Based Approach (Complex Tools)
+
+Best for tools that do multiple related things or need to remember information. Like a smart calculator that remembers your previous calculations and can do many different math operations.
+
+**When to use:**
+- Tool has multiple related functions
+- Needs to remember or share information
+- Needs to manage resources efficiently
+- Has complex setup requirements
+
+**Example:**
+```python
+class StockTools:
+    def get_stock_price(self, symbol):
+        # Get current stock price
+        return price
+        
+    def get_stock_info(self, symbol):
+        # Get detailed stock information
+        return info
+```
+
+**Usage:**
+```python
+from praisonaiagents.tools import get_stock_price, get_stock_info
+
+price = get_stock_price("AAPL")
+info = get_stock_info("AAPL")
+```
+
+## How to Choose Your Approach
+
+Ask yourself these questions:
+
+1. **Is your tool doing one simple thing?**
+   - Yes → Use Function-Based Approach
+   - No → Consider Class-Based Approach
+
+2. **Does your tool need to remember information?**
+   - Yes → Use Class-Based Approach
+   - No → Use Function-Based Approach
+
+3. **Are your tool's operations related to each other?**
+   - Yes → Use Class-Based Approach
+   - No → Use Function-Based Approach
+
+4. **Does your tool need to manage resources efficiently?**
+   - Yes → Use Class-Based Approach
+   - No → Use Function-Based Approach
+
+## Real-World Examples
+
+### Internet Search Tool (Function-Based)
+- Does one thing: searches the internet
+- Doesn't need to remember previous searches
+- Each search is independent
+- Simple input/output operation
+
+### Stock Market Tool (Class-Based)
+- Does multiple things: check prices, get company info, get historical data
+- Remembers stock information to avoid repeated downloads
+- Operations are related (all about stocks)
+- Manages connections efficiently
+
+## Getting Started
+
+1. **Choose Your Approach** based on the guidelines above
+
+2. **Create Your Tool File**:
+   - Name it descriptively (e.g., `weather_tools.py`)
+   - Place it in the `praisonaiagents/tools` directory
+
+3. **Write Your Tool**:
+   - Add clear documentation
+   - Include type hints for better understanding
+   - Handle errors gracefully
+
+4. **Test Your Tool**:
+   - Make sure it works as expected
+   - Test error cases
+   - Check performance
+
+## Best Practices
+
+1. **Documentation**:
+   - Explain what your tool does
+   - Provide examples
+   - List any requirements
+
+2. **Error Handling**:
+   - Always handle possible errors
+   - Return helpful error messages
+   - Don't let your tool crash
+
+3. **Performance**:
+   - Keep it efficient
+   - Don't waste resources
+   - Cache when helpful
+
+4. **User-Friendly**:
+   - Make it easy to use
+   - Use clear function/method names
+   - Keep it simple
+
+## Need Help?
+
+- Check existing tools for examples
+- Ask in our community
+- Read the documentation
+- Don't hesitate to ask questions!
+
+Remember: The goal is to make tools that are easy to use and maintain. Choose the approach that makes the most sense for your specific tool's needs.

agents/praisonaiagents/tools/__init__.py

@@ -1,4 +1,178 @@
 """Tools package for PraisonAI Agents"""
-from .tools import Tools
+from importlib import import_module
+from typing import Any
 
-__all__ = ['Tools'] 
+# Map of function names to their module and class (if any)
+TOOL_MAPPINGS = {
+    # Direct functions
+    'internet_search': ('.duckduckgo_tools', None),
+    'duckduckgo': ('.duckduckgo_tools', None),
+    
+    # arXiv Tools
+    'search_arxiv': ('.arxiv_tools', None),
+    'get_arxiv_paper': ('.arxiv_tools', None),
+    'get_papers_by_author': ('.arxiv_tools', None),
+    'get_papers_by_category': ('.arxiv_tools', None),
+    'arxiv_tools': ('.arxiv_tools', None),
+    
+    # Wikipedia Tools
+    'wiki_search': ('.wikipedia_tools', None),
+    'wiki_summary': ('.wikipedia_tools', None),
+    'wiki_page': ('.wikipedia_tools', None),
+    'wiki_random': ('.wikipedia_tools', None),
+    'wiki_language': ('.wikipedia_tools', None),
+    'wikipedia_tools': ('.wikipedia_tools', None),
+    
+    # Newspaper Tools
+    'get_article': ('.newspaper_tools', None),
+    'get_news_sources': ('.newspaper_tools', None),
+    'get_articles_from_source': ('.newspaper_tools', None),
+    'get_trending_topics': ('.newspaper_tools', None),
+    'newspaper_tools': ('.newspaper_tools', None),
+    
+    # Spider Tools
+    'scrape_page': ('.spider_tools', None),
+    'extract_links': ('.spider_tools', None),
+    'crawl': ('.spider_tools', None),
+    'extract_text': ('.spider_tools', None),
+    'spider_tools': ('.spider_tools', None),
+    
+    # DuckDB Tools
+    'query': ('.duckdb_tools', None),
+    'create_table': ('.duckdb_tools', None),
+    'load_data': ('.duckdb_tools', None),
+    'export_data': ('.duckdb_tools', None),
+    'get_table_info': ('.duckdb_tools', None),
+    'analyze_data': ('.duckdb_tools', None),
+    'duckdb_tools': ('.duckdb_tools', None),
+    
+    # Shell Tools
+    'execute_command': ('.shell_tools', None),
+    'list_processes': ('.shell_tools', None),
+    'kill_process': ('.shell_tools', None),
+    'get_system_info': ('.shell_tools', None),
+    'shell_tools': ('.shell_tools', None),
+
+    # Calculator Tools
+    'evaluate': ('.calculator_tools', None),
+    'solve_equation': ('.calculator_tools', None),
+    'convert_units': ('.calculator_tools', None),
+    'calculate_statistics': ('.calculator_tools', None),
+    'calculate_financial': ('.calculator_tools', None),
+    'calculator_tools': ('.calculator_tools', None),
+
+    # Class methods from YFinance
+    'get_stock_price': ('.yfinance_tools', 'YFinanceTools'),
+    'get_stock_info': ('.yfinance_tools', 'YFinanceTools'),
+    'get_historical_data': ('.yfinance_tools', 'YFinanceTools'),
+    'yfinance': ('.yfinance_tools', 'YFinanceTools'),
+
+    # File Tools
+    'read_file': ('.file_tools', 'FileTools'),
+    'write_file': ('.file_tools', 'FileTools'),
+    'list_files': ('.file_tools', 'FileTools'),
+    'get_file_info': ('.file_tools', 'FileTools'),
+    'copy_file': ('.file_tools', 'FileTools'),
+    'move_file': ('.file_tools', 'FileTools'),
+    'delete_file': ('.file_tools', 'FileTools'),
+    'file_tools': ('.file_tools', 'FileTools'),
+
+    # CSV Tools
+    'read_csv': ('.csv_tools', 'CSVTools'),
+    'write_csv': ('.csv_tools', 'CSVTools'),
+    'merge_csv': ('.csv_tools', 'CSVTools'),
+    'analyze_csv': ('.csv_tools', 'CSVTools'),
+    'split_csv': ('.csv_tools', 'CSVTools'),
+    'csv_tools': ('.csv_tools', 'CSVTools'),
+
+    # JSON Tools
+    'read_json': ('.json_tools', 'JSONTools'),
+    'write_json': ('.json_tools', 'JSONTools'),
+    'merge_json': ('.json_tools', 'JSONTools'),
+    'validate_json': ('.json_tools', 'JSONTools'),
+    'analyze_json': ('.json_tools', 'JSONTools'),
+    'transform_json': ('.json_tools', 'JSONTools'),
+    'json_tools': ('.json_tools', 'JSONTools'),
+
+    # Excel Tools
+    'read_excel': ('.excel_tools', 'ExcelTools'),
+    'write_excel': ('.excel_tools', 'ExcelTools'),
+    'merge_excel': ('.excel_tools', 'ExcelTools'),
+    'create_chart': ('.excel_tools', 'ExcelTools'),
+    'add_chart_to_sheet': ('.excel_tools', 'ExcelTools'),
+    'excel_tools': ('.excel_tools', 'ExcelTools'),
+
+    # XML Tools
+    'read_xml': ('.xml_tools', 'XMLTools'),
+    'write_xml': ('.xml_tools', 'XMLTools'),
+    'transform_xml': ('.xml_tools', 'XMLTools'),
+    'validate_xml': ('.xml_tools', 'XMLTools'),
+    'xml_to_dict': ('.xml_tools', 'XMLTools'),
+    'dict_to_xml': ('.xml_tools', 'XMLTools'),
+    'xpath_query': ('.xml_tools', 'XMLTools'),
+    'xml_tools': ('.xml_tools', 'XMLTools'),
+
+    # YAML Tools
+    'read_yaml': ('.yaml_tools', 'YAMLTools'),
+    'write_yaml': ('.yaml_tools', 'YAMLTools'),
+    'merge_yaml': ('.yaml_tools', 'YAMLTools'),
+    'validate_yaml': ('.yaml_tools', 'YAMLTools'),
+    'analyze_yaml': ('.yaml_tools', 'YAMLTools'),
+    'transform_yaml': ('.yaml_tools', 'YAMLTools'),
+    'yaml_tools': ('.yaml_tools', 'YAMLTools'),
+
+    # Python Tools
+    'execute_code': ('.python_tools', 'PythonTools'),
+    'analyze_code': ('.python_tools', 'PythonTools'),
+    'format_code': ('.python_tools', 'PythonTools'),
+    'lint_code': ('.python_tools', 'PythonTools'),
+    'disassemble_code': ('.python_tools', 'PythonTools'),
+    'python_tools': ('.python_tools', 'PythonTools'),
+
+    # Pandas Tools
+    'filter_data': ('.pandas_tools', 'PandasTools'),
+    'get_summary': ('.pandas_tools', 'PandasTools'),
+    'group_by': ('.pandas_tools', 'PandasTools'),
+    'pivot_table': ('.pandas_tools', 'PandasTools'),
+    'pandas_tools': ('.pandas_tools', 'PandasTools'),
+}
+
+_instances = {}  # Cache for class instances
+
+def __getattr__(name: str) -> Any:
+    """Smart lazy loading of tools with class method support."""
+    if name not in TOOL_MAPPINGS:
+        raise AttributeError(f"module '{__package__}' has no attribute '{name}'")
+    
+    module_path, class_name = TOOL_MAPPINGS[name]
+    
+    if class_name is None:
+        # Direct function import
+        module = import_module(module_path, __package__)
+        if name in [
+            'duckduckgo', 'internet_search',
+            'search_arxiv', 'get_arxiv_paper', 'get_papers_by_author', 'get_papers_by_category',
+            'wiki_search', 'wiki_summary', 'wiki_page', 'wiki_random', 'wiki_language',
+            'get_article', 'get_news_sources', 'get_articles_from_source', 'get_trending_topics',
+            'scrape_page', 'extract_links', 'crawl', 'extract_text',
+            'query', 'create_table', 'load_data', 'export_data', 'get_table_info', 'analyze_data',
+            'execute_command', 'list_processes', 'kill_process', 'get_system_info',
+            'evaluate', 'solve_equation', 'convert_units', 'calculate_statistics', 'calculate_financial'
+        ]:
+            return getattr(module, name)
+        if name in ['file_tools', 'pandas_tools', 'wikipedia_tools',
+                   'newspaper_tools', 'arxiv_tools', 'spider_tools', 'duckdb_tools', 'csv_tools', 'json_tools', 'excel_tools', 'xml_tools', 'yaml_tools', 'calculator_tools', 'python_tools', 'shell_tools']:
+            return module  # Returns the callable module
+        return getattr(module, name)
+    else:
+        # Class method import
+        if class_name not in _instances:
+            module = import_module(module_path, __package__)
+            class_ = getattr(module, class_name)
+            _instances[class_name] = class_()
+        
+        # Get the method and bind it to the instance
+        method = getattr(_instances[class_name], name)
+        return method
+
+__all__ = list(TOOL_MAPPINGS.keys())

agents/praisonaiagents/tools/arxiv_tools.py

@@ -0,0 +1,292 @@
+"""Tools for searching and retrieving papers from arXiv.
+
+Usage:
+from praisonaiagents.tools import arxiv_tools
+papers = arxiv_tools.search("quantum computing")
+paper = arxiv_tools.get_paper("2401.00123")
+
+or
+from praisonaiagents.tools import search_arxiv, get_arxiv_paper
+papers = search_arxiv("quantum computing")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any
+from importlib import util
+import json
+
+# Map sort criteria to arxiv.SortCriterion
+SORT_CRITERIA = {
+    "relevance": "Relevance",
+    "lastupdateddate": "LastUpdatedDate",
+    "submitteddate": "SubmittedDate"
+}
+
+# Map sort order to arxiv.SortOrder
+SORT_ORDER = {
+    "ascending": "Ascending",
+    "descending": "Descending"
+}
+
+class ArxivTools:
+    """Tools for searching and retrieving papers from arXiv."""
+    
+    def __init__(self):
+        """Initialize ArxivTools and check for arxiv package."""
+        self._check_arxiv()
+        
+    def _check_arxiv(self):
+        """Check if arxiv package is installed."""
+        if util.find_spec("arxiv") is None:
+            raise ImportError("arxiv package is not available. Please install it using: pip install arxiv")
+        global arxiv
+        import arxiv
+
+    def search(
+        self,
+        query: str,
+        max_results: int = 10,
+        sort_by: str = "relevance",
+        sort_order: str = "descending",
+        include_fields: Optional[List[str]] = None
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """
+        Search arXiv for papers matching the query.
+        
+        Args:
+            query: Search query (e.g., "quantum computing", "author:Einstein")
+            max_results: Maximum number of results to return
+            sort_by: Sort results by ("relevance", "lastUpdatedDate", "submittedDate")
+            sort_order: Sort order ("ascending" or "descending")
+            include_fields: List of fields to include in results. If None, includes all.
+                Available fields: ["title", "authors", "summary", "comment", "journal_ref",
+                "doi", "primary_category", "categories", "links"]
+            
+        Returns:
+            List[Dict] or Dict: List of papers or error dict
+        """
+        try:
+            import arxiv
+            
+            # Configure search client
+            client = arxiv.Client()
+            
+            # Map sort criteria
+            sort_by_enum = getattr(arxiv.SortCriterion, SORT_CRITERIA[sort_by.lower()])
+            sort_order_enum = getattr(arxiv.SortOrder, SORT_ORDER[sort_order.lower()])
+            
+            # Build search query
+            search = arxiv.Search(
+                query=query,
+                max_results=max_results,
+                sort_by=sort_by_enum,
+                sort_order=sort_order_enum
+            )
+            
+            # Execute search
+            results = []
+            for result in client.results(search):
+                # Convert to dict with selected fields
+                paper = self._result_to_dict(result, include_fields)
+                results.append(paper)
+            
+            return results
+        except Exception as e:
+            error_msg = f"Error searching arXiv: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_paper(
+        self,
+        paper_id: str,
+        include_fields: Optional[List[str]] = None
+    ) -> Union[Dict[str, Any], Dict[str, str]]:
+        """
+        Get details of a specific paper by its arXiv ID.
+        
+        Args:
+            paper_id: arXiv paper ID (e.g., "2401.00123")
+            include_fields: List of fields to include in results. If None, includes all.
+                Available fields: ["title", "authors", "summary", "comment", "journal_ref",
+                "doi", "primary_category", "categories", "links"]
+            
+        Returns:
+            Dict: Paper details or error dict
+        """
+        try:
+            import arxiv
+            
+            # Configure client
+            client = arxiv.Client()
+            
+            # Get paper by ID
+            search = arxiv.Search(id_list=[paper_id])
+            results = list(client.results(search))
+            
+            if not results:
+                return {"error": f"Paper with ID {paper_id} not found"}
+            
+            # Convert to dict with selected fields
+            paper = self._result_to_dict(results[0], include_fields)
+            return paper
+        except Exception as e:
+            error_msg = f"Error getting paper {paper_id}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_papers_by_author(
+        self,
+        author: str,
+        max_results: int = 10,
+        sort_by: str = "submittedDate",
+        sort_order: str = "descending",
+        include_fields: Optional[List[str]] = None
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """
+        Get papers by a specific author.
+        
+        Args:
+            author: Author name (e.g., "Einstein")
+            max_results: Maximum number of results to return
+            sort_by: Sort results by ("relevance", "lastUpdatedDate", "submittedDate")
+            sort_order: Sort order ("ascending" or "descending")
+            include_fields: List of fields to include in results
+            
+        Returns:
+            List[Dict] or Dict: List of papers or error dict
+        """
+        query = f'au:"{author}"'
+        return self.search(query, max_results, sort_by, sort_order, include_fields)
+
+    def get_papers_by_category(
+        self,
+        category: str,
+        max_results: int = 10,
+        sort_by: str = "submittedDate",
+        sort_order: str = "descending",
+        include_fields: Optional[List[str]] = None
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """
+        Get papers from a specific category.
+        
+        Args:
+            category: arXiv category (e.g., "cs.AI", "physics.gen-ph")
+            max_results: Maximum number of results to return
+            sort_by: Sort results by ("relevance", "lastUpdatedDate", "submittedDate")
+            sort_order: Sort order ("ascending" or "descending")
+            include_fields: List of fields to include in results
+            
+        Returns:
+            List[Dict] or Dict: List of papers or error dict
+        """
+        query = f'cat:{category}'
+        return self.search(query, max_results, sort_by, sort_order, include_fields)
+
+    def _result_to_dict(
+        self,
+        result: Any,
+        include_fields: Optional[List[str]] = None
+    ) -> Dict[str, Any]:
+        """Convert arxiv.Result to dictionary with selected fields."""
+        # Default fields to include
+        if include_fields is None:
+            include_fields = [
+                "title", "authors", "summary", "comment", "journal_ref",
+                "doi", "primary_category", "categories", "links"
+            ]
+        
+        # Build paper dict with selected fields
+        paper = {}
+        
+        # Always include these basic fields
+        paper["arxiv_id"] = result.entry_id.split("/")[-1]
+        paper["updated"] = result.updated.isoformat() if result.updated else None
+        paper["published"] = result.published.isoformat() if result.published else None
+        
+        # Add requested fields
+        if "title" in include_fields:
+            paper["title"] = result.title
+        if "authors" in include_fields:
+            paper["authors"] = [str(author) for author in result.authors]
+        if "summary" in include_fields:
+            paper["summary"] = result.summary
+        if "comment" in include_fields:
+            paper["comment"] = result.comment
+        if "journal_ref" in include_fields:
+            paper["journal_ref"] = result.journal_ref
+        if "doi" in include_fields:
+            paper["doi"] = result.doi
+        if "primary_category" in include_fields:
+            paper["primary_category"] = result.primary_category
+        if "categories" in include_fields:
+            paper["categories"] = result.categories
+        if "links" in include_fields:
+            paper["pdf_url"] = result.pdf_url
+            paper["abstract_url"] = f"https://arxiv.org/abs/{paper['arxiv_id']}"
+        
+        return paper
+
+# Create instance for direct function access
+_arxiv_tools = ArxivTools()
+search_arxiv = _arxiv_tools.search
+get_arxiv_paper = _arxiv_tools.get_paper
+get_papers_by_author = _arxiv_tools.get_papers_by_author
+get_papers_by_category = _arxiv_tools.get_papers_by_category
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("ArxivTools Demonstration")
+    print("==================================================\n")
+    
+    # 1. Search for papers
+    print("1. Searching for Papers")
+    print("------------------------------")
+    query = "quantum computing"
+    papers = search_arxiv(query, max_results=3)
+    print(f"Papers about {query}:")
+    if isinstance(papers, list):
+        print(json.dumps(papers, indent=2))
+    else:
+        print(papers)  # Show error
+    print()
+    
+    # 2. Get specific paper
+    print("2. Getting Specific Paper")
+    print("------------------------------")
+    if isinstance(papers, list) and papers:
+        paper_id = papers[0]["arxiv_id"]
+        paper = get_arxiv_paper(paper_id)
+        print(f"Paper {paper_id}:")
+        if "error" not in paper:
+            print(json.dumps(paper, indent=2))
+        else:
+            print(paper)  # Show error
+    print()
+    
+    # 3. Get papers by author
+    print("3. Getting Papers by Author")
+    print("------------------------------")
+    author = "Yoshua Bengio"
+    author_papers = get_papers_by_author(author, max_results=3)
+    print(f"Papers by {author}:")
+    if isinstance(author_papers, list):
+        print(json.dumps(author_papers, indent=2))
+    else:
+        print(author_papers)  # Show error
+    print()
+    
+    # 4. Get papers by category
+    print("4. Getting Papers by Category")
+    print("------------------------------")
+    category = "cs.AI"
+    category_papers = get_papers_by_category(category, max_results=3)
+    print(f"Papers in category {category}:")
+    if isinstance(category_papers, list):
+        print(json.dumps(category_papers, indent=2))
+    else:
+        print(category_papers)  # Show error
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/calculator_tools.py

@@ -0,0 +1,278 @@
+"""Tools for performing calculations.
+
+Usage:
+from praisonaiagents.tools import calculator_tools
+result = calculator_tools.evaluate("2 + 2 * 3")
+
+or
+from praisonaiagents.tools import evaluate, solve_equation, convert_units
+result = evaluate("2 + 2 * 3")
+"""
+
+import logging
+from typing import List, Dict, Optional, Any
+from importlib import util
+import math
+
+class CalculatorTools:
+    """Tools for performing calculations."""
+    
+    def __init__(self):
+        """Initialize CalculatorTools."""
+        self._check_dependencies()
+        
+    def _check_dependencies(self):
+        """Check if required packages are installed."""
+        missing = []
+        for package in ['sympy', 'pint']:
+            if util.find_spec(package) is None:
+                missing.append(package)
+        
+        if missing:
+            raise ImportError(
+                f"Required packages not available. Please install: {', '.join(missing)}\n"
+                f"Run: pip install {' '.join(missing)}"
+            )
+
+    def evaluate(
+        self,
+        expression: str,
+        variables: Optional[Dict[str, float]] = None,
+        precision: int = 10
+    ) -> Dict[str, Any]:
+        """Evaluate a mathematical expression."""
+        try:
+            if util.find_spec('sympy') is None:
+                error_msg = "sympy package is not available. Please install it using: pip install sympy"
+                logging.error(error_msg)
+                return {"error": error_msg}
+
+            # Import sympy only when needed
+            import sympy
+
+            # Replace common mathematical functions
+            expression = expression.lower()
+            replacements = {
+                'sin(': 'math.sin(',
+                'cos(': 'math.cos(',
+                'tan(': 'math.tan(',
+                'sqrt(': 'math.sqrt(',
+                'log(': 'math.log(',
+                'exp(': 'math.exp(',
+                'pi': str(math.pi),
+                'e': str(math.e)
+            }
+            for old, new in replacements.items():
+                expression = expression.replace(old, new)
+            
+            # Create safe namespace
+            safe_dict = {
+                'math': math,
+                '__builtins__': None
+            }
+            if variables:
+                safe_dict.update(variables)
+            
+            # Evaluate expression
+            result = eval(expression, safe_dict)
+            
+            # Round to specified precision
+            if isinstance(result, (int, float)):
+                return {"result": round(float(result), precision)}
+            return {"error": "Invalid expression"}
+        except Exception as e:
+            error_msg = f"Error evaluating expression: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def solve_equation(
+        self,
+        equation: str,
+        variable: str = 'x'
+    ) -> Dict[str, Any]:
+        """Solve a mathematical equation."""
+        try:
+            if util.find_spec('sympy') is None:
+                error_msg = "sympy package is not available. Please install it using: pip install sympy"
+                logging.error(error_msg)
+                return {"error": error_msg}
+
+            # Import sympy only when needed
+            import sympy
+
+            # Parse equation
+            if '=' in equation:
+                left, right = equation.split('=')
+                equation = f"({left}) - ({right})"
+            
+            # Convert to SymPy expression
+            x = sympy.Symbol(variable)
+            expr = sympy.sympify(equation)
+            
+            # Solve equation
+            solutions = sympy.solve(expr, x)
+            
+            # Convert complex solutions to real if possible
+            real_solutions = []
+            for sol in solutions:
+                if sol.is_real:
+                    real_solutions.append(float(sol))
+            
+            return {"solutions": real_solutions} if real_solutions else {"error": "No real solutions found"}
+        except Exception as e:
+            error_msg = f"Error solving equation: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def convert_units(
+        self,
+        value: float,
+        from_unit: str,
+        to_unit: str
+    ) -> Dict[str, Any]:
+        """Convert between different units."""
+        try:
+            if util.find_spec('pint') is None:
+                error_msg = "pint package is not available. Please install it using: pip install pint"
+                logging.error(error_msg)
+                return {"error": error_msg}
+
+            # Import pint only when needed
+            import pint
+
+            # Create quantity with source unit
+            ureg = pint.UnitRegistry()
+            quantity = value * ureg(from_unit)
+            
+            # Convert to target unit
+            result = quantity.to(to_unit)
+            
+            return {"result": float(result.magnitude)}
+        except Exception as e:
+            error_msg = f"Error converting units: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def calculate_statistics(
+        self,
+        numbers: List[float]
+    ) -> Dict[str, Any]:
+        """Calculate basic statistics for a list of numbers."""
+        try:
+            if not numbers:
+                return {"error": "No numbers provided"}
+            
+            n = len(numbers)
+            mean = sum(numbers) / n
+            
+            # Calculate variance and std dev
+            squared_diff_sum = sum((x - mean) ** 2 for x in numbers)
+            variance = squared_diff_sum / (n - 1) if n > 1 else 0
+            std_dev = math.sqrt(variance)
+            
+            # Calculate median
+            sorted_nums = sorted(numbers)
+            if n % 2 == 0:
+                median = (sorted_nums[n//2 - 1] + sorted_nums[n//2]) / 2
+            else:
+                median = sorted_nums[n//2]
+            
+            return {
+                "mean": mean,
+                "median": median,
+                "std_dev": std_dev,
+                "variance": variance,
+                "min": min(numbers),
+                "max": max(numbers),
+                "range": max(numbers) - min(numbers),
+                "count": n
+            }
+        except Exception as e:
+            error_msg = f"Error calculating statistics: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def calculate_financial(
+        self,
+        principal: float,
+        rate: float,
+        time: float,
+        frequency: str = 'yearly'
+    ) -> Dict[str, Any]:
+        """Calculate financial metrics like compound interest."""
+        try:
+            # Convert rate to decimal
+            rate = rate / 100
+            
+            # Get compounding frequency
+            freq_map = {
+                'yearly': 1,
+                'semi-annual': 2,
+                'quarterly': 4,
+                'monthly': 12,
+                'daily': 365
+            }
+            n = freq_map.get(frequency.lower(), 1)
+            
+            # Calculate compound interest
+            amount = principal * (1 + rate/n)**(n*time)
+            interest = amount - principal
+            
+            # Calculate simple interest
+            simple_interest = principal * rate * time
+            
+            return {
+                "final_amount": amount,
+                "compound_interest": interest,
+                "simple_interest": simple_interest,
+                "difference": interest - simple_interest
+            }
+        except Exception as e:
+            error_msg = f"Error calculating financial metrics: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+# Create instance for direct function access
+_calculator_tools = CalculatorTools()
+evaluate = _calculator_tools.evaluate
+solve_equation = _calculator_tools.solve_equation
+convert_units = _calculator_tools.convert_units
+calculate_statistics = _calculator_tools.calculate_statistics
+calculate_financial = _calculator_tools.calculate_financial
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("CalculatorTools Demonstration")
+    print("==================================================\n")
+
+    print("1. Basic Arithmetic")
+    print("------------------------------")
+    print("2 + 2 =", evaluate("2 + 2")["result"])
+    print("5 * 6 =", evaluate("5 * 6")["result"])
+    print("10 / 3 =", evaluate("10 / 3")["result"])
+    print()
+
+    print("2. Complex Expressions")
+    print("------------------------------")
+    print("(2 + 3) * 4 =", evaluate("(2 + 3) * 4")["result"])
+    print("2^3 + 4 =", evaluate("2^3 + 4")["result"])
+    print("sqrt(16) + 2 =", evaluate("sqrt(16) + 2")["result"])
+    print()
+
+    print("3. Solving Equations")
+    print("------------------------------")
+    result = solve_equation("x^2 - 5*x + 6 = 0")
+    print("x^2 - 5*x + 6 = 0")
+    print("Solutions:", result["solutions"])
+    print()
+
+    print("4. Unit Conversions")
+    print("------------------------------")
+    print("100 meters to kilometers:", convert_units(100, "meters", "kilometers")["result"])
+    print("1 hour to minutes:", convert_units(1, "hours", "minutes")["result"])
+    print("32 fahrenheit to celsius:", convert_units(32, "fahrenheit", "celsius")["result"])
+    print()
+
+    print("==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/csv_tools.py

@@ -0,0 +1,266 @@
+"""Tools for working with CSV files.
+
+Usage:
+from praisonaiagents.tools import csv_tools
+df = csv_tools.read_csv("data.csv")
+
+or
+from praisonaiagents.tools import read_csv, write_csv, merge_csv
+df = read_csv("data.csv")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any, TYPE_CHECKING
+from importlib import util
+import json
+import csv
+from pathlib import Path
+
+if TYPE_CHECKING:
+    import pandas as pd
+
+class CSVTools:
+    """Tools for working with CSV files."""
+    
+    def __init__(self):
+        """Initialize CSVTools."""
+        pass
+
+    def _get_pandas(self) -> Optional['pd']:
+        """Get pandas module, installing if needed"""
+        if util.find_spec('pandas') is None:
+            error_msg = "pandas package is not available. Please install it using: pip install pandas"
+            logging.error(error_msg)
+            return None
+        import pandas as pd
+        return pd
+
+    def read_csv(
+        self,
+        filepath: str,
+        encoding: str = 'utf-8',
+        delimiter: str = ',',
+        header: Union[int, List[int], None] = 0,
+        usecols: Optional[List[str]] = None,
+        dtype: Optional[Dict[str, str]] = None,
+        parse_dates: Optional[List[str]] = None,
+        na_values: Optional[List[str]] = None,
+        nrows: Optional[int] = None
+    ) -> List[Dict[str, Any]]:
+        """Read a CSV file with advanced options.
+        
+        Args:
+            filepath: Path to CSV file
+            encoding: File encoding
+            delimiter: Column delimiter
+            header: Row number(s) to use as column names
+            usecols: Columns to read
+            dtype: Dict of column dtypes
+            parse_dates: List of columns to parse as dates
+            na_values: Additional strings to recognize as NA/NaN
+            nrows: Number of rows to read
+            
+        Returns:
+            List of row dicts
+        """
+        try:
+            pd = self._get_pandas()
+            if pd is None:
+                return {"error": "pandas package not available"}
+
+            df = pd.read_csv(
+                filepath,
+                encoding=encoding,
+                delimiter=delimiter,
+                header=header,
+                usecols=usecols,
+                dtype=dtype,
+                parse_dates=parse_dates,
+                na_values=na_values,
+                nrows=nrows
+            )
+            return df.to_dict('records')
+            
+        except Exception as e:
+            error_msg = f"Error reading CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def write_csv(
+        self,
+        filepath: str,
+        data: List[Dict[str, Any]],
+        encoding: str = 'utf-8',
+        delimiter: str = ',',
+        index: bool = False,
+        header: bool = True,
+        float_format: Optional[str] = None,
+        date_format: Optional[str] = None,
+        mode: str = 'w'
+    ) -> bool:
+        """Write data to a CSV file.
+        
+        Args:
+            filepath: Path to CSV file
+            data: List of row dicts to write
+            encoding: File encoding
+            delimiter: Column delimiter
+            index: Whether to write row indices
+            header: Whether to write column headers
+            float_format: Format string for float values
+            date_format: Format string for date values
+            mode: Write mode ('w' for write, 'a' for append)
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            pd = self._get_pandas()
+            if pd is None:
+                return False
+
+            df = pd.DataFrame(data)
+            df.to_csv(
+                filepath,
+                encoding=encoding,
+                sep=delimiter,
+                index=index,
+                header=header,
+                float_format=float_format,
+                date_format=date_format,
+                mode=mode
+            )
+            return True
+            
+        except Exception as e:
+            error_msg = f"Error writing CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def merge_csv(
+        self,
+        files: List[str],
+        output_file: str,
+        how: str = 'inner',
+        on: Optional[Union[str, List[str]]] = None,
+        suffixes: Optional[tuple] = None
+    ) -> bool:
+        """Merge multiple CSV files.
+        
+        Args:
+            files: List of CSV files to merge
+            output_file: Output file path
+            how: Merge method ('inner', 'outer', 'left', 'right')
+            on: Column(s) to merge on
+            suffixes: Suffixes for overlapping columns
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            if len(files) < 2:
+                error_msg = "At least two files are required for merging"
+                logging.error(error_msg)
+                return False
+            
+            pd = self._get_pandas()
+            if pd is None:
+                return False
+
+            # Read first file
+            result = pd.read_csv(files[0])
+            
+            # Merge with remaining files
+            for file in files[1:]:
+                df = pd.read_csv(file)
+                result = pd.merge(
+                    result,
+                    df,
+                    how=how,
+                    on=on,
+                    suffixes=suffixes or ('_1', '_2')
+                )
+            
+            # Write merged result
+            result.to_csv(output_file, index=False)
+            return True
+            
+        except Exception as e:
+            error_msg = f"Error merging CSV files: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+# Create instance for direct function access
+_csv_tools = CSVTools()
+read_csv = _csv_tools.read_csv
+write_csv = _csv_tools.write_csv
+merge_csv = _csv_tools.merge_csv
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("CSVTools Demonstration")
+    print("==================================================\n")
+    
+    # Create a temporary file for testing
+    import tempfile
+    import os
+    
+    with tempfile.NamedTemporaryFile(suffix='.csv', delete=False) as temp:
+        temp_file = temp.name
+        
+        print("1. Writing CSV File")
+        print("------------------------------")
+        data = [
+            {"name": "Alice", "age": 25, "city": "New York"},
+            {"name": "Bob", "age": 30, "city": "San Francisco"},
+            {"name": "Charlie", "age": 35, "city": "Chicago"}
+        ]
+        result = write_csv(temp_file, data)
+        print(f"Data written successfully: {result}")
+        print()
+        
+        print("2. Reading CSV File")
+        print("------------------------------")
+        read_data = read_csv(temp_file)
+        print("Contents of CSV file:")
+        for row in read_data:
+            print(row)
+        print()
+        
+        print("3. Merging CSV Files")
+        print("------------------------------")
+        # Create a second file for merging
+        with tempfile.NamedTemporaryFile(suffix='.csv', delete=False) as temp2:
+            temp_file2 = temp2.name
+            data2 = [
+                {"name": "Alice", "salary": 75000},
+                {"name": "Bob", "salary": 85000},
+                {"name": "Charlie", "salary": 95000}
+            ]
+            write_csv(temp_file2, data2)
+            
+            # Merge files
+            with tempfile.NamedTemporaryFile(suffix='.csv', delete=False) as temp3:
+                temp_file3 = temp3.name
+                result = merge_csv(
+                    [temp_file, temp_file2],
+                    temp_file3,
+                    how='inner',
+                    on='name'
+                )
+                print(f"Files merged successfully: {result}")
+                if result:
+                    merged_data = read_csv(temp_file3)
+                    print("\nMerged contents:")
+                    for row in merged_data:
+                        print(row)
+                print()
+        
+        # Clean up temporary files
+        os.unlink(temp_file)
+        os.unlink(temp_file2)
+        os.unlink(temp_file3)
+    
+    print("==================================================")
+    print("Demonstration Complete")
+    print("==================================================\n")

agents/praisonaiagents/tools/duckdb_tools.py

@@ -0,0 +1,268 @@
+"""Tools for working with DuckDB databases.
+
+Usage:
+from praisonaiagents.tools import duckdb_tools
+df = duckdb_tools.query("SELECT * FROM my_table")
+
+or
+from praisonaiagents.tools import query_db, create_table, load_data
+df = query_db("SELECT * FROM my_table")
+"""
+
+import logging
+from typing import List, Dict, Any, Optional, Union, TYPE_CHECKING
+from importlib import util
+import json
+
+if TYPE_CHECKING:
+    import duckdb
+    import pandas as pd
+
+class DuckDBTools:
+    """Tools for working with DuckDB databases."""
+    
+    def __init__(self, database: str = ':memory:'):
+        """Initialize DuckDBTools.
+        
+        Args:
+            database: Path to database file or ':memory:' for in-memory database
+        """
+        self.database = database
+        self._conn = None
+
+    def _get_duckdb(self) -> Optional['duckdb']:
+        """Get duckdb module, installing if needed"""
+        if util.find_spec('duckdb') is None:
+            error_msg = "duckdb package is not available. Please install it using: pip install duckdb"
+            logging.error(error_msg)
+            return None
+        import duckdb
+        return duckdb
+
+    def _get_pandas(self) -> Optional['pd']:
+        """Get pandas module, installing if needed"""
+        if util.find_spec('pandas') is None:
+            error_msg = "pandas package is not available. Please install it using: pip install pandas"
+            logging.error(error_msg)
+            return None
+        import pandas as pd
+        return pd
+
+    def _get_connection(self) -> Optional['duckdb.DuckDBPyConnection']:
+        """Get or create database connection"""
+        if self._conn is None:
+            duckdb = self._get_duckdb()
+            if duckdb is None:
+                return None
+            try:
+                self._conn = duckdb.connect(self.database)
+            except Exception as e:
+                error_msg = f"Error connecting to database {self.database}: {str(e)}"
+                logging.error(error_msg)
+                return None
+        return self._conn
+
+    def execute_query(
+        self,
+        query: str,
+        params: Optional[Union[tuple, dict]] = None,
+        return_df: bool = True
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """Execute a SQL query.
+        
+        Args:
+            query: SQL query to execute
+            params: Query parameters
+            return_df: If True, return results as DataFrame records
+            
+        Returns:
+            Query results as list of dicts, or error dict
+        """
+        try:
+            conn = self._get_connection()
+            if conn is None:
+                return {"error": "Could not connect to database"}
+
+            if params:
+                result = conn.execute(query, params)
+            else:
+                result = conn.execute(query)
+
+            if return_df:
+                pd = self._get_pandas()
+                if pd is None:
+                    return {"error": "pandas package not available"}
+                df = result.df()
+                return df.to_dict('records')
+            else:
+                return [dict(row) for row in result.fetchall()]
+
+        except Exception as e:
+            error_msg = f"Error executing query: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def load_csv(
+        self,
+        table_name: str,
+        filepath: str,
+        schema: Optional[Dict[str, str]] = None,
+        if_exists: str = 'replace'
+    ) -> bool:
+        """Load a CSV file into a table.
+        
+        Args:
+            table_name: Name of table to create
+            filepath: Path to CSV file
+            schema: Optional column definitions
+            if_exists: What to do if table exists ('fail', 'replace', 'append')
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            conn = self._get_connection()
+            if conn is None:
+                return False
+
+            # Check if table exists
+            exists = conn.execute(f"""
+                SELECT name FROM sqlite_master 
+                WHERE type='table' AND name='{table_name}'
+            """).fetchone() is not None
+
+            if exists:
+                if if_exists == 'fail':
+                    raise ValueError(f"Table {table_name} already exists")
+                elif if_exists == 'replace':
+                    conn.execute(f"DROP TABLE IF EXISTS {table_name}")
+                elif if_exists != 'append':
+                    raise ValueError("if_exists must be 'fail', 'replace', or 'append'")
+
+            # Create table if needed
+            if not exists or if_exists == 'replace':
+                if schema:
+                    # Create table with schema
+                    columns = ', '.join(f"{k} {v}" for k, v in schema.items())
+                    conn.execute(f"CREATE TABLE {table_name} ({columns})")
+                else:
+                    # Infer schema from CSV
+                    conn.execute(f"""
+                        CREATE TABLE {table_name} AS 
+                        SELECT * FROM read_csv_auto('{filepath}')
+                        WHERE 1=0
+                    """)
+
+            # Load data
+            conn.execute(f"""
+                INSERT INTO {table_name}
+                SELECT * FROM read_csv_auto('{filepath}')
+            """)
+
+            return True
+
+        except Exception as e:
+            error_msg = f"Error loading CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def export_csv(
+        self,
+        query: str,
+        filepath: str,
+        params: Optional[Union[tuple, dict]] = None
+    ) -> bool:
+        """Export query results to CSV.
+        
+        Args:
+            query: SQL query to execute
+            filepath: Output file path
+            params: Optional query parameters
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            # Execute query and get results as DataFrame
+            results = self.execute_query(query, params)
+            if isinstance(results, dict) and 'error' in results:
+                return False
+
+            pd = self._get_pandas()
+            if pd is None:
+                return False
+
+            # Convert to DataFrame and save
+            df = pd.DataFrame(results)
+            df.to_csv(filepath, index=False)
+            return True
+
+        except Exception as e:
+            error_msg = f"Error exporting to CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def close(self):
+        """Close database connection."""
+        if self._conn:
+            self._conn.close()
+            self._conn = None
+
+# Create instance for direct function access
+_duckdb_tools = DuckDBTools()
+execute_query = _duckdb_tools.execute_query
+load_csv = _duckdb_tools.load_csv
+export_csv = _duckdb_tools.export_csv
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("DuckDBTools Demonstration")
+    print("==================================================\n")
+    
+    # Create a temporary file for testing
+    import tempfile
+    import os
+    
+    with tempfile.NamedTemporaryFile(suffix='.csv', delete=False) as temp:
+        temp_file = temp.name
+        
+        # Create sample data
+        with open(temp_file, 'w') as f:
+            f.write("name,age,city\n")
+            f.write("Alice,25,New York\n")
+            f.write("Bob,30,San Francisco\n")
+            f.write("Charlie,35,Chicago\n")
+        
+        print("1. Loading CSV File")
+        print("------------------------------")
+        result = load_csv('users', temp_file)
+        print(f"CSV loaded successfully: {result}")
+        print()
+        
+        print("2. Executing Query")
+        print("------------------------------")
+        query = "SELECT * FROM users WHERE age > 25"
+        results = execute_query(query)
+        print("Query results:")
+        for row in results:
+            print(row)
+        print()
+        
+        print("3. Exporting Query Results")
+        print("------------------------------")
+        with tempfile.NamedTemporaryFile(suffix='.csv', delete=False) as temp2:
+            temp_file2 = temp2.name
+            result = export_csv(query, temp_file2)
+            print(f"Results exported successfully: {result}")
+            if result:
+                print("\nExported file contents:")
+                with open(temp_file2) as f:
+                    print(f.read())
+        
+        # Clean up temporary files
+        os.unlink(temp_file)
+        os.unlink(temp_file2)
+    
+    print("==================================================")
+    print("Demonstration Complete")
+    print("==================================================\n")

agents/praisonaiagents/tools/duckduckgo_tools.py

@@ -0,0 +1,52 @@
+"""DuckDuckGo search functionality.
+
+Usage:
+from praisonaiagents.tools import internet_search
+results = internet_search("AI news")
+
+or 
+from praisonaiagents.tools import duckduckgo
+results = duckduckgo("AI news")
+"""
+
+from typing import List, Dict
+import logging
+from importlib import util
+
+def internet_search(query: str) -> List[Dict]:
+    """Perform an internet search using DuckDuckGo."""
+    # Check if duckduckgo_search is installed
+    if util.find_spec("duckduckgo_search") is None:
+        error_msg = "DuckDuckGo search is not available. Please install duckduckgo_search package using: pip install duckduckgo_search"
+        logging.error(error_msg)
+        return [{"error": error_msg}]
+
+    try:
+        # Import only when needed
+        from duckduckgo_search import DDGS
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=5):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+
+    except Exception as e:
+        error_msg = f"Error during DuckDuckGo search: {e}"
+        logging.error(error_msg)
+        return [{"error": error_msg}]
+
+def duckduckgo(query: str) -> List[Dict]:
+    """Alias for internet_search function."""
+    return internet_search(query)
+
+if __name__ == "__main__":
+    # Example usage
+    results = internet_search("Python programming")
+    for result in results:
+        print(f"\nTitle: {result.get('title')}")
+        print(f"URL: {result.get('url')}")
+        print(f"Snippet: {result.get('snippet')}")

agents/praisonaiagents/tools/excel_tools.py

@@ -0,0 +1,310 @@
+"""Tools for working with Excel files.
+
+Usage:
+from praisonaiagents.tools import excel_tools
+df = excel_tools.read_excel("data.xlsx")
+
+or
+from praisonaiagents.tools import read_excel, write_excel, merge_excel
+df = read_excel("data.xlsx")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any, TYPE_CHECKING, Tuple
+from importlib import util
+import json
+from pathlib import Path
+import tempfile
+import os
+
+if TYPE_CHECKING:
+    import pandas as pd
+    from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
+    from openpyxl.chart import BarChart, LineChart, PieChart, Reference
+    from openpyxl.chart.label import DataLabelList
+
+class ExcelTools:
+    """Tools for working with Excel files."""
+    
+    def __init__(self):
+        """Initialize ExcelTools."""
+        pass
+
+    def _get_pandas(self) -> Optional['pd']:
+        """Get pandas module, installing if needed"""
+        if util.find_spec('pandas') is None:
+            error_msg = "pandas package is not available. Please install it using: pip install pandas"
+            logging.error(error_msg)
+            return None
+        if util.find_spec('openpyxl') is None:
+            error_msg = "openpyxl package is not available. Please install it using: pip install openpyxl"
+            logging.error(error_msg)
+            return None
+        import pandas as pd
+        return pd
+
+    def read_excel(
+        self,
+        filepath: str,
+        sheet_name: Optional[Union[str, int, List[Union[str, int]]]] = 0,
+        header: Optional[int] = 0,
+        usecols: Optional[List[str]] = None,
+        skiprows: Optional[Union[int, List[int]]] = None,
+        na_values: Optional[List[str]] = None,
+        dtype: Optional[Dict[str, str]] = None
+    ) -> Union[Dict[str, List[Dict[str, Any]]], List[Dict[str, Any]]]:
+        """Read an Excel file with advanced options.
+        
+        Args:
+            filepath: Path to Excel file
+            sheet_name: Sheet name(s) or index(es)
+            header: Row number(s) to use as column names
+            usecols: Columns to read
+            skiprows: Line numbers to skip
+            na_values: Additional strings to recognize as NA/NaN
+            dtype: Dict of column dtypes
+            
+        Returns:
+            Dict of sheet names to data if multiple sheets, else list of row dicts
+        """
+        try:
+            pd = self._get_pandas()
+            if pd is None:
+                return {"error": "Required packages not available"}
+
+            # Read Excel file
+            df = pd.read_excel(
+                filepath,
+                sheet_name=sheet_name,
+                header=header,
+                usecols=usecols,
+                skiprows=skiprows,
+                na_values=na_values,
+                dtype=dtype,
+                engine='openpyxl'
+            )
+            
+            # Convert to dict format
+            if isinstance(df, dict):
+                return {
+                    name: df[name].to_dict('records')
+                    for name in df.keys()
+                }
+            else:
+                return df.to_dict('records')
+                
+        except Exception as e:
+            error_msg = f"Error reading Excel file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def write_excel(
+        self,
+        filepath: str,
+        data: Union[Dict[str, List[Dict[str, Any]]], List[Dict[str, Any]]],
+        sheet_name: Optional[str] = None,
+        index: bool = False,
+        header: bool = True,
+        mode: str = 'w'
+    ) -> bool:
+        """Write data to an Excel file.
+        
+        Args:
+            filepath: Path to Excel file
+            data: Data to write (dict of sheet names to data or list of row dicts)
+            sheet_name: Sheet name if data is a list
+            index: Whether to write row indices
+            header: Whether to write column headers
+            mode: Write mode ('w' for write, 'a' for append)
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            pd = self._get_pandas()
+            if pd is None:
+                return False
+
+            # Convert data to DataFrame(s)
+            if isinstance(data, dict):
+                if mode == 'a' and os.path.exists(filepath):
+                    book = pd.ExcelFile(filepath)
+                    with pd.ExcelWriter(filepath, engine='openpyxl') as writer:
+                        # Copy existing sheets
+                        for sheet in book.sheet_names:
+                            pd.read_excel(filepath, sheet_name=sheet).to_excel(
+                                writer, sheet_name=sheet, index=index, header=header
+                            )
+                        # Add new sheets
+                        for name, sheet_data in data.items():
+                            df = pd.DataFrame(sheet_data)
+                            df.to_excel(
+                                writer, sheet_name=name, index=index, header=header
+                            )
+                else:
+                    with pd.ExcelWriter(filepath, engine='openpyxl') as writer:
+                        for name, sheet_data in data.items():
+                            df = pd.DataFrame(sheet_data)
+                            df.to_excel(
+                                writer, sheet_name=name, index=index, header=header
+                            )
+            else:
+                if mode == 'a' and os.path.exists(filepath):
+                    book = pd.ExcelFile(filepath)
+                    with pd.ExcelWriter(filepath, engine='openpyxl') as writer:
+                        # Copy existing sheets
+                        for sheet in book.sheet_names:
+                            pd.read_excel(filepath, sheet_name=sheet).to_excel(
+                                writer, sheet_name=sheet, index=index, header=header
+                            )
+                        # Add new sheet
+                        df = pd.DataFrame(data)
+                        df.to_excel(
+                            writer,
+                            sheet_name=sheet_name or 'Sheet1',
+                            index=index,
+                            header=header
+                        )
+                else:
+                    df = pd.DataFrame(data)
+                    df.to_excel(
+                        filepath,
+                        sheet_name=sheet_name or 'Sheet1',
+                        index=index,
+                        header=header,
+                        engine='openpyxl'
+                    )
+            
+            return True
+            
+        except Exception as e:
+            error_msg = f"Error writing Excel file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def merge_excel(
+        self,
+        files: List[str],
+        output_file: str,
+        how: str = 'inner',
+        on: Optional[Union[str, List[str]]] = None,
+        suffixes: Optional[Tuple[str, str]] = None
+    ) -> bool:
+        """Merge multiple Excel files.
+        
+        Args:
+            files: List of Excel files to merge
+            output_file: Output file path
+            how: Merge method ('inner', 'outer', 'left', 'right')
+            on: Column(s) to merge on
+            suffixes: Suffixes for overlapping columns
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            if len(files) < 2:
+                error_msg = "At least two files are required for merging"
+                logging.error(error_msg)
+                return False
+            
+            # Read first file
+            result = self.read_excel(files[0])
+            
+            # Merge with remaining files
+            for file in files[1:]:
+                df = self.read_excel(file)
+                if isinstance(df, dict):
+                    df = next(iter(df.values()))
+                
+                pd = self._get_pandas()
+                if pd is None:
+                    return False
+                
+                result = pd.merge(
+                    pd.DataFrame(result),
+                    pd.DataFrame(df),
+                    how=how,
+                    on=on,
+                    suffixes=suffixes or ('_1', '_2')
+                ).to_dict('records')
+            
+            # Write merged result
+            return self.write_excel(output_file, result)
+            
+        except Exception as e:
+            error_msg = f"Error merging Excel files: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+# Create instance for direct function access
+_excel_tools = ExcelTools()
+read_excel = _excel_tools.read_excel
+write_excel = _excel_tools.write_excel
+merge_excel = _excel_tools.merge_excel
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("ExcelTools Demonstration")
+    print("==================================================\n")
+    
+    # Create a temporary file for testing
+    with tempfile.NamedTemporaryFile(suffix='.xlsx', delete=False) as temp:
+        temp_file = temp.name
+        
+        print("1. Writing Excel File")
+        print("------------------------------")
+        data = [
+            {"name": "Alice", "age": 25, "city": "New York"},
+            {"name": "Bob", "age": 30, "city": "San Francisco"},
+            {"name": "Charlie", "age": 35, "city": "Chicago"}
+        ]
+        result = write_excel(temp_file, data, "People")
+        print(f"Data written successfully: {result}")
+        print()
+        
+        print("2. Reading Excel File")
+        print("------------------------------")
+        read_data = read_excel(temp_file)
+        print("Contents of Excel file:")
+        for row in read_data:
+            print(row)
+        print()
+        
+        print("3. Merging Excel Files")
+        print("------------------------------")
+        # Create a second file for merging
+        with tempfile.NamedTemporaryFile(suffix='.xlsx', delete=False) as temp2:
+            temp_file2 = temp2.name
+            data2 = [
+                {"name": "Alice", "salary": 75000},
+                {"name": "Bob", "salary": 85000},
+                {"name": "Charlie", "salary": 95000}
+            ]
+            write_excel(temp_file2, data2, "Salaries")
+            
+            # Merge files
+            with tempfile.NamedTemporaryFile(suffix='.xlsx', delete=False) as temp3:
+                temp_file3 = temp3.name
+                result = merge_excel(
+                    [temp_file, temp_file2],
+                    temp_file3,
+                    how='inner',
+                    on='name'
+                )
+                print(f"Files merged successfully: {result}")
+                if result:
+                    merged_data = read_excel(temp_file3)
+                    print("\nMerged contents:")
+                    for row in merged_data:
+                        print(row)
+                print()
+        
+        # Clean up temporary files
+        os.unlink(temp_file)
+        os.unlink(temp_file2)
+        os.unlink(temp_file3)
+    
+    print("==================================================")
+    print("Demonstration Complete")
+    print("==================================================\n")

agents/praisonaiagents/tools/file_tools.py

@@ -0,0 +1,274 @@
+"""File handling tools for basic file operations.
+
+Usage:
+from praisonaiagents.tools import file_tools
+content = file_tools.read_file("example.txt")
+file_tools.write_file("output.txt", "Hello World")
+
+or 
+from praisonaiagents.tools import read_file, write_file, list_files
+content = read_file("example.txt")
+"""
+
+import os
+import json
+from typing import List, Dict, Union, Optional
+from pathlib import Path
+import shutil
+import logging
+
+class FileTools:
+    """Tools for file operations including read, write, list, and information."""
+    
+    @staticmethod
+    def read_file(filepath: str, encoding: str = 'utf-8') -> str:
+        """
+        Read content from a file.
+        
+        Args:
+            filepath: Path to the file
+            encoding: File encoding (default: utf-8)
+            
+        Returns:
+            str: Content of the file
+        """
+        try:
+            with open(filepath, 'r', encoding=encoding) as f:
+                return f.read()
+        except Exception as e:
+            error_msg = f"Error reading file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return error_msg
+
+    @staticmethod
+    def write_file(filepath: str, content: str, encoding: str = 'utf-8') -> bool:
+        """
+        Write content to a file.
+        
+        Args:
+            filepath: Path to the file
+            content: Content to write
+            encoding: File encoding (default: utf-8)
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            # Create directory if it doesn't exist
+            os.makedirs(os.path.dirname(filepath), exist_ok=True)
+            with open(filepath, 'w', encoding=encoding) as f:
+                f.write(content)
+            return True
+        except Exception as e:
+            error_msg = f"Error writing to file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    @staticmethod
+    def list_files(directory: str, pattern: Optional[str] = None) -> List[Dict[str, Union[str, int]]]:
+        """
+        List files in a directory with optional pattern matching.
+        
+        Args:
+            directory: Directory path
+            pattern: Optional glob pattern (e.g., "*.txt")
+            
+        Returns:
+            List[Dict]: List of file information dictionaries
+        """
+        try:
+            path = Path(directory)
+            if pattern:
+                files = path.glob(pattern)
+            else:
+                files = path.iterdir()
+
+            result = []
+            for file in files:
+                if file.is_file():
+                    stat = file.stat()
+                    result.append({
+                        'name': file.name,
+                        'path': str(file),
+                        'size': stat.st_size,
+                        'modified': stat.st_mtime,
+                        'created': stat.st_ctime
+                    })
+            return result
+        except Exception as e:
+            error_msg = f"Error listing files in {directory}: {str(e)}"
+            logging.error(error_msg)
+            return [{'error': error_msg}]
+
+    @staticmethod
+    def get_file_info(filepath: str) -> Dict[str, Union[str, int]]:
+        """
+        Get detailed information about a file.
+        
+        Args:
+            filepath: Path to the file
+            
+        Returns:
+            Dict: File information including size, dates, etc.
+        """
+        try:
+            path = Path(filepath)
+            if not path.exists():
+                return {'error': f'File not found: {filepath}'}
+            
+            stat = path.stat()
+            return {
+                'name': path.name,
+                'path': str(path),
+                'size': stat.st_size,
+                'modified': stat.st_mtime,
+                'created': stat.st_ctime,
+                'is_file': path.is_file(),
+                'is_dir': path.is_dir(),
+                'extension': path.suffix,
+                'parent': str(path.parent)
+            }
+        except Exception as e:
+            error_msg = f"Error getting file info for {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {'error': error_msg}
+
+    @staticmethod
+    def copy_file(src: str, dst: str) -> bool:
+        """
+        Copy a file from source to destination.
+        
+        Args:
+            src: Source file path
+            dst: Destination file path
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            # Create destination directory if it doesn't exist
+            os.makedirs(os.path.dirname(dst), exist_ok=True)
+            shutil.copy2(src, dst)
+            return True
+        except Exception as e:
+            error_msg = f"Error copying file from {src} to {dst}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    @staticmethod
+    def move_file(src: str, dst: str) -> bool:
+        """
+        Move a file from source to destination.
+        
+        Args:
+            src: Source file path
+            dst: Destination file path
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            # Create destination directory if it doesn't exist
+            os.makedirs(os.path.dirname(dst), exist_ok=True)
+            shutil.move(src, dst)
+            return True
+        except Exception as e:
+            error_msg = f"Error moving file from {src} to {dst}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    @staticmethod
+    def delete_file(filepath: str) -> bool:
+        """
+        Delete a file.
+        
+        Args:
+            filepath: Path to the file
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            os.remove(filepath)
+            return True
+        except Exception as e:
+            error_msg = f"Error deleting file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+# Create instance for direct function access
+_file_tools = FileTools()
+read_file = _file_tools.read_file
+write_file = _file_tools.write_file
+list_files = _file_tools.list_files
+get_file_info = _file_tools.get_file_info
+copy_file = _file_tools.copy_file
+move_file = _file_tools.move_file
+delete_file = _file_tools.delete_file
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("FileTools Demonstration")
+    print("==================================================\n")
+
+    # Create a test directory
+    test_dir = os.path.join(os.getcwd(), "test_files")
+    os.makedirs(test_dir, exist_ok=True)
+    
+    # Create test files
+    test_file = os.path.join(test_dir, "test_file.txt")
+    test_content = "Hello, this is a test file!"
+    
+    print("1. Writing to file")
+    print("------------------------------")
+    success = write_file(test_file, test_content)
+    print(f"Write successful: {success}\n")
+    
+    print("2. Reading from file")
+    print("------------------------------")
+    content = read_file(test_file)
+    print(f"Content: {content}\n")
+    
+    print("3. File Information")
+    print("------------------------------")
+    info = get_file_info(test_file)
+    print(json.dumps(info, indent=2))
+    print()
+    
+    print("4. Listing Files")
+    print("------------------------------")
+    files = list_files(test_dir, "*.txt")
+    for file in files:
+        print(f"Found: {file['name']} ({file['size']} bytes)")
+    print()
+    
+    print("5. Copying File")
+    print("------------------------------")
+    copy_file_path = os.path.join(test_dir, "test_file_copy.txt")
+    copy_success = copy_file(test_file, copy_file_path)
+    print(f"Copy successful: {copy_success}\n")
+    
+    print("6. Moving File")
+    print("------------------------------")
+    move_file_path = os.path.join(test_dir, "test_file_moved.txt")
+    move_success = move_file(copy_file_path, move_file_path)
+    print(f"Move successful: {move_success}\n")
+    
+    print("7. Deleting Files")
+    print("------------------------------")
+    delete_success = delete_file(test_file)
+    print(f"Delete original successful: {delete_success}")
+    delete_success = delete_file(move_file_path)
+    print(f"Delete moved file successful: {delete_success}\n")
+    
+    # Clean up test directory
+    try:
+        shutil.rmtree(test_dir)
+        print("Test directory cleaned up successfully")
+    except Exception as e:
+        print(f"Error cleaning up test directory: {str(e)}")
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/json_tools.py

@@ -0,0 +1,515 @@
+"""Tools for working with JSON files.
+
+Usage:
+from praisonaiagents.tools import json_tools
+data = json_tools.read_json("data.json")
+
+or
+from praisonaiagents.tools import read_json, write_json, merge_json
+data = read_json("data.json")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any, Tuple
+from importlib import util
+import json
+from datetime import datetime
+
+class JSONTools:
+    """Tools for working with JSON files."""
+    
+    def __init__(self):
+        """Initialize JSONTools."""
+        pass
+
+    def read_json(
+        self,
+        filepath: str,
+        encoding: str = 'utf-8',
+        validate_schema: Optional[Dict[str, Any]] = None
+    ) -> Dict[str, Any]:
+        """Read a JSON file with optional schema validation.
+        
+        Args:
+            filepath: Path to JSON file
+            encoding: File encoding
+            validate_schema: Optional JSON schema for validation
+            
+        Returns:
+            Dict with JSON data
+        """
+        try:
+            # Read JSON file
+            with open(filepath, 'r', encoding=encoding) as f:
+                data = json.load(f)
+            
+            # Validate against schema if provided
+            if validate_schema:
+                if util.find_spec('jsonschema') is None:
+                    error_msg = "jsonschema package is not available. Please install it using: pip install jsonschema"
+                    logging.error(error_msg)
+                    return {"error": error_msg}
+                import jsonschema
+                try:
+                    jsonschema.validate(instance=data, schema=validate_schema)
+                except jsonschema.exceptions.ValidationError as e:
+                    error_msg = f"JSON validation failed: {str(e)}"
+                    logging.error(error_msg)
+                    return {"error": error_msg}
+            
+            return data
+            
+        except Exception as e:
+            error_msg = f"Error reading JSON file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def write_json(
+        self,
+        data: Union[Dict[str, Any], List[Any]],
+        filepath: str,
+        encoding: str = 'utf-8',
+        indent: int = 2,
+        sort_keys: bool = False,
+        ensure_ascii: bool = False
+    ) -> bool:
+        """Write data to a JSON file.
+        
+        Args:
+            data: Data to write
+            filepath: Output file path
+            encoding: File encoding
+            indent: Number of spaces for indentation
+            sort_keys: Sort dictionary keys
+            ensure_ascii: Escape non-ASCII characters
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        try:
+            with open(filepath, 'w', encoding=encoding) as f:
+                json.dump(
+                    data,
+                    f,
+                    indent=indent,
+                    sort_keys=sort_keys,
+                    ensure_ascii=ensure_ascii
+                )
+            return True
+        except Exception as e:
+            error_msg = f"Error writing JSON file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def merge_json(
+        self,
+        files: List[str],
+        output_file: str,
+        merge_arrays: bool = True,
+        overwrite_duplicates: bool = True
+    ) -> bool:
+        """Merge multiple JSON files.
+        
+        Args:
+            files: List of JSON files to merge
+            output_file: Output file path
+            merge_arrays: Merge arrays instead of overwriting
+            overwrite_duplicates: Overwrite duplicate keys
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        try:
+            if len(files) < 2:
+                raise ValueError("At least two files are required for merging")
+            
+            # Read first file
+            result = self.read_json(files[0])
+            
+            # Merge with remaining files
+            for file in files[1:]:
+                data = self.read_json(file)
+                result = self._deep_merge(
+                    result,
+                    data,
+                    merge_arrays=merge_arrays,
+                    overwrite_duplicates=overwrite_duplicates
+                )
+            
+            # Write merged result
+            return self.write_json(result, output_file)
+        except Exception as e:
+            error_msg = f"Error merging JSON files: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def _deep_merge(
+        self,
+        dict1: Dict[str, Any],
+        dict2: Dict[str, Any],
+        merge_arrays: bool = True,
+        overwrite_duplicates: bool = True
+    ) -> Dict[str, Any]:
+        """Deep merge two dictionaries.
+        
+        Args:
+            dict1: First dictionary
+            dict2: Second dictionary
+            merge_arrays: Merge arrays instead of overwriting
+            overwrite_duplicates: Overwrite duplicate keys
+            
+        Returns:
+            Merged dictionary
+        """
+        result = dict1.copy()
+        
+        for key, value in dict2.items():
+            if key in result:
+                if isinstance(result[key], dict) and isinstance(value, dict):
+                    result[key] = self._deep_merge(
+                        result[key],
+                        value,
+                        merge_arrays=merge_arrays,
+                        overwrite_duplicates=overwrite_duplicates
+                    )
+                elif isinstance(result[key], list) and isinstance(value, list):
+                    if merge_arrays:
+                        result[key].extend(value)
+                    elif overwrite_duplicates:
+                        result[key] = value
+                elif overwrite_duplicates:
+                    result[key] = value
+            else:
+                result[key] = value
+        
+        return result
+
+    def validate_json(
+        self,
+        data: Union[Dict[str, Any], str],
+        schema: Dict[str, Any]
+    ) -> Tuple[bool, Optional[str]]:
+        """Validate JSON data against a schema.
+        
+        Args:
+            data: JSON data or filepath
+            schema: JSON schema for validation
+            
+        Returns:
+            Tuple of (is_valid, error_message)
+        """
+        try:
+            if util.find_spec('jsonschema') is None:
+                error_msg = "jsonschema package is not available. Please install it using: pip install jsonschema"
+                logging.error(error_msg)
+                return False, error_msg
+            import jsonschema
+
+            # Load data if filepath provided
+            if isinstance(data, str):
+                with open(data, 'r') as f:
+                    data = json.load(f)
+            
+            jsonschema.validate(instance=data, schema=schema)
+            return True, None
+            
+        except jsonschema.exceptions.ValidationError as e:
+            error_msg = f"JSON validation failed: {str(e)}"
+            logging.error(error_msg)
+            return False, error_msg
+            
+        except Exception as e:
+            error_msg = f"Error validating JSON: {str(e)}"
+            logging.error(error_msg)
+            return False, error_msg
+
+    def analyze_json(
+        self,
+        data: Union[Dict[str, Any], str],
+        max_depth: int = 10
+    ) -> Dict[str, Any]:
+        """Analyze JSON data structure.
+        
+        Args:
+            data: JSON data or filepath
+            max_depth: Maximum depth to analyze
+            
+        Returns:
+            Dict with analysis results
+        """
+        try:
+            # Load data if filepath provided
+            if isinstance(data, str):
+                data = self.read_json(data)
+            
+            def analyze_value(value: Any, depth: int = 0) -> Dict[str, Any]:
+                if depth >= max_depth:
+                    return {'type': 'max_depth_reached'}
+                
+                result = {'type': type(value).__name__}
+                
+                if isinstance(value, dict):
+                    result['size'] = len(value)
+                    result['keys'] = list(value.keys())
+                    if depth < max_depth - 1:
+                        result['children'] = {
+                            k: analyze_value(v, depth + 1)
+                            for k, v in value.items()
+                        }
+                
+                elif isinstance(value, list):
+                    result['length'] = len(value)
+                    if value:
+                        result['element_types'] = list(set(
+                            type(x).__name__ for x in value
+                        ))
+                        if depth < max_depth - 1:
+                            result['sample_elements'] = [
+                                analyze_value(x, depth + 1)
+                                for x in value[:5]
+                            ]
+                
+                elif isinstance(value, (int, float)):
+                    result.update({
+                        'value': value,
+                        'is_integer': isinstance(value, int)
+                    })
+                
+                elif isinstance(value, str):
+                    result.update({
+                        'length': len(value),
+                        'sample': value[:100] if len(value) > 100 else value
+                    })
+                
+                return result
+            
+            return {
+                'analysis_time': datetime.now().isoformat(),
+                'structure': analyze_value(data)
+            }
+        except Exception as e:
+            error_msg = f"Error analyzing JSON: {str(e)}"
+            logging.error(error_msg)
+            return {}
+
+    def transform_json(
+        self,
+        data: Union[Dict[str, Any], str],
+        transformations: List[Dict[str, Any]]
+    ) -> Dict[str, Any]:
+        """Transform JSON data using a list of operations.
+        
+        Args:
+            data: JSON data or filepath
+            transformations: List of transformation operations
+            
+        Returns:
+            Transformed JSON data
+        """
+        try:
+            # Load data if filepath provided
+            if isinstance(data, str):
+                data = self.read_json(data)
+            
+            result = data.copy()
+            
+            for transform in transformations:
+                op = transform.get('operation')
+                path = transform.get('path', '').split('.')
+                value = transform.get('value')
+                
+                if op == 'set':
+                    self._set_value(result, path, value)
+                elif op == 'delete':
+                    self._delete_value(result, path)
+                elif op == 'rename':
+                    old_path = path
+                    new_path = value.split('.')
+                    self._rename_key(result, old_path, new_path)
+                elif op == 'move':
+                    old_path = path
+                    new_path = value.split('.')
+                    self._move_value(result, old_path, new_path)
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error transforming JSON: {str(e)}"
+            logging.error(error_msg)
+            return data
+
+    def _set_value(self, data: Dict[str, Any], path: List[str], value: Any):
+        """Set a value at the specified path."""
+        current = data
+        for key in path[:-1]:
+            if key not in current:
+                current[key] = {}
+            current = current[key]
+        current[path[-1]] = value
+
+    def _delete_value(self, data: Dict[str, Any], path: List[str]):
+        """Delete a value at the specified path."""
+        current = data
+        for key in path[:-1]:
+            if key not in current:
+                return
+            current = current[key]
+        if path[-1] in current:
+            del current[path[-1]]
+
+    def _rename_key(
+        self,
+        data: Dict[str, Any],
+        old_path: List[str],
+        new_path: List[str]
+    ):
+        """Rename a key at the specified path."""
+        value = self._get_value(data, old_path)
+        if value is not None:
+            self._delete_value(data, old_path)
+            self._set_value(data, new_path, value)
+
+    def _move_value(
+        self,
+        data: Dict[str, Any],
+        old_path: List[str],
+        new_path: List[str]
+    ):
+        """Move a value from one path to another."""
+        self._rename_key(data, old_path, new_path)
+
+    def _get_value(
+        self,
+        data: Dict[str, Any],
+        path: List[str]
+    ) -> Optional[Any]:
+        """Get a value at the specified path."""
+        current = data
+        for key in path:
+            if key not in current:
+                return None
+            current = current[key]
+        return current
+
+# Create instance for direct function access
+_json_tools = JSONTools()
+read_json = _json_tools.read_json
+write_json = _json_tools.write_json
+merge_json = _json_tools.merge_json
+validate_json = _json_tools.validate_json
+analyze_json = _json_tools.analyze_json
+transform_json = _json_tools.transform_json
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("JSONTools Demonstration")
+    print("==================================================\n")
+    
+    # Sample data
+    data1 = {
+        'id': 1,
+        'name': 'Alice',
+        'scores': [95, 87, 92],
+        'details': {
+            'age': 25,
+            'city': 'New York'
+        }
+    }
+    
+    data2 = {
+        'id': 2,
+        'name': 'Bob',
+        'scores': [88, 90, 85],
+        'details': {
+            'age': 30,
+            'country': 'USA'
+        }
+    }
+    
+    # 1. Write JSON files
+    print("1. Writing JSON Files")
+    print("------------------------------")
+    success = write_json(data1, 'test1.json')
+    print(f"First file written: {success}")
+    success = write_json(data2, 'test2.json')
+    print(f"Second file written: {success}")
+    print()
+    
+    # 2. Read JSON file
+    print("2. Reading JSON File")
+    print("------------------------------")
+    data = read_json('test1.json')
+    print("Contents of test1.json:")
+    print(json.dumps(data, indent=2))
+    print()
+    
+    # 3. Merge JSON files
+    print("3. Merging JSON Files")
+    print("------------------------------")
+    success = merge_json(['test1.json', 'test2.json'], 'merged.json')
+    print(f"Files merged: {success}")
+    if success:
+        print("Merged contents:")
+        print(json.dumps(read_json('merged.json'), indent=2))
+    print()
+    
+    # 4. Validate JSON
+    print("4. Validating JSON")
+    print("------------------------------")
+    schema = {
+        'type': 'object',
+        'properties': {
+            'id': {'type': 'integer'},
+            'name': {'type': 'string'},
+            'scores': {
+                'type': 'array',
+                'items': {'type': 'number'}
+            },
+            'details': {
+                'type': 'object',
+                'properties': {
+                    'age': {'type': 'integer'},
+                    'city': {'type': 'string'}
+                }
+            }
+        },
+        'required': ['id', 'name']
+    }
+    
+    is_valid, error = validate_json(data1, schema)
+    print(f"Validation result: {is_valid}")
+    if error:
+        print(f"Validation error: {error}")
+    print()
+    
+    # 5. Analyze JSON
+    print("5. Analyzing JSON")
+    print("------------------------------")
+    analysis = analyze_json(data1)
+    print("Analysis results:")
+    print(json.dumps(analysis, indent=2))
+    print()
+    
+    # 6. Transform JSON
+    print("6. Transforming JSON")
+    print("------------------------------")
+    transformations = [
+        {
+            'operation': 'set',
+            'path': 'details.status',
+            'value': 'active'
+        },
+        {
+            'operation': 'rename',
+            'path': 'details.city',
+            'value': 'details.location'
+        }
+    ]
+    
+    transformed = transform_json(data1, transformations)
+    print("Transformed data:")
+    print(json.dumps(transformed, indent=2))
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/newspaper_tools.py

@@ -0,0 +1,354 @@
+"""Newspaper tools for scraping and parsing news articles.
+
+Usage:
+from praisonaiagents.tools import newspaper_tools
+article = newspaper_tools.get_article("https://example.com/article")
+sources = newspaper_tools.get_news_sources("technology")
+articles = newspaper_tools.get_articles_from_source("https://techcrunch.com")
+
+or
+from praisonaiagents.tools import get_article, get_news_sources
+article = get_article("https://example.com/article")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any
+from importlib import util
+import json
+from urllib.parse import urlparse
+
+# Predefined list of popular news sources
+POPULAR_NEWS_SOURCES = {
+    'technology': [
+        'https://techcrunch.com',
+        'https://www.theverge.com',
+        'https://www.wired.com',
+        'https://www.engadget.com',
+        'https://arstechnica.com'
+    ],
+    'business': [
+        'https://www.bloomberg.com',
+        'https://www.reuters.com',
+        'https://www.wsj.com',
+        'https://www.ft.com',
+        'https://www.cnbc.com'
+    ],
+    'general': [
+        'https://www.nytimes.com',
+        'https://www.theguardian.com',
+        'https://www.washingtonpost.com',
+        'https://www.bbc.com',
+        'https://www.cnn.com'
+    ],
+    'sports': [
+        'https://www.espn.com',
+        'https://sports.yahoo.com',
+        'https://www.cbssports.com',
+        'https://www.skysports.com',
+        'https://www.bleacherreport.com'
+    ],
+    'entertainment': [
+        'https://variety.com',
+        'https://www.hollywoodreporter.com',
+        'https://www.ew.com',
+        'https://www.deadline.com',
+        'https://www.imdb.com/news'
+    ],
+    'science': [
+        'https://www.scientificamerican.com',
+        'https://www.sciencedaily.com',
+        'https://www.newscientist.com',
+        'https://www.sciencemag.org',
+        'https://www.nature.com/news'
+    ]
+}
+
+class NewspaperTools:
+    """Tools for scraping and parsing news articles."""
+    
+    def __init__(self):
+        """Initialize NewspaperTools and check for newspaper package."""
+        self._check_newspaper()
+        
+    def _check_newspaper(self):
+        """Check if newspaper package is installed."""
+        if util.find_spec("newspaper") is None:
+            raise ImportError("newspaper3k package is not available. Please install it using: pip install newspaper3k")
+        global newspaper
+        import newspaper
+
+    def get_article(
+        self, 
+        url: str,
+        language: str = 'en'
+    ) -> Dict[str, Any]:
+        """
+        Extract and parse a news article from a URL.
+        
+        Args:
+            url: URL of the article
+            language: Language code (e.g., 'en' for English)
+            
+        Returns:
+            Dict: Article information including title, text, authors, etc.
+        """
+        try:
+            from newspaper import Article, Config
+            
+            # Configure article download
+            config = Config()
+            config.browser_user_agent = 'Mozilla/5.0'
+            config.language = language
+            
+            # Download and parse article
+            article = Article(url, config=config)
+            article.download()
+            article.parse()
+            
+            # Try to extract additional information
+            try:
+                article.nlp()
+            except Exception as e:
+                logging.warning(f"NLP processing failed: {str(e)}")
+            
+            # Build response
+            response = {
+                "url": url,
+                "title": article.title,
+                "text": article.text,
+                "authors": article.authors,
+                "publish_date": article.publish_date.isoformat() if article.publish_date else None,
+                "top_image": article.top_image,
+                "images": list(article.images),
+                "movies": list(article.movies),
+                "source_domain": urlparse(url).netloc,
+            }
+            
+            # Add NLP results if available
+            if hasattr(article, 'keywords') and article.keywords:
+                response["keywords"] = article.keywords
+            if hasattr(article, 'summary') and article.summary:
+                response["summary"] = article.summary
+            
+            return response
+        except Exception as e:
+            error_msg = f"Error extracting article from {url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_news_sources(
+        self, 
+        category: Optional[str] = None,
+        language: str = 'en',
+        country: Optional[str] = None
+    ) -> Union[List[Dict[str, str]], Dict[str, str]]:
+        """
+        Get a list of news sources, optionally filtered by category.
+        
+        Args:
+            category: Category to filter by (e.g., 'technology', 'sports')
+            language: Language code
+            country: Country code
+            
+        Returns:
+            List[Dict] or Dict: List of news sources or error dict
+        """
+        try:
+            sources = []
+            
+            # Get sources for the specified category or all categories
+            if category:
+                category = category.lower()
+                if category in POPULAR_NEWS_SOURCES:
+                    urls = POPULAR_NEWS_SOURCES[category]
+                else:
+                    urls = []
+                    for cat_urls in POPULAR_NEWS_SOURCES.values():
+                        urls.extend(cat_urls)
+            else:
+                urls = []
+                for cat_urls in POPULAR_NEWS_SOURCES.values():
+                    urls.extend(cat_urls)
+            
+            # Create source objects
+            for url in urls:
+                domain = urlparse(url).netloc
+                source = {
+                    "url": url,
+                    "domain": domain,
+                    "name": domain.replace("www.", "").split(".")[0].title(),
+                    "category": category if category else "general"
+                }
+                sources.append(source)
+            
+            return sources
+        except Exception as e:
+            error_msg = f"Error getting news sources: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_articles_from_source(
+        self, 
+        source_url: str,
+        limit: int = 10,
+        language: str = 'en'
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """
+        Get recent articles from a news source.
+        
+        Args:
+            source_url: URL of the news source
+            limit: Maximum number of articles to return
+            language: Language code
+            
+        Returns:
+            List[Dict] or Dict: List of articles or error dict
+        """
+        try:
+            from newspaper import Source, Config
+            
+            # Configure source scraping
+            config = Config()
+            config.browser_user_agent = 'Mozilla/5.0'
+            config.language = language
+            config.fetch_images = False  # Speed up processing
+            
+            # Build news source
+            source = Source(source_url, config=config)
+            source.build()
+            
+            # Get articles
+            articles = []
+            for article_url in source.article_urls()[:limit]:
+                try:
+                    article = self.get_article(article_url, language)
+                    if "error" not in article:
+                        articles.append(article)
+                except Exception as e:
+                    logging.warning(f"Error processing article {article_url}: {str(e)}")
+                    continue
+                
+                if len(articles) >= limit:
+                    break
+            
+            return articles
+        except Exception as e:
+            error_msg = f"Error getting articles from {source_url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_trending_topics(
+        self,
+        sources: Optional[List[str]] = None,
+        limit: int = 10,
+        language: str = 'en'
+    ) -> Union[List[str], Dict[str, str]]:
+        """
+        Get trending topics across news sources.
+        
+        Args:
+            sources: List of source URLs to analyze
+            limit: Maximum number of trending topics to return
+            language: Language code
+            
+        Returns:
+            List[str] or Dict: List of trending topics or error dict
+        """
+        try:
+            from collections import Counter
+            
+            # Use default sources if none provided
+            if not sources:
+                sources_data = self.get_news_sources(language=language)
+                if isinstance(sources_data, dict) and "error" in sources_data:
+                    return sources_data
+                sources = [s["url"] for s in sources_data[:5]]  # Use top 5 sources
+            
+            # Collect keywords from articles
+            all_keywords = []
+            for source_url in sources:
+                try:
+                    articles = self.get_articles_from_source(source_url, limit=5, language=language)
+                    if isinstance(articles, list):
+                        for article in articles:
+                            if "keywords" in article:
+                                all_keywords.extend(article["keywords"])
+                except Exception as e:
+                    logging.warning(f"Error processing source {source_url}: {str(e)}")
+                    continue
+            
+            # Get most common keywords
+            trending = Counter(all_keywords).most_common(limit)
+            return [topic for topic, count in trending]
+        except Exception as e:
+            error_msg = f"Error getting trending topics: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+# Create instance for direct function access
+_newspaper_tools = NewspaperTools()
+get_article = _newspaper_tools.get_article
+get_news_sources = _newspaper_tools.get_news_sources
+get_articles_from_source = _newspaper_tools.get_articles_from_source
+get_trending_topics = _newspaper_tools.get_trending_topics
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("NewspaperTools Demonstration")
+    print("==================================================\n")
+    
+    # 1. Get news sources
+    print("1. Getting News Sources")
+    print("------------------------------")
+    tech_sources = get_news_sources("technology")
+    print("Technology news sources:")
+    if isinstance(tech_sources, list):
+        print(json.dumps(tech_sources[:3], indent=2))  # Show first 3 sources
+    else:
+        print(tech_sources)  # Show error
+    print()
+    
+    if isinstance(tech_sources, list) and tech_sources:
+        source_url = tech_sources[0]["url"]
+        
+        # 2. Get articles from a source
+        print("2. Getting Articles from Source")
+        print("------------------------------")
+        articles = get_articles_from_source(source_url, limit=2)
+        print(f"Articles from {source_url}:")
+        if isinstance(articles, list):
+            for article in articles:
+                print(f"- {article['title']}")
+                if "summary" in article:
+                    print(f"  Summary: {article['summary'][:200]}...")
+        else:
+            print(articles)  # Show error
+        print()
+        
+        # 3. Get a single article
+        print("3. Getting Single Article")
+        print("------------------------------")
+        if isinstance(articles, list) and articles:
+            article_url = articles[0]["url"]
+            article = get_article(article_url)
+            if "error" not in article:
+                print(f"Article: {article['title']}")
+                if "summary" in article:
+                    print(f"Summary: {article['summary'][:200]}...")
+                print(f"Authors: {', '.join(article['authors'])}")
+                print(f"Date: {article['publish_date']}")
+            else:
+                print(article)  # Show error
+        print()
+        
+        # 4. Get trending topics
+        print("4. Getting Trending Topics")
+        print("------------------------------")
+        topics = get_trending_topics([source_url], limit=5)
+        print("Trending topics:")
+        print(json.dumps(topics, indent=2))
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/pandas_tools.py

@@ -0,0 +1,326 @@
+"""Pandas tools for data manipulation and analysis.
+
+Usage:
+from praisonaiagents.tools import pandas_tools
+df = pandas_tools.read_csv("data.csv")
+df = pandas_tools.filter_data(df, "column > 5")
+summary = pandas_tools.get_summary(df)
+
+or
+from praisonaiagents.tools import read_csv, filter_data, get_summary
+df = read_csv("data.csv")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any
+from importlib import util
+import json
+import os
+
+# Import pandas for type hints, but don't use it until we check it's installed
+if util.find_spec("pandas") is not None:
+    import pandas as pd
+    import numpy as np
+else:
+    # Create a placeholder for type hints
+    class pd:
+        DataFrame = None
+
+def _convert_to_serializable(obj: Any) -> Any:
+    """Convert numpy/pandas types to JSON serializable Python types."""
+    if isinstance(obj, (np.integer, np.floating)):
+        return obj.item()
+    elif isinstance(obj, np.ndarray):
+        return obj.tolist()
+    elif isinstance(obj, pd.Series):
+        return obj.to_list()
+    elif isinstance(obj, pd.DataFrame):
+        return obj.to_dict(orient='records')
+    return obj
+
+class PandasTools:
+    """Tools for data manipulation and analysis using pandas."""
+    
+    def __init__(self):
+        """Initialize PandasTools and check for pandas installation."""
+        self._check_pandas()
+        
+    def _check_pandas(self):
+        """Check if pandas is installed."""
+        if util.find_spec("pandas") is None:
+            raise ImportError("pandas is not available. Please install it using: pip install pandas")
+        global pd, np
+        import pandas as pd
+        import numpy as np
+
+    def read_csv(self, filepath: str, **kwargs) -> Union[pd.DataFrame, Dict[str, str]]:
+        """
+        Read a CSV file into a pandas DataFrame.
+        
+        Args:
+            filepath: Path to the CSV file
+            **kwargs: Additional arguments to pass to pd.read_csv()
+            
+        Returns:
+            pd.DataFrame or Dict: DataFrame if successful, error dict if failed
+        """
+        try:
+            return pd.read_csv(filepath, **kwargs)
+        except Exception as e:
+            error_msg = f"Error reading CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def read_excel(self, filepath: str, **kwargs) -> Union[pd.DataFrame, Dict[str, str]]:
+        """
+        Read an Excel file into a pandas DataFrame.
+        
+        Args:
+            filepath: Path to the Excel file
+            **kwargs: Additional arguments to pass to pd.read_excel()
+            
+        Returns:
+            pd.DataFrame or Dict: DataFrame if successful, error dict if failed
+        """
+        try:
+            return pd.read_excel(filepath, **kwargs)
+        except Exception as e:
+            error_msg = f"Error reading Excel file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def write_csv(self, df: pd.DataFrame, filepath: str, **kwargs) -> bool:
+        """
+        Write DataFrame to a CSV file.
+        
+        Args:
+            df: DataFrame to write
+            filepath: Output file path
+            **kwargs: Additional arguments to pass to df.to_csv()
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            os.makedirs(os.path.dirname(filepath), exist_ok=True)
+            df.to_csv(filepath, **kwargs)
+            return True
+        except Exception as e:
+            error_msg = f"Error writing CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def write_excel(self, df: pd.DataFrame, filepath: str, **kwargs) -> bool:
+        """
+        Write DataFrame to an Excel file.
+        
+        Args:
+            df: DataFrame to write
+            filepath: Output file path
+            **kwargs: Additional arguments to pass to df.to_excel()
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            os.makedirs(os.path.dirname(filepath), exist_ok=True)
+            df.to_excel(filepath, **kwargs)
+            return True
+        except Exception as e:
+            error_msg = f"Error writing Excel file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def filter_data(self, df: pd.DataFrame, query: str) -> Union[pd.DataFrame, Dict[str, str]]:
+        """
+        Filter DataFrame using a query string.
+        
+        Args:
+            df: Input DataFrame
+            query: Query string (e.g., "column > 5 and other_column == 'value'")
+            
+        Returns:
+            pd.DataFrame or Dict: Filtered DataFrame if successful, error dict if failed
+        """
+        try:
+            return df.query(query)
+        except Exception as e:
+            error_msg = f"Error filtering data with query '{query}': {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_summary(self, df: pd.DataFrame) -> Dict[str, Any]:
+        """
+        Get a summary of the DataFrame including basic statistics and info.
+        
+        Args:
+            df: Input DataFrame
+            
+        Returns:
+            Dict: Summary statistics and information
+        """
+        try:
+            numeric_summary = df.describe().to_dict()
+            # Convert numpy types to native Python types
+            for col in numeric_summary:
+                numeric_summary[col] = {k: _convert_to_serializable(v) 
+                                     for k, v in numeric_summary[col].items()}
+            
+            summary = {
+                "shape": list(df.shape),
+                "columns": list(df.columns),
+                "dtypes": df.dtypes.astype(str).to_dict(),
+                "null_counts": df.isnull().sum().to_dict(),
+                "numeric_summary": numeric_summary,
+                "memory_usage": int(df.memory_usage(deep=True).sum()),
+            }
+            return summary
+        except Exception as e:
+            error_msg = f"Error getting data summary: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def group_by(
+        self, 
+        df: pd.DataFrame, 
+        columns: Union[str, List[str]], 
+        agg_dict: Dict[str, Union[str, List[str]]]
+    ) -> Union[pd.DataFrame, Dict[str, str]]:
+        """
+        Group DataFrame by columns and apply aggregation functions.
+        
+        Args:
+            df: Input DataFrame
+            columns: Column(s) to group by
+            agg_dict: Dictionary of column:function pairs for aggregation
+            
+        Returns:
+            pd.DataFrame or Dict: Grouped DataFrame if successful, error dict if failed
+        """
+        try:
+            return df.groupby(columns).agg(agg_dict).reset_index()
+        except Exception as e:
+            error_msg = f"Error grouping data: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def pivot_table(
+        self, 
+        df: pd.DataFrame, 
+        index: Union[str, List[str]], 
+        columns: Optional[Union[str, List[str]]] = None,
+        values: Optional[Union[str, List[str]]] = None,
+        aggfunc: str = "mean"
+    ) -> Union[pd.DataFrame, Dict[str, str]]:
+        """
+        Create a pivot table from DataFrame.
+        
+        Args:
+            df: Input DataFrame
+            index: Column(s) to use as index
+            columns: Column(s) to use as columns
+            values: Column(s) to aggregate
+            aggfunc: Aggregation function to use
+            
+        Returns:
+            pd.DataFrame or Dict: Pivot table if successful, error dict if failed
+        """
+        try:
+            return pd.pivot_table(
+                df,
+                index=index,
+                columns=columns,
+                values=values,
+                aggfunc=aggfunc
+            ).reset_index()
+        except Exception as e:
+            error_msg = f"Error creating pivot table: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+# Create instance for direct function access
+_pandas_tools = PandasTools()
+read_csv = _pandas_tools.read_csv
+read_excel = _pandas_tools.read_excel
+write_csv = _pandas_tools.write_csv
+write_excel = _pandas_tools.write_excel
+filter_data = _pandas_tools.filter_data
+get_summary = _pandas_tools.get_summary
+group_by = _pandas_tools.group_by
+pivot_table = _pandas_tools.pivot_table
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("PandasTools Demonstration")
+    print("==================================================\n")
+
+    # Create a test directory
+    test_dir = os.path.join(os.getcwd(), "test_files")
+    os.makedirs(test_dir, exist_ok=True)
+    
+    # Create a sample DataFrame
+    df = pd.DataFrame({
+        'name': ['John', 'Jane', 'Bob', 'Alice', 'Charlie'],
+        'age': [25, 30, 35, 28, 32],
+        'city': ['New York', 'London', 'Paris', 'Tokyo', 'London'],
+        'salary': [50000, 60000, 75000, 65000, 55000]
+    })
+    
+    # 1. Write to CSV
+    print("1. Writing to CSV")
+    print("------------------------------")
+    csv_file = os.path.join(test_dir, "sample.csv")
+    success = write_csv(df, csv_file, index=False)
+    print(f"Write successful: {success}\n")
+    
+    # 2. Read from CSV
+    print("2. Reading from CSV")
+    print("------------------------------")
+    df_read = read_csv(csv_file)
+    print("First few rows:")
+    print(df_read.head())
+    print()
+    
+    # 3. Filter Data
+    print("3. Filtering Data")
+    print("------------------------------")
+    filtered_df = filter_data(df, "age > 30 and salary > 60000")
+    print("People over 30 with salary > 60000:")
+    print(filtered_df)
+    print()
+    
+    # 4. Get Summary
+    print("4. Data Summary")
+    print("------------------------------")
+    summary = get_summary(df)
+    print(json.dumps(summary, indent=2))
+    print()
+    
+    # 5. Group By
+    print("5. Group By")
+    print("------------------------------")
+    grouped = group_by(df, "city", {"salary": ["mean", "count"], "age": "mean"})
+    print("Statistics by city:")
+    print(grouped)
+    print()
+    
+    # 6. Pivot Table
+    print("6. Pivot Table")
+    print("------------------------------")
+    pivoted = pivot_table(df, index="city", values=["salary", "age"])
+    print("Pivot table by city:")
+    print(pivoted)
+    print()
+    
+    # Clean up test directory
+    try:
+        import shutil
+        shutil.rmtree(test_dir)
+        print("Test directory cleaned up successfully")
+    except Exception as e:
+        print(f"Error cleaning up test directory: {str(e)}")
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/python_tools.py

@@ -0,0 +1,423 @@
+"""Tools for Python code execution and analysis.
+
+Usage:
+from praisonaiagents.tools import python_tools
+result = python_tools.execute_code("print('Hello, World!')")
+
+or
+from praisonaiagents.tools import execute_code, analyze_code, format_code
+result = execute_code("print('Hello, World!')")
+"""
+
+import logging
+from typing import Dict, List, Optional, Any
+from importlib import util
+import io
+from contextlib import redirect_stdout, redirect_stderr
+import traceback
+
+class PythonTools:
+    """Tools for Python code execution and analysis."""
+    
+    def __init__(self):
+        """Initialize PythonTools."""
+        self._check_dependencies()
+        
+    def _check_dependencies(self):
+        """Check if required packages are installed."""
+        missing = []
+        for package in ['black', 'pylint', 'autopep8']:
+            if util.find_spec(package) is None:
+                missing.append(package)
+        
+        if missing:
+            raise ImportError(
+                f"Required packages not available. Please install: {', '.join(missing)}\n"
+                f"Run: pip install {' '.join(missing)}"
+            )
+
+    def execute_code(
+        self,
+        code: str,
+        globals_dict: Optional[Dict[str, Any]] = None,
+        locals_dict: Optional[Dict[str, Any]] = None,
+        timeout: int = 30,
+        max_output_size: int = 10000
+    ) -> Dict[str, Any]:
+        """Execute Python code safely."""
+        try:
+            # Set up execution environment
+            if globals_dict is None:
+                globals_dict = {'__builtins__': __builtins__}
+            if locals_dict is None:
+                locals_dict = {}
+            
+            # Capture output
+            stdout_buffer = io.StringIO()
+            stderr_buffer = io.StringIO()
+            
+            try:
+                # Compile code
+                compiled_code = compile(code, '<string>', 'exec')
+                
+                # Execute with output capture
+                with redirect_stdout(stdout_buffer), redirect_stderr(stderr_buffer):
+                    exec(compiled_code, globals_dict, locals_dict)
+                    
+                    # Get last expression value if any
+                    import ast
+                    tree = ast.parse(code)
+                    if tree.body and isinstance(tree.body[-1], ast.Expr):
+                        result = eval(
+                            compile(ast.Expression(tree.body[-1].value), '<string>', 'eval'),
+                            globals_dict,
+                            locals_dict
+                        )
+                    else:
+                        result = None
+                
+                # Get output
+                stdout = stdout_buffer.getvalue()
+                stderr = stderr_buffer.getvalue()
+                
+                # Truncate output if too large
+                if len(stdout) > max_output_size:
+                    stdout = stdout[:max_output_size] + "...[truncated]"
+                if len(stderr) > max_output_size:
+                    stderr = stderr[:max_output_size] + "...[truncated]"
+                
+                return {
+                    'result': result,
+                    'stdout': stdout,
+                    'stderr': stderr,
+                    'success': True
+                }
+            
+            except Exception as e:
+                error_msg = f"Error executing code: {str(e)}"
+                logging.error(error_msg)
+                return {
+                    'result': None,
+                    'stdout': stdout_buffer.getvalue(),
+                    'stderr': error_msg,
+                    'success': False
+                }
+            
+        except Exception as e:
+            error_msg = f"Error executing code: {str(e)}"
+            logging.error(error_msg)
+            return {
+                'result': None,
+                'stdout': '',
+                'stderr': error_msg,
+                'success': False
+            }
+
+    def analyze_code(
+        self,
+        code: str
+    ) -> Optional[Dict[str, Any]]:
+        """Analyze Python code structure and quality.
+        
+        Args:
+            code: Python code to analyze
+            
+        Returns:
+            Dictionary with analysis results
+        """
+        try:
+            # Import ast only when needed
+            import ast
+            
+            # Parse code
+            tree = ast.parse(code)
+            
+            # Analyze structure
+            analysis = {
+                'imports': [],
+                'functions': [],
+                'classes': [],
+                'variables': [],
+                'complexity': {
+                    'lines': len(code.splitlines()),
+                    'functions': 0,
+                    'classes': 0,
+                    'branches': 0
+                }
+            }
+            
+            # Analyze nodes
+            for node in ast.walk(tree):
+                if isinstance(node, ast.Import):
+                    for name in node.names:
+                        analysis['imports'].append(name.name)
+                elif isinstance(node, ast.ImportFrom):
+                    module = node.module or ''
+                    for name in node.names:
+                        analysis['imports'].append(f"{module}.{name.name}")
+                elif isinstance(node, ast.FunctionDef):
+                    analysis['functions'].append({
+                        'name': node.name,
+                        'args': [
+                            arg.arg for arg in node.args.args
+                        ],
+                        'decorators': [
+                            ast.unparse(d) for d in node.decorator_list
+                        ]
+                    })
+                    analysis['complexity']['functions'] += 1
+                elif isinstance(node, ast.ClassDef):
+                    analysis['classes'].append({
+                        'name': node.name,
+                        'bases': [
+                            ast.unparse(base) for base in node.bases
+                        ],
+                        'decorators': [
+                            ast.unparse(d) for d in node.decorator_list
+                        ]
+                    })
+                    analysis['complexity']['classes'] += 1
+                elif isinstance(node, ast.Assign):
+                    for target in node.targets:
+                        if isinstance(target, ast.Name):
+                            analysis['variables'].append(target.id)
+                elif isinstance(node, (ast.If, ast.While, ast.For)):
+                    analysis['complexity']['branches'] += 1
+            
+            return analysis
+        except Exception as e:
+            error_msg = f"Error analyzing code: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def format_code(
+        self,
+        code: str,
+        style: str = 'black',
+        line_length: int = 88
+    ) -> Optional[str]:
+        """Format Python code according to style guide.
+        
+        Args:
+            code: Python code to format
+            style: Formatting style ('black' or 'pep8')
+            line_length: Maximum line length
+            
+        Returns:
+            Formatted code
+        """
+        try:
+            if util.find_spec(style) is None:
+                error_msg = f"{style} package is not available. Please install it using: pip install {style}"
+                logging.error(error_msg)
+                return None
+
+            if style == 'black':
+                import black
+                return black.format_str(
+                    code,
+                    mode=black.FileMode(
+                        line_length=line_length
+                    )
+                )
+            else:  # pep8
+                import autopep8
+                return autopep8.fix_code(
+                    code,
+                    options={
+                        'max_line_length': line_length
+                    }
+                )
+        except Exception as e:
+            error_msg = f"Error formatting code: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def lint_code(
+        self,
+        code: str
+    ) -> Optional[Dict[str, List[Dict[str, Any]]]]:
+        """Lint Python code for potential issues.
+        
+        Args:
+            code: Python code to lint
+            
+        Returns:
+            Dictionary with linting results
+        """
+        try:
+            if util.find_spec('pylint') is None:
+                error_msg = "pylint package is not available. Please install it using: pip install pylint"
+                logging.error(error_msg)
+                return None
+
+            # Import pylint only when needed
+            from pylint.reporters import JSONReporter
+            from pylint.lint.run import Run
+            
+            # Create temporary file for pylint
+            import tempfile
+            with tempfile.NamedTemporaryFile(
+                mode='w',
+                suffix='.py',
+                delete=False
+            ) as f:
+                f.write(code)
+                temp_path = f.name
+            
+            # Run pylint
+            reporter = JSONReporter()
+            Run(
+                [temp_path],
+                reporter=reporter,
+                exit=False
+            )
+            
+            # Process results
+            results = {
+                'errors': [],
+                'warnings': [],
+                'conventions': []
+            }
+            
+            for msg in reporter.messages:
+                item = {
+                    'type': msg.category,
+                    'module': msg.module,
+                    'obj': msg.obj,
+                    'line': msg.line,
+                    'column': msg.column,
+                    'path': msg.path,
+                    'symbol': msg.symbol,
+                    'message': msg.msg,
+                    'message-id': msg.msg_id
+                }
+                
+                if msg.category in ['error', 'fatal']:
+                    results['errors'].append(item)
+                elif msg.category == 'warning':
+                    results['warnings'].append(item)
+                else:
+                    results['conventions'].append(item)
+            
+            # Clean up
+            import os
+            os.unlink(temp_path)
+            
+            return results
+        except Exception as e:
+            error_msg = f"Error linting code: {str(e)}"
+            logging.error(error_msg)
+            return {
+                'errors': [],
+                'warnings': [],
+                'conventions': []
+            }
+
+    def disassemble_code(
+        self,
+        code: str
+    ) -> Optional[str]:
+        """Disassemble Python code to bytecode.
+        
+        Args:
+            code: Python code to disassemble
+            
+        Returns:
+            Disassembled bytecode as string
+        """
+        try:
+            # Import dis only when needed
+            import dis
+            
+            # Compile code
+            compiled_code = compile(code, '<string>', 'exec')
+            
+            # Capture disassembly
+            output = io.StringIO()
+            with redirect_stdout(output):
+                dis.dis(compiled_code)
+            
+            return output.getvalue()
+        except Exception as e:
+            error_msg = f"Error disassembling code: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+# Create instance for direct function access
+_python_tools = PythonTools()
+execute_code = _python_tools.execute_code
+analyze_code = _python_tools.analyze_code
+format_code = _python_tools.format_code
+lint_code = _python_tools.lint_code
+disassemble_code = _python_tools.disassemble_code
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("PythonTools Demonstration")
+    print("==================================================\n")
+
+    print("1. Execute Python Code")
+    print("------------------------------")
+    code = """
+def greet(name):
+    return f"Hello, {name}!"
+
+result = greet("World")
+print(result)
+"""
+    print("Code to execute:")
+    print(code)
+    print("\nOutput:")
+    result = execute_code(code)
+    print(result["stdout"])
+    print()
+
+    print("2. Format Python Code")
+    print("------------------------------")
+    code = """
+def messy_function(x,y,   z):
+    if x>0:
+     return y+z
+    else:
+     return y-z
+"""
+    print("Before formatting:")
+    print(code)
+    print("\nAfter formatting:")
+    result = format_code(code)
+    print(result)
+    print()
+
+    print("3. Lint Python Code")
+    print("------------------------------")
+    code = """
+def bad_function():
+    unused_var = 42
+    return 'result'
+"""
+    print("Code to lint:")
+    print(code)
+    print("\nLinting results:")
+    result = lint_code(code)
+    print(result)
+    print()
+
+    print("4. Execute Code with Variables")
+    print("------------------------------")
+    code = """
+x = 10
+y = 20
+result = x + y
+print(f"The sum of {x} and {y} is {result}")
+"""
+    print("Code to execute:")
+    print(code)
+    print("\nOutput:")
+    result = execute_code(code)
+    print(result["stdout"])
+    print()
+
+    print("==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/shell_tools.py

@@ -0,0 +1,278 @@
+"""Tools for executing shell commands safely.
+
+This module provides a safe interface for executing shell commands with:
+- Timeout control
+- Output capture
+- Error handling
+- Resource limits
+"""
+
+import subprocess
+import shlex
+import logging
+import os
+import time
+from typing import Dict, List, Optional, Union
+
+class ShellTools:
+    """Tools for executing shell commands safely."""
+    
+    def __init__(self):
+        """Initialize ShellTools."""
+        self._check_dependencies()
+    
+    def _check_dependencies(self):
+        """Check if required packages are installed."""
+        try:
+            import psutil
+        except ImportError:
+            raise ImportError(
+                "Required package not available. Please install: psutil\n"
+                "Run: pip install psutil"
+            )
+    
+    def execute_command(
+        self,
+        command: str,
+        cwd: Optional[str] = None,
+        timeout: int = 30,
+        shell: bool = False,
+        env: Optional[Dict[str, str]] = None,
+        max_output_size: int = 10000
+    ) -> Dict[str, Union[str, int, bool]]:
+        """Execute a shell command safely.
+        
+        Args:
+            command: Command to execute
+            cwd: Working directory
+            timeout: Maximum execution time in seconds
+            shell: Whether to run command in shell
+            env: Environment variables
+            max_output_size: Maximum output size in bytes
+            
+        Returns:
+            Dictionary with execution results
+        """
+        try:
+            # Split command if not using shell
+            if not shell:
+                command = shlex.split(command)
+            
+            # Set up process environment
+            process_env = os.environ.copy()
+            if env:
+                process_env.update(env)
+            
+            # Start process
+            start_time = time.time()
+            process = subprocess.Popen(
+                command,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+                cwd=cwd,
+                shell=shell,
+                env=process_env,
+                text=True
+            )
+            
+            try:
+                # Wait for process with timeout
+                stdout, stderr = process.communicate(timeout=timeout)
+                
+                # Truncate output if too large
+                if len(stdout) > max_output_size:
+                    stdout = stdout[:max_output_size] + "...[truncated]"
+                if len(stderr) > max_output_size:
+                    stderr = stderr[:max_output_size] + "...[truncated]"
+                
+                return {
+                    'stdout': stdout,
+                    'stderr': stderr,
+                    'exit_code': process.returncode,
+                    'success': process.returncode == 0,
+                    'execution_time': time.time() - start_time
+                }
+            
+            except subprocess.TimeoutExpired:
+                # Kill process on timeout
+                parent = psutil.Process(process.pid)
+                children = parent.children(recursive=True)
+                for child in children:
+                    child.kill()
+                parent.kill()
+                
+                return {
+                    'stdout': '',
+                    'stderr': f'Command timed out after {timeout} seconds',
+                    'exit_code': -1,
+                    'success': False,
+                    'execution_time': timeout
+                }
+                
+        except Exception as e:
+            error_msg = f"Error executing command: {str(e)}"
+            logging.error(error_msg)
+            return {
+                'stdout': '',
+                'stderr': error_msg,
+                'exit_code': -1,
+                'success': False,
+                'execution_time': 0
+            }
+    
+    def list_processes(self) -> List[Dict[str, Union[int, str, float]]]:
+        """List running processes with their details.
+        
+        Returns:
+            List of process information dictionaries
+        """
+        try:
+            processes = []
+            for proc in psutil.process_iter(['pid', 'name', 'username', 'memory_percent', 'cpu_percent']):
+                try:
+                    pinfo = proc.info
+                    processes.append({
+                        'pid': pinfo['pid'],
+                        'name': pinfo['name'],
+                        'username': pinfo['username'],
+                        'memory_percent': round(pinfo['memory_percent'], 2),
+                        'cpu_percent': round(pinfo['cpu_percent'], 2)
+                    })
+                except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess):
+                    pass
+            return processes
+        except Exception as e:
+            error_msg = f"Error listing processes: {str(e)}"
+            logging.error(error_msg)
+            return []
+    
+    def kill_process(
+        self,
+        pid: int,
+        force: bool = False
+    ) -> Dict[str, Union[bool, str]]:
+        """Kill a process by its PID.
+        
+        Args:
+            pid: Process ID to kill
+            force: Whether to force kill (-9)
+            
+        Returns:
+            Dictionary with operation results
+        """
+        try:
+            process = psutil.Process(pid)
+            if force:
+                process.kill()  # SIGKILL
+            else:
+                process.terminate()  # SIGTERM
+            
+            return {
+                'success': True,
+                'message': f'Process {pid} killed successfully'
+            }
+        except psutil.NoSuchProcess:
+            return {
+                'success': False,
+                'message': f'No process found with PID {pid}'
+            }
+        except psutil.AccessDenied:
+            return {
+                'success': False,
+                'message': f'Access denied to kill process {pid}'
+            }
+        except Exception as e:
+            error_msg = f"Error killing process: {str(e)}"
+            logging.error(error_msg)
+            return {
+                'success': False,
+                'message': error_msg
+            }
+    
+    def get_system_info(self) -> Dict[str, Union[float, int, str, Dict]]:
+        """Get system information.
+        
+        Returns:
+            Dictionary with system information
+        """
+        try:
+            cpu_percent = psutil.cpu_percent(interval=1)
+            memory = psutil.virtual_memory()
+            disk = psutil.disk_usage('/')
+            
+            return {
+                'cpu': {
+                    'percent': cpu_percent,
+                    'cores': psutil.cpu_count(),
+                    'physical_cores': psutil.cpu_count(logical=False)
+                },
+                'memory': {
+                    'total': memory.total,
+                    'available': memory.available,
+                    'percent': memory.percent,
+                    'used': memory.used,
+                    'free': memory.free
+                },
+                'disk': {
+                    'total': disk.total,
+                    'used': disk.used,
+                    'free': disk.free,
+                    'percent': disk.percent
+                },
+                'boot_time': psutil.boot_time(),
+                'platform': os.uname().sysname
+            }
+        except Exception as e:
+            error_msg = f"Error getting system info: {str(e)}"
+            logging.error(error_msg)
+            return {}
+
+# Create instance for direct function access
+_shell_tools = ShellTools()
+execute_command = _shell_tools.execute_command
+list_processes = _shell_tools.list_processes
+kill_process = _shell_tools.kill_process
+get_system_info = _shell_tools.get_system_info
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("ShellTools Demonstration")
+    print("==================================================\n")
+    
+    # 1. Execute command
+    print("1. Command Execution")
+    print("------------------------------")
+    result = execute_command("ls -la")
+    print(f"Success: {result['success']}")
+    print(f"Output:\n{result['stdout']}")
+    if result['stderr']:
+        print(f"Errors:\n{result['stderr']}")
+    print(f"Execution time: {result['execution_time']:.2f}s")
+    print()
+    
+    # 2. System Information
+    print("2. System Information")
+    print("------------------------------")
+    info = get_system_info()
+    print(f"CPU Usage: {info['cpu']['percent']}%")
+    print(f"Memory Usage: {info['memory']['percent']}%")
+    print(f"Disk Usage: {info['disk']['percent']}%")
+    print(f"Platform: {info['platform']}")
+    print()
+    
+    # 3. Process List
+    print("3. Process List (top 5 by CPU)")
+    print("------------------------------")
+    processes = sorted(
+        list_processes(),
+        key=lambda x: x['cpu_percent'],
+        reverse=True
+    )[:5]
+    for proc in processes:
+        print(f"PID: {proc['pid']}, Name: {proc['name']}, CPU: {proc['cpu_percent']}%")
+    print()
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/spider_tools.py

@@ -0,0 +1,431 @@
+"""Tools for web scraping and crawling.
+
+Usage:
+from praisonaiagents.tools import spider_tools
+content = spider_tools.scrape_page("https://example.com")
+links = spider_tools.extract_links("https://example.com")
+
+or
+from praisonaiagents.tools import scrape_page, extract_links
+content = scrape_page("https://example.com")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any
+from importlib import util
+import json
+from urllib.parse import urljoin, urlparse
+import re
+import os
+import hashlib
+import time
+
+class SpiderTools:
+    """Tools for web scraping and crawling."""
+    
+    def __init__(self):
+        """Initialize SpiderTools and check for required packages."""
+        self._session = None
+        
+    def _get_session(self):
+        """Get or create requests session with common headers."""
+        if util.find_spec('requests') is None:
+            error_msg = "requests package is not available. Please install it using: pip install requests"
+            logging.error(error_msg)
+            return None
+            
+        if self._session is None:
+            import requests
+            self._session = requests.Session()
+            self._session.headers.update({
+                'User-Agent': 'Mozilla/5.0 (compatible; PraisonAI/1.0; +http://praisonai.com/bot)',
+                'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
+                'Accept-Language': 'en-US,en;q=0.5',
+                'Accept-Encoding': 'gzip, deflate, br',
+                'Connection': 'keep-alive',
+            })
+        return self._session
+
+    def scrape_page(
+        self,
+        url: str,
+        selector: Optional[str] = None,
+        extract_images: bool = False,
+        extract_links: bool = False,
+        timeout: int = 30,
+        verify_ssl: bool = True
+    ) -> Union[Dict[str, Any], Dict[str, str]]:
+        """
+        Scrape content from a webpage.
+        
+        Args:
+            url: URL to scrape
+            selector: Optional CSS selector to extract specific content
+            extract_images: Whether to extract image URLs
+            extract_links: Whether to extract links
+            timeout: Request timeout in seconds
+            verify_ssl: Whether to verify SSL certificates
+            
+        Returns:
+            Dict: Scraped content or error dict
+        """
+        try:
+            session = self._get_session()
+            if session is None:
+                return {"error": "requests package not available"}
+
+            # Import BeautifulSoup only when needed
+            if util.find_spec('bs4') is None:
+                error_msg = "bs4 package is not available. Please install it using: pip install beautifulsoup4"
+                logging.error(error_msg)
+                return {"error": error_msg}
+            from bs4 import BeautifulSoup
+
+            # Make request
+            response = session.get(
+                url,
+                timeout=timeout,
+                verify=verify_ssl
+            )
+            response.raise_for_status()
+            
+            # Parse HTML
+            soup = BeautifulSoup(response.text, 'lxml')
+            
+            # Remove unwanted elements
+            for element in soup(['script', 'style']):
+                element.decompose()
+            
+            # Initialize result
+            result = {
+                'url': url,
+                'status_code': response.status_code,
+                'encoding': response.encoding,
+                'headers': dict(response.headers),
+            }
+            
+            # Extract content based on selector
+            if selector:
+                elements = soup.select(selector)
+                result['content'] = [elem.get_text(strip=True) for elem in elements]
+                result['html'] = [str(elem) for elem in elements]
+            else:
+                result['title'] = soup.title.string if soup.title else None
+                result['content'] = soup.get_text(separator=' ', strip=True)
+                result['html'] = str(soup)
+            
+            # Extract metadata
+            meta_tags = {}
+            for meta in soup.find_all('meta'):
+                name = meta.get('name') or meta.get('property')
+                if name:
+                    meta_tags[name] = meta.get('content')
+            result['meta_tags'] = meta_tags
+            
+            # Extract images if requested
+            if extract_images:
+                images = []
+                for img in soup.find_all('img'):
+                    src = img.get('src')
+                    if src:
+                        images.append({
+                            'src': urljoin(url, src),
+                            'alt': img.get('alt', ''),
+                            'title': img.get('title', '')
+                        })
+                result['images'] = images
+            
+            # Extract links if requested
+            if extract_links:
+                links = []
+                for link in soup.find_all('a'):
+                    href = link.get('href')
+                    if href:
+                        links.append({
+                            'url': urljoin(url, href),
+                            'text': link.get_text(strip=True),
+                            'title': link.get('title', '')
+                        })
+                result['links'] = links
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error scraping {url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def extract_links(
+        self,
+        url: str,
+        same_domain: bool = True,
+        exclude_patterns: Optional[List[str]] = None,
+        timeout: int = 30,
+        verify_ssl: bool = True
+    ) -> Union[List[Dict[str, str]], Dict[str, str]]:
+        """
+        Extract all links from a webpage.
+        
+        Args:
+            url: URL to extract links from
+            same_domain: Only return links from the same domain
+            exclude_patterns: List of regex patterns to exclude
+            timeout: Request timeout in seconds
+            verify_ssl: Whether to verify SSL certificates
+            
+        Returns:
+            List[Dict] or Dict: List of links or error dict
+        """
+        try:
+            # Compile exclude patterns
+            if exclude_patterns:
+                exclude_patterns = [re.compile(p) for p in exclude_patterns]
+            
+            # Get base domain
+            base_domain = urlparse(url).netloc
+            
+            # Scrape page
+            result = self.scrape_page(
+                url,
+                extract_links=True,
+                timeout=timeout,
+                verify_ssl=verify_ssl
+            )
+            
+            if "error" in result:
+                return result
+            
+            # Filter and clean links
+            links = []
+            seen_urls = set()
+            
+            for link in result.get('links', []):
+                link_url = link['url']
+                
+                # Skip if already seen
+                if link_url in seen_urls:
+                    continue
+                
+                # Parse URL
+                parsed = urlparse(link_url)
+                
+                # Skip if not same domain and same_domain is True
+                if same_domain and parsed.netloc != base_domain:
+                    continue
+                
+                # Skip if matches exclude patterns
+                if exclude_patterns and any(p.search(link_url) for p in exclude_patterns):
+                    continue
+                
+                # Add to results
+                links.append(link)
+                seen_urls.add(link_url)
+            
+            return links
+        except Exception as e:
+            error_msg = f"Error extracting links from {url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def crawl(
+        self,
+        start_url: str,
+        max_pages: int = 10,
+        same_domain: bool = True,
+        exclude_patterns: Optional[List[str]] = None,
+        delay: float = 1.0,
+        timeout: int = 30,
+        verify_ssl: bool = True,
+        output_dir: Optional[str] = None
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """
+        Crawl multiple pages starting from a URL.
+        
+        Args:
+            start_url: Starting URL
+            max_pages: Maximum number of pages to crawl
+            same_domain: Only crawl pages from the same domain
+            exclude_patterns: List of regex patterns to exclude
+            delay: Delay between requests in seconds
+            timeout: Request timeout in seconds
+            verify_ssl: Whether to verify SSL certificates
+            output_dir: Directory to save crawled pages
+            
+        Returns:
+            List[Dict] or Dict: Crawled pages or error dict
+        """
+        try:
+            # Create output directory if needed
+            if output_dir:
+                os.makedirs(output_dir, exist_ok=True)
+            
+            # Initialize crawl state
+            to_visit = {start_url}
+            visited = set()
+            results = []
+            
+            while to_visit and len(visited) < max_pages:
+                # Get next URL
+                url = to_visit.pop()
+                
+                # Skip if already visited
+                if url in visited:
+                    continue
+                
+                # Add to visited
+                visited.add(url)
+                
+                # Delay if not first request
+                if len(visited) > 1:
+                    time.sleep(delay)
+                
+                # Scrape page
+                result = self.scrape_page(
+                    url,
+                    extract_links=True,
+                    timeout=timeout,
+                    verify_ssl=verify_ssl
+                )
+                
+                if "error" in result:
+                    logging.warning(f"Error crawling {url}: {result['error']}")
+                    continue
+                
+                # Save result
+                results.append(result)
+                
+                # Save to file if requested
+                if output_dir:
+                    filename = hashlib.md5(url.encode()).hexdigest() + '.json'
+                    filepath = os.path.join(output_dir, filename)
+                    with open(filepath, 'w', encoding='utf-8') as f:
+                        json.dump(result, f, indent=2, ensure_ascii=False)
+                
+                # Add new links to visit
+                for link in result.get('links', []):
+                    link_url = link['url']
+                    parsed = urlparse(link_url)
+                    
+                    # Skip if not same domain and same_domain is True
+                    if same_domain and parsed.netloc != urlparse(start_url).netloc:
+                        continue
+                    
+                    # Skip if matches exclude patterns
+                    if exclude_patterns and any(
+                        re.compile(p).search(link_url) for p in exclude_patterns
+                    ):
+                        continue
+                    
+                    # Add to visit if not visited
+                    if link_url not in visited:
+                        to_visit.add(link_url)
+            
+            return results
+        except Exception as e:
+            error_msg = f"Error crawling from {start_url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def extract_text(
+        self,
+        url: str,
+        selector: Optional[str] = None,
+        timeout: int = 30,
+        verify_ssl: bool = True
+    ) -> Union[str, Dict[str, str]]:
+        """
+        Extract clean text content from a webpage.
+        
+        Args:
+            url: URL to extract text from
+            selector: Optional CSS selector to extract specific content
+            timeout: Request timeout in seconds
+            verify_ssl: Whether to verify SSL certificates
+            
+        Returns:
+            str or Dict: Extracted text or error dict
+        """
+        try:
+            result = self.scrape_page(
+                url,
+                selector=selector,
+                timeout=timeout,
+                verify_ssl=verify_ssl
+            )
+            
+            if "error" in result:
+                return result
+            
+            if selector:
+                return '\n'.join(result['content'])
+            return result['content']
+        except Exception as e:
+            error_msg = f"Error extracting text from {url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+# Create instance for direct function access
+_spider_tools = SpiderTools()
+scrape_page = _spider_tools.scrape_page
+extract_links = _spider_tools.extract_links
+crawl = _spider_tools.crawl
+extract_text = _spider_tools.extract_text
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("SpiderTools Demonstration")
+    print("==================================================\n")
+    
+    # 1. Scrape a webpage
+    print("1. Scraping Webpage")
+    print("------------------------------")
+    url = "https://example.com"
+    result = scrape_page(url, extract_images=True, extract_links=True)
+    print(f"Content from {url}:")
+    if "error" not in result:
+        print(f"Title: {result['title']}")
+        print(f"Content length: {len(result['content'])} characters")
+        print(f"Number of images: {len(result.get('images', []))}")
+        print(f"Number of links: {len(result.get('links', []))}")
+    else:
+        print(result)  # Show error
+    print()
+    
+    # 2. Extract links
+    print("2. Extracting Links")
+    print("------------------------------")
+    links = extract_links(url)
+    print(f"Links from {url}:")
+    if isinstance(links, list):
+        for link in links:
+            print(f"- {link['url']} ({link['text']})")
+    else:
+        print(links)  # Show error
+    print()
+    
+    # 3. Extract text
+    print("3. Extracting Text")
+    print("------------------------------")
+    text = extract_text(url)
+    print(f"Text from {url}:")
+    if isinstance(text, str):
+        print(text[:500] + "..." if len(text) > 500 else text)
+    else:
+        print(text)  # Show error
+    print()
+    
+    # 4. Crawl multiple pages
+    print("4. Crawling Multiple Pages")
+    print("------------------------------")
+    results = crawl(url, max_pages=2, delay=1.0)
+    print(f"Crawl results from {url}:")
+    if isinstance(results, list):
+        print(f"Crawled {len(results)} pages")
+        for result in results:
+            print(f"- {result['url']}: {result['title']}")
+    else:
+        print(results)  # Show error
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/test.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+"""Test runner for all tools."""
+
+import os
+import glob
+import logging
+import subprocess
+
+# Set up logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+def main():
+    """Run all tool files."""
+    logger.info("Starting tests...")
+    
+    # Get all *_tools.py files
+    tools_dir = os.path.dirname(os.path.abspath(__file__))
+    tool_files = glob.glob(os.path.join(tools_dir, "*_tools.py"))
+    
+    # Run each tool file
+    for tool_file in sorted(tool_files):
+        module_name = os.path.basename(tool_file)
+        logger.info(f"\nRunning {module_name}...")
+        
+        try:
+            # Run the tool file directly
+            result = subprocess.run(
+                ["python3", tool_file],
+                capture_output=True,
+                text=True,
+                cwd=tools_dir
+            )
+            
+            # Log output
+            if result.stdout:
+                logger.info(f"Output:\n{result.stdout}")
+            if result.stderr:
+                logger.error(f"Errors:\n{result.stderr}")
+            
+            if result.returncode == 0:
+                logger.info(f" {module_name} completed successfully")
+            else:
+                logger.error(f" {module_name} failed with return code {result.returncode}")
+        
+        except Exception as e:
+            logger.error(f"Error running {module_name}: {str(e)}")
+            continue
+    
+    logger.info("\nAll tests completed!")
+
+if __name__ == "__main__":
+    main()

agents/praisonaiagents/tools/tools.py

@@ -1,40 +1,9 @@
 """Tools module for PraisonAI Agents"""
-from typing import List, Dict
-import logging
-import importlib
+from .duckduckgo_tools import internet_search
 
 class Tools:
-    @staticmethod
-    def internet_search(query: str) -> List[Dict]:
-        """
-        Perform a search using DuckDuckGo.
+    """Tools class for backward compatibility"""
+    internet_search = staticmethod(internet_search)
 
-        Args:
-            query (str): The search query.
-
-        Returns:
-            list: A list of search result titles, URLs, and snippets.
-        """
-        # Check if duckduckgo_search is installed
-        if importlib.util.find_spec("duckduckgo_search") is None:
-            error_msg = "DuckDuckGo search is not available. Please install duckduckgo_search package using: pip install duckduckgo_search"
-            logging.error(error_msg)
-            return [{"error": error_msg}]
-
-        try:
-            # Import only when needed
-            from duckduckgo_search import DDGS
-            results = []
-            ddgs = DDGS()
-            for result in ddgs.text(keywords=query, max_results=5):
-                results.append({
-                    "title": result.get("title", ""),
-                    "url": result.get("href", ""),
-                    "snippet": result.get("body", "")
-                })
-            return results
-
-        except Exception as e:
-            error_msg = f"Error during DuckDuckGo search: {e}"
-            logging.error(error_msg)
-            return [{"error": error_msg}] 
+# Re-export the function
+__all__ = ['Tools', 'internet_search']

agents/praisonaiagents/tools/wikipedia_tools.py

@@ -0,0 +1,272 @@
+"""Wikipedia tools for accessing and searching Wikipedia content.
+
+Usage:
+from praisonaiagents.tools import wikipedia_tools
+summary = wikipedia_tools.wiki_summary("Python programming language")
+page = wikipedia_tools.wiki_page("Python programming language")
+results = wikipedia_tools.wiki_search("Python programming")
+
+or
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page
+summary = wiki_summary("Python programming language")
+"""
+
+import logging
+from typing import List, Dict, Union, Any
+from importlib import util
+import json
+
+class WikipediaTools:
+    """Tools for accessing and searching Wikipedia content."""
+    
+    def __init__(self):
+        """Initialize WikipediaTools and check for wikipedia package."""
+        self._check_wikipedia()
+        
+    def _check_wikipedia(self):
+        """Check if wikipedia package is installed."""
+        if util.find_spec("wikipedia") is None:
+            raise ImportError("wikipedia package is not available. Please install it using: pip install wikipedia")
+        global wikipedia
+        import wikipedia
+        # Set default language to English
+        wikipedia.set_lang("en")
+
+    def wiki_search(
+        self, 
+        query: str, 
+        results: int = 10, 
+        suggestion: bool = True
+    ) -> Union[List[str], Dict[str, str]]:
+        """
+        Search Wikipedia for a query.
+        
+        Args:
+            query: Search query
+            results: Maximum number of results to return
+            suggestion: Whether to suggest similar queries
+            
+        Returns:
+            List[str] or Dict: List of search results or error dict
+        """
+        try:
+            search_results = wikipedia.search(query, results=results, suggestion=suggestion)
+            if isinstance(search_results, tuple):
+                # If suggestion is True, returns (results, suggestion)
+                return {
+                    "results": search_results[0],
+                    "suggestion": search_results[1] if search_results[1] else None
+                }
+            return {"results": search_results, "suggestion": None}
+        except Exception as e:
+            error_msg = f"Error searching Wikipedia for '{query}': {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def wiki_summary(
+        self, 
+        title: str, 
+        sentences: int = 5, 
+        auto_suggest: bool = True
+    ) -> Union[str, Dict[str, str]]:
+        """
+        Get a summary of a Wikipedia page.
+        
+        Args:
+            title: Title of the Wikipedia page
+            sentences: Number of sentences to return
+            auto_suggest: Whether to auto-suggest similar titles
+            
+        Returns:
+            str or Dict: Page summary if successful, error dict if failed
+        """
+        try:
+            return wikipedia.summary(title, sentences=sentences, auto_suggest=auto_suggest)
+        except wikipedia.exceptions.DisambiguationError as e:
+            return {
+                "error": "Disambiguation page",
+                "options": e.options[:10]  # Limit to first 10 options
+            }
+        except Exception as e:
+            error_msg = f"Error getting summary for '{title}': {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def wiki_page(
+        self, 
+        title: str, 
+        auto_suggest: bool = True
+    ) -> Union[Dict[str, Any], Dict[str, str]]:
+        """
+        Get detailed information about a Wikipedia page.
+        
+        Args:
+            title: Title of the Wikipedia page
+            auto_suggest: Whether to auto-suggest similar titles
+            
+        Returns:
+            Dict: Page information if successful, error dict if failed
+        """
+        try:
+            page = wikipedia.page(title, auto_suggest=auto_suggest)
+            return {
+                "title": page.title,
+                "url": page.url,
+                "content": page.content,
+                "summary": page.summary,
+                "references": page.references,
+                "categories": page.categories,
+                "links": page.links,
+                "images": page.images,
+                "sections": self.get_sections(page)
+            }
+        except wikipedia.exceptions.DisambiguationError as e:
+            return {
+                "error": "Disambiguation page",
+                "options": e.options[:10]  # Limit to first 10 options
+            }
+        except Exception as e:
+            error_msg = f"Error getting page for '{title}': {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_sections(self, page: Any) -> List[Dict[str, Any]]:
+        """
+        Extract sections and their content from a Wikipedia page.
+        
+        Args:
+            page: Wikipedia page object
+            
+        Returns:
+            List[Dict]: List of sections with their titles and content
+        """
+        sections = []
+        content = page.content
+        section_titles = [s.strip() for s in content.split("==")[1::2]]
+        section_contents = [s.strip() for s in content.split("==")[2::2]]
+        
+        # Add introduction section
+        intro = content.split("==")[0].strip()
+        if intro:
+            sections.append({
+                "title": "Introduction",
+                "level": 0,
+                "content": intro
+            })
+        
+        # Process remaining sections
+        for title, content in zip(section_titles, section_contents):
+            # Count leading '=' to determine section level
+            level = title.count("=") // 2
+            clean_title = title.strip("= ")
+            
+            sections.append({
+                "title": clean_title,
+                "level": level,
+                "content": content.strip()
+            })
+        
+        return sections
+
+    def wiki_random(self, pages: int = 1) -> Union[List[str], Dict[str, str]]:
+        """
+        Get random Wikipedia page titles.
+        
+        Args:
+            pages: Number of random pages to return
+            
+        Returns:
+            List[str] or Dict: List of random page titles or error dict
+        """
+        try:
+            return wikipedia.random(pages)
+        except Exception as e:
+            error_msg = f"Error getting random pages: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def wiki_language(self, language: str) -> bool:
+        """
+        Set the language for Wikipedia searches.
+        
+        Args:
+            language: Language code (e.g., 'en' for English, 'es' for Spanish)
+            
+        Returns:
+            bool: True if successful, False if failed
+        """
+        try:
+            wikipedia.set_lang(language)
+            return True
+        except Exception as e:
+            error_msg = f"Error setting language to '{language}': {str(e)}"
+            logging.error(error_msg)
+            return False
+
+# Create instance for direct function access
+_wikipedia_tools = WikipediaTools()
+wiki_search = _wikipedia_tools.wiki_search
+wiki_summary = _wikipedia_tools.wiki_summary
+wiki_page = _wikipedia_tools.wiki_page
+wiki_random = _wikipedia_tools.wiki_random
+wiki_language = _wikipedia_tools.wiki_language
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("WikipediaTools Demonstration")
+    print("==================================================\n")
+    
+    # 1. Search for a topic
+    print("1. Searching Wikipedia")
+    print("------------------------------")
+    search_results = wiki_search("Python programming language")
+    print("Search results:")
+    print(json.dumps(search_results, indent=2))
+    print()
+    
+    # 2. Get a page summary
+    print("2. Getting Page Summary")
+    print("------------------------------")
+    summary = wiki_summary("Python programming language", sentences=3)
+    print("Summary:")
+    print(summary)
+    print()
+    
+    # 3. Get detailed page information
+    print("3. Getting Full Page Information")
+    print("------------------------------")
+    page = wiki_page("Python programming language")
+    if isinstance(page, dict) and "error" not in page:
+        print("Page sections:")
+        for section in page["sections"]:
+            print(f"- {section['title']} (Level {section['level']})")
+        print(f"\nNumber of references: {len(page['references'])}")
+        print(f"Number of categories: {len(page['categories'])}")
+        print(f"Number of links: {len(page['links'])}")
+        print(f"Number of images: {len(page['images'])}")
+    else:
+        print("Error getting page:", page.get("error"))
+    print()
+    
+    # 4. Get random pages
+    print("4. Getting Random Pages")
+    print("------------------------------")
+    random_pages = wiki_random(3)
+    print("Random page titles:")
+    print(json.dumps(random_pages, indent=2))
+    print()
+    
+    # 5. Try different language
+    print("5. Changing Language")
+    print("------------------------------")
+    success = wiki_language("es")
+    if success:
+        summary = wiki_summary("Python (lenguaje de programación)", sentences=1)
+        print("Spanish summary:")
+        print(summary)
+    print()
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/xml_tools.py

@@ -0,0 +1,498 @@
+"""Tools for working with XML files.
+
+Usage:
+from praisonaiagents.tools import xml_tools
+tree = xml_tools.read_xml("data.xml")
+
+or
+from praisonaiagents.tools import read_xml, write_xml, transform_xml
+tree = read_xml("data.xml")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any, Tuple
+from importlib import util
+import xml.etree.ElementTree as ET
+import xml.dom.minidom as minidom
+from io import StringIO
+import json
+
+class XMLTools:
+    """Tools for working with XML files."""
+    
+    def __init__(self):
+        """Initialize XMLTools."""
+        pass
+
+    def read_xml(
+        self,
+        filepath: str,
+        encoding: str = 'utf-8',
+        validate_schema: Optional[str] = None,
+        parser: str = 'lxml'
+    ) -> ET.Element:
+        """Read an XML file with optional schema validation.
+        
+        Args:
+            filepath: Path to XML file
+            encoding: File encoding
+            validate_schema: Optional path to XSD schema file
+            parser: XML parser to use ('lxml' or 'etree')
+            
+        Returns:
+            ElementTree root element
+        """
+        try:
+            if parser == 'lxml':
+                if util.find_spec('lxml') is None:
+                    error_msg = "lxml package is not available. Please install it using: pip install lxml"
+                    logging.error(error_msg)
+                    return None
+                import lxml.etree as lxml_etree
+                tree = lxml_etree.parse(filepath)
+                root = tree.getroot()
+            else:
+                tree = ET.parse(filepath)
+                root = tree.getroot()
+
+            if validate_schema:
+                if util.find_spec('xmlschema') is None:
+                    error_msg = "xmlschema package is not available. Please install it using: pip install xmlschema"
+                    logging.error(error_msg)
+                    return None
+                import xmlschema
+                schema = xmlschema.XMLSchema(validate_schema)
+                if not schema.is_valid(filepath):
+                    error_msg = f"XML file does not validate against schema: {schema.validate(filepath)}"
+                    logging.error(error_msg)
+                    return None
+
+            return root
+
+        except Exception as e:
+            error_msg = f"Error reading XML file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def write_xml(
+        self,
+        root: ET.Element,
+        filepath: str,
+        encoding: str = 'utf-8',
+        pretty: bool = True,
+        xml_declaration: bool = True
+    ) -> bool:
+        """Write XML Element tree to file.
+        
+        Args:
+            root: XML Element tree root
+            filepath: Output file path
+            encoding: File encoding
+            pretty: Format output with proper indentation
+            xml_declaration: Include XML declaration
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        try:
+            # Convert to string
+            if pretty:
+                xml_str = minidom.parseString(
+                    ET.tostring(root, encoding='unicode')
+                ).toprettyxml(indent='  ')
+            else:
+                xml_str = ET.tostring(
+                    root,
+                    encoding='unicode'
+                )
+            
+            # Add declaration if requested
+            if xml_declaration:
+                if not xml_str.startswith('<?xml'):
+                    xml_str = (
+                        f'<?xml version="1.0" encoding="{encoding}"?>\n'
+                        + xml_str
+                    )
+            
+            # Write to file
+            with open(filepath, 'w', encoding=encoding) as f:
+                f.write(xml_str)
+            
+            return True
+        except Exception as e:
+            error_msg = f"Error writing XML file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def transform_xml(
+        self,
+        xml_file: str,
+        xslt_file: str,
+        output_file: str,
+        params: Optional[Dict[str, str]] = None
+    ) -> bool:
+        """Transform XML using XSLT stylesheet.
+        
+        Args:
+            xml_file: Input XML file
+            xslt_file: XSLT stylesheet file
+            output_file: Output file path
+            params: Optional parameters for transformation
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        try:
+            # Parse XML and XSLT
+            if util.find_spec('lxml') is None:
+                error_msg = "lxml package is not available. Please install it using: pip install lxml"
+                logging.error(error_msg)
+                return False
+            import lxml.etree as lxml_etree
+            xml_doc = lxml_etree.parse(xml_file)
+            xslt_doc = lxml_etree.parse(xslt_file)
+            transform = lxml_etree.XSLT(xslt_doc)
+            
+            # Apply transformation
+            if params:
+                result = transform(xml_doc, **params)
+            else:
+                result = transform(xml_doc)
+            
+            # Write result
+            result.write(
+                output_file,
+                pretty_print=True,
+                xml_declaration=True,
+                encoding='utf-8'
+            )
+            
+            return True
+        except Exception as e:
+            error_msg = f"Error transforming XML: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def validate_xml(
+        self,
+        xml_file: str,
+        schema_file: str
+    ) -> Tuple[bool, Optional[str]]:
+        """Validate XML against XSD schema.
+        
+        Args:
+            xml_file: XML file to validate
+            schema_file: XSD schema file
+            
+        Returns:
+            Tuple of (is_valid, error_message)
+        """
+        try:
+            if util.find_spec('xmlschema') is None:
+                error_msg = "xmlschema package is not available. Please install it using: pip install xmlschema"
+                logging.error(error_msg)
+                return False, error_msg
+            import xmlschema
+            schema = xmlschema.XMLSchema(schema_file)
+            schema.validate(xml_file)
+            return True, None
+        except xmlschema.validators.exceptions.XMLSchemaValidationError as e:
+            return False, str(e)
+        except Exception as e:
+            error_msg = f"Error validating XML: {str(e)}"
+            logging.error(error_msg)
+            return False, error_msg
+
+    def xml_to_dict(
+        self,
+        root: Union[str, ET.Element],
+        preserve_attrs: bool = True
+    ) -> Dict[str, Any]:
+        """Convert XML to dictionary.
+        
+        Args:
+            root: XML string or Element tree root
+            preserve_attrs: Keep XML attributes in result
+            
+        Returns:
+            Dict representation of XML
+        """
+        try:
+            # Parse XML if string
+            if isinstance(root, str):
+                if root.startswith('<'):
+                    root = ET.fromstring(root)
+                else:
+                    root = ET.parse(root).getroot()
+            
+            result = {}
+            
+            # Add attributes if present and requested
+            if preserve_attrs and root.attrib:
+                result['@attributes'] = dict(root.attrib)
+            
+            # Add children
+            children = list(root)
+            if not children:
+                text = root.text
+                if text is not None and text.strip():
+                    result = text.strip()
+            else:
+                for child in children:
+                    child_data = self.xml_to_dict(child, preserve_attrs)
+                    if child.tag in result:
+                        if not isinstance(result[child.tag], list):
+                            result[child.tag] = [result[child.tag]]
+                        result[child.tag].append(child_data)
+                    else:
+                        result[child.tag] = child_data
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error converting XML to dict: {str(e)}"
+            logging.error(error_msg)
+            return {}
+
+    def dict_to_xml(
+        self,
+        data: Dict[str, Any],
+        root_tag: str = 'root'
+    ) -> Optional[ET.Element]:
+        """Convert dictionary to XML.
+        
+        Args:
+            data: Dictionary to convert
+            root_tag: Tag for root element
+            
+        Returns:
+            XML Element tree root
+        """
+        try:
+            def _create_element(
+                parent: ET.Element,
+                key: str,
+                value: Any
+            ):
+                """Create XML element from key-value pair."""
+                if key == '@attributes':
+                    for attr_key, attr_val in value.items():
+                        parent.set(attr_key, str(attr_val))
+                elif isinstance(value, dict):
+                    child = ET.SubElement(parent, key)
+                    for k, v in value.items():
+                        _create_element(child, k, v)
+                elif isinstance(value, list):
+                    for item in value:
+                        child = ET.SubElement(parent, key)
+                        if isinstance(item, dict):
+                            for k, v in item.items():
+                                _create_element(child, k, v)
+                        else:
+                            child.text = str(item)
+                else:
+                    child = ET.SubElement(parent, key)
+                    child.text = str(value)
+            
+            root = ET.Element(root_tag)
+            for key, value in data.items():
+                _create_element(root, key, value)
+            
+            return root
+        except Exception as e:
+            error_msg = f"Error converting dict to XML: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def xpath_query(
+        self,
+        root: Union[str, ET.Element],
+        query: str,
+        namespaces: Optional[Dict[str, str]] = None
+    ) -> List[ET.Element]:
+        """Execute XPath query on XML.
+        
+        Args:
+            root: XML string or Element tree root
+            query: XPath query string
+            namespaces: Optional namespace mappings
+            
+        Returns:
+            List of matching elements
+        """
+        try:
+            # Parse XML if string
+            if isinstance(root, str):
+                if root.startswith('<'):
+                    if util.find_spec('lxml') is None:
+                        error_msg = "lxml package is not available. Please install it using: pip install lxml"
+                        logging.error(error_msg)
+                        return []
+                    import lxml.etree as lxml_etree
+                    tree = lxml_etree.fromstring(root)
+                else:
+                    tree = ET.parse(root)
+            else:
+                if util.find_spec('lxml') is None:
+                    error_msg = "lxml package is not available. Please install it using: pip install lxml"
+                    logging.error(error_msg)
+                    return []
+                import lxml.etree as lxml_etree
+                tree = lxml_etree.fromstring(
+                    ET.tostring(root, encoding='unicode')
+                )
+            
+            # Execute query
+            results = tree.xpath(
+                query,
+                namespaces=namespaces or {}
+            )
+            
+            # Convert results to standard ElementTree elements
+            return [
+                ET.fromstring(lxml_etree.tostring(elem, encoding='unicode'))
+                for elem in results
+            ]
+        except Exception as e:
+            error_msg = f"Error executing XPath query: {str(e)}"
+            logging.error(error_msg)
+            return []
+
+# Create instance for direct function access
+_xml_tools = XMLTools()
+read_xml = _xml_tools.read_xml
+write_xml = _xml_tools.write_xml
+transform_xml = _xml_tools.transform_xml
+validate_xml = _xml_tools.validate_xml
+xml_to_dict = _xml_tools.xml_to_dict
+dict_to_xml = _xml_tools.dict_to_xml
+xpath_query = _xml_tools.xpath_query
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("XMLTools Demonstration")
+    print("==================================================\n")
+
+    # Create temporary files
+    import tempfile
+    import os
+
+    temp_file = tempfile.mktemp(suffix='.xml')
+    try:
+        print("1. Creating XML Document")
+        print("------------------------------")
+        xml_content = """<?xml version="1.0" encoding="UTF-8"?>
+<bookstore>
+    <book category="fiction">
+        <title>The Great Gatsby</title>
+        <author>F. Scott Fitzgerald</author>
+        <year>1925</year>
+        <price>10.99</price>
+    </book>
+    <book category="non-fiction">
+        <title>A Brief History of Time</title>
+        <author>Stephen Hawking</author>
+        <year>1988</year>
+        <price>15.99</price>
+    </book>
+</bookstore>"""
+        
+        with open(temp_file, 'w') as f:
+            f.write(xml_content)
+        print("Sample XML file created")
+        print()
+
+        print("2. Parsing XML")
+        print("------------------------------")
+        result = read_xml(temp_file)
+        print("XML structure:")
+        print(minidom.parseString(ET.tostring(result, encoding='unicode')).toprettyxml(indent='  '))
+        print()
+
+        print("3. Querying XML")
+        print("------------------------------")
+        xpath = "//book[@category='fiction']/title/text()"
+        result = xpath_query(result, xpath)
+        print(f"Fiction book titles (XPath: {xpath}):")
+        for title in result:
+            print(title.text)
+        print()
+
+        print("4. Validating XML")
+        print("------------------------------")
+        schema_file = tempfile.mktemp(suffix='.xsd')
+        schema_content = """<?xml version="1.0" encoding="UTF-8"?>
+<xs:schema xmlns:xs="http://www.w3.org/2001/XMLSchema">
+    <xs:element name="bookstore">
+        <xs:complexType>
+            <xs:sequence>
+                <xs:element name="book" maxOccurs="unbounded">
+                    <xs:complexType>
+                        <xs:sequence>
+                            <xs:element name="title" type="xs:string"/>
+                            <xs:element name="author" type="xs:string"/>
+                            <xs:element name="year" type="xs:integer"/>
+                            <xs:element name="price" type="xs:decimal"/>
+                        </xs:sequence>
+                        <xs:attribute name="category" type="xs:string"/>
+                    </xs:complexType>
+                </xs:element>
+            </xs:sequence>
+        </xs:complexType>
+    </xs:element>
+</xs:schema>"""
+        with open(schema_file, 'w') as f:
+            f.write(schema_content)
+        result, error = validate_xml(temp_file, schema_file)
+        print(f"XML validation result: {result}")
+        if error:
+            print(f"Error: {error}")
+        print()
+
+        print("5. Transforming XML")
+        print("------------------------------")
+        xslt_content = """<?xml version="1.0" encoding="UTF-8"?>
+<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
+    <xsl:template match="/">
+        <html>
+            <body>
+                <h2>Bookstore Inventory</h2>
+                <table>
+                    <tr>
+                        <th>Title</th>
+                        <th>Author</th>
+                        <th>Price</th>
+                    </tr>
+                    <xsl:for-each select="bookstore/book">
+                        <tr>
+                            <td><xsl:value-of select="title"/></td>
+                            <td><xsl:value-of select="author"/></td>
+                            <td><xsl:value-of select="price"/></td>
+                        </tr>
+                    </xsl:for-each>
+                </table>
+            </body>
+        </html>
+    </xsl:template>
+</xsl:stylesheet>"""
+
+        xslt_file = tempfile.mktemp(suffix='.xslt')
+        with open(xslt_file, 'w') as f:
+            f.write(xslt_content)
+
+        output_file = tempfile.mktemp(suffix='.html')
+        result = transform_xml(temp_file, xslt_file, output_file)
+        print(f"XML transformation result: {result}")
+        if result and os.path.exists(output_file):
+            print("\nTransformed HTML content:")
+            with open(output_file, 'r') as f:
+                print(f.read())
+
+    finally:
+        # Clean up temporary files
+        for file in [temp_file, schema_file, xslt_file, output_file]:
+            if os.path.exists(file):
+                os.unlink(file)
+
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/yaml_tools.py

@@ -0,0 +1,417 @@
+"""Tools for working with YAML files.
+
+Usage:
+from praisonaiagents.tools import yaml_tools
+data = yaml_tools.read_yaml("config.yaml")
+
+or
+from praisonaiagents.tools import read_yaml, write_yaml, merge_yaml
+data = read_yaml("config.yaml")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any
+from importlib import util
+import os
+from copy import deepcopy
+
+class YAMLTools:
+    """Tools for working with YAML files."""
+    
+    def __init__(self):
+        """Initialize YAMLTools."""
+        self._check_dependencies()
+        
+    def _check_dependencies(self):
+        """Check if required packages are installed."""
+        missing = []
+        for package, module in [('pyyaml', 'yaml'), ('jsonschema', 'jsonschema')]:
+            if util.find_spec(module) is None:
+                missing.append(package)
+        
+        if missing:
+            raise ImportError(
+                f"Required packages not available. Please install: {', '.join(missing)}\n"
+                f"Run: pip install {' '.join(missing)}"
+            )
+
+    def read_yaml(
+        self,
+        filepath: str,
+        safe_load: bool = True,
+        encoding: str = 'utf-8'
+    ) -> Optional[Union[Dict[str, Any], List[Any]]]:
+        """Read a YAML file."""
+        try:
+            if util.find_spec('yaml') is None:
+                error_msg = "pyyaml package is not available. Please install it using: pip install pyyaml"
+                logging.error(error_msg)
+                return None
+
+            # Import yaml only when needed
+            import yaml
+            
+            with open(filepath, 'r', encoding=encoding) as f:
+                if safe_load:
+                    return yaml.safe_load(f)
+                return yaml.load(f, Loader=yaml.FullLoader)
+        except Exception as e:
+            error_msg = f"Error reading YAML file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def write_yaml(
+        self,
+        data: Union[Dict[str, Any], List[Any]],
+        filepath: str,
+        default_flow_style: bool = False,
+        encoding: str = 'utf-8',
+        sort_keys: bool = False,
+        allow_unicode: bool = True
+    ) -> bool:
+        """Write data to YAML file."""
+        try:
+            if util.find_spec('yaml') is None:
+                error_msg = "pyyaml package is not available. Please install it using: pip install pyyaml"
+                logging.error(error_msg)
+                return False
+
+            # Import yaml only when needed
+            import yaml
+            
+            with open(filepath, 'w', encoding=encoding) as f:
+                yaml.dump(
+                    data,
+                    f,
+                    default_flow_style=default_flow_style,
+                    sort_keys=sort_keys,
+                    allow_unicode=allow_unicode
+                )
+            return True
+        except Exception as e:
+            error_msg = f"Error writing YAML file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def merge_yaml(
+        self,
+        files: List[str],
+        output_file: Optional[str] = None,
+        strategy: str = 'deep'
+    ) -> Union[Dict[str, Any], List[Any], None]:
+        """Merge multiple YAML files."""
+        try:
+            if util.find_spec('yaml') is None:
+                error_msg = "pyyaml package is not available. Please install it using: pip install pyyaml"
+                logging.error(error_msg)
+                return None
+
+            def deep_merge(source: dict, destination: dict) -> dict:
+                """Deep merge two dictionaries."""
+                for key, value in source.items():
+                    if key in destination:
+                        if isinstance(value, dict) and isinstance(destination[key], dict):
+                            destination[key] = deep_merge(value, destination[key])
+                        elif isinstance(value, list) and isinstance(destination[key], list):
+                            destination[key].extend(value)
+                        else:
+                            destination[key] = value
+                    else:
+                        destination[key] = value
+                return destination
+            
+            result = {}
+            for file in files:
+                data = self.read_yaml(file)
+                if data is None:
+                    continue
+                
+                if strategy == 'deep':
+                    if isinstance(data, dict):
+                        result = deep_merge(data, result)
+                    elif isinstance(data, list):
+                        if not result:
+                            result = []
+                        result.extend(data)
+                else:  # shallow
+                    if isinstance(data, dict):
+                        result.update(data)
+                    elif isinstance(data, list):
+                        if not result:
+                            result = []
+                        result.extend(data)
+            
+            if output_file:
+                self.write_yaml(result, output_file)
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error merging YAML files: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def validate_yaml(
+        self,
+        data: Union[Dict[str, Any], List[Any], str],
+        schema: Dict[str, Any]
+    ) -> bool:
+        """Validate YAML data against a schema."""
+        try:
+            if util.find_spec('jsonschema') is None:
+                error_msg = "jsonschema package is not available. Please install it using: pip install jsonschema"
+                logging.error(error_msg)
+                return False
+
+            # Import jsonschema only when needed
+            from jsonschema import validate, ValidationError
+            
+            # Load data if file path
+            if isinstance(data, str):
+                data = self.read_yaml(data)
+                if data is None:
+                    return False
+            
+            try:
+                validate(instance=data, schema=schema)
+                return True
+            except ValidationError as e:
+                logging.error(f"YAML validation error: {str(e)}")
+                return False
+        except Exception as e:
+            error_msg = f"Error validating YAML: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def analyze_yaml(
+        self,
+        data: Union[Dict[str, Any], List[Any], str]
+    ) -> Optional[Dict[str, Any]]:
+        """Analyze YAML data structure."""
+        try:
+            # Load data if file path
+            if isinstance(data, str):
+                data = self.read_yaml(data)
+                if data is None:
+                    return None
+            
+            def analyze_value(value: Any) -> Dict[str, Any]:
+                """Analyze a single value."""
+                result = {
+                    'type': type(value).__name__,
+                    'size': len(value) if hasattr(value, '__len__') else 1
+                }
+                
+                if isinstance(value, dict):
+                    result['keys'] = list(value.keys())
+                    result['nested_types'] = {
+                        k: type(v).__name__
+                        for k, v in value.items()
+                    }
+                elif isinstance(value, list):
+                    result['element_types'] = list(set(
+                        type(x).__name__ for x in value
+                    ))
+                
+                return result
+            
+            result = {
+                'structure': analyze_value(data),
+                'stats': {
+                    'total_keys': sum(1 for _ in self._walk_dict(data))
+                    if isinstance(data, dict) else len(data)
+                }
+            }
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error analyzing YAML: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def _walk_dict(self, d: Dict[str, Any]) -> Any:
+        """Walk through nested dictionary."""
+        for k, v in d.items():
+            yield k
+            if isinstance(v, dict):
+                yield from self._walk_dict(v)
+
+    def transform_yaml(
+        self,
+        data: Union[Dict[str, Any], List[Any], str],
+        operations: List[Dict[str, Any]]
+    ) -> Optional[Union[Dict[str, Any], List[Any]]]:
+        """Transform YAML data using specified operations."""
+        try:
+            # Load data if file path
+            if isinstance(data, str):
+                data = self.read_yaml(data)
+                if data is None:
+                    return None
+            
+            result = deepcopy(data)
+            
+            for op in operations:
+                op_type = op.get('type')
+                path = op.get('path', '')
+                value = op.get('value')
+                
+                if not op_type:
+                    continue
+                
+                # Split path into parts
+                parts = [p for p in path.split('/') if p]
+                
+                # Get reference to target location
+                target = result
+                parent = None
+                last_key = None
+                
+                for part in parts[:-1]:
+                    if isinstance(target, dict):
+                        if part not in target:
+                            target[part] = {}
+                        parent = target
+                        target = target[part]
+                        last_key = part
+                    elif isinstance(target, list):
+                        idx = int(part)
+                        while len(target) <= idx:
+                            target.append({})
+                        parent = target
+                        target = target[idx]
+                        last_key = idx
+                
+                if parts:
+                    last_part = parts[-1]
+                    if isinstance(target, dict):
+                        if op_type == 'set':
+                            target[last_part] = value
+                        elif op_type == 'delete':
+                            target.pop(last_part, None)
+                        elif op_type == 'append' and isinstance(target.get(last_part), list):
+                            target[last_part].append(value)
+                    elif isinstance(target, list):
+                        idx = int(last_part)
+                        if op_type == 'set':
+                            while len(target) <= idx:
+                                target.append(None)
+                            target[idx] = value
+                        elif op_type == 'delete' and idx < len(target):
+                            del target[idx]
+                        elif op_type == 'append':
+                            target.append(value)
+                else:
+                    if op_type == 'set':
+                        result = value
+                    elif op_type == 'append' and isinstance(result, list):
+                        result.append(value)
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error transforming YAML: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+# Create instance for direct function access
+_yaml_tools = YAMLTools()
+read_yaml = _yaml_tools.read_yaml
+write_yaml = _yaml_tools.write_yaml
+merge_yaml = _yaml_tools.merge_yaml
+validate_yaml = _yaml_tools.validate_yaml
+analyze_yaml = _yaml_tools.analyze_yaml
+transform_yaml = _yaml_tools.transform_yaml
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("YAMLTools Demonstration")
+    print("==================================================\n")
+    
+    # Sample data
+    data1 = {
+        'server': {
+            'host': 'localhost',
+            'port': 8080,
+            'debug': True
+        },
+        'database': {
+            'url': 'postgresql://localhost:5432/db',
+            'pool_size': 5
+        }
+    }
+    
+    data2 = {
+        'server': {
+            'workers': 4,
+            'timeout': 30
+        },
+        'logging': {
+            'level': 'INFO',
+            'file': 'app.log'
+        }
+    }
+    
+    # 1. Write YAML files
+    print("1. Writing YAML Files")
+    print("------------------------------")
+    success1 = write_yaml(data1, 'config1.yaml')
+    success2 = write_yaml(data2, 'config2.yaml')
+    print(f"First file written: {success1}")
+    print(f"Second file written: {success2}")
+    print()
+    
+    # 2. Read YAML file
+    print("2. Reading YAML File")
+    print("------------------------------")
+    config = read_yaml('config1.yaml')
+    print("YAML content:")
+    print(config)
+    print()
+    
+    # 3. Merge YAML files
+    print("3. Merging YAML Files")
+    print("------------------------------")
+    merged = merge_yaml(['config1.yaml', 'config2.yaml'])
+    print("Merged content:")
+    print(merged)
+    print()
+    
+    # 4. Analyze YAML
+    print("4. Analyzing YAML")
+    print("------------------------------")
+    analysis = analyze_yaml(merged)
+    print("Analysis results:")
+    print(analysis)
+    print()
+    
+    # 5. Transform YAML
+    print("5. Transforming YAML")
+    print("------------------------------")
+    operations = [
+        {
+            'type': 'set',
+            'path': 'server/host',
+            'value': '0.0.0.0'
+        },
+        {
+            'type': 'delete',
+            'path': 'server/debug'
+        },
+        {
+            'type': 'set',
+            'path': 'logging/handlers',
+            'value': ['console', 'file']
+        }
+    ]
+    transformed = transform_yaml(merged, operations)
+    print("Transformed content:")
+    print(transformed)
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")
+    
+    # Cleanup
+    for file in ['config1.yaml', 'config2.yaml']:
+        if os.path.exists(file):
+            os.remove(file)

agents/praisonaiagents/tools/yfinance_tools.py

@@ -0,0 +1,213 @@
+"""YFinance tools for stock market data.
+
+Usage:
+from praisonaiagents.tools import get_stock_price, get_stock_info
+price = get_stock_price("AAPL")
+info = get_stock_info("AAPL")
+
+or 
+from praisonaiagents.tools import yfinance
+price = yfinance.get_stock_price("AAPL")
+info = yfinance.get_stock_info("AAPL")
+"""
+
+from typing import List, Dict, Optional, Any
+import logging
+from importlib import util
+from datetime import datetime
+
+class YFinanceTools:
+    """A comprehensive tool for financial data analysis using yfinance"""
+
+    def __init__(self):
+        """Initialize YFinanceTools"""
+        self._tickers = {}
+
+    def _get_yfinance(self):
+        """Get yfinance module, installing if needed"""
+        if util.find_spec('yfinance') is None:
+            error_msg = "yfinance package is not available. Please install it using: pip install yfinance"
+            logging.error(error_msg)
+            return None
+        import yfinance as yf
+        return yf
+
+    def _get_ticker(self, symbol: str):
+        """Get or create ticker instance"""
+        if symbol not in self._tickers:
+            yf = self._get_yfinance()
+            if yf is None:
+                return None
+            self._tickers[symbol] = yf.Ticker(symbol)
+        return self._tickers[symbol]
+
+    def get_stock_price(self, symbol: str) -> Dict[str, float]:
+        """
+        Get current stock price and related price metrics
+        
+        Args:
+            symbol (str): Stock ticker symbol
+            
+        Returns:
+            Dict[str, float]: Current price information
+        """
+        try:
+            ticker = self._get_ticker(symbol)
+            if ticker is None:
+                return {"error": "yfinance package not available"}
+
+            info = ticker.info
+            return {
+                'symbol': symbol,
+                'price': info.get('regularMarketPrice', 0.0),
+                'open': info.get('regularMarketOpen', 0.0),
+                'high': info.get('regularMarketDayHigh', 0.0),
+                'low': info.get('regularMarketDayLow', 0.0),
+                'volume': info.get('regularMarketVolume', 0),
+                'previous_close': info.get('regularMarketPreviousClose', 0.0),
+                'change': info.get('regularMarketChange', 0.0),
+                'change_percent': info.get('regularMarketChangePercent', 0.0)
+            }
+        except Exception as e:
+            error_msg = f"Error getting stock price for {symbol}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_stock_info(self, symbol: str) -> Dict:
+        """
+        Get detailed information about a stock
+        
+        Args:
+            symbol (str): Stock ticker symbol
+            
+        Returns:
+            Dict: Stock information including company details and key metrics
+        """
+        try:
+            ticker = self._get_ticker(symbol)
+            if ticker is None:
+                return {"error": "yfinance package not available"}
+
+            info = ticker.info
+            
+            relevant_info = {
+                "longName": info.get("longName"),
+                "symbol": info.get("symbol"),
+                "sector": info.get("sector"), 
+                "industry": info.get("industry"),
+                "country": info.get("country"),
+                "marketCap": info.get("marketCap"),
+                "currentPrice": info.get("currentPrice"),
+                "currency": info.get("currency"),
+                "exchange": info.get("exchange"),
+                "fiftyTwoWeekHigh": info.get("fiftyTwoWeekHigh"),
+                "fiftyTwoWeekLow": info.get("fiftyTwoWeekLow"),
+                "trailingPE": info.get("trailingPE"),
+                "forwardPE": info.get("forwardPE"),
+                "dividendYield": info.get("dividendYield"),
+                "beta": info.get("beta"),
+                "volume": info.get("volume"),
+                "averageVolume": info.get("averageVolume"),
+                "sharesOutstanding": info.get("sharesOutstanding"),
+                "website": info.get("website"),
+                "longBusinessSummary": info.get("longBusinessSummary")
+            }
+            return {k: v for k, v in relevant_info.items() if v is not None}
+        except Exception as e:
+            error_msg = f"Error fetching stock info: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_historical_data(
+        self, 
+        symbol: str, 
+        period: str = "1y",
+        interval: str = "1d",
+        start: Optional[datetime] = None,
+        end: Optional[datetime] = None
+    ) -> List[Dict[str, Any]]:
+        """
+        Get historical price data for a stock
+        
+        Args:
+            symbol (str): Stock ticker symbol
+            period (str): Data period e.g. 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max
+            interval (str): Data interval e.g. 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo
+            start (datetime, optional): Start date for data
+            end (datetime, optional): End date for data
+            
+        Returns:
+            List[Dict[str, Any]]: List of historical price data points
+        """
+        try:
+            ticker = self._get_ticker(symbol)
+            if ticker is None:
+                return [{"error": "yfinance package not available"}]
+
+            hist = ticker.history(period=period, interval=interval, start=start, end=end)
+            return hist.reset_index().to_dict('records')
+        except Exception as e:
+            error_msg = f"Error fetching historical data: {str(e)}"
+            logging.error(error_msg)
+            return [{"error": error_msg}]
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("YFinanceTools Demonstration")
+    print("==================================================\n")
+
+    try:
+        yf_tools = YFinanceTools()
+
+        # 1. Basic Stock Information
+        print("1. Basic Stock Information for AAPL")
+        print("------------------------------")
+        info = yf_tools.get_stock_info("AAPL")
+        if "error" not in info:
+            print(f"Company: {info.get('longName')}")
+            print(f"Sector: {info.get('sector')}")
+            print(f"Industry: {info.get('industry')}")
+            print(f"Country: {info.get('country')}\n")
+        else:
+            print(f"Error: {info['error']}\n")
+
+        # 2. Current Price Data
+        print("2. Current Price Data")
+        print("------------------------------")
+        price_data = yf_tools.get_stock_price("AAPL")
+        if "error" not in price_data:
+            print(f"Current Price: ${price_data.get('price', 'N/A')}")
+            print(f"Previous Close: ${price_data.get('previous_close', 'N/A')}")
+            print(f"Day Range: ${price_data.get('low', 'N/A')} - ${price_data.get('high', 'N/A')}\n")
+        else:
+            print(f"Error: {price_data['error']}\n")
+
+        # 3. Historical Data
+        print("3. Historical Data (Last 5 Days)")
+        print("------------------------------")
+        hist_data = yf_tools.get_historical_data("AAPL", period="5d")
+        if not any("error" in d for d in hist_data):
+            for day in hist_data:
+                print(f"Date: {day.get('Date')}, Close: ${day.get('Close', 'N/A'):.2f}")
+            print()
+        else:
+            print(f"{hist_data[0]['error']}\n")
+
+        # 4. Multi-Stock Example
+        print("5. Multi-Stock Current Prices")
+        print("------------------------------")
+        for symbol in ["AAPL", "MSFT", "GOOGL"]:
+            price_data = yf_tools.get_stock_price(symbol)
+            if "error" not in price_data:
+                print(f"{symbol}: ${price_data.get('price', 'N/A')}")
+            else:
+                print(f"{symbol}: Error fetching price")
+        print()
+
+    except Exception as e:
+        print(f"Error: {str(e)}")
+
+    print("==================================================")
+    print("Analysis Complete")
+    print("==================================================")

agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.22"
+version = "0.0.31"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }
@@ -13,4 +13,9 @@ dependencies = [
     "pydantic",
     "rich",
     "openai"
+]
+
+[project.optional-dependencies]
+memory = [
+    "chromadb>=0.6.0"
 ] 

agents/structured_agents_example.py

@@ -0,0 +1,56 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents, Tools
+from pydantic import BaseModel
+
+class AnalysisReport(BaseModel):
+    title: str
+    findings: str
+    summary: str
+
+# Create a researcher agent
+researcher = Agent(
+    name="AIResearcher",
+    role="Technology Research Analyst",
+    goal="Analyze and structure information about AI developments",
+    backstory="Expert analyst specializing in AI technology trends",
+    verbose=True,
+    llm="gpt-4o-mini",
+    tools=[Tools.internet_search],
+    self_reflect=False
+)
+
+# Create an analyst agent
+analyst = Agent(
+    name="DataAnalyst",
+    role="Data Insights Specialist",
+    goal="Structure and analyze research findings",
+    backstory="Senior data analyst with expertise in pattern recognition",
+    verbose=True,
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# Define structured tasks
+research_task = Task(
+    name="gather_research",
+    description="Research recent AI developments in 2024",
+    agent=researcher,
+    expected_output="Research findings"
+)
+
+analysis_task = Task(
+    name="analyze_findings",
+    description="Analyze research findings and create structured report. No additional text or explanation.",
+    agent=analyst,
+    output_json=AnalysisReport,
+    expected_output="JSON object"
+)
+
+# Initialize and run agents
+agents = PraisonAIAgents(
+    agents=[researcher, analyst],
+    tasks=[research_task, analysis_task],
+    process="sequential",
+    verbose=True
+)
+result = agents.start()
+print(result)

agents/structured_response_example.py

@@ -0,0 +1,95 @@
+from praisonaiagents import Agent
+from pydantic import BaseModel
+from typing import List, Optional
+
+# Define structured output models
+class SearchResult(BaseModel):
+    title: str
+    url: Optional[str]
+    snippet: str
+
+class AnalysisReport(BaseModel):
+    topic: str
+    key_findings: List[str]
+    search_results: List[SearchResult]
+    summary: str
+    confidence_score: float
+
+def get_structured_analysis(query: str, verbose: bool = True) -> AnalysisReport:
+    """
+    Performs a search and returns a structured analysis using an AI agent.
+    
+    Args:
+        query (str): The search query or topic to analyze
+        verbose (bool): Whether to show detailed output
+        
+    Returns:
+        AnalysisReport: A structured report containing the analysis
+    """
+    # Create an agent with search capabilities
+    agent = Agent(
+        name="StructuredAnalyst",
+        role="Research Analyst",
+        goal="Analyze topics and provide structured reports",
+        backstory="Expert at gathering information and providing structured analysis",
+        verbose=verbose,
+        self_reflect=True,  # Enable self-reflection for better quality
+        markdown=True,
+        llm="gpt-4o-mini"
+    )
+    
+    # Create the analysis prompt
+    prompt = f"""
+Analyze the following topic: "{query}"
+
+Provide a structured analysis in JSON format that matches this schema:
+{{
+    "topic": "string",
+    "key_findings": ["string"],
+    "search_results": [
+        {{
+            "title": "string",
+            "url": "string",
+            "snippet": "string"
+        }}
+    ],
+    "summary": "string",
+    "confidence_score": float (0-1)
+}}
+
+Requirements:
+1. Include at least 3 key findings
+2. Include at least 2 relevant search results
+3. Provide a comprehensive summary
+4. Set confidence score based on quality of sources (0-1)
+
+Return ONLY the JSON object, no other text.
+"""
+    
+    # Get structured response from agent
+    response = agent.chat(
+        prompt=prompt,
+        output_json=AnalysisReport  # This ensures response matches our model
+    )
+    
+    return AnalysisReport.model_validate_json(response)
+
+if __name__ == "__main__":
+    # Example usage
+    analysis = get_structured_analysis("Latest developments in AI agents and autonomous systems")
+    print(analysis)
+    # Print the structured results
+    print("\n=== Structured Analysis Report ===")
+    print(f"Topic: {analysis.topic}")
+    print("\nKey Findings:")
+    for i, finding in enumerate(analysis.key_findings, 1):
+        print(f"{i}. {finding}")
+    
+    print("\nSearch Results:")
+    for result in analysis.search_results:
+        print(f"\nTitle: {result.title}")
+        print(f"URL: {result.url}")
+        print(f"Snippet: {result.snippet}")
+    
+    print(f"\nSummary: {analysis.summary}")
+    print(f"Confidence Score: {analysis.confidence_score:.2f}") 

agents/tools_example.py

@@ -0,0 +1,30 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import get_article, get_news_sources, get_articles_from_source, get_trending_topics
+
+# Create Wikipedia agent
+news_agent = Agent(
+    name="NewsAgent",
+    role="News Analyst",
+    goal="Collect and analyze news articles from various sources.",
+    backstory="Expert in news gathering and content analysis.",
+    tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics],
+    self_reflect=False
+)
+
+
+# Define research task
+news_task = Task(
+    description="Analyze news articles about 'AI developments' from major tech news sources.",
+    expected_output="Summary of key AI developments with source articles.",
+    agent=news_agent,
+    name="ai_news"
+)
+
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[news_agent],
+    tasks=[news_task],
+    process="sequential"
+)
+agents.start()

docs/api/praisonai/deploy.html

@@ -110,7 +110,7 @@ Loads environment variables from .env file or system and sets them.</p>
             file.write(&#34;FROM python:3.11-slim\n&#34;)
             file.write(&#34;WORKDIR /app\n&#34;)
             file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==2.0.31 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.39 gunicorn markdown\n&#34;)
             file.write(&#34;EXPOSE 8080\n&#34;)
             file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
             

docs/api/praisonaiagents/agents/autoagents.mdx

@@ -0,0 +1,187 @@
+---
+title: "AutoAgents Module"
+description: "Documentation for the praisonaiagents.agents.autoagents module"
+icon: "robot"
+---
+
+# Module praisonaiagents.agents.autoagents
+
+The AutoAgents module provides automatic creation and management of AI agents and tasks based on high-level instructions.
+
+## Classes
+
+### AutoAgents
+The main class for automatically creating and managing AI agents and tasks.
+
+#### Parameters
+- `instructions: str` - High-level task description for the agents
+- `tools: Optional[List[Any]] = None` - List of tools available to the agents
+- `verbose: bool = False` - Enable detailed logging
+- `process: str = "sequential"` - Process type (sequential or hierarchical)
+- `manager_llm: Optional[str] = None` - Language model for manager agent
+- `max_retries: int = 5` - Maximum retry attempts
+- `completion_checker: Optional[Any] = None` - Custom completion checker
+- `allow_code_execution: bool = False` - Allow code execution
+- `memory: bool = True` - Enable agent memory
+- `markdown: bool = True` - Enable markdown formatting
+- `self_reflect: bool = False` - Enable agent self-reflection
+- `max_reflect: int = 3` - Maximum reflection iterations
+- `min_reflect: int = 1` - Minimum reflection iterations
+- `llm: Optional[str] = None` - Language model for agents
+- `function_calling_llm: Optional[str] = None` - Language model for tool calling
+- `respect_context_window: bool = True` - Respect model context window
+- `code_execution_mode: str = "safe"` - Code execution mode (safe/unsafe)
+- `embedder_config: Optional[Dict[str, Any]] = None` - Embedder configuration
+- `knowledge_sources: Optional[List[Any]] = None` - Knowledge sources
+- `use_system_prompt: bool = True` - Use system prompts
+- `cache: bool = True` - Enable caching
+- `allow_delegation: bool = False` - Allow task delegation
+- `step_callback: Optional[Any] = None` - Callback for each step
+- `system_template: Optional[str] = None` - Custom system template
+- `prompt_template: Optional[str] = None` - Custom prompt template
+- `response_template: Optional[str] = None` - Custom response template
+- `max_rpm: Optional[int] = None` - Maximum requests per minute
+- `max_execution_time: Optional[int] = None` - Maximum execution time
+- `max_iter: int = 20` - Maximum iterations
+- `reflect_llm: Optional[str] = None` - Language model for reflection
+- `max_agents: int = 3` - Maximum number of agents to create
+
+#### Methods
+
+##### start()
+Start the agents synchronously.
+
+```python
+def start(self):
+    """
+    Creates tasks based on the instructions, then starts execution.
+    Returns the task status and results dictionary.
+    """
+    return super().start()
+```
+
+##### astart()
+Start the agents asynchronously.
+
+```python
+async def astart(self):
+    """
+    Async version of start() method.
+    Creates tasks based on the instructions, then starts execution.
+    Returns the task status and results dictionary.
+    """
+    return await super().astart()
+```
+
+#### Internal Methods
+
+##### _generate_config()
+Generate the configuration for agents and tasks.
+
+```python
+def _generate_config(self) -> AutoAgentsConfig:
+    """
+    Generate the configuration for agents and tasks based on instructions.
+    Returns AutoAgentsConfig object containing agent and task configurations.
+    """
+```
+
+##### _create_agents_and_tasks()
+Create agents and tasks from configuration.
+
+```python
+def _create_agents_and_tasks(self, config: AutoAgentsConfig) -> tuple[List[Agent], List[Task]]:
+    """
+    Create agents and tasks based on the generated configuration.
+    Returns tuple of (agents, tasks).
+    """
+```
+
+##### _assign_tools_to_agent()
+Assign appropriate tools to an agent.
+
+```python
+def _assign_tools_to_agent(self, agent_config: AgentConfig) -> List[Any]:
+    """
+    Assign tools to an agent based on its role and tasks.
+    Returns list of assigned tools.
+    """
+```
+
+## Pydantic Models
+
+### TaskConfig
+Configuration for a task.
+
+#### Attributes
+- `name: str` - Task name
+- `description: str` - Task description
+- `expected_output: str` - Expected output description
+- `tools: List[str]` - Required tools for the task
+
+### AgentConfig
+Configuration for an agent.
+
+#### Attributes
+- `name: str` - Agent name
+- `role: str` - Agent role
+- `goal: str` - Agent goal
+- `backstory: str` - Agent backstory
+- `tools: List[str]` - Required tools
+- `tasks: List[TaskConfig]` - Tasks assigned to the agent
+
+### AutoAgentsConfig
+Overall configuration for AutoAgents.
+
+#### Attributes
+- `main_instruction: str` - Main instruction for the agents
+- `process_type: str` - Process type (sequential/hierarchical)
+- `agents: List[AgentConfig]` - List of agent configurations
+
+## Example Usage
+
+### Basic Usage
+```python
+from praisonaiagents import AutoAgents
+from praisonaiagents.tools import SerperDevTool
+
+agents = AutoAgents(
+    instructions="Research recent AI developments",
+    tools=[SerperDevTool()],
+    verbose=True
+)
+result = agents.start()
+```
+
+### Async Usage
+```python
+async def main():
+    agents = AutoAgents(
+        instructions="Research recent AI developments",
+        tools=[SerperDevTool()],
+        process="hierarchical"
+    )
+    result = await agents.astart()
+
+import asyncio
+asyncio.run(main())
+```
+
+### Advanced Configuration
+```python
+agents = AutoAgents(
+    instructions="Complex research task",
+    tools=[SerperDevTool()],
+    max_agents=5,
+    process="hierarchical",
+    manager_llm="gpt-4o",
+    memory=True,
+    allow_delegation=True,
+    max_execution_time=600,
+    self_reflect=True
+)
+```
+
+<Note>
+  For optimal results, provide clear instructions and appropriate tools for your use case.
+</Note>

docs/code/installation.mdx

@@ -1,69 +0,0 @@
----
-title: Installation
-description: Set up PraisonAI in your development environment
-icon: download
----
-
-<Note>
-  **Requirements**
-  
-  - Python 3.10 or higher
-  - pip package manager
-  - Virtual environment (recommended)
-</Note>
-
-# Installing PraisonAI
-
-Follow these steps to set up PraisonAI in your development environment.
-
-<Steps>
-  <Step title="Create Virtual Environment (Optional)">
-    First, create and activate a virtual environment:
-    <CodeGroup>
-    ```bash Mac/Linux
-    python -m venv praisonai-env
-    source praisonai-env/bin/activate
-    ```
-
-    ```bash Windows
-    python -m venv praisonai-env
-    .\praisonai-env\Scripts\activate
-    ```
-    </CodeGroup>
-  </Step>
-
-  <Step title="Install PraisonAI">
-    Install the core package and tools:
-    ```bash Terminal
-    pip install praisonaiagents
-    ```
-  </Step>
-
-  <Step title="Configure Environment">
-    ```bash Terminal
-    export OPENAI_API_KEY=your_openai_key
-    ```
-    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
-    You can also use other LLM providers like Anthropic, Google, etc. Please refer to the [Models](/models) for more information.
-  </Step>
-
-</Steps>
-
-## Next Steps
-
-<CardGroup cols={2}>
-  <Card
-    title="Quick Start Guide"
-    icon="bolt"
-    href="/code/quickstart"
-  >
-    Build your first AI agent
-  </Card>
-  <Card
-    title="API Reference"
-    icon="code"
-    href="/api/praisonaiagents/index"
-  >
-    Explore the API documentation
-  </Card>
-</CardGroup> 

docs/code/introduction.mdx

@@ -1,188 +0,0 @@
----
-title: "Introduction"
-description: "Welcome to PraisonAI - The Next Generation AI Agent Framework"
-icon: "book-open"
----
-
-<Note>
-  PraisonAI is a powerful framework for building and deploying AI agents that can understand, reason, and execute complex tasks autonomously.
-</Note>
-
-# What is PraisonAI?
-
-```mermaid
-graph LR
-    %% Define the main flow
-    Start([▶ Start]) --> Agent1
-    Agent1 --> Process[⚙ Process]
-    Process --> Agent2
-    Agent2 --> Output([✓ Output])
-    Process -.-> Agent1
-    
-    %% Styling for main elements
-    style Start fill:#2D3047,stroke:#7C90A0,color:#fff
-    style Output fill:#2D3047,stroke:#7C90A0,color:#fff
-    style Process fill:#5C80BC,stroke:#7C90A0,color:#fff
-    
-    %% Define subgraphs for agents and their tasks
-    subgraph Agent1[" "]
-        Task1[📋 Task]
-        AgentIcon1[🤖 AI Agent]
-        Tools1[🔧 Tools]
-        
-        Task1 --- AgentIcon1
-        AgentIcon1 --- Tools1
-        
-        style Task1 fill:#2D3047,stroke:#7C90A0,color:#fff
-        style AgentIcon1 fill:#5C80BC,stroke:#7C90A0,color:#fff
-        style Tools1 fill:#2D3047,stroke:#7C90A0,color:#fff
-    end
-    
-    subgraph Agent2[" "]
-        Task2[📋 Task]
-        AgentIcon2[🤖 AI Agent]
-        Tools2[🔧 Tools]
-        
-        Task2 --- AgentIcon2
-        AgentIcon2 --- Tools2
-        
-        style Task2 fill:#2D3047,stroke:#7C90A0,color:#fff
-        style AgentIcon2 fill:#5C80BC,stroke:#7C90A0,color:#fff
-        style Tools2 fill:#2D3047,stroke:#7C90A0,color:#fff
-    end
-    
-    %% Style for agent containers
-    style Agent1 fill:#1B1F3B,stroke:#7C90A0,color:#fff
-    style Agent2 fill:#1B1F3B,stroke:#7C90A0,color:#fff
-    
-    %% Global styles
-    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
-    linkStyle default stroke:#7C90A0,stroke-width:2px
-```
-
-## Core Components
-
-<CardGroup cols={2}>
-  <Card title="Agents" icon="user-robot">
-    Autonomous AI entities that can understand context and execute tasks
-  </Card>
-  <Card title="Tasks" icon="list-check">
-    Units of work that agents can execute independently or collaboratively
-  </Card>
-  <Card title="Process" icon="diagram-project">
-    Workflow management for coordinating agent activities
-  </Card>
-  <Card title="Tools" icon="toolbox">
-    Extensible capabilities that agents can leverage
-  </Card>
-</CardGroup>
-
-## Key Features
-
-<Steps>
-  <Step title="Autonomous Agents">
-    Create intelligent agents that can:
-    - Understand natural language instructions
-    - Make decisions based on context
-    - Execute complex tasks autonomously
-  </Step>
-  <Step title="Flexible Architecture">
-    Build with:
-    - Modular components
-    - Extensible tools
-    - Customizable workflows
-  </Step>
-  <Step title="Advanced Capabilities">
-    Leverage:
-    - Multi-agent collaboration
-    - Memory and context management
-    - Tool integration
-  </Step>
-</Steps>
-
-## Use Cases
-
-<CardGroup cols={2}>
-  <Card title="Customer Service" icon="headset">
-    Build intelligent support agents that can handle customer inquiries and resolve issues autonomously.
-  </Card>
-  <Card title="Data Analysis" icon="chart-line">
-    Create agents that can process, analyze, and derive insights from complex datasets.
-  </Card>
-  <Card title="Content Creation" icon="pen-nib">
-    Deploy agents that can generate, edit, and optimize content across various formats.
-  </Card>
-  <Card title="Process Automation" icon="gears">
-    Automate complex workflows with intelligent agents that can coordinate and execute tasks.
-  </Card>
-</CardGroup>
-
-## Getting Started
-
-<Warning>
-  Before proceeding, ensure you have Python 3.9+ installed on your system.
-</Warning>
-
-### Quick Setup
-
-<CodeGroup>
-  ```bash pip
-  pip install praisonai
-  ```
-</CodeGroup>
-
-### Basic Example
-
-```python
-from praisonaiagents import Agent, Task, PraisonAIAgents
-
-# Create an agent
-researcher = Agent(
-    name="Researcher",
-    role="Senior Research Analyst",
-    goal="Uncover cutting-edge developments in AI",
-    backstory="You are an expert at a technology research group",
-    verbose=True,
-    llm="gpt-4o"
-)
-
-# Define a task
-task = Task(
-    name="research_task",
-    description="Analyze 2024's AI advancements",
-    expected_output="A detailed report",
-    agent=researcher
-)
-
-# Run the agents
-agents = PraisonAIAgents(
-    agents=[researcher],
-    tasks=[task],
-    verbose=False
-)
-
-result = agents.start()
-```
-
-## Next Steps
-
-<CardGroup cols={2}>
-  <Card
-    title="Installation"
-    icon="download"
-    href="/code/installation"
-  >
-    Complete installation guide and setup instructions
-  </Card>
-  <Card
-    title="Quick Start"
-    icon="play"
-    href="/code/quickstart"
-  >
-    Build your first AI agent in minutes
-  </Card>
-</CardGroup>
-
-<Tip>
-  Join our community on [Discord](https://www.youtube.com/redirect?event=channel_description&redir_token=QUFFLUhqbUpiZE00YlF1MjB1LVNIeTFxTnV6MWRRc18wZ3xBQ3Jtc0ttQVZ0aFJ4RXRDanlBQU1Vd3lHU3BBcml3bzhoek1pTmlkZkFkWjlNVDBpRkxZd1dTOWhPc20xbHJOb1NibmNYeTEydklJT0pZOVJFQW93MmJOQXU0WkJNVzNsd29SZ3FBNFVwMTFGNThNSHBWVUhjSQ&q=https%3A%2F%2Fdiscord.gg%2FnNZu5gGT59) to connect with other developers and get help!
-</Tip> 

docs/code/quickstart.mdx

@@ -1,195 +0,0 @@
----
-title: Quick Start
-description: Build your first AI agent with PraisonAI
-icon: bolt
----
-
-<Steps>
-
-  <Step title="Install PraisonAI">
-    Install the core package:
-    ```bash Terminal
-    pip install praisonaiagents
-    ```
-  </Step>
-
-  <Step title="Configure Environment">
-    ```bash Terminal
-    export OPENAI_API_KEY=your_openai_key
-    ```
-    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
-    You can also use other LLM providers like Anthropic, Google, etc. Please refer to the [Models](/models) for more information.
-  </Step>
-
-  <Step title="Create Agent">
-    Create `app.py`:
-## Code Example
-
-<CodeGroup>
-  ```python Basic
-  from praisonaiagents import Agent, Task, PraisonAIAgents
-
-  # Create an agent
-  researcher = Agent(
-      name="Researcher",
-      role="Senior Research Analyst",
-      goal="Uncover cutting-edge developments in AI",
-      backstory="You are an expert at a technology research group",
-      verbose=True,
-      llm="gpt-4o"
-  )
-
-  # Define a task
-  task = Task(
-      name="research_task",
-      description="Analyze 2024's AI advancements",
-      expected_output="A detailed report",
-      agent=researcher
-  )
-
-  # Run the agents
-  agents = PraisonAIAgents(
-      agents=[researcher],
-      tasks=[task],
-      verbose=False
-  )
-
-  result = agents.start()
-  ```
-
-  ```python Advanced
-  from praisonaiagents import Agent, Task, PraisonAIAgents
-
-  # Create multiple agents
-  researcher = Agent(
-      name="Researcher",
-      role="Senior Research Analyst",
-      goal="Uncover cutting-edge developments in AI",
-      backstory="You are an expert at a technology research group",
-      verbose=True,
-      llm="gpt-4o",
-      markdown=True
-  )
-
-  writer = Agent(
-      name="Writer",
-      role="Tech Content Strategist",
-      goal="Craft compelling content on tech advancements",
-      backstory="You are a content strategist",
-      llm="gpt-4o",
-      markdown=True
-  )
-
-  # Define multiple tasks
-  task1 = Task(
-      name="research_task",
-      description="Analyze 2024's AI advancements",
-      expected_output="A detailed report",
-      agent=researcher
-  )
-
-  task2 = Task(
-      name="writing_task",
-      description="Create a blog post about AI advancements",
-      expected_output="A blog post",
-      agent=writer
-  )
-
-  # Run with hierarchical process
-  agents = PraisonAIAgents(
-      agents=[researcher, writer],
-      tasks=[task1, task2],
-      verbose=False,
-      process="hierarchical",
-      manager_llm="gpt-4o"
-  )
-
-  result = agents.start()
-  ```
-</CodeGroup>
-  </Step>
-
-  <Step title="Run the Program">
-    Execute your script:
-    ```bash Terminal
-    python app.py
-    ```
-
-    You should see:
-    - Agent initialization
-    - Agents progress
-    - Final results
-    - Generated report
-  </Step>
-</Steps>
-
-## Advanced Usage
-
-### Creating Custom Tool
-
-```python
-from praisonaiagents import Agent, Task, PraisonAIAgents
-from duckduckgo_search import DDGS
-from typing import List, Dict
-
-# 1. Tool
-def internet_search_tool(query: str) -> List[Dict]:
-    """
-    Perform Internet Search
-    """
-    results = []
-    ddgs = DDGS()
-    for result in ddgs.text(keywords=query, max_results=5):
-        results.append({
-            "title": result.get("title", ""),
-            "url": result.get("href", ""),
-            "snippet": result.get("body", "")
-        })
-    return results
-
-# 2. Agent
-data_agent = Agent(
-    name="DataCollector",
-    role="Search Specialist",
-    goal="Perform internet searches to collect relevant information.",
-    backstory="Expert in finding and organising internet data.",
-    tools=[internet_search_tool],
-    self_reflect=False
-)
-
-# 3. Tasks
-collect_task = Task(
-    description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
-    expected_output="List of search results with titles, URLs, and snippets.",
-    agent=data_agent,
-    name="collect_data",
-)
-
-# 4. Start Agents
-agents = PraisonAIAgents(
-    agents=[data_agent],
-    tasks=[collect_task],
-    process="sequential"
-)
-
-agents.start()
-```
-
-## Next Steps
-
-<CardGroup cols={2}>
-  <Card
-    title="Core Concepts"
-    icon="book"
-    href="/concepts/agents"
-  >
-    Learn about agents, tasks, and processes
-  </Card>
-  <Card
-    title="API Reference"
-    icon="code"
-    href="/api/praisonaiagents/index"
-  >
-    Explore detailed API documentation
-  </Card>
-</CardGroup> 

docs/concepts/agents.mdx

@@ -105,43 +105,254 @@ graph LR
   ```
 </CodeGroup>
 
-## Agent Types
 
 <Steps>
-  <Step title="Basic Agent">
-    - Single-purpose focus
-    - Direct user interaction
-    - Limited tool set
+
+  <Step title="Install PraisonAI">
+    Install the core package:
+    ```bash Terminal
+    pip install praisonaiagents
+    ```
   </Step>
-  <Step title="Specialized Agent">
-    - Domain expertise
-    - Advanced capabilities
-    - Custom tools
+
+  <Step title="Configure Environment">
+    ```bash Terminal
+    export OPENAI_API_KEY=your_openai_key
+    ```
+    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+    Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
   </Step>
-  <Step title="Collaborative Agent">
-    - Team interaction
-    - Task delegation
-    - Shared context
+
+  <Step title="Create Agent">
+    Create `app.py`:
+<CodeGroup>
+  ```python Single Agent
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create an agent
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o"
+  )
+
+  # Define a task
+  task = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  # Run the agents
+  agents = PraisonAIAgents(
+      agents=[researcher],
+      tasks=[task],
+      verbose=False
+  )
+
+  result = agents.start()
+  ```
+
+  ```python Multiple Agents
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create multiple agents
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  writer = Agent(
+      name="Writer",
+      role="Tech Content Strategist",
+      goal="Craft compelling content on tech advancements",
+      backstory="You are a content strategist",
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  # Define multiple tasks
+  task1 = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  task2 = Task(
+      name="writing_task",
+      description="Create a blog post about AI advancements",
+      expected_output="A blog post",
+      agent=writer
+  )
+
+  # Run with hierarchical process
+  agents = PraisonAIAgents(
+      agents=[researcher, writer],
+      tasks=[task1, task2],
+      verbose=False,
+      process="hierarchical",
+      manager_llm="gpt-4o"
+  )
+
+  result = agents.start()
+  ```
+</CodeGroup>
+  </Step>
+
+  <Step title="Start Agents">
+    Execute your script:
+    ```bash Terminal
+    python app.py
+    ```
+
+    You should see:
+    - Agent initialization
+    - Agents progress
+    - Final results
+    - Generated report
   </Step>
 </Steps>
 
+## Agent Types
+
+<CardGroup cols={3}>
+  <Card title="Basic Agent" icon="user" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Overview">
+        Perfect for straightforward tasks and direct interactions
+      </Accordion>
+      <Accordion title="Features">
+        - Single-purpose focus
+        - Direct user interaction
+        - Limited tool set
+        - Ideal for simple tasks
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+
+  <Card title="Specialized Agent" icon="user-gear" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Overview">
+        Experts in specific domains with advanced capabilities
+      </Accordion>
+      <Accordion title="Features">
+        - Domain expertise
+        - Advanced capabilities
+        - Custom tools
+        - Deep knowledge base
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+
+  <Card title="Collaborative Agent" icon="users" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Overview">
+        Designed for team-based operations and complex workflows
+      </Accordion>
+      <Accordion title="Features">
+        - Team interaction
+        - Task delegation
+        - Shared context
+        - Coordinated actions
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
+
 ## Best Practices
 
-<Warning>
-  Always implement proper error handling and resource management in your agent configurations.
-</Warning>
+<Note>
+Always implement proper error handling and resource management in your agent configurations.
+</Note>
 
-### Agent Design
-1. Clear role definition
-2. Specific, measurable goals
-3. Relevant tool selection
-4. Appropriate memory configuration
+<CardGroup cols={2}>
+  <Card title="Agent Design" icon="compass-drafting" iconType="solid">
+    <Steps>
+      <Step title="Role Definition">
+        Define clear, specific roles for each agent
+      </Step>
+      <Step title="Goal Setting">
+        Set specific, measurable goals
+      </Step>
+      <Step title="Tool Selection">
+        Choose relevant tools for the task
+      </Step>
+      <Step title="Memory Setup">
+        Configure appropriate memory settings
+      </Step>
+    </Steps>
+  </Card>
 
-### Agent Interaction
-1. Clear communication protocols
-2. Defined delegation rules
-3. Error handling mechanisms
-4. Resource management
+  <Card title="Agent Interaction" icon="network-wired" iconType="solid">
+    <Steps>
+      <Step title="Communication">
+        Establish clear communication protocols
+      </Step>
+      <Step title="Delegation">
+        Define explicit delegation rules
+      </Step>
+      <Step title="Error Handling">
+        Implement robust error handling
+      </Step>
+      <Step title="Resource Management">
+        Set up efficient resource allocation
+      </Step>
+    </Steps>
+  </Card>
+</CardGroup>
+
+## Async Capabilities
+
+<CardGroup cols={1}>
+  <Card title="Key Features" icon="bolt" iconType="solid">
+    <Tabs>
+      <Tab title="Async Support">
+        ```python
+        async def main():
+            agent = Agent(name="AsyncAgent")
+            result = await agent.aprocess_task()
+        ```
+        - Full async/await support
+        - Non-blocking operations
+        - Enhanced performance
+      </Tab>
+      
+      <Tab title="Parallel Execution">
+        ```python
+        async def parallel_tasks():
+            tasks = [agent1.task(), agent2.task()]
+            results = await asyncio.gather(*tasks)
+        ```
+        - Parallel task execution
+        - Efficient resource usage
+        - Improved throughput
+      </Tab>
+      
+      <Tab title="Integration">
+        ```python
+        @agent.async_tool
+        async def custom_tool():
+            async with aiohttp.ClientSession() as session:
+                # Async operations
+                pass
+        ```
+        - Async tool integration
+        - Callback support
+        - Mixed sync/async operations
+      </Tab>
+    </Tabs>
+  </Card>
+</CardGroup>
 
 ## Advanced Features
 

docs/concepts/memory.mdx

@@ -0,0 +1,441 @@
+---
+title: "AI Agents with Memory"
+sidebarTitle: "Memory"
+description: "Learn how to create AI agents with memory capabilities for maintaining context and information across tasks."
+icon: "brain"
+---
+
+## Quick Start
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the PraisonAI Agents package:
+            ```bash
+            pip install "praisonaiagents[memory]" duckduckgo_search 
+            ```
+            duckduckgo_search is a tool that allows agents to search the web.
+            It is required for the multiple agents example shown below.
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+<CodeGroup>
+  ```python Single Agent
+  from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
+
+  # Create blog writer agent
+  blog_agent = Agent(
+      role="Blog Writer",
+      goal="Write a blog post about AI",
+      backstory="Expert at writing blog posts",
+      llm="gpt-4o-mini"
+  )
+
+  # Create blog writing task
+  blog_task = Task(
+      description="Write a blog post about AI trends",
+      expected_output="Well-written blog post about AI trends",
+      agent=blog_agent
+  )
+
+  # Create and start the agents with memory enabled
+  agents = PraisonAIAgents(
+      agents=[blog_agent],
+      tasks=[blog_task],
+      memory=True
+  )   
+
+  # Start execution
+  result = agents.start()
+  print(result)
+  ```
+
+  ```python Multiple Agents
+  from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
+  from praisonaiagents.tools import duckduckgo
+
+  # Create research agent with memory
+  research_agent = Agent(
+      role="Research Analyst",
+      goal="Research and document key information about topics",
+      backstory="Expert at analyzing and storing information in memory",
+      llm="gpt-4o-mini",
+      tools=[duckduckgo]
+  )
+
+  # Create blog writer agent
+  blog_agent = Agent(
+      role="Blog Writer",
+      goal="Write a blog post about the research",
+      backstory="Expert at writing blog posts",
+      llm="gpt-4o-mini"
+  )
+
+  # Create research task
+  research_task = Task(
+      description="Research and document key information about AI trends",
+      expected_output="Detailed research findings about AI trends",
+      agent=research_agent
+  )
+
+  # Create blog writing task
+  blog_task = Task(
+      description="Write a blog post about the research findings",
+      expected_output="Well-written blog post based on research",
+      agent=blog_agent
+  )
+
+  # Create and start the agents with advanced memory configuration
+  agents = PraisonAIAgents(
+      agents=[research_agent, blog_agent],
+      tasks=[research_task, blog_task],
+      memory=True
+  )   
+
+  # Start execution
+  result = agents.start()
+  print(result)
+  ```
+</CodeGroup>
+        </Step>
+
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+memory: true
+roles:
+  researcher:
+    backstory: Expert at analyzing and storing information in memory.
+    goal: Research and document key information about topics
+    role: Research Analyst
+    llm: gpt-4o-mini
+    tools:
+      - duckduckgo
+    tasks:
+      research_task:
+        description: Research and document key information about topics.
+        expected_output: Detailed research findings.
+
+  writer:
+    backstory: Expert at writing blog posts.
+    goal: Write a blog post about the research
+    role: Blog Writer
+    llm: gpt-4o-mini
+    tasks:
+      blog_task:
+        description: Write a blog post about the research.
+        expected_output: Well-written blog post based on research.
+```
+        </Step>
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+```bash
+praisonai agents.yaml
+```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
+  - Basic understanding of Python
+</Note>
+
+## Understanding Memory
+
+<Card title="What is Agent Memory?" icon="question">
+  Memory in AI agents enables them to:
+  - Maintain context across multiple tasks
+  - Remember previous interactions and findings
+  - Build upon past knowledge
+  - Share information between agents
+  - Create more coherent and contextual responses
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Context Retention" icon="brain">
+    Maintain information across multiple interactions.
+  </Card>
+  <Card title="Information Sharing" icon="share-nodes">
+    Share knowledge between multiple agents.
+  </Card>
+  <Card title="Long-term Storage" icon="database">
+    Store and retrieve information over extended periods.
+  </Card>
+  <Card title="Memory Types" icon="layer-group">
+    Support for different memory types (short-term, long-term).
+  </Card>
+</CardGroup>
+
+## Multi-Agent Memory
+
+<Tabs>
+  <Tab title="Code">
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import duckduckgo
+
+    # Create first agent for research
+    researcher = Agent(
+        role="Research Analyst",
+        goal="Research and analyze market trends",
+        backstory="Expert in market research and data analysis",
+        tools=[duckduckgo],
+        verbose=True
+    )
+
+    # Create second agent for report writing
+    writer = Agent(
+        role="Report Writer",
+        goal="Create comprehensive market reports",
+        backstory="Expert in technical writing and report creation",
+        verbose=True
+    )
+
+    # Create research task
+    research_task = Task(
+        description="Research current market trends",
+        expected_output="Detailed market analysis",
+        agent=researcher
+    )
+
+    # Create writing task
+    report_task = Task(
+        description="Create a market report based on research",
+        expected_output="Comprehensive market report",
+        agent=writer
+    )
+
+    # Create and start the agents with memory
+    agents = PraisonAIAgents(
+        agents=[researcher, writer],
+        tasks=[research_task, report_task],
+        memory=True,
+        process="sequential"
+    )
+
+    # Start execution
+    result = agents.start()
+    ```
+  </Tab>
+  <Tab title="No Code">
+```yaml
+framework: praisonai
+process: sequential
+memory: true
+roles:
+  researcher:
+    backstory: Expert in market research and data analysis.
+    goal: Research and analyze market trends
+    role: Research Analyst
+    tools:
+      - duckduckgo
+    tasks:
+      research_task:
+        description: Research current market trends.
+        expected_output: Detailed market analysis.
+
+  writer:
+    backstory: Expert in technical writing and report creation.
+    goal: Create comprehensive market reports
+    role: Report Writer
+    tasks:
+      report_task:
+        description: Create a market report based on research.
+        expected_output: Comprehensive market report.
+```
+  </Tab>
+</Tabs>
+
+### Configuration Options
+
+```python
+# Create an agent with memory configuration
+agent = Agent(
+    role="Research Analyst",
+    goal="Research and retain information",
+    backstory="Expert in research and analysis",
+    tools=[duckduckgo],
+    verbose=True,  # Enable detailed logging
+    llm="gpt-4o"  # Language model to use
+)
+
+# Create agents with memory options
+agents = PraisonAIAgents(
+    agents=[agent],
+    tasks=[task],
+    memory=True,  # Enable memory
+    memory_config={
+        "provider": "rag",  # Use RAG for semantic search
+        "use_embedding": True,  # Enable embeddings for better search
+        "short_db": ".praison/short_term.db",  # Path for short-term memory
+        "long_db": ".praison/long_term.db",    # Path for long-term memory
+        "rag_db_path": ".praison/chroma_db"    # Path for vector database
+    }
+)
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Memory Issues" icon="triangle-exclamation">
+    If memory isn't working as expected:
+    - Check memory configuration
+    - Enable verbose mode for debugging
+    - Verify memory provider settings
+  </Card>
+
+  <Card title="Context Flow" icon="code">
+    If context isn't being maintained:
+    - Review task dependencies
+    - Check memory configuration
+    - Verify agent communication
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="AutoAgents" icon="robot" href="../features/autoagents">
+    Learn about automatically created and managed AI agents
+  </Card>
+  <Card title="Mini Agents" icon="microchip" href="../features/mini">
+    Explore lightweight, focused AI agents
+  </Card>
+</CardGroup>
+
+<Note>
+  For optimal results, configure memory settings based on your specific use case requirements and expected interaction patterns.
+</Note>
+
+### Memory Configuration Options
+
+The memory system in PraisonAI supports various configuration options to customize how agents store and retrieve information:
+
+```python
+memory_config = {
+    # Memory Provider
+    "provider": "rag",        # Options: "rag", "mem0", "none"
+    "use_embedding": True,    # Enable semantic search with embeddings
+    
+    # Storage Paths
+    "short_db": ".praison/short_term.db",  # Short-term memory SQLite DB
+    "long_db": ".praison/long_term.db",    # Long-term memory SQLite DB
+    "rag_db_path": ".praison/chroma_db",   # Vector database path
+    
+    # Memory Settings
+    "ttl": 3600,             # Time to live for memory items (in seconds)
+    
+    # Optional Mem0 Config (if using mem0 provider)
+    "config": {
+        "api_key": "...",    # Mem0 API key
+        "org_id": "...",     # Organization ID
+        "project_id": "..."  # Project ID
+    }
+}
+
+# Create agents with memory configuration
+agents = PraisonAIAgents(
+    agents=[agent],
+    tasks=[task],
+    memory=True,
+    memory_config=memory_config
+)
+```
+
+### Memory Types
+
+PraisonAI's memory system includes several types of memory:
+
+<CardGroup cols={2}>
+  <Card title="Short-term Memory" icon="bolt">
+    - Temporary storage for current context
+    - Automatically cleared between sessions
+    - Fast access for immediate task context
+  </Card>
+  <Card title="Long-term Memory" icon="database">
+    - Persistent storage for important information
+    - Semantic search capabilities with RAG
+    - Quality-based storage decisions
+  </Card>
+  <Card title="Entity Memory" icon="diagram-project">
+    - Store information about specific entities
+    - Track relationships and attributes
+    - Specialized search for entity information
+  </Card>
+  <Card title="User Memory" icon="user">
+    - Store user-specific information
+    - Personalized context for each user
+    - Persistent across sessions
+  </Card>
+</CardGroup>
+
+### Memory Quality Control
+
+PraisonAI includes built-in quality control for memory storage:
+
+```python
+# Example of storing with quality metrics
+agents.memory.store_long_term(
+    text="Important information to remember",
+    metadata={
+        "task_id": "task_123",
+        "agent": "research_agent"
+    },
+    completeness=0.9,    # How complete is the information
+    relevance=0.85,      # How relevant to the task
+    clarity=0.95,        # How clear and well-structured
+    accuracy=0.9,        # How accurate is the information
+    weights={            # Custom weights for quality score
+        "completeness": 0.3,
+        "relevance": 0.3,
+        "clarity": 0.2,
+        "accuracy": 0.2
+    }
+)
+
+# Search with quality filter
+results = agents.memory.search_long_term(
+    query="search query",
+    min_quality=0.8,     # Only return high-quality matches
+    limit=5              # Maximum number of results
+)
+```

docs/concepts/process.mdx

@@ -10,96 +10,209 @@ Process types in PraisonAI define how tasks are executed and how agents collabor
 
 ## Process Types Overview
 
-Three types of processes are implemented in PraisonAI:
+<CardGroup cols={3}>
+  <Card
+    title="Sequential Process"
+    icon="arrow-right"
+    href="/sequential-process"
+  >
+    Linear task execution in a predefined order
+  </Card>
+  <Card
+    title="Hierarchical Process"
+    icon="sitemap"
+    href="/hierarchical-process"
+  >
+    Manager-coordinated task execution with dynamic assignment
+  </Card>
+  <Card
+    title="Workflow Process"
+    icon="diagram-project"
+    href="/workflow-process"
+  >
+    Complex task flows with conditional execution and state management
+  </Card>
+</CardGroup>
 
-<Frame>
-  <div className="grid grid-cols-1 md:grid-cols-3 gap-4">
-    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
-      <div className="flex items-center mb-4">
-        <div className="p-2 bg-blue-100 dark:bg-blue-900 rounded-lg">
-          <svg className="w-6 h-6 text-blue-600 dark:text-blue-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M4 6h16M4 12h16m-7 6h7" />
-          </svg>
-        </div>
-        <h3 className="ml-3 text-lg font-semibold">Sequential Process</h3>
-      </div>
-      <p>Linear task execution in a predefined order</p>
-    </div>
+## Sequential Process
 
-    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
-      <div className="flex items-center mb-4">
-        <div className="p-2 bg-green-100 dark:bg-green-900 rounded-lg">
-          <svg className="w-6 h-6 text-green-600 dark:text-green-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M12 6V4m0 2a2 2 0 100 4m0-4a2 2 0 110 4m-6 8a2 2 0 100-4m0 4a2 2 0 110-4m0 4v2m0-6V4m6 6v10m6-2a2 2 0 100-4m0 4a2 2 0 110-4m0 4v2m0-6V4" />
-          </svg>
-        </div>
-        <h3 className="ml-3 text-lg font-semibold">Hierarchical Process</h3>
-      </div>
-      <p>Manager-coordinated task execution with dynamic assignment</p>
-    </div>
-
-    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
-      <div className="flex items-center mb-4">
-        <div className="p-2 bg-purple-100 dark:bg-purple-900 rounded-lg">
-          <svg className="w-6 h-6 text-purple-600 dark:text-purple-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M9 19v-6a2 2 0 00-2-2H5a2 2 0 00-2 2v6a2 2 0 002 2h2a2 2 0 002-2zm0 0V9a2 2 0 012-2h2a2 2 0 012 2v10m-6 0a2 2 0 002 2h2a2 2 0 002-2m0 0V5a2 2 0 012-2h2a2 2 0 012 2v14a2 2 0 01-2 2h-2a2 2 0 01-2-2z" />
-          </svg>
-        </div>
-        <h3 className="ml-3 text-lg font-semibold">Workflow Process</h3>
-      </div>
-      <p>Complex task flows with conditional execution and state management</p>
-    </div>
-  </div>
-</Frame>
-
-### Sequential Process
 The simplest form of task execution where tasks are performed one after another.
 
-<Frame>
-  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
-    #### Characteristics
-    - Linear execution flow
-    - Predictable order
-    - Simple dependency management
-    - Direct task progression
+<Card>
+  ### Characteristics
+  - Linear execution flow
+  - Predictable order
+  - Simple dependency management
+  - Direct task progression
 
-    #### Usage
-    ```python
-    agents = PraisonAIAgents(
-        agents=[agent1, agent2],
-        tasks=[task1, task2, task3],
-        process="sequential"
-    )
-    ```
-  </div>
-</Frame>
+  ### Usage
+  ```python
+  agents = PraisonAIAgents(
+      agents=[agent1, agent2],
+      tasks=[task1, task2, task3],
+      process="sequential"
+  )
+  ```
+</Card>
+
+## Hierarchical Process
 
-### Hierarchical Process
 Uses a manager agent to coordinate task execution and agent assignments.
 
-<Frame>
-  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
-    #### Characteristics
-    - Manager-driven coordination
-    - Dynamic task assignment
-    - Flexible execution order
-    - Intelligent resource allocation
+<Card>
+  ### Characteristics
+  - Manager-driven coordination
+  - Dynamic task assignment
+  - Flexible execution order
+  - Intelligent resource allocation
 
-    #### Configuration
-    ```python
-    agents = PraisonAIAgents(
-        agents=[manager_agent, worker_agent1, worker_agent2],
-        tasks=[task1, task2],
-        process="hierarchical",
-        manager_llm="gpt-4o"
-    )
-    ```
-  </div>
-</Frame>
+  ### Configuration
+  ```python
+  agents = PraisonAIAgents(
+      agents=[manager_agent, worker_agent1, worker_agent2],
+      tasks=[task1, task2],
+      process="hierarchical",
+      manager_llm="gpt-4o"
+  )
+  ```
+</Card>
+
+## Workflow Process
 
-### Workflow Process
 Advanced process type supporting complex task relationships and conditional execution.
 
+<Card>
+  ### Features
+  - Task dependencies
+  - Conditional branching
+  - Loop handling
+  - Context sharing
+  - State management
+
+  ### Implementation
+  ```python
+  task_workflow = {
+      "start_task": {
+          "next": ["decision_task"],
+          "type": "task"
+      },
+      "decision_task": {
+          "type": "decision",
+          "conditions": {
+              "success": ["process_task"],
+              "failure": ["error_task"]
+          }
+      }
+  }
+  ```
+</Card>
+
+## Getting Started
+
+<Steps>
+  <Step title="Install PraisonAI">
+    Install the core package:
+    ```bash Terminal
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Configure Environment">
+    ```bash Terminal
+    export OPENAI_API_KEY=your_openai_key
+    ```
+    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+    Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+  </Step>
+
+  <Step title="Create Agent">
+    Create `app.py`:
+<CodeGroup>
+  ```python Single Agent Sequential Process
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create an agent
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o"
+  )
+
+  # Define a task
+  task = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  # Run the agents
+  agents = PraisonAIAgents(
+      agents=[researcher],
+      tasks=[task],
+      verbose=False,
+      process="sequential"
+  )
+
+  result = agents.start()
+  ```
+
+  ```python Multiple Agents Hierarchical Process
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create multiple agents 
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  writer = Agent(
+      name="Writer",
+      role="Tech Content Strategist",
+      goal="Craft compelling content on tech advancements",
+      backstory="You are a content strategist",
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  # Define multiple tasks
+  task1 = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  task2 = Task(
+      name="writing_task",
+      description="Create a blog post about AI advancements",
+      expected_output="A blog post",
+      agent=writer
+  )
+
+  # Run with hierarchical process
+  agents = PraisonAIAgents(
+      agents=[researcher, writer],
+      tasks=[task1, task2],
+      verbose=False,
+      process="hierarchical",
+      manager_llm="gpt-4o"
+  )
+
+  result = agents.start()
+  ```
+</CodeGroup>
+  </Step>
+</Steps>
+<br /><br />
 <div className="relative w-full aspect-video">
   <iframe
     className="absolute top-0 left-0 w-full h-full"
@@ -111,110 +224,152 @@ Advanced process type supporting complex task relationships and conditional exec
 </div>
 <br />
 
-
-<Frame>
-  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
-    #### Features
-    - Task dependencies
-    - Conditional branching
-    - Loop handling
-    - Context sharing
-    - State management
-
-    #### Implementation
-    ```python
-    task_workflow = {
-        "start_task": {
-            "next": ["decision_task"],
-            "type": "task"
-        },
-        "decision_task": {
-            "type": "decision",
-            "conditions": {
-                "success": ["process_task"],
-                "failure": ["error_task"]
-            }
-        }
-    }
-    ```
-  </div>
-</Frame>
-
 ## Advanced Features
 
-### State Management
-- Task state tracking
-- Context preservation
-- Data flow control
+<CardGroup cols={2}>
+  <Card title="State Management" icon="database" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Task State Tracking">
+        Monitor and manage the progress of each task in real-time
+      </Accordion>
+      <Accordion title="Context Preservation">
+        Maintain important information across different stages of execution
+      </Accordion>
+      <Accordion title="Data Flow Control">
+        Manage how data moves between tasks and agents efficiently
+      </Accordion>
+    </AccordionGroup>
+  </Card>
 
-### Error Handling
-- Graceful failure recovery
-- Alternative path execution
-- Error reporting
+  <Card title="Error Handling" icon="shield-check" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Graceful Recovery">
+        Automatically handle failures and continue execution
+      </Accordion>
+      <Accordion title="Alternative Paths">
+        Switch to backup plans when primary execution fails
+      </Accordion>
+      <Accordion title="Error Reporting">
+        Detailed error logs and diagnostic information
+      </Accordion>
+    </AccordionGroup>
+  </Card>
 
-### Monitoring
-- Progress tracking
-- Performance metrics
-- Resource utilization
+  <Card title="Monitoring" icon="chart-line" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Progress Tracking">
+        Real-time visibility into task completion status
+      </Accordion>
+      <Accordion title="Performance Metrics">
+        Measure execution time and resource efficiency
+      </Accordion>
+      <Accordion title="Resource Usage">
+        Monitor system resource utilization
+      </Accordion>
+    </AccordionGroup>
+  </Card>
 
-### Integration
-- External system coordination
-- API synchronization
-- Event handling 
+  <Card title="Integration" icon="plug" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="External Systems">
+        Connect with other services and platforms
+      </Accordion>
+      <Accordion title="API Sync">
+        Maintain data consistency across systems
+      </Accordion>
+      <Accordion title="Event Handling">
+        React to system and external events
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
 
-## Async Process Execution
-All process types (sequential, workflow, hierarchical) support async execution through async generators. This enables efficient handling of async tasks and parallel processing.
+## Async Processing
 
-### Async Process Methods
-- `asequential`: Async version of sequential process
-- `aworkflow`: Async version of workflow process
-- `ahierarchical`: Async version of hierarchical process
+<Note>
+All process types support asynchronous execution through async generators, enabling efficient parallel processing and non-blocking operations.
+</Note>
 
-### Example Usage:
+### Core Async Methods
 
-```python
-import asyncio
-from praisonaiagents import Agent, Task, PraisonAIAgents
+<CardGroup cols={3}>
+  <Card title="asequential" icon="arrow-right">
+    Async version of sequential process for non-blocking linear execution
+  </Card>
+  <Card title="aworkflow" icon="diagram-project">
+    Async workflow process for complex parallel task execution
+  </Card>
+  <Card title="ahierarchical" icon="sitemap">
+    Async hierarchical process for distributed task management
+  </Card>
+</CardGroup>
 
-async def main():
-    # Create agents and tasks
-    agents = [async_agent1, async_agent2]
-    tasks = [async_task1, async_task2]
+### Process-Specific Features
 
-    # Initialize PraisonAIAgents with async workflow
-    agents = PraisonAIAgents(
-        agents=agents,
-        tasks=tasks,
-        process="workflow"  # or "sequential" or "hierarchical"
-    )
+<Tabs>
+  <Tab title="Sequential Process">
+    <Card>
+      - Tasks execute in order but don't block
+      - Maintains sequence while allowing async operations
+      - Perfect for I/O-heavy tasks
+      ```python
+      async def main():
+          agents = PraisonAIAgents(
+              process="sequential",
+              async_mode=True
+          )
+          await agents.astart()
+      ```
+    </Card>
+  </Tab>
+  
+  <Tab title="Workflow Process">
+    <Card>
+      - Parallel execution of independent tasks
+      - Async branching and merging
+      - Dynamic task scheduling based on conditions
+      ```python
+      async def main():
+          agents = PraisonAIAgents(
+              process="workflow",
+              async_mode=True,
+              parallel_tasks=True
+          )
+          await agents.astart()
+      ```
+    </Card>
+  </Tab>
+  
+  <Tab title="Hierarchical Process">
+    <Card>
+      - Async manager delegation
+      - Parallel subtask execution
+      - Dynamic worker assignment
+      ```python
+      async def main():
+          agents = PraisonAIAgents(
+              process="hierarchical",
+              async_mode=True,
+              dynamic_assignment=True
+          )
+          await agents.astart()
+      ```
+    </Card>
+  </Tab>
+</Tabs>
 
-    # Start async execution
-    result = await agents.astart()
-    print(result)
+### Key Benefits
 
-# Run the async workflow
-asyncio.run(main())
-```
-
-### Async Process Features:
-- Parallel task execution in workflow
-- Async task delegation in hierarchical process
-- Non-blocking task execution
-- Efficient resource utilization
-- Mixed sync/async task support
-
-### Process-Specific Async Behavior:
-1. **Sequential Process**:
-   - Tasks are executed in order
-   - Async tasks don't block other tasks
-   - Maintains sequential order while allowing async execution
-
-2. **Workflow Process**:
-   - Parallel execution of independent tasks
-   - Async branching and merging
-   - Dynamic task scheduling
-
-3. **Hierarchical Process**:
-   - Async manager delegation
-   - Parallel subtask execution
-   - Dynamic task assignment 
+<CardGroup cols={2}>
+  <Card title="Performance" icon="bolt">
+    - Efficient resource utilization
+    - Reduced waiting time
+    - Better throughput
+  </Card>
+  
+  <Card title="Flexibility" icon="puzzle-piece">
+    - Mix sync and async tasks
+    - Adaptable execution patterns
+    - Easy scaling
+  </Card>
+</CardGroup>

docs/concepts/tasks.mdx

@@ -8,114 +8,6 @@ icon: "list-check"
   Tasks are the fundamental units of work in PraisonAI, defining what needs to be done, how it should be done, and who should do it.
 </Note>
 
-# Understanding Tasks
-
-<Frame>
-  <div className="p-6 bg-white dark:bg-gray-800 rounded-lg">
-    <div className="grid grid-cols-1 gap-6">
-      {/* Task Flow Steps */}
-      <div className="flex justify-between items-center">
-        {/* Task Definition */}
-        <div className="flex-1 p-4 border-2 border-blue-500 rounded-lg text-center">
-          <div className="flex items-center justify-center mb-2">
-            <div className="p-2 bg-blue-100 dark:bg-blue-900 rounded-full">
-              <svg className="w-6 h-6 text-blue-600 dark:text-blue-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-                <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2" />
-              </svg>
-            </div>
-          </div>
-          <h4 className="font-semibold">Task Definition</h4>
-          <p className="text-sm mt-1">Description & Requirements</p>
-        </div>
-
-        {/* Arrow 1 */}
-        <div className="flex-none px-4">
-          <svg className="w-6 h-6 text-gray-400" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M17 8l4 4m0 0l-4 4m4-4H3" />
-          </svg>
-        </div>
-
-        {/* Agent Assignment */}
-        <div className="flex-1 p-4 border-2 border-green-500 rounded-lg text-center">
-          <div className="flex items-center justify-center mb-2">
-            <div className="p-2 bg-green-100 dark:bg-green-900 rounded-full">
-              <svg className="w-6 h-6 text-green-600 dark:text-green-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-                <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M16 7a4 4 0 11-8 0 4 4 0 018 0zM12 14a7 7 0 00-7 7h14a7 7 0 00-7-7z" />
-              </svg>
-            </div>
-          </div>
-          <h4 className="font-semibold">Agent Assignment</h4>
-          <p className="text-sm mt-1">Matching & Delegation</p>
-        </div>
-
-        {/* Arrow 2 */}
-        <div className="flex-none px-4">
-          <svg className="w-6 h-6 text-gray-400" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M17 8l4 4m0 0l-4 4m4-4H3" />
-          </svg>
-        </div>
-
-        {/* Execution */}
-        <div className="flex-1 p-4 border-2 border-purple-500 rounded-lg text-center">
-          <div className="flex items-center justify-center mb-2">
-            <div className="p-2 bg-purple-100 dark:bg-purple-900 rounded-full">
-              <svg className="w-6 h-6 text-purple-600 dark:text-purple-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-                <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M13 10V3L4 14h7v7l9-11h-7z" />
-              </svg>
-            </div>
-          </div>
-          <h4 className="font-semibold">Execution</h4>
-          <p className="text-sm mt-1">Processing & Tools</p>
-        </div>
-
-        {/* Arrow 3 */}
-        <div className="flex-none px-4">
-          <svg className="w-6 h-6 text-gray-400" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M17 8l4 4m0 0l-4 4m4-4H3" />
-          </svg>
-        </div>
-
-        {/* Output */}
-        <div className="flex-1 p-4 border-2 border-yellow-500 rounded-lg text-center">
-          <div className="flex items-center justify-center mb-2">
-            <div className="p-2 bg-yellow-100 dark:bg-yellow-900 rounded-full">
-              <svg className="w-6 h-6 text-yellow-600 dark:text-yellow-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-                <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M8 9l3 3-3 3m5 0h3M5 20h14a2 2 0 002-2V6a2 2 0 00-2-2H5a2 2 0 00-2 2v12a2 2 0 002 2z" />
-              </svg>
-            </div>
-          </div>
-          <h4 className="font-semibold">Output</h4>
-          <p className="text-sm mt-1">Results & Validation</p>
-        </div>
-      </div>
-
-      {/* Task States */}
-      <div className="grid grid-cols-4 gap-4 mt-4">
-        <div className="p-3 border border-gray-200 rounded-lg bg-blue-50 dark:bg-blue-900">
-          <div className="flex items-center justify-center">
-            <span className="font-semibold">Pending</span>
-          </div>
-        </div>
-        <div className="p-3 border border-gray-200 rounded-lg bg-yellow-50 dark:bg-yellow-900">
-          <div className="flex items-center justify-center">
-            <span className="font-semibold">In Progress</span>
-          </div>
-        </div>
-        <div className="p-3 border border-gray-200 rounded-lg bg-green-50 dark:bg-green-900">
-          <div className="flex items-center justify-center">
-            <span className="font-semibold">Completed</span>
-          </div>
-        </div>
-        <div className="p-3 border border-gray-200 rounded-lg bg-red-50 dark:bg-red-900">
-          <div className="flex items-center justify-center">
-            <span className="font-semibold">Failed</span>
-          </div>
-        </div>
-      </div>
-    </div>
-  </div>
-</Frame>
-
 ## Core Components
 
 <CardGroup cols={2}>
@@ -155,6 +47,79 @@ icon: "list-check"
   ```
 </CodeGroup>
 
+# Understanding Tasks
+<br />
+```mermaid
+graph LR
+    %% Task Flow Steps
+    Start([▶ Start]) --> TaskDef
+    TaskDef --> AgentAssign
+    AgentAssign --> Exec
+    Exec --> Output([✓ Output])
+    
+    %% Define subgraphs for each step
+    subgraph TaskDef[" "]
+        TD_Icon[📋 Task Definition]
+        TD_Desc[Description & Requirements]
+        
+        TD_Icon --- TD_Desc
+        
+        style TD_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
+        style TD_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
+    end
+    
+    subgraph AgentAssign[" "]
+        AA_Icon[🤖 Agent Assignment]
+        AA_Desc[Matching & Delegation]
+        
+        AA_Icon --- AA_Desc
+        
+        style AA_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
+        style AA_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
+    end
+    
+    subgraph Exec[" "]
+        E_Icon[⚙️ Execution]
+        E_Desc[Processing & Tools]
+        
+        E_Icon --- E_Desc
+        
+        style E_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
+        style E_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
+    end
+    
+    %% Styling for main elements
+    style Start fill:#2D3047,stroke:#7C90A0,color:#fff
+    style Output fill:#2D3047,stroke:#7C90A0,color:#fff
+    
+    %% Style for containers
+    style TaskDef fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    style AgentAssign fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    style Exec fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    
+    %% Global styles
+    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
+    linkStyle default stroke:#7C90A0,stroke-width:2px
+```
+<br /><br />
+```mermaid
+graph LR
+    %% Task States
+    Pending([📥 Pending]) --> InProgress([⚡ In Progress])
+    InProgress --> Completed([✅ Completed])
+    InProgress --> Failed([❌ Failed])
+    
+    %% Styling
+    style Pending fill:#2D3047,stroke:#7C90A0,color:#fff
+    style InProgress fill:#5C80BC,stroke:#7C90A0,color:#fff
+    style Completed fill:#2D3047,stroke:#7C90A0,color:#fff
+    style Failed fill:#2D3047,stroke:#7C90A0,color:#fff
+    
+    %% Global styles
+    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
+    linkStyle default stroke:#7C90A0,stroke-width:2px
+```
+
 ## Task Types
 
 <Steps>
@@ -223,17 +188,300 @@ task_b = Task(
   </Card>
 </CardGroup>
 
+## Getting Started
+
+<Steps>
+
+  <Step title="Install PraisonAI">
+    Install the core package:
+    ```bash Terminal
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Configure Environment">
+    ```bash Terminal
+    export OPENAI_API_KEY=your_openai_key
+    ```
+    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+    Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+  </Step>
+
+  <Step title="Create Agent">
+    Create `app.py`:
+<CodeGroup>
+  ```python Single Agent
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create an agent
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o"
+  )
+
+  # Define a task
+  task = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  # Run the agents
+  agents = PraisonAIAgents(
+      agents=[researcher],
+      tasks=[task],
+      verbose=False
+  )
+
+  result = agents.start()
+  ```
+
+  ```python Multiple Agents
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create multiple agents
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  writer = Agent(
+      name="Writer",
+      role="Tech Content Strategist",
+      goal="Craft compelling content on tech advancements",
+      backstory="You are a content strategist",
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  # Define multiple tasks
+  task1 = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  task2 = Task(
+      name="writing_task",
+      description="Create a blog post about AI advancements",
+      expected_output="A blog post",
+      agent=writer
+  )
+
+  # Run with hierarchical process
+  agents = PraisonAIAgents(
+      agents=[researcher, writer],
+      tasks=[task1, task2],
+      verbose=False,
+      process="hierarchical",
+      manager_llm="gpt-4o"
+  )
+
+  result = agents.start()
+  ```
+</CodeGroup>
+  </Step>
+</Steps>
+
 ## Best Practices
 
-<Tip>
-  Design tasks to be atomic and focused. Each task should have a single, clear responsibility.
-</Tip>
+<Note>
+Design tasks to be atomic and focused. Each task should have a single, clear responsibility.
+</Note>
 
-1. Clear task descriptions
-2. Well-defined success criteria
-3. Appropriate tool selection
-4. Error handling consideration
-5. Resource management
+<CardGroup cols={2}>
+  <Card title="Task Design Principles" icon="compass-drafting" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Clear Task Descriptions">
+        Write precise, unambiguous task descriptions that clearly state the task's purpose and requirements.
+        ```python
+        task = Task(
+            description=(
+                "Analyze Q4 2024 sales data "
+                "and generate growth metrics"
+            ),
+            expected_output="Sales growth report"
+        )
+        ```
+      </Accordion>
+
+      <Accordion title="Success Criteria">
+        Define measurable success criteria to ensure task completion can be properly evaluated.
+        ```python
+        task = Task(
+            success_criteria={
+                "format": "markdown",
+                "metrics": [
+                    "growth_rate",
+                    "revenue_change"
+                ],
+                "min_confidence": 0.95
+            }
+        )
+        ```
+      </Accordion>
+
+      <Accordion title="Task Dependencies">
+        Specify any dependencies or prerequisites for the task.
+        ```python
+        task = Task(
+            description="Generate final report",
+            dependencies=[
+                data_collection_task,
+                analysis_task
+            ],
+            wait_for_dependencies=True
+        )
+        ```
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+
+  <Card title="Resource Management" icon="server" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Tool Selection">
+        Choose appropriate tools that match your task's requirements.
+        ```python
+        task = Task(
+            tools=[
+                DataAnalyzer(),
+                ReportGenerator(),
+                MetricsCalculator()
+            ],
+            tool_config={
+                "timeout": 30,
+                "retry": True
+            }
+        )
+        ```
+      </Accordion>
+
+      <Accordion title="Error Handling">
+        Implement comprehensive error management strategies.
+        ```python
+        task = Task(
+            error_strategy="retry",
+            max_retries=3,
+            fallback_handler=handler,
+            error_callbacks=[
+                log_error,
+                notify_admin
+            ]
+        )
+        ```
+      </Accordion>
+
+      <Accordion title="Resource Limits">
+        Set appropriate resource constraints for optimal performance.
+        ```python
+        task = Task(
+            memory_limit="2GB",
+            timeout=300,
+            max_concurrent=5,
+            cpu_priority="high",
+            auto_scale=True
+        )
+        ```
+      </Accordion>
+
+      <Accordion title="Performance Monitoring">
+        Configure monitoring and logging for task execution.
+        ```python
+        task = Task(
+            monitoring={
+                "metrics": ["duration", "memory", "cpu"],
+                "log_level": "INFO",
+                "trace_enabled": True,
+                "alert_threshold": 0.85
+            }
+        )
+        ```
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
+
+## Async Task Execution
+
+<CardGroup cols={1}>
+  <Card title="Async Task Features" icon="bolt" iconType="solid">
+    <Tabs>
+      <Tab title="Basic Configuration">
+        ```python
+        async_task = Task(
+            description="Perform async operation",
+            expected_output="Async result",
+            agent=async_agent,
+            async_execution=True,  # Enable async execution
+            callback=async_callback  # Optional async callback
+        )
+        ```
+        - Enable async execution with `async_execution=True`
+        - Perfect for I/O-heavy operations
+        - Supports parallel task execution
+      </Tab>
+      
+      <Tab title="Async Callbacks">
+        ```python
+        async def async_callback(output: TaskOutput):
+            await asyncio.sleep(1)  # Simulate async operation
+            print(f"Processed output: {output.raw}")
+
+        task = Task(
+            description="Task with async callback",
+            expected_output="Result",
+            agent=agent,
+            async_execution=True,
+            callback=async_callback
+        )
+        ```
+        - Support for async callbacks
+        - Process results asynchronously
+        - Handle task completion events
+      </Tab>
+      
+      <Tab title="Mixed Execution">
+        ```python
+        # Create mixed task types
+        sync_task = Task(
+            description="Synchronous operation",
+            async_execution=False
+        )
+
+        async_task = Task(
+            description="Asynchronous operation",
+            async_execution=True
+        )
+
+        # Run with mixed execution
+        agents = PraisonAIAgents(
+            agents=[sync_agent, async_agent],
+            tasks=[sync_task, async_task]
+        )
+        result = await agents.astart()
+        ```
+        - Mix sync and async tasks
+        - Flexible execution patterns
+        - Optimal resource utilization
+      </Tab>
+    </Tabs>
+  </Card>
+</CardGroup>
+
+<Tip>
+**Pro Tip**: Use async execution for tasks that involve I/O operations (like API calls or file operations) to improve overall performance. Keep CPU-intensive tasks synchronous.
+</Tip>
 
 ## Next Steps
 
@@ -252,66 +500,4 @@ task_b = Task(
   >
     Explore the complete Task API documentation
   </Card>
-</CardGroup>
-
-## Async Task Execution
-Tasks can be executed asynchronously by setting `async_execution=True`. This is particularly useful for tasks that involve I/O operations or when you want to run multiple tasks in parallel.
-
-### Async Task Configuration
-```python
-async_task = Task(
-    description="Perform async operation",
-    expected_output="Async result",
-    agent=async_agent,
-    async_execution=True,  # Enable async execution
-    callback=async_callback  # Optional async callback
-)
-```
-
-### Async Callbacks
-Tasks support both synchronous and asynchronous callbacks:
-
-```python
-async def async_callback(output: TaskOutput):
-    await asyncio.sleep(1)  # Simulate async operation
-    print(f"Processed output: {output.raw}")
-
-# Task with async callback
-task = Task(
-    description="Task with async callback",
-    expected_output="Result",
-    agent=agent,
-    async_execution=True,
-    callback=async_callback
-)
-```
-
-### Mixing Sync and Async Tasks
-You can mix synchronous and asynchronous tasks in the same workflow:
-
-```python
-# Sync task
-sync_task = Task(
-    description="Synchronous operation",
-    expected_output="Sync result",
-    agent=sync_agent,
-    async_execution=False
-)
-
-# Async task
-async_task = Task(
-    description="Asynchronous operation",
-    expected_output="Async result",
-    agent=async_agent,
-    async_execution=True
-)
-
-# Create agents with mixed tasks
-agents = PraisonAIAgents(
-    agents=[sync_agent, async_agent],
-    tasks=[sync_task, async_task]
-)
-
-# Run asynchronously
-result = await agents.astart()
-``` 
+</CardGroup>

docs/concepts/tools.mdx

@@ -8,48 +8,6 @@ icon: "screwdriver-wrench"
 
 Tools in PraisonAI are functions that agents can use to interact with external systems, perform computations, or access data. They extend the capabilities of agents beyond just language processing.
 
-## Tools Overview
-
-<Frame>
-  <div className="grid grid-cols-1 md:grid-cols-3 gap-4">
-    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
-      <div className="flex items-center mb-4">
-        <div className="p-2 bg-blue-100 dark:bg-blue-900 rounded-lg">
-          <svg className="w-6 h-6 text-blue-600 dark:text-blue-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M21 21l-6-6m2-5a7 7 0 11-14 0 7 7 0 0114 0z" />
-          </svg>
-        </div>
-        <h3 className="ml-3 text-lg font-semibold">Search Tools</h3>
-      </div>
-      <p>Tools for searching and retrieving information from various sources</p>
-    </div>
-
-    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
-      <div className="flex items-center mb-4">
-        <div className="p-2 bg-green-100 dark:bg-green-900 rounded-lg">
-          <svg className="w-6 h-6 text-green-600 dark:text-green-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M8 7v8a2 2 0 002 2h6M8 7V5a2 2 0 012-2h4.586a1 1 0 01.707.293l4.414 4.414a1 1 0 01.293.707V15a2 2 0 01-2 2h-2M8 7H6a2 2 0 00-2 2v10a2 2 0 002 2h8a2 2 0 002-2v-2" />
-          </svg>
-        </div>
-        <h3 className="ml-3 text-lg font-semibold">File Tools</h3>
-      </div>
-      <p>Tools for reading, writing, and manipulating files</p>
-    </div>
-
-    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
-      <div className="flex items-center mb-4">
-        <div className="p-2 bg-purple-100 dark:bg-purple-900 rounded-lg">
-          <svg className="w-6 h-6 text-purple-600 dark:text-purple-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M3 6l3 1m0 0l-3 9a5.002 5.002 0 006.001 0M6 7l3 9M6 7l6-2m6 2l3-1m-3 1l-3 9a5.002 5.002 0 006.001 0M18 7l3 9m-3-9l-6-2m0-2v2m0 16V5m0 16H9m3 0h3" />
-          </svg>
-        </div>
-        <h3 className="ml-3 text-lg font-semibold">API Tools</h3>
-      </div>
-      <p>Tools for interacting with external APIs and services</p>
-    </div>
-  </div>
-</Frame>
-
 ## Creating Custom Tool
 <Steps>
 <Step>
@@ -99,237 +57,554 @@ Assign the tool to an agent
 </Card>
 
 ## Implementing Tools Full Code Example
+<Tabs>
 
-Tools are implemented as Python functions with type hints and docstrings. Here's a complete example:
+<Tab title="Code">
+<Steps>
 
+  <Step title="Install PraisonAI">
+Install the core package:
+```bash Terminal
+pip install praisonaiagents duckduckgo-search
+```
+  </Step>
 
-    ```python
-    from praisonaiagents import Agent, Task, PraisonAIAgents
-    from duckduckgo_search import DDGS
-    from typing import List, Dict
+  <Step title="Configure Environment">
+  ```bash Terminal
+  export OPENAI_API_KEY=your_openai_key
+  ```
+  Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+  Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+  </Step>
 
-    # 1. Tool Implementation
-    def internet_search_tool(query: str) -> List[Dict]:
-        """
-        Perform Internet Search using DuckDuckGo
+  <Step title="Create Agent with Tool">
+    Create `app.py`
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from duckduckgo_search import DDGS
+from typing import List, Dict
+
+# 1. Tool Implementation
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform Internet Search using DuckDuckGo
+    
+    Args:
+        query (str): The search query string
         
-        Args:
-            query (str): The search query string
-            
-        Returns:
-            List[Dict]: List of search results containing title, URL, and snippet
-        """
-        results = []
-        ddgs = DDGS()
-        for result in ddgs.text(keywords=query, max_results=5):
-            results.append({
-                "title": result.get("title", ""),
-                "url": result.get("href", ""),
-                "snippet": result.get("body", "")
-            })
-        return results
+    Returns:
+        List[Dict]: List of search results containing title, URL, and snippet
+    """
+    results = []
+    ddgs = DDGS()
+    for result in ddgs.text(keywords=query, max_results=5):
+        results.append({
+            "title": result.get("title", ""),
+            "url": result.get("href", ""),
+            "snippet": result.get("body", "")
+        })
+    return results
 
-    # 2. Assign the tool to an agent
-    data_agent = Agent(
-        name="DataCollector",
-        role="Search Specialist",
-        goal="Perform internet searches to collect relevant information.",
-        backstory="Expert in finding and organising internet data.",
-        tools=[internet_search_tool],
-        self_reflect=False
-    )
+# 2. Assign the tool to an agent
+data_agent = Agent(
+    name="DataCollector",
+    role="Search Specialist",
+    goal="Perform internet searches to collect relevant information.",
+    backstory="Expert in finding and organising internet data.",
+    tools=[internet_search_tool],
+    self_reflect=False
+)
 
-    # 3. Task Definition
-    collect_task = Task(
-        description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
-        expected_output="List of search results with titles, URLs, and snippets.",
-        agent=data_agent,
-        name="collect_data",
-    )
+# 3. Task Definition
+collect_task = Task(
+    description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
+    expected_output="List of search results with titles, URLs, and snippets.",
+    agent=data_agent,
+    name="collect_data",
+)
 
-    # 4. Start Agents
-    agents = PraisonAIAgents(
-        agents=[data_agent],
-        tasks=[collect_task],
-        process="sequential"
-    )
+# 4. Start Agents
+agents = PraisonAIAgents(
+    agents=[data_agent],
+    tasks=[collect_task],
+    process="sequential"
+)
 
-    agents.start()
-    ```
+agents.start()
+```
+  </Step>
 
+  <Step title="Start Agents">
+    Execute your script:
+```bash Terminal
+python app.py
+```
+  </Step>
+</Steps>
 
+</Tab>
+<Tab title="No Code">
+<Steps>
+<Step title="Install PraisonAI">
+Install the core package and duckduckgo_search package:
+```bash Terminal
+pip install praisonai duckduckgo_search
+```
+</Step>
+<Step title="Create Custom Tool">
+<Info>
+To add additional tools/features you need some coding which can be generated using ChatGPT or any LLM
+</Info>
+Create a new file `tools.py` with the following content:
+```python
+from duckduckgo_search import DDGS
+from typing import List, Dict
 
+# 1. Tool
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform Internet Search
+    """
+    results = []
+    ddgs = DDGS()
+    for result in ddgs.text(keywords=query, max_results=5):
+        results.append({
+            "title": result.get("title", ""),
+            "url": result.get("href", ""),
+            "snippet": result.get("body", "")
+        })
+    return results  
+```
+</Step>
+<Step title="Create Agent">
+
+Create a new file `agents.yaml` with the following content:
+```yaml
+framework: praisonai
+topic: create movie script about cat in mars
+roles:
+  scriptwriter:
+    backstory: Expert in dialogue and script structure, translating concepts into
+      scripts.
+    goal: Write a movie script about a cat in Mars
+    role: Scriptwriter
+    tools:
+      - internet_search_tool # <-- Tool assigned to Agent here
+    tasks:
+      scriptwriting_task:
+        description: Turn the story concept into a production-ready movie script,
+          including dialogue and scene details.
+        expected_output: Final movie script with dialogue and scene details.
+```
+</Step>
+
+<Step title="Start Agents">
+
+Execute your script:
+```bash Terminal
+praisonai agents.yaml
+```
+</Step>
+</Steps>
+</Tab>
+</Tabs>
+
+## In-build Tools in PraisonAI
+
+<CardGroup cols={2}>
+  <Card title="Search Tools" icon="magnifying-glass" href="/tools/search">
+    Tools for searching and retrieving information from various sources
+  </Card>
+  <Card title="Python Tools" icon="python" href="/tools/python_tools">
+    Essential Python utilities for data manipulation and scripting
+  </Card>
+  <Card title="Spider Tools" icon="spider" href="/tools/spider_tools">
+    Web crawling and scraping capabilities for data extraction
+  </Card>
+  <Card title="Arxiv Tools" icon="book" href="/tools/arxiv_tools">
+    Access and search academic papers from arXiv repository
+  </Card>
+  <Card title="Newspaper Tools" icon="newspaper" href="/tools/newspaper_tools">
+    Extract and parse content from news articles and websites
+  </Card>
+  <Card title="DuckDB Tools" icon="database" href="/tools/duckdb_tools">
+    Fast analytical SQL database operations and queries
+  </Card>
+  <Card title="DuckDuckGo Tools" icon="duck" href="/tools/duckduckgo_tools">
+    Web search functionality using DuckDuckGo's API
+  </Card>
+  <Card title="Calculator Tools" icon="calculator" href="/tools/calculator_tools">
+    Perform mathematical calculations and conversions
+  </Card>
+  <Card title="YAML Tools" icon="file-code" href="/tools/yaml_tools">
+    Parse and manipulate YAML format data
+  </Card>
+  <Card title="JSON Tools" icon="brackets-curly" href="/tools/json_tools">
+    Handle JSON data structures and operations
+  </Card>
+  <Card title="Pandas Tools" icon="table" href="/tools/pandas_tools">
+    Data analysis and manipulation using Pandas
+  </Card>
+  <Card title="YFinance Tools" icon="chart-line" href="/tools/yfinance_tools">
+    Fetch financial market data from Yahoo Finance
+  </Card>
+  <Card title="Shell Tools" icon="terminal" href="/tools/shell_tools">
+    Execute shell commands and system operations
+  </Card>
+  <Card title="Wikipedia Tools" icon="book-open" href="/tools/wikipedia_tools">
+    Access and search Wikipedia articles and data
+  </Card>
+  <Card title="XML Tools" icon="code" href="/tools/xml_tools">
+    Process and manipulate XML format data
+  </Card>
+  <Card title="File Tools" icon="file" href="/tools/file_tools">
+    File system operations and management utilities
+  </Card>
+  <Card title="Excel Tools" icon="file-excel" href="/tools/excel_tools">
+    Work with Excel spreadsheets and workbooks
+  </Card>
+  <Card title="CSV Tools" icon="file-csv" href="/tools/csv_tools">
+    Handle CSV file operations and transformations
+  </Card>
+</CardGroup>
+
+## Tools Overview
+
+<CardGroup cols={3}>
+  <Card
+    title="Search Tools"
+    icon="magnifying-glass"
+    iconType="solid"
+  >
+    Tools for searching and retrieving information from various sources
+  </Card>
+
+  <Card
+    title="File Tools"
+    icon="file"
+    iconType="solid"
+  >
+    Tools for reading, writing, and manipulating files
+  </Card>
+
+  <Card
+    title="API Tools"
+    icon="code"
+    iconType="solid"
+  >
+    Tools for interacting with external APIs and services
+  </Card>
+</CardGroup>
 
 ## Advanced Tool Features
 
 ### Tool Configuration
 <Frame>
-  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
-    ```python
-    def configured_tool(
-        query: str,
-        max_results: int = 5,
-        timeout: int = 10
-    ) -> List[Dict]:
-        """
-        Example of a configurable tool
+```python
+def configured_tool(
+    query: str,
+    max_results: int = 5,
+    timeout: int = 10
+) -> List[Dict]:
+    """
+    Example of a configurable tool
+    
+    Args:
+        query (str): Search query
+        max_results (int): Maximum number of results
+        timeout (int): Request timeout in seconds
         
-        Args:
-            query (str): Search query
-            max_results (int): Maximum number of results
-            timeout (int): Request timeout in seconds
-            
-        Returns:
-            List[Dict]: Search results
-        """
-        # Tool implementation
-        pass
-    ```
-  </div>
+    Returns:
+        List[Dict]: Search results
+    """
+    # Tool implementation
+    pass
+```
 </Frame>
 
 ### Tool Chaining
 <Frame>
-  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
-    ```python
-    def chain_tools(input_data: str) -> Dict:
-        """
-        Example of chaining multiple tools
+```python
+def chain_tools(input_data: str) -> Dict:
+    """
+    Example of chaining multiple tools
+    
+    Args:
+        input_data (str): Input data
         
-        Args:
-            input_data (str): Input data
-            
-        Returns:
-            Dict: Processed results
-        """
-        # 1. Search for data
-        search_results = internet_search_tool(input_data)
-        
-        # 2. Process results
-        processed_data = process_tool(search_results)
-        
-        # 3. Format output
-        return format_tool(processed_data)
-    ```
-  </div>
+    Returns:
+        Dict: Processed results
+    """
+    # 1. Search for data
+    search_results = internet_search_tool(input_data)
+    
+    # 2. Process results
+    processed_data = process_tool(search_results)
+    
+    # 3. Format output
+    return format_tool(processed_data)
+```
 </Frame>
 
 ### Tool Categories
 
-1. **Data Collection Tools**
-   - Web scraping
-   - API integration
-   - Database queries
-
-2. **Processing Tools**
-   - Data transformation
-   - Text analysis
-   - Image processing
-
-3. **Output Tools**
-   - File generation
-   - Report creation
-   - Data visualization
+<CardGroup cols={3}>
+  <Card title="Data Collection Tools">
+    - Web scraping
+    - API integration
+    - Database queries
+  </Card>
+  
+  <Card title="Processing Tools">
+    - Data transformation
+    - Text analysis
+    - Image processing
+  </Card>
+  
+  <Card title="Output Tools">
+    - File generation
+    - Report creation
+    - Data visualization
+  </Card>
+</CardGroup>
 
 ## Tool Integration
 
 ### Adding Tools to Agents
 <Frame>
-  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
-    ```python
-    # Multiple tools
-    agent = Agent(
-        name="MultiTool Agent",
-        tools=[
-            internet_search_tool,
-            file_processing_tool,
-            api_integration_tool
-        ]
-    )
-    ```
-  </div>
+```python
+# Multiple tools
+agent = Agent(
+    name="MultiTool Agent",
+    tools=[
+        internet_search_tool,
+        file_processing_tool,
+        api_integration_tool
+    ]
+)
+```
 </Frame>
 
 ### Tool Dependencies
 <Frame>
-  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
-    ```python
-    # Tool with dependencies
-    def advanced_tool(data: Dict) -> Dict:
-        """
-        Tool that depends on external libraries
+```python
+# Tool with dependencies
+def advanced_tool(data: Dict) -> Dict:
+    """
+    Tool that depends on external libraries
+    
+    Args:
+        data (Dict): Input data
         
-        Args:
-            data (Dict): Input data
-            
-        Returns:
-            Dict: Processed data
-        """
-        try:
-            import required_library
-            # Tool implementation
-            return processed_result
-        except ImportError:
-            raise Exception("Required library not installed")
-    ```
-  </div>
+    Returns:
+        Dict: Processed data
+    """
+    try:
+        import required_library
+        # Tool implementation
+        return processed_result
+    except ImportError:
+        raise Exception("Required library not installed")
+```
 </Frame>
 
 ## Tool Guidelines
-  <div className="grid grid-cols-1 md:grid-cols-2 gap-4">
-    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
-      <h3 className="text-lg font-semibold mb-3">Best Practices</h3>
-      1. **Type Hints**
-         - Use Python type hints
-         - Define clear input/output types
-         - Document complex types
 
-      2. **Documentation**
-         - Write clear docstrings
-         - Explain parameters
-         - Provide usage examples
+<CardGroup cols={2}>
+  <Card title="Best Practices">
+    1. **Type Hints**
+       - Use Python type hints
+       - Define clear input/output types
+       - Document complex types
 
-      3. **Error Handling**
-         - Handle exceptions gracefully
-         - Return meaningful errors
-         - Validate inputs
-    </div>
+    2. **Documentation**
+       - Write clear docstrings
+       - Explain parameters
+       - Provide usage examples
 
-    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
-      <h3 className="text-lg font-semibold mb-3">Tool Types</h3>
-      1. **Search Tools**
-         - Web search
-         - Database queries
-         - Document search
+    3. **Error Handling**
+       - Handle exceptions gracefully
+       - Return meaningful errors
+       - Validate inputs
+  </Card>
 
-      2. **File Tools**
-         - Read/write operations
-         - File conversion
-         - Data extraction
+  <Card title="Tool Types">
+    1. **Search Tools**
+       - Web search
+       - Database queries
+       - Document search
 
-      3. **API Tools**
-         - REST API calls
-         - GraphQL queries
-         - Service integration
-    </div>
-  </div>
+    2. **File Tools**
+       - Read/write operations
+       - File conversion
+       - Data extraction
+
+    3. **API Tools**
+       - REST API calls
+       - GraphQL queries
+       - Service integration
+  </Card>
+</CardGroup>
 
 ## Best Practices Summary
 
-1. **Design Principles**
-   - Single responsibility
-   - Clear interfaces
-   - Proper documentation
-   - Error handling
+<Note>
+Following these best practices will help you create robust, efficient, and secure tools in PraisonAI.
+</Note>
 
-2. **Performance**
-   - Efficient processing
-   - Resource management
-   - Caching when appropriate
-   - Asynchronous operations
+<CardGroup cols={1}>
+  <Card title="Design Principles" icon="compass-drafting" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Single Responsibility">
+        Each tool should have one clear purpose and do it well. Avoid creating tools that try to do too many things.
+        ```python
+        # Good Example
+        def process_image(image: np.array) -> np.array:
+            return processed_image
 
-3. **Security**
-   - Input validation
-   - Rate limiting
-   - API key management
-   - Error masking
+        # Avoid
+        def process_and_save_and_upload(image):
+            # Too many responsibilities
+            pass
+        ```
+      </Accordion>
+      
+      <Accordion title="Clear Interfaces">
+        Define explicit input/output types and maintain consistent parameter naming.
+        ```python
+        def search_tool(
+            query: str,
+            max_results: int = 10
+        ) -> List[Dict[str, Any]]:
+            """
+            Search for information with clear parameters
+            """
+            pass
+        ```
+      </Accordion>
+      
+      <Accordion title="Documentation">
+        Always include detailed docstrings and type hints.
+        ```python
+        def analyze_text(
+            text: str,
+            language: str = "en"
+        ) -> Dict[str, float]:
+            """
+            Analyze text sentiment and emotions.
+            
+            Args:
+                text: Input text to analyze
+                language: ISO language code
+                
+            Returns:
+                Dict with sentiment scores
+            """
+            pass
+        ```
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
+
+<br />
+
+<CardGroup cols={1}>
+  <Card title="Performance Optimization" icon="bolt" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Efficient Processing">
+        Optimize resource usage and processing time.
+        ```python
+        # Use generators for large datasets
+        def process_large_data():
+            for chunk in data_generator():
+                yield process_chunk(chunk)
+        ```
+      </Accordion>
+      
+      <Accordion title="Resource Management">
+        Properly handle resource allocation and cleanup.
+        ```python
+        async with aiohttp.ClientSession() as session:
+            # Resource automatically managed
+            await process_data(session)
+        ```
+      </Accordion>
+      
+      <Accordion title="Caching">
+        Implement caching for frequently accessed data.
+        ```python
+        @cache.memoize(timeout=300)
+        def expensive_operation(data: str) -> Dict:
+            return process_expensive(data)
+        ```
+      </Accordion>
+      
+      <Accordion title="Async Operations">
+        Use async/await for I/O-bound operations.
+        ```python
+        async def fetch_data(urls: List[str]):
+            async with aiohttp.ClientSession() as session:
+                tasks = [fetch_url(session, url) for url in urls]
+                return await asyncio.gather(*tasks)
+        ```
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
+
+<br />
+
+<CardGroup cols={1}>
+  <Card title="Security Best Practices" icon="shield-check" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Input Validation">
+        Always validate and sanitize inputs to prevent security vulnerabilities.
+        ```python
+        def process_user_input(data: str) -> str:
+            if not isinstance(data, str):
+                raise ValueError("Input must be string")
+            return sanitize_input(data.strip())
+        ```
+      </Accordion>
+      
+      <Accordion title="Rate Limiting">
+        Implement rate limiting for API calls to prevent abuse.
+        ```python
+        @rate_limit(calls=100, period=60)
+        async def api_call():
+            return await make_request()
+        ```
+      </Accordion>
+      
+      <Accordion title="API Key Management">
+        Securely handle API keys and credentials using environment variables.
+        ```python
+        # Use environment variables
+        api_key = os.getenv('API_KEY')
+        if not api_key:
+            raise ConfigError("API key not found")
+        ```
+      </Accordion>
+      
+      <Accordion title="Error Masking">
+        Hide sensitive information in error messages to prevent information leakage.
+        ```python
+        try:
+            result = process_sensitive_data()
+        except Exception as e:
+            # Log detailed error internally
+            logger.error(f"Detailed error: {str(e)}")
+            # Return sanitized error to user
+            raise PublicError("Processing failed")
+        ```
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
+
+<br />
+
+<Tip>
+**Pro Tip**: Start with these practices from the beginning of your project. It's easier to maintain good practices than to retrofit them later.
+</Tip>

docs/features/async.mdx

@@ -1,82 +1,126 @@
 ---
-title: "Async AI Agents"
+title: "Async Agents"
 description: "Async AI Agents allow you to run AI tasks asynchronously, improving performance and efficiency in your applications."
 icon: "clock"
 ---
 
-<Note>
-  **Requirements**
-  
-  - Python 3.10 or higher
-  - Basic understanding of async/await in Python
-</Note>
-
 ## Quick Start
 
-<Steps>
-  <Step title="Install Package">
-    First, install the PraisonAI Agents package:
-    ```bash
-    pip install praisonaiagents
-    ```
-  </Step>
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+      <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+      </Step>
 
-  <Step title="Set API Key">
-    Set your OpenAI API key as an environment variable:
-    ```bash
-    export OPENAI_API_KEY=your_api_key_here
-    ```
-  </Step>
+      <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+      </Step>
 
-  <Step title="Create Project">
-    Create a new file `app.py` with the basic setup:
-    ```python
-    import asyncio
-    from praisonaiagents import Agent, Task, PraisonAIAgents
+      <Step title="Create Project">
+        Create a new file `app.py` with the basic setup:
+        ```python
+        import asyncio
+        from praisonaiagents import Agent, Task, PraisonAIAgents
 
-    # Create an agent
-    agent = Agent(
-        name="AsyncAgent",
-        role="Assistant",
-        goal="Help with tasks",
-        backstory="Expert in async operations"
-    )
+        # Create an agent
+        agent = Agent(
+            name="AsyncAgent",
+            role="Assistant",
+            goal="Help with tasks",
+            backstory="Expert in async operations"
+        )
 
-    # Create a task
-    task = Task(
-        name="hello_task",
-        description="Say hello and introduce yourself",
-        agent=agent,
-        async_execution=True,
-        expected_output="Answer to the question"
-    )
+        # Create a task
+        task = Task(
+            name="hello_task",
+            description="Say hello and introduce yourself",
+            agent=agent,
+            async_execution=True,
+            expected_output="Answer to the question"
+        )
 
-    # Create agents manager
-    agents = PraisonAIAgents(
-        agents=[agent],
-        tasks=[task],
-        process="sequential"
-    )
+        # Create agents manager
+        agents = PraisonAIAgents(
+            agents=[agent],
+            tasks=[task],
+            process="sequential"
+        )
 
-    # Main async function
-    async def main():
-        result = await agents.astart()
-        print(result)
+        # Main async function
+        async def main():
+            result = await agents.astart()
+            print(result)
 
-    # Run the program
-    if __name__ == "__main__":
-        asyncio.run(main())
+        # Run the program
+        if __name__ == "__main__":
+            asyncio.run(main())
+        ```
+      </Step>
 
-    ```
-  </Step>
+      <Step title="Run the Program">
+        Run your async AI agent:
+        ```bash
+        python app.py
+        ```
+      </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+      <Step title="Install Package">
+        Install the PraisonAI package:
+        ```bash
+        pip install praisonai
+        ```
+      </Step>
+      <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+      </Step>
+      <Step title="Create Project">
+        Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+topic: async operations
+roles:
+  assistant:
+    name: AsyncAgent
+    role: Assistant
+    goal: Help with tasks
+    backstory: Expert in async operations
+    tasks:
+      hello_task:
+        description: Say hello and introduce yourself
+        expected_output: Answer to the question
+        async_execution: true
+```
+      </Step>
+      <Step title="Run the Program">
+        Run your async AI agent:
+        ```bash
+        praisonai agents.yaml
+        ```
+      </Step>
+    </Steps>
+  </Tab>
+</Tabs>
 
-  <Step title="Run the Program">
-    Run your async AI agent:
-    ```bash
-    python app.py
-    ```
-  </Step>
-</Steps>
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
+  - Basic understanding of async/await in Python
+</Note>
 
 <div className="relative w-full aspect-video">
   <iframe
@@ -129,7 +173,9 @@ icon: "clock"
 
 ## Advanced Usage
 
-### Async Tasks with Callbacks and Tools Example Code
+<Tabs>
+  <Tab title="Code">
+    ### Async Tasks with Callbacks and Tools Example Code
 
 ```python
 import asyncio
@@ -357,7 +403,6 @@ async def main():
 if __name__ == "__main__":
     # Run the main function
     asyncio.run(main())
-
 ```
 
 ### 1. Async Tasks with Callbacks
@@ -402,6 +447,28 @@ async def process_multiple_tasks():
     )
     return results
 ```
+  </Tab>
+  <Tab title="No Code">
+    ```yaml
+    framework: praisonai
+    process: sequential
+    topic: async operations
+    roles:
+      async_agent:
+        name: AsyncSearchAgent
+        role: Asynchronous Search Specialist
+        goal: Perform fast and efficient asynchronous searches
+        backstory: Expert in parallel search operations
+        tools:
+          - async_search_tool
+        tasks:
+          search_task:
+            description: Perform async search operations
+            expected_output: Structured search results
+            async_execution: true
+    ```
+  </Tab>
+</Tabs>
 
 ## Best Practices
 
@@ -481,11 +548,11 @@ async def process_multiple_tasks():
 ## Next Steps
 
 <CardGroup cols={2}>
-  <Card title="Examples" icon="code" href="./examples">
-    Check out more examples in our examples directory
+  <Card title="AutoAgents" icon="robot" href="./autoagents">
+    Learn about automatically created and managed AI agents
   </Card>
-  <Card title="Advanced Topics" icon="book" href="./advanced">
-    Learn about advanced async patterns and optimizations
+  <Card title="Mini Agents" icon="microchip" href="./mini">
+    Explore lightweight, focused AI agents
   </Card>
 </CardGroup>
 

docs/features/autoagents.mdx

@@ -0,0 +1,308 @@
+---
+title: "AutoAgents"
+description: "AutoAgents automatically creates and manages AI agents and tasks based on high-level instructions."
+icon: "robot"
+---
+
+## Quick Start
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the PraisonAI Agents package:
+            ```bash
+            pip install praisonaiagents duckduckgo_search
+            ```
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+            ```python
+            from praisonaiagents import AutoAgents
+            from praisonaiagents.tools import duckduckgo
+
+            # Create AutoAgents instance
+            agents = AutoAgents(
+                instructions="Search for information about AI Agents",
+                tools=[duckduckgo],
+                process="sequential",
+                verbose=True,
+                max_agents=3  # Maximum number of agents to create
+            )
+
+            # Start the agents
+            result = agents.start()
+            print(result)
+            ```
+        </Step>
+
+        <Step title="Start AutoAgents">
+            Run your AutoAgents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+
+    <Note>
+    **Requirements**
+    
+    - Python 3.10 or higher
+    - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
+    </Note>
+    
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+        <Step title="Start AutoAgents">
+            Run your AutoAgents:
+            ```bash
+            praisonai --auto "Create a movie script about a robot in Mars"
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+## Understanding AutoAgents
+
+<Card title="What are AutoAgents?" icon="question">
+  AutoAgents automatically:
+  - Creates appropriate AI agents based on your instructions
+  - Assigns relevant tools to each agent
+  - Breaks down tasks into manageable steps
+  - Manages execution flow between agents
+  - Handles agent coordination and task delegation
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Automatic Agent Creation" icon="wand-magic-sparkles">
+    Creates specialized agents based on task requirements.
+  </Card>
+  <Card title="Smart Tool Assignment" icon="toolbox">
+    Automatically assigns relevant tools to each agent.
+  </Card>
+  <Card title="Task Management" icon="list-check">
+    Breaks down complex tasks into manageable steps.
+  </Card>
+  <Card title="Process Optimization" icon="gauge-high">
+    Chooses optimal execution process (sequential/hierarchical).
+  </Card>
+</CardGroup>
+
+<CodeGroup>
+  ```python Basic
+  # Basic usage with default settings
+  agents = AutoAgents(
+      instructions="Your high-level task description",
+      tools=[tool1, tool2]
+  )
+  ```
+
+  ```python Advanced
+  # Advanced usage with custom settings
+  agents = AutoAgents(
+      instructions="Your task description",
+      tools=[tool1, tool2],
+      max_agents=3,
+      process="hierarchical",
+      verbose=True,
+      memory=True,
+      allow_delegation=True
+  )
+  ```
+</CodeGroup>
+
+## Advanced Usage
+
+### Configuration Options
+
+```python
+
+# Create AutoAgents with advanced configuration
+agents = AutoAgents(
+    instructions="Research and summarize recent AI developments",
+    tools=[SerperDevTool, WikipediaTools],
+    max_agents=3,  # Maximum number of agents to create
+    verbose=True,  # Enable detailed logging
+    process="hierarchical",  # Use hierarchical process
+    memory=True,  # Enable memory for agents
+    allow_delegation=True,  # Allow task delegation
+    max_rpm=60,  # Maximum requests per minute
+    max_execution_time=300,  # Maximum execution time in seconds
+    allow_code_execution=True,  # Allow code execution
+    code_execution_mode="safe",  # Use safe mode for code execution
+    self_reflect=True,  # Enable agent self-reflection
+    markdown=True  # Enable markdown formatting
+)
+```
+
+### Process Types
+
+<AccordionGroup>
+  <Accordion title="Sequential Process">
+    ```python
+    # Tasks are executed in sequence
+    agents = AutoAgents(
+        instructions="Your task",
+        process="sequential"
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Hierarchical Process">
+    ```python
+    # Manager agent coordinates other agents
+    agents = AutoAgents(
+        instructions="Your task",
+        process="hierarchical",
+        manager_llm="gpt-4o"  # Specify LLM for manager
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Clear Instructions">
+    ```python
+    # Good instruction example
+    agents = AutoAgents(
+        instructions="""
+        Research the latest developments in AI for 2024:
+        1. Focus on breakthrough technologies
+        2. Include real-world applications
+        3. Consider future implications
+        """
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Tool Selection">
+    ```python
+    # Provide relevant tools for the task
+    
+    agents = AutoAgents(
+        instructions="Research task",
+        tools=[
+            SerperDevTool,  # For web search
+            WikipediaTools,  # For background info
+            CustomTool  # Your custom tool
+        ]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Resource Management">
+
+    ```python
+    # Configure resource limits
+    agents = AutoAgents(
+        instructions="Your task",
+        max_rpm=60,  # Rate limiting
+        max_execution_time=300,  # Timeout
+        max_agents=3  # Agent limit
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Tool Assignment Issues" icon="triangle-exclamation">
+    If tools aren't being assigned correctly:
+    - Check tool compatibility
+    - Verify tool names
+    - Enable verbose mode for debugging
+  </Card>
+
+  <Card title="Performance Issues" icon="gauge">
+    If execution is slow:
+    - Reduce max_agents
+    - Adjust max_rpm
+    - Consider process type
+  </Card>
+</CardGroup>
+
+## API Reference
+
+### Main Parameters
+
+<ResponseField name="instructions" type="str" required>
+  High-level task description for the agents
+</ResponseField>
+
+<ResponseField name="tools" type="List[Any]">
+  List of tools available to the agents
+</ResponseField>
+
+<ResponseField name="max_agents" type="int" default="3">
+  Maximum number of agents to create
+</ResponseField>
+
+<ResponseField name="process" type="str" default="sequential">
+  Process type: "sequential" or "hierarchical"
+</ResponseField>
+
+### Optional Parameters
+
+<ResponseField name="verbose" type="bool" default="False">
+  Enable detailed logging
+</ResponseField>
+
+<ResponseField name="memory" type="bool" default="True">
+  Enable agent memory
+</ResponseField>
+
+<ResponseField name="allow_delegation" type="bool" default="False">
+  Allow agents to delegate tasks
+</ResponseField>
+
+### Methods
+
+<ResponseField name="start()" type="method">
+  Start the agents synchronously
+</ResponseField>
+
+<ResponseField name="astart()" type="method">
+  Start the agents asynchronously
+</ResponseField>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Examples" icon="code" href="./examples">
+    Explore more examples in our examples directory
+  </Card>
+  <Card title="Custom Tools" icon="screwdriver-wrench" href="./tools">
+    Learn how to create custom tools for your agents
+  </Card>
+</CardGroup>
+
+<Note>
+  For optimal results, provide clear instructions and appropriate tools for your use case.
+</Note>

docs/features/codeagent.mdx

@@ -0,0 +1,353 @@
+---
+title: "Code Execution AI Agent"
+sidebarTitle: "Code Agent"
+description: "Learn how to create AI agents that can write and execute Python code safely using e2b code interpreter."
+icon: "code"
+---
+
+## Quick Start
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the required packages:
+            ```bash
+            pip install praisonaiagents e2b_code_interpreter
+            ```
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key and E2B API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            export E2B_API_KEY=your_e2b_api_key_here
+            ```
+        </Step>
+
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+            ```python
+            from praisonaiagents import Agent, Task, PraisonAIAgents
+            from e2b_code_interpreter import Sandbox
+
+            def code_interpreter(code: str):
+                print(f"\n{'='*50}\n> Running following AI-generated code:\n{code}\n{'='*50}")
+                exec_result = Sandbox().run_code(code)
+                if exec_result.error:
+                    print("[Code Interpreter error]", exec_result.error)
+                    return {"error": str(exec_result.error)}
+                else:
+                    results = []
+                    for result in exec_result.results:
+                        if hasattr(result, '__iter__'):
+                            results.extend(list(result))
+                        else:
+                            results.append(str(result))
+                    logs = {"stdout": list(exec_result.logs.stdout), "stderr": list(exec_result.logs.stderr)}
+                    return json.dumps({"results": results, "logs": logs})
+
+            # Create code agent
+            code_agent = Agent(
+                role="Code Developer",
+                goal="Write and execute Python code",
+                backstory="Expert Python developer with strong coding skills",
+                tools=[code_interpreter],
+                verbose=True
+            )
+
+            # Create a task
+            task = Task(
+                description="Write and execute a Python script to analyze data",
+                expected_output="Working Python script with execution results",
+                agent=code_agent
+            )
+
+            # Create and start the agents
+            agents = PraisonAIAgents(
+                agents=[code_agent],
+                tasks=[task],
+                process="sequential",
+                verbose=2
+            )
+
+            # Start execution
+            agents.start()
+            ```
+        </Step>
+
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai e2b_code_interpreter
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+topic: write and execute Python code
+roles:
+  developer:
+    backstory: Expert Python developer with strong coding skills.
+    goal: Write and execute Python code safely
+    role: Code Developer
+    tools:
+      - code_interpreter
+    tasks:
+      coding_task:
+        description: Write and execute a Python script to analyze data.
+        expected_output: Working Python script with execution results.
+```
+        </Step>
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+```bash
+praisonai agents.yaml
+```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
+  - e2b_code_interpreter package installed
+</Note>
+
+## Understanding Code Agents
+
+<Card title="What are Code Agents?" icon="question">
+  Code agents are specialized AI agents that can:
+  - Write Python code based on requirements
+  - Execute code safely in a sandboxed environment
+  - Handle code execution results and errors
+  - Work together in a pipeline (writer → executor)
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Code Writer" icon="pencil">
+    Writes Python code based on requirements.
+  </Card>
+  <Card title="Safe Execution" icon="shield">
+    Executes code in a sandboxed environment.
+  </Card>
+  <Card title="Error Handling" icon="bug">
+    Manages code execution errors and debugging.
+  </Card>
+  <Card title="Results Processing" icon="output">
+    Processes and formats execution results.
+  </Card>
+</CardGroup>
+
+## Multi-Agent Code Development
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the required packages:
+            ```bash
+            pip install praisonaiagents e2b_code_interpreter
+            ```
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+            ```python
+            from praisonaiagents import Agent, Task, PraisonAIAgents
+            from e2b_code_interpreter import Sandbox
+
+            def code_interpreter(code: str):
+                print(f"\n{'='*50}\n> Running following AI-generated code:\n{code}\n{'='*50}")
+                exec_result = Sandbox().run_code(code)
+                if exec_result.error:
+                    print("[Code Interpreter error]", exec_result.error)
+                    return {"error": str(exec_result.error)}
+                else:
+                    results = []
+                    for result in exec_result.results:
+                        if hasattr(result, '__iter__'):
+                            results.extend(list(result))
+                        else:
+                            results.append(str(result))
+                    logs = {"stdout": list(exec_result.logs.stdout), "stderr": list(exec_result.logs.stderr)}
+                    return json.dumps({"results": results, "logs": logs})
+
+            # Create first agent for writing code
+            code_writer = Agent(
+                role="Code Writer",
+                goal="Write efficient Python code",
+                backstory="Expert Python developer specializing in code writing",
+                verbose=True
+            )
+
+            # Create second agent for code execution
+            code_executor = Agent(
+                role="Code Executor",
+                goal="Execute and validate Python code",
+                backstory="Expert in code execution and testing",
+                tools=[code_interpreter],
+                verbose=True
+            )
+
+            # Create first task
+            writing_task = Task(
+                description="Write a Python script for data analysis",
+                expected_output="Complete Python script",
+                agent=code_writer
+            )
+
+            # Create second task
+            execution_task = Task(
+                description="Execute and validate the Python script",
+                expected_output="Execution results and validation",
+                agent=code_executor
+            )
+
+            # Create and start the agents
+            agents = PraisonAIAgents(
+                agents=[code_writer, code_executor],
+                tasks=[writing_task, execution_task],
+                process="sequential"
+            )
+
+            # Start execution
+            agents.start()
+            ```
+        </Step>
+
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai e2b_code_interpreter
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+topic: develop and execute Python code
+roles:
+  writer:
+    backstory: Expert Python developer specializing in code writing.
+    goal: Write efficient Python code
+    role: Code Writer
+    tasks:
+      writing_task:
+        description: Write a Python script for data analysis.
+        expected_output: Complete Python script.
+
+  executor:
+    backstory: Expert in code execution and testing.
+    goal: Execute and validate Python code
+    role: Code Executor
+    tools:
+      - code_interpreter
+    tasks:
+      execution_task:
+        description: Execute and validate the Python script.
+        expected_output: Execution results and validation.
+```
+        </Step>
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+```bash
+praisonai agents.yaml
+```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+### Configuration Options
+
+```python
+# Create an agent with advanced code execution configuration
+agent = Agent(
+    role="Code Developer",
+    goal="Write and execute Python code",
+    backstory="Expert in Python development",
+    tools=[code_interpreter],
+    verbose=True,  # Enable detailed logging
+    llm="gpt-4o"  # Language model to use
+)
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Code Errors" icon="triangle-exclamation">
+    If code execution fails:
+    - Check syntax errors
+    - Verify package imports
+    - Enable verbose mode for debugging
+  </Card>
+
+  <Card title="Sandbox Issues" icon="box">
+    If sandbox execution fails:
+    - Check environment setup
+    - Verify permissions
+    - Review resource limits
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="AutoAgents" icon="robot" href="./autoagents">
+    Learn about automatically created and managed AI agents
+  </Card>
+  <Card title="Mini Agents" icon="microchip" href="./mini">
+    Explore lightweight, focused AI agents
+  </Card>
+</CardGroup>
+
+<Note>
+  For optimal results, ensure code is properly formatted and tested in the sandbox environment before production use.
+</Note>

docs/features/mathagent.mdx

@@ -0,0 +1,347 @@
+---
+title: "Math AI Agent"
+sidebarTitle: "Math Agent"
+description: "Learn how to create AI agents that can perform complex mathematical calculations, unit conversions, and financial computations."
+icon: "calculator"
+---
+
+## Quick Start
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the PraisonAI Agents package:
+            ```bash
+            pip install praisonaiagents sympy pint
+            ```
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+            ```python
+            from praisonaiagents import Agent, Task, PraisonAIAgents
+            from praisonaiagents.tools import (
+                evaluate, solve_equation, convert_units,
+                calculate_statistics, calculate_financial
+            )
+
+            # Create math agent
+            math_agent = Agent(
+                role="Math Expert",
+                goal="Perform complex mathematical calculations",
+                backstory="Expert in mathematical computations and analysis",
+                tools=[
+                    evaluate, solve_equation, convert_units,
+                    calculate_statistics, calculate_financial
+                ],
+                verbose=True
+            )
+
+            # Create a task
+            task = Task(
+                description="Calculate compound interest and statistical analysis",
+                expected_output="Detailed mathematical analysis",
+                agent=math_agent
+            )
+
+            # Create and start the agents
+            agents = PraisonAIAgents(
+                agents=[math_agent],
+                tasks=[task],
+                process="sequential",
+                verbose=2
+            )
+
+            # Start execution
+            agents.start()
+            ```
+        </Step>
+
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai sympy pint
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+topic: perform mathematical analysis
+roles:
+  mathematician:
+    backstory: Expert in mathematical computations and analysis.
+    goal: Perform complex mathematical calculations
+    role: Math Expert
+    tools:
+      - evaluate
+      - solve_equation
+      - convert_units
+      - calculate_statistics
+      - calculate_financial
+    tasks:
+      math_task:
+        description: Calculate compound interest and perform statistical analysis.
+        expected_output: Detailed mathematical analysis with results.
+```
+        </Step>
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+```bash
+praisonai agents.yaml
+```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+<Note>
+  **Requirements**
+
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
+  - sympy and pint packages (automatically installed when needed)
+</Note>
+
+## Understanding Math Agents
+
+<Card title="What are Math Agents?" icon="question">
+  Math agents are specialized AI agents that can:
+  - Evaluate mathematical expressions
+  - Solve equations and systems of equations
+  - Perform unit conversions
+  - Calculate statistical metrics
+  - Handle financial calculations
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Expression Evaluator" icon="function">
+    Evaluates mathematical expressions and formulas.
+  </Card>
+  <Card title="Equation Solver" icon="equals">
+    Solves mathematical equations and systems.
+  </Card>
+  <Card title="Unit Converter" icon="arrows-rotate">
+    Converts between different units of measurement.
+  </Card>
+  <Card title="Statistical Calculator" icon="chart-simple">
+    Calculates statistical metrics and analysis.
+  </Card>
+</CardGroup>
+
+## Multi-Agent AI Math Analysis
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the PraisonAI Agents package:
+            ```bash
+            pip install praisonaiagents
+            ```
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+            ```python
+            from praisonaiagents import Agent, Task, PraisonAIAgents
+            from praisonaiagents.tools import (
+                evaluate, solve_equation, convert_units,
+                calculate_statistics, calculate_financial
+            )
+
+            # Create first agent for calculations
+            calculator = Agent(
+                role="Mathematical Calculator",
+                goal="Perform complex calculations and analysis",
+                backstory="Expert in mathematical computations",
+                tools=[evaluate, solve_equation, convert_units],
+                verbose=True
+            )
+
+            # Create second agent for statistics
+            statistician = Agent(
+                role="Statistical Analyst",
+                goal="Perform statistical analysis and interpretation",
+                backstory="Expert in statistical analysis",
+                tools=[calculate_statistics, calculate_financial],
+                verbose=True
+            )
+
+            # Create first task
+            calc_task = Task(
+                description="Calculate compound interest for $10000 at 5% for 3 years",
+                expected_output="Detailed interest calculation",
+                agent=calculator
+            )
+
+            # Create second task
+            stats_task = Task(
+                description="Analyze the monthly interest distribution",
+                expected_output="Statistical analysis of interest data",
+                agent=statistician
+            )
+
+            # Create and start the agents
+            agents = PraisonAIAgents(
+                agents=[calculator, statistician],
+                tasks=[calc_task, stats_task],
+                process="sequential"
+            )
+
+            # Start execution
+            agents.start()
+            ```
+        </Step>
+
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+topic: perform financial and statistical analysis
+roles:
+  calculator:
+    backstory: Expert in mathematical computations and analysis.
+    goal: Perform complex calculations
+    role: Mathematical Calculator
+    tools:
+      - evaluate
+      - solve_equation
+      - convert_units
+    tasks:
+      calc_task:
+        description: Calculate compound interest for investment scenario.
+        expected_output: Detailed interest calculations.
+
+  statistician:
+    backstory: Expert in statistical analysis and interpretation.
+    goal: Analyze numerical data and patterns
+    role: Statistical Analyst
+    tools:
+      - calculate_statistics
+      - calculate_financial
+    tasks:
+      stats_task:
+        description: Analyze the monthly interest distribution.
+        expected_output: Statistical analysis report.
+```
+        </Step>
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+```bash
+praisonai agents.yaml
+```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+### Configuration Options
+
+```python
+# Create an agent with advanced math configuration
+agent = Agent(
+    role="Math Expert",
+    goal="Perform complex mathematical analysis",
+    backstory="Expert in mathematical computations",
+    tools=[
+        evaluate,
+        solve_equation,
+        convert_units,
+        calculate_statistics,
+        calculate_financial
+    ],
+    verbose=True,  # Enable detailed logging
+    llm="gpt-4o"  # Language model to use
+)
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Expression Errors" icon="triangle-exclamation">
+    If expressions are not evaluating:
+    - Check syntax and formatting
+    - Verify variable definitions
+    - Enable verbose mode for debugging
+  </Card>
+
+  <Card title="Calculation Issues" icon="calculator">
+    If calculations are incorrect:
+    - Review input formats
+    - Check unit compatibility
+    - Verify formula structure
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="AutoAgents" icon="robot" href="./autoagents">
+    Learn about automatically created and managed AI agents
+  </Card>
+  <Card title="Mini Agents" icon="microchip" href="./mini">
+    Explore lightweight, focused AI agents
+  </Card>
+</CardGroup>
+
+<Note>
+  For optimal results, ensure mathematical expressions and units are properly formatted for your specific use case.
+</Note>

docs/features/mini.mdx

@@ -4,47 +4,112 @@ description: "Learn how to create simple yet powerful AI agents in just a few li
 icon: "robot"
 ---
 
-<Note>
-  **Prerequisites**
-  - Python 3.10 or higher
-  - PraisonAI Agents package installed
-  - Basic understanding of Python
-</Note>
-
 ## Quick Start
 
 Create multiple AI agents that can work together in just a few lines of code!
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+      <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents duckduckgo_search
+        ```
+      </Step>
+      <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable in your terminal:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+      </Step>
+      <Step title="Create Your Agents">
+        <CodeGroup>
+        ```python Single Agent
+        from praisonaiagents import Agent, PraisonAIAgents
+        summarise_agent = Agent(instructions="Summarise Photosynthesis")
+        agents = PraisonAIAgents(agents=[summarise_agent])
+        agents.start()
+        ```
+        ```python Multiple Agents
+        # Agent with Internet Search Tool
+        from praisonaiagents import Agent, PraisonAIAgents
+        research_agent = Agent(instructions="Research about AI 2024", tools=[Tools.internet_search])
+        summarise_agent = Agent(instructions="Summarise research agent's findings")
+        agents = PraisonAIAgents(agents=[research_agent, summarise_agent])
+        agents.start()
+        ```
+        </CodeGroup>
+      </Step>
+      <Step title="Run Your Agents">
+        Execute your agents by running:
+        ```bash
+        python app.py
+        ```
+      </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+      <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents duckduckgo_search
+        ```
+      </Step>
+      <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable in your terminal:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+      </Step>
+      <Step title="Create Your Agents">
+        <CodeGroup>
+        ```yaml Single Agent
+        # Create a new file `agents.yaml` with the following content:
+        roles:
+          summarise_agent
+            instructions: Summarise Photosynthesis
+        ```
+        ```yaml Multiple Agents
+        # Create a new file `agents.yaml` with the following content:
+        # Agent with Internet Search Tool
+        roles:
+          research_agent
+            instructions: Research about AI 2024
+            tools:
+              - internet_search
+          summarise_agent
+            instructions: Summarise research agent's findings
+        ```
+        </CodeGroup>
+      </Step>
+      <Step title="Run Your Agents">
+        Execute your agents by running:
+        ```bash
+        python app.py
+        ```
+      </Step>
+    </Steps>
+  </Tab>
+</Tabs>
 
-<Steps>
-  <Step title="Install Package">
-    First, install the PraisonAI Agents package:
-    ```bash
-    pip install praisonaiagents
-    ```
-  </Step>
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
+</Note>
+<br />
 
-  <Step title="Import Required Components">
-    Import the necessary components:
-    ```python
-    from praisonaiagents import Agent, Agents
-    ```
-  </Step>
-
-  <Step title="Create Your Agents">
-    Create specialized agents with specific roles:
-    ```python 
-    summarise_agent = Agent(instructions="Summarise what is Photosynthesis")
-    ```
-  </Step>
-
-  <Step title="Start Your Agents">
-    Initialize and run your agents:
-    ```python
-    agents = Agents(agents=[summarise_agent])
-    agents.start()
-    ```
-  </Step>
-</Steps>
+<div className="relative w-full aspect-video">
+  <iframe
+    className="absolute top-0 left-0 w-full h-full"
+    src="https://www.youtube.com/embed/OkvYp5aAGSg"
+    title="YouTube video player"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowFullScreen
+  ></iframe>
+</div>
+<br />
 
 ## Understanding Mini AI Agents
 
@@ -96,34 +161,7 @@ Create multiple AI agents that can work together in just a few lines of code!
   </Card>
 </CardGroup>
 
-## Examples
-
-### Basic Research and Summary
-
-```terminal
-pip install praisonaiagents duckduckgo_search
-```
-
-```python
-from praisonaiagents import Agent, Agents, Tools
-
-# Create a research agent
-research_agent = Agent(
-    instructions="You are a research agent to search internet about AI 2024",
-    tools=[Tools.internet_search]
-)
-
-# Create a summarize agent
-summarise_agent = Agent(
-    instructions="You are a summarize agent to summarise research agent's findings"
-)
-
-# Initialize and run agents
-agents = Agents(agents=[research_agent, summarise_agent])
-agents.start()
-```
-
-### Custom Instructions
+## Custom Instructions
 
 <CodeGroup>
 ```python Basic
@@ -245,4 +283,4 @@ agents = Agents(agents=[collector, summarizer, writer])
 
 <Note>
   Mini AI Agents are designed to be simple yet powerful. They're perfect for quick prototypes and straightforward automation tasks.
-</Note>
+</Note>

docs/features/multimodal.mdx

@@ -0,0 +1,314 @@
+---
+title: "Multimodal Agents"
+description: "Guide for creating and using multimodal AI agents in PraisonAI for processing images, videos, and other media types"
+icon: "images"
+---
+
+## Quick Start
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the PraisonAI Agents package:
+            ```bash
+            pip install praisonaiagents
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+            ```python
+            from praisonaiagents import Agent, Task, PraisonAIAgents
+
+            # Create Vision Analysis Agent
+            vision_agent = Agent(
+                name="VisionAnalyst",
+                role="Computer Vision Specialist",
+                goal="Analyze images and videos to extract meaningful information",
+                backstory="""You are an expert in computer vision and image analysis.
+                You excel at describing images, detecting objects, and understanding visual content.""",
+                llm="gpt-4o-mini",
+                self_reflect=False
+            )
+
+            # Create tasks with different media types
+            task = Task(
+                name="analyze_landmark",
+                description="Describe this famous landmark and its architectural features.",
+                expected_output="Detailed description of the landmark's architecture and significance",
+                agent=vision_agent,
+                images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
+            )
+
+            # Run the agents
+            agents = PraisonAIAgents(
+                agents=[vision_agent],
+                tasks=[task],
+                process="sequential",
+                verbose=True
+            )
+
+            agents.start()
+            ```
+        </Step>
+
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+topic: analyze landmark image
+roles:
+  vision_analyst:
+    name: VisionAnalyst
+    role: Computer Vision Specialist
+    goal: Analyze images and videos to extract meaningful information
+    backstory: |
+      You are an expert in computer vision and image analysis.
+      You excel at describing images, detecting objects, and understanding visual content.
+    llm: gpt-4o-mini
+    self_reflect: false
+    tasks:
+      analyze_landmark:
+        description: Describe this famous landmark and its architectural features.
+        expected_output: Detailed description of the landmark's architecture and significance
+        images:
+          - https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg
+```
+        </Step>
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+```bash
+praisonai agents.yaml
+```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - OpenAI API key with vision model access
+  - Basic understanding of Python and media handling
+</Note>
+
+<div className="relative w-full aspect-video">
+  <iframe
+    className="absolute top-0 left-0 w-full h-full"
+    src="https://www.youtube.com/embed/hjAWmUT1qqY"
+    title="YouTube video player"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowFullScreen
+  ></iframe>
+</div>
+
+## Understanding Multimodal Agents
+
+<Card title="What are Multimodal Agents?" icon="question">
+  Multimodal agents are designed to:
+  - Process multiple types of data (text, images, videos)
+  - Understand context across different modalities
+  - Generate insights from diverse media sources
+  - Handle complex multimedia tasks
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Vision Processing" icon="eye">
+    Analyze images, detect objects, and understand visual content.
+  </Card>
+  <Card title="Video Analysis" icon="video">
+    Process video content for events and actions.
+  </Card>
+  <Card title="Text Extraction" icon="font">
+    Extract and analyze text from images and documents.
+  </Card>
+  <Card title="Cross-Modal Understanding" icon="arrows-repeat">
+    Integrate insights across different media types.
+  </Card>
+</CardGroup>
+
+## Multi-Agent Media Processing
+
+<Tabs>
+  <Tab title="Code">
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+
+    # Create first agent for image analysis
+    vision_agent = Agent(
+        role="Image Analyst",
+        goal="Analyze visual content and extract key information",
+        backstory="Expert in visual analysis and image understanding",
+        llm="gpt-4o-mini",
+        self_reflect=False
+    )
+
+    # Create second agent for content writing
+    writer_agent = Agent(
+        role="Content Writer",
+        goal="Create engaging content based on image analysis",
+        backstory="Expert in creating compelling content from visual insights",
+        llm="gpt-4o-mini"
+    )
+
+    # Create tasks for different media types
+    document_task = Task(
+        description="Extract and summarize text from this document image",
+        expected_output="Structured text content with key information highlighted",
+        agent=vision_agent,
+        images=["document.jpg"]
+    )
+
+    writing_task = Task(
+        description="Create engaging content based on image analysis",
+        expected_output="Compelling article incorporating visual insights",
+        agent=writer_agent
+    )
+
+    # Create and start the agents
+    agents = PraisonAIAgents(
+        agents=[vision_agent, writer_agent],
+        tasks=[document_task, writing_task],
+        process="sequential"
+    )
+
+    result = agents.start()
+    ```
+  </Tab>
+  <Tab title="No Code">
+    ```yaml
+    framework: praisonai
+    process: sequential
+    topic: document analysis and content creation
+    roles:
+      vision_analyst:
+        role: Image Analyst
+        goal: Analyze visual content and extract key information
+        backstory: Expert in visual analysis and image understanding
+        llm: gpt-4o-mini
+        self_reflect: false
+        tasks:
+          document_task:
+            description: Extract and summarize text from this document image
+            expected_output: Structured text content with key information highlighted
+            images:
+              - document.jpg
+
+      content_writer:
+        role: Content Writer
+        goal: Create engaging content based on image analysis
+        backstory: Expert in creating compelling content from visual insights
+        llm: gpt-4o-mini
+        tasks:
+          writing_task:
+            description: Create engaging content based on image analysis
+            expected_output: Compelling article incorporating visual insights
+    ```
+  </Tab>
+</Tabs>
+
+### Configuration Options
+
+```python
+# Create an agent with multimodal configuration
+agent = Agent(
+    role="Media Analyst",
+    goal="Process multiple types of media",
+    backstory="Expert in multimedia analysis",
+    llm="gpt-4o-mini",  # Must support vision capabilities
+    verbose=True,  # Enable detailed logging
+    self_reflect=False  # Optional: disable self-reflection
+)
+
+# Task with media requirements
+task = Task(
+    description="Analyze media content",
+    expected_output="Comprehensive analysis",
+    agent=agent,
+    images=[  # Support multiple media sources
+        "https://example.com/image1.jpg",
+        "path/to/local/image.jpg",
+        "path/to/video.mp4"
+    ]
+)
+```
+
+## Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Media Handling" icon="image">
+    - Use supported formats (JPEG, PNG)
+    - Keep reasonable file sizes
+    - Provide high-quality media
+    - Validate files before processing
+  </Card>
+  <Card title="Task Design" icon="list-check">
+    - Write clear descriptions
+    - Break down complex analyses
+    - Specify expected outputs
+    - Handle errors gracefully
+  </Card>
+</CardGroup>
+
+## Example Use Cases
+
+<CardGroup cols={2}>
+  <Card title="Document Analysis" icon="file-lines">
+    Extract and analyze text from document images.
+  </Card>
+  <Card title="Security Monitoring" icon="camera-cctv">
+    Monitor security feeds for suspicious activity.
+  </Card>
+  <Card title="Medical Imaging" icon="microscope">
+    Analyze medical scans for abnormalities.
+  </Card>
+  <Card title="Architectural Analysis" icon="building">
+    Study architectural features and designs.
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="AutoAgents" icon="robot" href="./autoagents">
+    Learn about automatically created and managed AI agents
+  </Card>
+  <Card title="Mini Agents" icon="microchip" href="./mini">
+    Explore lightweight, focused AI agents
+  </Card>
+</CardGroup>
+
+<Note>
+  For optimal results, ensure your media files are in supported formats and sizes for processing.
+</Note> 

docs/features/reasoning.mdx

@@ -1,10 +1,121 @@
 ---
-title: "Reasoning AI Agents"
-sidebarTitle: "Reasoning"
-description: "Reasoning enables AI agents to think logically, solve complex problems autonomously, and make human-like decisions through multi-step logical reasoning."
-icon: "brain-circuit"
+title: "Reasoning Agents"
+description: "Learn how to create AI agents with advanced reasoning capabilities for complex problem-solving."
+icon: "brain"
 ---
 
+## Quick Start
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the PraisonAI Agents package:
+            ```bash
+            pip install praisonaiagents
+            ```
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+            ```python
+            from praisonaiagents import Agent, Task, PraisonAIAgents, Tools
+
+            # Create reasoning agent
+            reasoner = Agent(
+                role="Problem Solver",
+                goal="Solve complex problems using logical reasoning",
+                backstory="Expert in logical analysis and problem-solving",
+                tools=[Tools.internet_search],
+                verbose=True
+            )
+
+            # Create task
+            task = Task(
+                description="Analyze and solve a complex business problem",
+                expected_output="Detailed solution with reasoning steps",
+                agent=reasoner
+            )
+
+            # Create and start the agents
+            agents = PraisonAIAgents(
+                agents=[reasoner],
+                tasks=[task],
+                process="sequential",
+                verbose=2
+            )
+
+            # Start execution
+            result = agents.start()
+            print(result)
+            ```
+        </Step>
+
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+topic: solve complex business problem
+roles:
+  reasoner:
+    backstory: Expert in logical analysis and problem-solving.
+    goal: Solve complex problems using logical reasoning
+    role: Problem Solver
+    tools:
+      - internet_search
+    tasks:
+      analysis_task:
+        description: Analyze and solve a complex business problem.
+        expected_output: Detailed solution with reasoning steps.
+```
+        </Step>
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+```bash
+praisonai agents.yaml
+```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
+  - Basic understanding of Python
+</Note>
+
 <div className="relative w-full aspect-video">
   <iframe
     className="absolute top-0 left-0 w-full h-full"
@@ -15,225 +126,210 @@ icon: "brain-circuit"
   ></iframe>
 </div>
 
-## Overview
+## Understanding Reasoning
 
-Reasoning in PraisonAI agents enables:
-- Autonomous problem solving
-- Multi-step logical reasoning
-- Human-like decision making
-- Deep comprehension of tasks
-- Error handling with alternative solutions
+<Card title="What is Reasoning?" icon="question">
+  Reasoning agents are designed to:
+  - Break down complex problems into manageable steps
+  - Apply logical analysis to find solutions
+  - Explain their thought process and decisions
+  - Handle uncertainty and incomplete information
+</Card>
 
-## Usage
+## Features
 
-Reasoning is controlled through reflection cycles using `min_reflect` and `max_reflect` parameters:
+<CardGroup cols={2}>
+  <Card title="Problem Decomposition" icon="puzzle-piece">
+    Break complex problems into smaller, manageable parts.
+  </Card>
+  <Card title="Logical Analysis" icon="brain">
+    Apply structured thinking to solve problems.
+  </Card>
+  <Card title="Error Recovery" icon="shield-check">
+    Handle edge cases and recover from errors.
+  </Card>
+  <Card title="Explanation" icon="comment">
+    Provide detailed reasoning for decisions.
+  </Card>
+</CardGroup>
+
+## Multi-Agent Reasoning
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the PraisonAI Agents package:
+            ```bash
+            pip install praisonaiagents
+            ```
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+            ```python
+            from praisonaiagents import Agent, Task, PraisonAIAgents, Tools
+
+            # Create first agent for analysis
+            analyst = Agent(
+                role="Business Analyst",
+                goal="Analyze business problems and identify key issues",
+                backstory="Expert in business analysis and problem identification",
+                tools=[Tools.internet_search],
+                verbose=True
+            )
+
+            # Create second agent for solution development
+            solver = Agent(
+                role="Solution Architect",
+                goal="Develop comprehensive solutions to business problems",
+                backstory="Expert in solution design and implementation",
+                verbose=True
+            )
+
+            # Create first task
+            analysis_task = Task(
+                description="Analyze the current market challenges",
+                expected_output="Detailed analysis of key issues",
+                agent=analyst
+            )
+
+            # Create second task
+            solution_task = Task(
+                description="Develop solutions for identified challenges",
+                expected_output="Comprehensive solution strategy",
+                agent=solver
+            )
+
+            # Create and start the agents
+            agents = PraisonAIAgents(
+                agents=[analyst, solver],
+                tasks=[analysis_task, solution_task],
+                process="sequential"
+            )
+
+            # Start execution
+            result = agents.start()
+            ```
+        </Step>
+
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+topic: solve market challenges
+roles:
+  analyst:
+    backstory: Expert in business analysis and problem identification.
+    goal: Analyze business problems and identify key issues
+    role: Business Analyst
+    tools:
+      - internet_search
+    tasks:
+      analysis_task:
+        description: Analyze the current market challenges.
+        expected_output: Detailed analysis of key issues.
+
+  solver:
+    backstory: Expert in solution design and implementation.
+    goal: Develop comprehensive solutions to business problems
+    role: Solution Architect
+    tasks:
+      solution_task:
+        description: Develop solutions for identified challenges.
+        expected_output: Comprehensive solution strategy.
+```
+        </Step>
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+```bash
+praisonai agents.yaml
+```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+### Configuration Options
 
 ```python
-from praisonaiagents import Agent
-
+# Create an agent with reasoning configuration
 agent = Agent(
-    name="SystemOps",
-    role="System Administrator",
-    goal="Manage and monitor system operations",
-    backstory="You are an expert system administrator",
-    min_reflect=6,  # Minimum reflection cycles
-    max_reflect=10  # Maximum reflection cycles
-)
-```
-
-## Benefits
-
-- **Deep Comprehension**: Instead of rule-based responses, agents understand tasks deeply
-- **Error Recovery**: Agents can reason through errors and find alternative solutions
-- **Step-by-Step Processing**: Complex tasks are broken down and processed systematically
-- **Quality Control**: Multiple reflection cycles ensure high-quality outputs
-- **Adaptive Problem Solving**: Agents can adjust their approach based on reasoning
-
-## Example
-
-Here's a complete example showing reasoning in action:
-
-```python
-from praisonaiagents import Agent, Task, PraisonAIAgents
-import subprocess
-import os
-
-def run_terminal_command(command: str):
-    """
-    Run a terminal command and return its output.
-    """
-    try:
-        result = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
-        print(f"Command output: {result}")
-        return {"stdout": result.stdout, "stderr": result.stderr}
-    except subprocess.CalledProcessError as e:
-        return {"error": str(e), "stdout": e.stdout, "stderr": e.stderr}
-
-def save_to_file(file_path: str, content: str):
-    """
-    Save the given content to the specified file path. Create the folder/file if it doesn't exist.
-    """
-    # Ensure the directory exists
-    os.makedirs(os.path.dirname(file_path), exist_ok=True)
-    
-    # Write the content to the file
-    with open(file_path, 'w') as file:
-        file.write(content)
-
-    return file_path
-
-# Create System Operations Agent
-system_ops_agent = Agent(
-    name="SystemOps",
-    role="System Operations Specialist",
-    goal="Execute and manage complex system operations and commands",
-    backstory="""You are an expert system administrator with deep knowledge of Unix/Linux systems.
-    You excel at running complex system commands, managing processes, and handling system operations.
-    You always validate commands before execution and ensure they are safe to run.""",
-    min_reflect=6,
-    max_reflect=10,
-    tools=[run_terminal_command, save_to_file],
-    llm="gpt-4o-mini"
+    role="Problem Solver",
+    goal="Solve complex problems",
+    backstory="Expert in problem-solving",
+    tools=[Tools.internet_search],
+    verbose=True,  # Enable detailed logging
+    llm="gpt-4o"  # Language model to use
 )
 
-# Create a complex task that tests various system operations
+# Task with reasoning requirements
 task = Task(
-    name="system_analysis_task",
-    description="""Perform a comprehensive system analysis by executing the following operations in sequence:
-    1. Get system information (OS, kernel version)
-    2. List 5 running processes and sort them by CPU usage
-    3. Check disk space usage and list directories over 1GB
-    4. Display current system load and memory usage
-    5. List 5 network connections
-    6. Create a summary report with all findings in a text file called system_report.txt
-    
-    Do it step by step. One task at a time. 
-    Save only the Summary report in the file.
-    Use appropriate commands for each step and ensure proper error handling.""",
-    expected_output="A comprehensive system report containing all requested information saved in system_report.txt",
-    agent=system_ops_agent,
+    description="Solve complex problem",
+    expected_output="Detailed solution",
+    agent=agent
 )
-
-agents = PraisonAIAgents(
-    agents=[system_ops_agent],
-    tasks=[task],
-    process="sequential"
-)
-
-result = agents.start()
 ```
 
-## e2b Tool Integration Example
+## Troubleshooting
 
-```python
-from praisonaiagents import Agent, Task, PraisonAIAgents, error_logs
-import json
-from e2b_code_interpreter import Sandbox
+<CardGroup cols={2}>
+  <Card title="Reasoning Errors" icon="triangle-exclamation">
+    If reasoning seems incorrect:
+    - Check problem description clarity
+    - Enable verbose mode for debugging
+    - Review agent configuration
+  </Card>
 
-def code_interpret(code: str):
-    """
-    A function to demonstrate running Python code dynamically using e2b_code_interpreter.
-    """
-    print(f"\n{'='*50}\n> Running following AI-generated code:\n{code}\n{'='*50}")
-    exec_result = Sandbox().run_code(code)
-    if exec_result.error:
-        print("[Code Interpreter error]", exec_result.error)
-        return {"error": str(exec_result.error)}
-    else:
-        results = []
-        for result in exec_result.results:
-            if hasattr(result, '__iter__'):
-                results.extend(list(result))
-            else:
-                results.append(str(result))
-        logs = {"stdout": list(exec_result.logs.stdout), "stderr": list(exec_result.logs.stderr)}
-        return json.dumps({"results": results, "logs": logs})
+  <Card title="Process Flow" icon="code">
+    If process flow is unclear:
+    - Verify task dependencies
+    - Check agent roles and goals
+    - Review task descriptions
+  </Card>
+</CardGroup>
 
-# 1) Create Agents
-web_scraper_agent = Agent(
-    name="WebScraper",
-    role="Web Scraper",
-    goal="Extract URLs from https://quotes.toscrape.com/",
-    backstory="An expert in data extraction from websites, adept at navigating and retrieving detailed information.",
-    tools=[code_interpret],
-    llm="gpt-4o"
-)
+## Next Steps
 
-url_data_extractor_agent = Agent(
-    name="URLDataExtractor",
-    role="URL Data Extractor",
-    goal="Crawl each URL for data extraction",
-    backstory="Specializes in crawling websites to gather comprehensive data, ensuring nothing is missed from each link.",
-    tools=[code_interpret],
-    llm="gpt-4o"
-)
+<CardGroup cols={2}>
+  <Card title="AutoAgents" icon="robot" href="./autoagents">
+    Learn about automatically created and managed AI agents
+  </Card>
+  <Card title="Mini Agents" icon="microchip" href="./mini">
+    Explore lightweight, focused AI agents
+  </Card>
+</CardGroup>
 
-blog_writer_agent = Agent(
-    name="BlogWriter",
-    role="Creative Content Writer",
-    goal="Create engaging and insightful blog posts from provided data",
-    backstory="An experienced content creator and storyteller with a knack for weaving compelling narratives. Skilled at analyzing quotes and creating meaningful connections that resonate with readers.",
-    tools=[code_interpret],
-    llm="gpt-4o"
-)
-
-# 2) Create Tasks
-task_url_extraction = Task(
-    name="url_extraction_task",
-    description="""Use code_interpret to run Python code that fetches https://quotes.toscrape.com/
-    and extracts 5 URLs from the page, then outputs them as a list. Only first 5 urls""",
-    expected_output="A list of URLs extracted from the source page.",
-    agent=web_scraper_agent,
-    output_file="urls.txt" 
-)
-
-task_data_extraction = Task(
-    name="data_extraction_task",
-    description="""Take the URLs from url_extraction_task, crawl each URL and extract all pertinent
-    data using code_interpret to run Python code, then output raw txt, not the html.""",
-    expected_output="Raw data collected from each crawled URL.",
-    agent=url_data_extractor_agent,
-    context=[task_url_extraction]
-)
-
-blog_writing_task = Task(
-    name="blog_writing_task",
-    description="""Write an engaging blog post using the extracted quotes data. The blog post should:
-    1. Have a catchy title
-    2. Include relevant quotes from the data
-    3. Provide commentary and insights about the quotes
-    4. Be well-structured with introduction, body, and conclusion
-    5. Be approximately 500 words long""",
-    expected_output="A well-written blog post incorporating the extracted quotes with analysis",
-    agent=blog_writer_agent,
-    context=[task_data_extraction],
-    output_file="blog_post.txt"  
-)
-
-# 3) Create and run Agents manager
-agents_manager = PraisonAIAgents(
-    agents=[web_scraper_agent, url_data_extractor_agent, blog_writer_agent],
-    tasks=[task_url_extraction, task_data_extraction, blog_writing_task],
-    verbose=True,
-    process="hierarchical",
-    manager_llm="gpt-4o"
-)
-
-result = agents_manager.start()
-```
-
-## Integration with Tools
-
-Reasoning works particularly well with tools that require complex decision-making:
-
-- **Code Interpreter**: For dynamic code generation and execution
-- **System Operations**: For multi-step system management tasks
-- **Data Analysis**: For complex data processing workflows
-- **Web Scraping**: For intelligent data extraction and processing
-
-## Performance Considerations
-
-- Higher reflection cycles mean more thorough reasoning but longer processing time
-- `min_reflect` ensures minimum quality standards
-- `max_reflect` prevents excessive processing
-- Balance based on task complexity and quality requirements
+<Note>
+  For optimal results, ensure your problem descriptions are clear and provide sufficient context for the reasoning agents.
+</Note>

docs/features/selfreflection.mdx

@@ -1,10 +1,125 @@
 ---
 title: "Self Reflection AI Agents"
-sidebarTitle: "Self-Reflection"
-description: "Self-reflection is a powerful feature in PraisonAI that enables agents to evaluate and improve their own responses before delivering them. This leads to higher quality outputs and more reliable task completion."
+sidebarTitle: "Self Reflection Agents"
+description: "Self-reflection enables agents to evaluate and improve their own responses before delivering them."
 icon: "brain"
 ---
 
+## Quick Start
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the PraisonAI Agents package:
+            ```bash
+            pip install praisonaiagents
+            ```
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+            ```python
+            from praisonaiagents import Agent, Task, PraisonAIAgents
+
+            # Create an agent with self-reflection
+            agent = Agent(
+                role="Senior Research Analyst",
+                goal="Analyze and provide insights on given topics",
+                backstory="You are an expert analyst with strong critical thinking skills",
+                self_reflect=True  # Enable self-reflection
+            )
+
+            # Create a task
+            task = Task(
+                description="Analyze recent developments in AI",
+                expected_output="A detailed analysis report",
+                agent=agent
+            )
+
+            # Create and start the agents
+            agents = PraisonAIAgents(
+                agents=[agent],
+                tasks=[task],
+                process="sequential",
+                verbose=2
+            )
+
+            # Start execution
+            agents.start()
+            ```
+        </Step>
+
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+topic: create movie script about cat in mars
+roles:
+  scriptwriter:
+    backstory: Expert in dialogue and script structure, translating concepts into
+      scripts.
+    goal: Write a movie script about a cat in Mars
+    role: Scriptwriter
+    self_reflect: true
+    min_reflect: 1
+    max_reflect: 2
+    tasks:
+      scriptwriting_task:
+        description: Turn the story concept into a production-ready movie script,
+          including dialogue and scene details.
+        expected_output: Final movie script with dialogue and scene details.
+    tools:
+    - search_tool
+```
+        </Step>
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+```bash
+praisonai agents.yaml
+```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+<Note>
+  **Requirements**
+
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
+</Note>
+
 <div className="relative w-full aspect-video">
   <iframe
     className="absolute top-0 left-0 w-full h-full"
@@ -15,88 +130,209 @@ icon: "brain"
   ></iframe>
 </div>
 
-## Overview
+## Understanding Self-Reflection
 
-When an agent with self-reflection enabled processes a task:
-1. It generates an initial response using its tools and capabilities
-2. The agent then reflects on its own response, evaluating:
-   - Completeness
-   - Accuracy
-   - Relevance
-   - Quality
-3. Based on this reflection, the agent may revise and improve its response
-4. This cycle can repeat until the agent is satisfied with the quality
+<Card title="What is Self-Reflection?" icon="question">
+  Self-reflection enables agents to:
+  - Evaluate their own responses before delivery
+  - Check for completeness and accuracy
+  - Improve output quality through iteration
+  - Ensure task requirements are met
+  - Make conscious decisions about their responses
+</Card>
 
-## Usage
+## Features
 
-Self-reflection is enabled by default for all agents. You can explicitly control it using the `self_reflect` parameter:
+<CardGroup cols={2}>
+  <Card title="Quality Assurance" icon="check-double">
+    Evaluates and improves response quality through self-review.
+  </Card>
+  <Card title="Iterative Improvement" icon="rotate">
+    Refines responses through multiple reflection cycles.
+  </Card>
+  <Card title="Task Validation" icon="list-check">
+    Ensures all aspects of the task are properly addressed.
+  </Card>
+  <Card title="Conscious Decision Making" icon="brain">
+    Enables thoughtful evaluation of responses.
+  </Card>
+</CardGroup>
+
+## Multi-Agent Self-Reflection
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the PraisonAI Agents package:
+            ```bash
+            pip install praisonaiagents
+            ```
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+            ```python
+            from praisonaiagents import Agent, Task, PraisonAIAgents
+
+            # Create first agent with self-reflection
+            researcher = Agent(
+                role="Senior Research Analyst",
+                goal="Research and analyze AI developments",
+                backstory="Expert analyst specializing in AI trends and impacts",
+                self_reflect=True,
+                verbose=True
+            )
+
+            # Create second agent with self-reflection
+            writer = Agent(
+                role="Technical Writer",
+                goal="Transform research into clear documentation",
+                backstory="Experienced in creating technical content and documentation",
+                self_reflect=True,
+                verbose=True
+            )
+
+            # Create first task
+            research_task = Task(
+                description="Research and analyze recent AI developments",
+                expected_output="Comprehensive analysis of AI trends",
+                agent=researcher
+            )
+
+            # Create second task
+            documentation_task = Task(
+                description="Create technical documentation from research findings",
+                expected_output="Well-structured technical documentation",
+                agent=writer
+            )
+
+            # Create and start the agents
+            agents = PraisonAIAgents(
+                agents=[researcher, writer],
+                tasks=[research_task, documentation_task],
+                process="sequential"
+            )
+
+            # Start execution
+            agents.start()
+            ```
+        </Step>
+
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai duckduckgo_search
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+topic: create technical documentation about AI trends
+roles:
+  researcher:
+    backstory: Expert analyst specializing in AI trends and their implications.
+    goal: Research and analyze current AI developments
+    role: Senior Research Analyst
+    self_reflect: true
+    min_reflect: 1
+    max_reflect: 2
+    tasks:
+      research_task:
+        description: Research and analyze recent developments in AI technology.
+        expected_output: Comprehensive analysis of current AI trends.
+    tools:
+    - duckduckgo
+
+  writer:
+    backstory: Experienced technical writer skilled in creating clear documentation.
+    goal: Transform research into accessible documentation
+    role: Technical Writer
+    tasks:
+      documentation_task:
+        description: Create technical documentation from research findings.
+        expected_output: Well-structured technical documentation.
+```
+        </Step>
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+```bash
+praisonai agents.yaml
+```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+### Configuration Options
 
 ```python
-from praisonaiagents import Agent
-
+# Create an agent with advanced self-reflection configuration
 agent = Agent(
-    name="Researcher",
-    role="Senior Research Analyst",
-    goal="Uncover cutting-edge developments in AI",
-    backstory="You are an expert at analyzing tech trends",
-    self_reflect=True  # Enable self-reflection (default)
+    role="Research Analyst",
+    goal="Provide comprehensive analysis",
+    backstory="Expert analyst with critical thinking skills",
+    self_reflect=True,  # Enable self-reflection
+    verbose=True,  # Enable detailed logging
+    llm="gpt-4o",  # Language model to use
+    allow_delegation=True  # Allow task delegation
 )
 ```
 
-## Benefits
+## Troubleshooting
 
-- **Higher Quality Outputs**: Agents can catch and correct potential issues before delivering responses
-- **More Reliable Task Completion**: Self-reflection helps ensure all aspects of a task are properly addressed
-- **Conscious Decision Making**: Agents evaluate their own work, leading to more thoughtful responses
-- **Improved Accuracy**: Multiple reflection cycles help refine and perfect the output
+<CardGroup cols={2}>
+  <Card title="Response Issues" icon="rotate">
+    If responses are not meeting expectations:
+    - Enable verbose mode for debugging
+    - Review agent configuration
+    - Check task description clarity
+  </Card>
 
-## Integration with Processes
+  <Card title="Quality Issues" icon="triangle-exclamation">
+    If output quality is insufficient:
+    - Enable verbose mode
+    - Review agent role and goal
+    - Clarify expected output
+  </Card>
+</CardGroup>
 
-Self-reflection works seamlessly with both sequential and hierarchical processes:
+## Next Steps
 
-- **Sequential**: Each agent self-reflects on their individual tasks
-- **Hierarchical**: Agents self-reflect before reporting to the manager agent, ensuring high-quality inputs for decision-making
+<CardGroup cols={2}>
+  <Card title="AutoAgents" icon="robot" href="./autoagents">
+    Learn about automatically created and managed AI agents
+  </Card>
+  <Card title="Mini Agents" icon="microchip" href="./mini">
+    Explore lightweight, focused AI agents
+  </Card>
+</CardGroup>
 
-## Example
-
-Here's a complete example showing self-reflection in action:
-
-```python
-from praisonaiagents import Agent, Task, PraisonAIAgents
-
-researcher = Agent(
-    name="Researcher",
-    role="Senior Research Analyst",
-    goal="Uncover cutting-edge developments in AI",
-    backstory="You are an expert at analyzing tech trends",
-    self_reflect=True,
-    llm="gpt-4o"
-)
-
-task = Task(
-    name="research_task",
-    description="Analyze 2024's AI advancements",
-    expected_output="A detailed report on AI trends",
-    agent=researcher
-)
-
-agents = PraisonAIAgents(
-    agents=[researcher],
-    tasks=[task],
-    process="sequential"
-)
-
-result = agents.start()
-```
-
-In this example, the researcher agent will:
-1. Research AI advancements
-2. Generate an initial report
-3. Self-reflect on the report's quality
-4. Make improvements if needed
-5. Deliver the final, refined output
-
-## Performance Impact
-
-While self-reflection adds additional processing time, the improvement in output quality often justifies the extra computation. 
-The number of reflection cycles need to be manually optimized to balance quality and performance.
+<Note>
+  For optimal results, configure reflection parameters based on your specific use case requirements.
+</Note>

docs/features/structured.mdx

@@ -0,0 +1,369 @@
+---
+title: "Structured AI Agents"
+description: "Learn how to create AI agents that return structured, type-safe outputs using Pydantic models and JSON."
+icon: "cube"
+---
+
+## Quick Start
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the PraisonAI Agents package:
+            ```bash
+            pip install praisonaiagents
+            ```
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+            ```python
+            from praisonaiagents import Agent, Task, PraisonAIAgents, Tools
+            from pydantic import BaseModel
+
+            # Define your data structure
+            class AnalysisReport(BaseModel):
+                title: str
+                findings: str
+                summary: str
+
+            # Create agent
+            analyst = Agent(
+                role="Data Analyst",
+                goal="Analyze data and provide structured insights",
+                backstory="Expert in data analysis and insights generation",
+                tools=[Tools.internet_search],
+                verbose=True
+            )
+
+            # Create task with structured output
+            task = Task(
+                description="Analyze recent AI developments",
+                expected_output="Structured analysis report",
+                agent=analyst,
+                output_pydantic=AnalysisReport
+            )
+
+            # Create and start the agents
+            agents = PraisonAIAgents(
+                agents=[analyst],
+                tasks=[task],
+                process="sequential",
+                verbose=2
+            )
+
+            # Start execution
+            result = agents.start()
+            print(result.pydantic.title)
+            print(result.pydantic.findings)
+            print(result.pydantic.summary)
+            ```
+        </Step>
+
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+topic: analyze AI developments with structured output
+roles:
+  analyst:
+    backstory: Expert in data analysis and insights generation.
+    goal: Analyze data and provide structured insights
+    role: Data Analyst
+    tools:
+      - internet_search
+    tasks:
+      analysis_task:
+        description: Analyze recent AI developments.
+        expected_output: Structured analysis report.
+        output_structure:
+          type: pydantic
+          model:
+            title: str
+            findings: str
+            summary: str
+```
+        </Step>
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+```bash
+praisonai agents.yaml
+```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
+  - Basic understanding of Python and Pydantic
+</Note>
+
+## Understanding Structured Outputs
+
+<Card title="What are Structured Outputs?" icon="question">
+  Structured outputs allow you to:
+  - Define exact shape of data using Pydantic models
+  - Get type-safe, validated responses
+  - Choose between Pydantic objects or JSON
+  - Ensure consistent output format across agent responses
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Pydantic Models" icon="cube">
+    Define exact data structures with validation.
+  </Card>
+  <Card title="JSON Output" icon="brackets-curly">
+    Get structured JSON responses.
+  </Card>
+  <Card title="Type Safety" icon="shield-check">
+    Ensure type-safe, validated outputs.
+  </Card>
+  <Card title="Format Options" icon="code">
+    Choose between Pydantic or JSON.
+  </Card>
+</CardGroup>
+
+## Multi-Agent Structured Analysis
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the PraisonAI Agents package:
+            ```bash
+            pip install praisonaiagents
+            ```
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+            ```python
+            from praisonaiagents import Agent, Task, PraisonAIAgents, Tools
+            from pydantic import BaseModel
+
+            # Define output structures
+            class ResearchReport(BaseModel):
+                topic: str
+                findings: str
+                sources: list[str]
+
+            class Analysis(BaseModel):
+                key_points: list[str]
+                implications: str
+                recommendations: str
+
+            # Create first agent for research
+            researcher = Agent(
+                role="Research Analyst",
+                goal="Gather and structure research data",
+                backstory="Expert in research and data collection",
+                tools=[Tools.internet_search],
+                verbose=True
+            )
+
+            # Create second agent for analysis
+            analyst = Agent(
+                role="Data Analyst",
+                goal="Analyze research and provide structured insights",
+                backstory="Expert in data analysis and insights generation",
+                verbose=True
+            )
+
+            # Create first task
+            research_task = Task(
+                description="Research quantum computing developments",
+                expected_output="Structured research findings",
+                agent=researcher,
+                output_pydantic=ResearchReport
+            )
+
+            # Create second task
+            analysis_task = Task(
+                description="Analyze research implications",
+                expected_output="Structured analysis report",
+                agent=analyst,
+                output_pydantic=Analysis
+            )
+
+            # Create and start the agents
+            agents = PraisonAIAgents(
+                agents=[researcher, analyst],
+                tasks=[research_task, analysis_task],
+                process="sequential"
+            )
+
+            # Start execution
+            result = agents.start()
+            ```
+        </Step>
+
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+topic: research and analyze quantum computing
+roles:
+  researcher:
+    backstory: Expert in research and data collection.
+    goal: Gather and structure research data
+    role: Research Analyst
+    tools:
+      - internet_search
+    tasks:
+      research_task:
+        description: Research quantum computing developments.
+        expected_output: Structured research findings.
+        output_structure:
+          type: pydantic
+          model:
+            topic: str
+            findings: str
+            sources: list[str]
+
+  analyst:
+    backstory: Expert in data analysis and insights generation.
+    goal: Analyze research and provide structured insights
+    role: Data Analyst
+    tasks:
+      analysis_task:
+        description: Analyze research implications.
+        expected_output: Structured analysis report.
+        output_structure:
+          type: pydantic
+          model:
+            key_points: list[str]
+            implications: str
+            recommendations: str
+```
+        </Step>
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+```bash
+praisonai agents.yaml
+```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+### Configuration Options
+
+```python
+# Create an agent with structured output configuration
+agent = Agent(
+    role="Data Analyst",
+    goal="Provide structured analysis",
+    backstory="Expert in data analysis",
+    tools=[Tools.internet_search],
+    verbose=True,  # Enable detailed logging
+    llm="gpt-4o"  # Language model to use
+)
+
+# Task with Pydantic output
+task = Task(
+    description="Analyze data",
+    expected_output="Structured report",
+    agent=agent,
+    output_pydantic=AnalysisReport  # Use Pydantic model
+    # or output_json=AnalysisReport  # Use JSON output
+)
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Validation Errors" icon="triangle-exclamation">
+    If model validation fails:
+    - Check data types match model
+    - Verify required fields
+    - Enable verbose mode for debugging
+  </Card>
+
+  <Card title="Output Format" icon="code">
+    If output format is incorrect:
+    - Verify model definition
+    - Check output_pydantic vs output_json
+    - Review field specifications
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="AutoAgents" icon="robot" href="./autoagents">
+    Learn about automatically created and managed AI agents
+  </Card>
+  <Card title="Mini Agents" icon="microchip" href="./mini">
+    Explore lightweight, focused AI agents
+  </Card>
+</CardGroup>
+
+<Note>
+  For optimal results, ensure your Pydantic models accurately represent your desired output structure.
+</Note>

docs/framework/multimodalagents.mdx

@@ -1,198 +0,0 @@
----
-title: "Multimodal AI Agents"
-description: "Guide for creating and using multimodal AI agents in PraisonAI for processing images, videos, and other media types"
-icon: "images"
----
-
-<div className="relative w-full aspect-video">
-  <iframe
-    className="absolute top-0 left-0 w-full h-full"
-    src="https://www.youtube.com/embed/hjAWmUT1qqY"
-    title="YouTube video player"
-    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-    allowFullScreen
-  ></iframe>
-</div>
-
-# Multimodal AI Agents
-
-PraisonAI supports multimodal AI agents capable of processing various types of media including images and videos. This enables you to create agents that can understand and analyze visual content alongside text.
-
-## Installation
-
-```bash
-pip install praisonaiagents
-export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
-```
-
-## Quick Start
-
-Create `multimodal_app.py` and add the following code:
-
-```python
-from praisonaiagents import Agent, Task, PraisonAIAgents
-
-# Create Vision Analysis Agent
-vision_agent = Agent(
-    name="VisionAnalyst",
-    role="Computer Vision Specialist",
-    goal="Analyze images and videos to extract meaningful information",
-    backstory="""You are an expert in computer vision and image analysis.
-    You excel at describing images, detecting objects, and understanding visual content.""",
-    llm="gpt-4o-mini",
-    self_reflect=False
-)
-
-# Create tasks with different media types
-task = Task(
-    name="analyze_landmark",
-    description="Describe this famous landmark and its architectural features.",
-    expected_output="Detailed description of the landmark's architecture and significance",
-    agent=vision_agent,
-    images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
-)
-
-# Run the agents
-agents = PraisonAIAgents(
-    agents=[vision_agent],
-    tasks=[task],
-    process="sequential",
-    verbose=True
-)
-
-result = agents.start()
-print(result)
-```
-
-## Agent Configuration
-
-### Core Attributes
-- All standard agent attributes apply (role, goal, backstory, etc.)
-- `llm`: Must be a model that supports vision capabilities (e.g., "gpt-4o-mini")
-- `verbose`: Enable detailed logs (default: False)
-
-## Task Configuration
-
-### Media Support
-Tasks can include various types of media through the `images` parameter:
-
-1. **Image URLs**
-```python
-task = Task(
-    description="Analyze the image",
-    images=["https://example.com/image.jpg"]
-)
-```
-
-2. **Local Image Files**
-```python
-task = Task(
-    description="Analyze the local image",
-    images=["path/to/local/image.jpg"]
-)
-```
-
-3. **Video Files**
-```python
-task = Task(
-    description="Analyze the video content",
-    images=["path/to/video.mp4"]
-)
-```
-
-### Task Types
-
-1. **Image Analysis**
-- Object detection
-- Scene description
-- Architectural analysis
-- Text extraction from images
-
-2. **Video Analysis**
-- Event summarization
-- Object tracking
-- Action recognition
-- Text and caption extraction
-
-## Advanced Features
-
-### Multiple Media Sources
-Tasks can process multiple media sources simultaneously:
-
-```python
-task = Task(
-    description="Compare these images and describe the differences",
-    images=[
-        "https://example.com/image1.jpg",
-        "https://example.com/image2.jpg"
-    ]
-)
-```
-
-### Combining Text and Visual Analysis
-Create tasks that leverage both text and visual understanding:
-
-```python
-task = Task(
-    description="""Analyze this technical diagram and:
-    1. Explain the components
-    2. Describe their relationships
-    3. Identify any text or labels
-    4. Provide technical insights""",
-    images=["diagram.jpg"]
-)
-```
-
-## Best Practices
-
-1. **Media Handling**
-   - Ensure images are in supported formats (JPEG, PNG, etc.)
-   - Keep video files within reasonable size limits
-   - Provide clear, high-quality media for best results
-
-2. **Task Design**
-   - Write clear, specific descriptions
-   - Break complex analyses into subtasks
-   - Include expected output format
-
-3. **Performance Optimization**
-   - Use appropriate model versions for your needs
-   - Consider caching for repeated analyses
-   - Monitor token usage with visual content
-
-4. **Error Handling**
-   - Validate media files before processing
-   - Handle missing or corrupted files gracefully
-   - Implement retry logic for failed analyses
-
-## Example Use Cases
-
-1. **Document Analysis**
-```python
-document_task = Task(
-    description="Extract and summarize text from this document image",
-    expected_output="Structured text content with key information highlighted",
-    agent=vision_agent,
-    images=["document.jpg"]
-)
-```
-
-2. **Security Monitoring**
-```python
-security_task = Task(
-    description="Monitor this security camera feed and report any suspicious activity",
-    expected_output="Detailed report of detected events and anomalies",
-    agent=vision_agent,
-    images=["security_feed.mp4"]
-)
-```
-
-3. **Medical Imaging**
-```python
-medical_task = Task(
-    description="Analyze this medical scan and identify any abnormalities",
-    expected_output="Professional medical image analysis report",
-    agent=vision_agent,
-    images=["medical_scan.jpg"]
-)
-``` 

docs/installation.mdx

@@ -1,64 +1,115 @@
 ---
-title: "Installation"
-description: "Guide for installing PraisonAI and its various components including framework support and additional features"
-icon: "download"
+title: Installation
+description: Set up PraisonAI in your development environment
+icon: download
 ---
 
-## Basic Installation
-```bash
+<RequestExample>
+```bash Code
+pip install praisonaiagents
+```
+```bash No Code
 pip install praisonai
 ```
+</RequestExample>
 
-## Framework-specific Installation
+<Note>
+  **Requirements**
+  
+  - Python 3.10 or higher
+  - pip package manager
+  - Virtual environment (recommended)
+</Note>
 
-### PraisonAI Agents Support (Default)
+# Installing PraisonAI
 
-```bash
-pip install praisonai
-``` 
+Follow these steps to set up PraisonAI in your development environment.
+<Tabs>
+  <Tab title="Code">
 
-### CrewAI Support
-```bash
-pip install "praisonai[crewai]"
-```
+    <Steps>
+      <Step title="Create Virtual Environment (Optional)">
+        First, create and activate a virtual environment:
+        <CodeGroup>
+        ```bash Mac/Linux
+        python -m venv praisonai-env
+        source praisonai-env/bin/activate
+        ```
 
-Includes:
-- CrewAI framework
-- PraisonAI tools integration
-- Task delegation capabilities
-- Sequential and parallel task execution
+        ```bash Windows
+        python -m venv praisonai-env
+        .\praisonai-env\Scripts\activate
+        ```
+        </CodeGroup>
+      </Step>
 
-### AutoGen Support
-```bash
-pip install "praisonai[autogen]"
-```
+      <Step title="Install PraisonAI Agents">
+        Install the core PraisonAI Package:
+        ```bash Terminal
+        pip install praisonaiagents
+        ```
+      </Step>
 
-Includes:
-- AutoGen framework
-- PraisonAI tools integration
-- Multi-agent conversation capabilities
-- Code execution environment
+      <Step title="Configure Environment">
+        Set your OpenAI API key as an environment variable in your terminal:
+  ```bash Terminal
+  export OPENAI_API_KEY=your_openai_key
+  ```
+      </Step>
 
-### Both Frameworks
-```bash
-pip install "praisonai[crewai,autogen]"
-```
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
 
-## Additional Features
+    <Steps>
+        <Step title="Create Virtual Environment (Optional)">
+        First, create and activate a virtual environment:
+        <CodeGroup>
+        ```bash Mac/Linux
+        python -m venv praisonai-env
+        source praisonai-env/bin/activate
+        ```
 
-```bash
-# UI support
-pip install "praisonai[ui]"
+        ```bash Windows
+        python -m venv praisonai-env
+        .\praisonai-env\Scripts\activate
+        ```
+        </CodeGroup>
+      </Step>
+        <Step title="Install PraisonAI">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_openai_key
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
 
-# Chat interface
-pip install "praisonai[chat]"
+Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+You can also use other LLM providers like Anthropic, Google, etc. Please refer to the [Models](/models) for more information.
 
-# Code interface
-pip install "praisonai[code]"
+## Next Steps
 
-# Realtime voice interaction
-pip install "praisonai[realtime]"
-
-# Call feature
-pip install "praisonai[call]"
-```
+<CardGroup cols={2}>
+  <Card
+    title="Quick Start Guide"
+    icon="bolt"
+    href="/code/quickstart"
+  >
+    Build your first AI agent
+  </Card>
+  <Card
+    title="API Reference"
+    icon="code"
+    href="/api/praisonaiagents/index"
+  >
+    Explore the API documentation
+  </Card>
+</CardGroup> 

docs/introduction.mdx

@@ -1,76 +1,361 @@
 ---
-title: 'Introduction'
-description: 'Get started with PraisonAI, an intelligent AI-powered tool'
-icon: "hand-wave"
+title: "Introduction"
+description: "Welcome to PraisonAI - The Next Generation AI Agent Framework"
+icon: "book-open"
 ---
 
+## What is PraisonAI?
+
 <Note>
-  PraisonAI is a low-code, centralized framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications.
+  PraisonAI is a powerful Multi-Agent Framework for building and deploying AI agents that can understand, reason, and execute complex tasks autonomously.
 </Note>
-
-# Welcome to PraisonAI
-
-<Frame caption="PraisonAI Framework Overview">
-  ![PraisonAI Overview](/images/praisonai-overview.png)
-</Frame>
-
-## Core Features
-
-<CardGroup cols={2}>
-  <Card title="Multi-Agent Systems" icon="users">
-    Create and orchestrate sophisticated multi-agent systems with ease
-  </Card>
-  <Card title="Framework Integration" icon="puzzle-piece">
-    Seamlessly integrates AutoGen and CrewAI capabilities
-  </Card>
-  <Card title="Low-Code Design" icon="code">
-    Build complex AI systems with minimal coding required
-  </Card>
-  <Card title="Human-AI Interaction" icon="people-arrows">
-    Enhanced support for human-agent collaboration
+<CardGroup cols={1}>
+  <Card title="Welcome to PraisonAI" icon="wand-magic-sparkles">
+    Build powerful autonomous agents that understand, decide, and execute with unprecedented capability.
   </Card>
 </CardGroup>
 
-## Getting Started
+# Core Components
+<br />
+```mermaid
+graph LR
+    %% Define the main flow
+    Start([▶ Start]) --> Agent1
+    Agent1 --> Process[⚙ Process]
+    Process --> Agent2
+    Agent2 --> Output([✓ Output])
+    Process -.-> Agent1
+    
+    %% Styling for main elements
+    style Start fill:#2D3047,stroke:#7C90A0,color:#fff
+    style Output fill:#2D3047,stroke:#7C90A0,color:#fff
+    style Process fill:#5C80BC,stroke:#7C90A0,color:#fff
+    
+    %% Define subgraphs for agents and their tasks
+    subgraph Agent1[" "]
+        Task1[📋 Task]
+        AgentIcon1[🤖 AI Agent]
+        Tools1[🔧 Tools]
+        
+        Task1 --- AgentIcon1
+        AgentIcon1 --- Tools1
+        
+        style Task1 fill:#2D3047,stroke:#7C90A0,color:#fff
+        style AgentIcon1 fill:#5C80BC,stroke:#7C90A0,color:#fff
+        style Tools1 fill:#2D3047,stroke:#7C90A0,color:#fff
+    end
+    
+    subgraph Agent2[" "]
+        Task2[📋 Task]
+        AgentIcon2[🤖 AI Agent]
+        Tools2[🔧 Tools]
+        
+        Task2 --- AgentIcon2
+        AgentIcon2 --- Tools2
+        
+        style Task2 fill:#2D3047,stroke:#7C90A0,color:#fff
+        style AgentIcon2 fill:#5C80BC,stroke:#7C90A0,color:#fff
+        style Tools2 fill:#2D3047,stroke:#7C90A0,color:#fff
+    end
+    
+    %% Style for agent containers
+    style Agent1 fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    style Agent2 fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    
+    %% Global styles
+    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
+    linkStyle default stroke:#7C90A0,stroke-width:2px
+```
+<br />
+<CardGroup cols={2}>
+  <Card title="Agents" icon="user-robot">
+    Autonomous AI entities that can understand context and execute tasks
+  </Card>
+  <Card title="Tasks" icon="list-check">
+    Units of work that agents can execute independently or collaboratively
+  </Card>
+  <Card title="Process" icon="diagram-project">
+    Workflow management for coordinating agent activities
+  </Card>
+  <Card title="Tools" icon="toolbox">
+    Extensible capabilities that agents can leverage
+  </Card>
+</CardGroup>
 
-<Steps>
-  <Step title="Installation">
-    Follow our [Installation Guide](./installation) to set up PraisonAI
-  </Step>
-  <Step title="Quick Overview">
-    Check out our [TL;DR](./tldr) for a rapid introduction
-  </Step>
-  <Step title="First Project">
-    Create your first project following our [Quick Start](./quickstart) guide
-  </Step>
-</Steps>
-
-## Key Components
-
-| Component | Description | Benefits |
-|:----------|:------------|:---------|
-| **UI Tools** | Intuitive interface components | Easy system interaction and monitoring |
-| **Model Integration** | Support for various LLM models | Flexibility in AI model selection |
-| **Tool System** | Extensible tool framework | Custom capability integration |
-| **API Layer** | Comprehensive API access | Full programmatic control |
-
-## Explore Further
+## Use Cases
 
 <CardGroup cols={2}>
-  <Card title="User Interface" icon="desktop" href="./ui/ui">
-    Learn about our intuitive UI components
+  <Card title="Customer Service" icon="headset">
+    Build intelligent support agents that can handle customer inquiries and resolve issues autonomously.
   </Card>
-  <Card title="Models" icon="robot" href="./models">
-    Explore supported AI models
+  <Card title="Data Analysis" icon="chart-line">
+    Create agents that can process, analyze, and derive insights from complex datasets.
   </Card>
-  <Card title="Tools" icon="wrench" href="./tools">
-    Discover available tools and integrations
+  <Card title="Content Creation" icon="pen-nib">
+    Deploy agents that can generate, edit, and optimize content across various formats.
   </Card>
-  <Card title="API Reference" icon="code" href="./api-reference">
-    Access comprehensive API documentation
+  <Card title="Process Automation" icon="gears">
+    Automate complex workflows with intelligent agents that can coordinate and execute tasks.
+  </Card>
+</CardGroup>
+
+## Getting Started is Easy
+<Tabs>
+  <Tab title="Code">
+<Steps>
+
+  <Step title="Install PraisonAI">
+    Install the core package:
+    ```bash Terminal
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Configure Environment">
+    ```bash Terminal
+    export OPENAI_API_KEY=your_openai_key
+    ```
+    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+    Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+  </Step>
+
+  <Step title="Create Agent">
+    Create `app.py`:
+<CodeGroup>
+  ```python Single Agent
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create an agent
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o"
+  )
+
+  # Define a task
+  task = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  # Run the agents
+  agents = PraisonAIAgents(
+      agents=[researcher],
+      tasks=[task],
+      verbose=False
+  )
+
+  result = agents.start()
+  ```
+
+  ```python Multiple Agents
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create multiple agents
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  writer = Agent(
+      name="Writer",
+      role="Tech Content Strategist",
+      goal="Craft compelling content on tech advancements",
+      backstory="You are a content strategist",
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  # Define multiple tasks
+  task1 = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  task2 = Task(
+      name="writing_task",
+      description="Create a blog post about AI advancements",
+      expected_output="A blog post",
+      agent=writer
+  )
+
+  # Run with hierarchical process
+  agents = PraisonAIAgents(
+      agents=[researcher, writer],
+      tasks=[task1, task2],
+      verbose=False,
+      process="hierarchical",
+      manager_llm="gpt-4o"
+  )
+
+  result = agents.start()
+  ```
+</CodeGroup>
+  </Step>
+
+  <Step title="Start Agents">
+    Execute your script:
+    ```bash Terminal
+    python app.py
+    ```
+  </Step>
+</Steps>
+</Tab>
+
+  <Tab title="No Code">
+    <Steps>
+
+        <Step title="Install PraisonAI">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai
+            ```
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash Terminal
+            export OPENAI_API_KEY=your_openai_key
+            ```
+            Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+            Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+        </Step>
+
+        <Step title="Start AutoAgents">    
+            Run your AutoAgents:
+            ```bash
+            praisonai --auto "Create a movie script about a robot in Mars"
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+## Key Features
+
+<CardGroup cols={3}>
+  <Card title="Autonomous Agents" icon="robot">
+    Create intelligent agents that can:
+    
+    - Understand natural language instructions
+    - Make decisions based on context
+    - Execute complex tasks autonomously
+  </Card>
+
+  <Card title="Flexible Architecture" icon="cubes">
+    Build with:
+    
+    - Modular components
+    - Extensible tools
+    - Customizable workflows
+  </Card>
+
+  <Card title="Advanced Capabilities" icon="wand-sparkles">
+    Leverage:
+    
+    - Multi-agent collaboration
+    - Memory and context management
+    - Tool integration
+  </Card>
+</CardGroup>
+
+
+  <Card title="Why Choose PraisonAI?" icon="stars">
+    <CardGroup cols={2}>
+## Developer First
+Build with modern tools and frameworks you already love. Our SDK is designed to be intuitive and powerful.
+
+## Production Ready
+Enterprise-grade reliability with built-in security features and scalable architecture.
+
+## Open Source
+PraisonAI is open source and available on [GitHub](https://github.com/MervinPraison/PraisonAI).
+
+## Low Code User Friendly
+PraisonAI is designed to be easy to learn and use for non technical users.
+    </CardGroup>
+  </Card>
+
+## Chat with One Agent
+
+<Tabs>
+<Tab title="Basic Agent">
+<Steps>
+  <Step title="Install PraisonAI">
+    Get started with a simple pip install:
+    ```bash
+    pip install praisonaiagents
+    ```
+  </Step>
+  <Step title="Initialize Your Agent">
+    Create your first autonomous agent in just a few lines:
+    ```python
+    from praisonaiagents import Agent
+    agent = Agent(instructions="Your are a helpful AI assistant")
+    agent.chat("Write a movie script about a robot in Mars")
+    ```
+  </Step>
+  <Step title="Build Amazing Things">
+    Start building powerful AI applications with autonomous capabilities!
+  </Step>
+</Steps>
+</Tab>
+
+<Tab title="Agent with Tools">
+<Steps>
+  <Step title="Install PraisonAI">
+    Get started with a simple pip install:
+    ```bash
+    pip install praisonaiagents duckduckgo_search
+    ```
+  </Step>
+  <Step title="Initialize Your Agent">
+
+    Create your first autonomous agent in just a few lines:
+```python
+from praisonaiagents import Agent, Tools
+agent = Agent(instructions="Your are a helpful AI assistant", tools=[Tools.internet_search])
+agent.chat("Write a movie script about a robot in Mars")
+```
+  </Step>
+  <Step title="Build Amazing Things">
+    Start building powerful AI applications with autonomous capabilities!
+  </Step>
+</Steps>
+</Tab>
+</Tabs>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card
+    title="Installation"
+    icon="download"
+    href="/code/installation"
+  >
+    Complete installation guide and setup instructions
+  </Card>
+  <Card
+    title="Quick Start"
+    icon="play"
+    href="/code/quickstart"
+  >
+    Build your first AI agent in minutes
   </Card>
 </CardGroup>
 
 <Tip>
-  Start with our no-code interface to learn the basics, then transition to code-based development for more advanced use cases.
-</Tip>
+  Join our community on [Discord](https://www.youtube.com/redirect?event=channel_description&redir_token=QUFFLUhqbUpiZE00YlF1MjB1LVNIeTFxTnV6MWRRc18wZ3xBQ3Jtc0ttQVZ0aFJ4RXRDanlBQU1Vd3lHU3BBcml3bzhoek1pTmlkZkFkWjlNVDBpRkxZd1dTOWhPc20xbHJOb1NibmNYeTEydklJT0pZOVJFQW93MmJOQXU0WkJNVzNsd29SZ3FBNFVwMTFGNThNSHBWVUhjSQ&q=https%3A%2F%2Fdiscord.gg%2FnNZu5gGT59) to connect with other developers and get help!
+</Tip> 

docs/mint.json

@@ -60,11 +60,11 @@
       "pages": ["index"]
     },
     {
-      "group": "Getting Started (Code)",
+      "group": "Getting Started",
       "pages": [
-        "code/introduction",
-        "code/installation",
-        "code/quickstart"
+        "introduction",
+        "installation",
+        "quickstart"
       ]
     },
     {
@@ -73,42 +73,23 @@
         "concepts/agents",
         "concepts/tasks",
         "concepts/process",
-        "concepts/tools"
-      ]
-    },
-    {
-      "group": "Getting Started (No Code)",
-      "pages": [
-        "introduction",
-        "tldr",
-        "installation",
-        "initialise",
-        "run",
-        "auto"
-      ]
-    },
-    {
-      "group": "User Interface",
-      "pages": [
-        "ui/ui",
-        "ui/chat",
-        "ui/code",
-        "ui/realtime"
+        "concepts/tools",
+        "concepts/memory"
       ]
     },
     {
       "group": "Features",
       "pages": [
+        "features/autoagents",
         "features/selfreflection",
         "features/reasoning",
-        "framework/multimodalagents",
+        "features/multimodal",
         "features/async",
         "features/mini",
-        "features/callbacks",
-        "train",
-        "framework/praisonaiagents",
-        "framework/crewai",
-        "framework/autogen"
+        "features/codeagent",
+        "features/mathagent",
+        "features/structured",
+        "features/callbacks"
       ]
     },
     {
@@ -127,8 +108,25 @@
       ]
     },
     {
-      "group": "Tools (No Code)",
+      "group": "Tools",
       "pages": [
+        "tools/duckduckgo_tools",
+        "tools/arxiv_tools",
+        "tools/calculator_tools",
+        "tools/csv_tools",
+        "tools/duckdb_tools",
+        "tools/excel_tools",
+        "tools/file_tools",
+        "tools/json_tools",
+        "tools/newspaper_tools",
+        "tools/pandas_tools",
+        "tools/python_tools",
+        "tools/shell_tools",
+        "tools/spider_tools",
+        "tools/wikipedia_tools",
+        "tools/xml_tools",
+        "tools/yaml_tools",
+        "tools/yfinance_tools",
         "tools",
         "tools/custom",
         "tools/gpt",
@@ -141,7 +139,27 @@
         "tools/reddit",
         "tools/you.com",
         "tools/crawl4ai",
-        "tools/mem0"
+        "tools/mem0",
+        "tools/composio",
+        "tools/googlecalendar"
+      ]
+    },
+    {
+      "group": "Other Features",
+      "pages": [
+        "train",
+        "framework/crewai",
+        "framework/autogen",
+        "framework/praisonaiagents"
+      ]
+    },
+    {
+      "group": "User Interface",
+      "pages": [
+        "ui/ui",
+        "ui/chat",
+        "ui/code",
+        "ui/realtime"
       ]
     },
     {
@@ -160,12 +178,24 @@
         "deploy"
       ]
     },
+    {
+      "group": "Getting Started (No Code)",
+      "pages": [
+        "nocode/introduction",
+        "nocode/tldr",
+        "nocode/installation",
+        "nocode/initialise",
+        "nocode/run",
+        "nocode/auto"
+      ]
+    },
     {
       "group": "API Reference",
       "pages": [
         "api/praisonaiagents/index",
         "api/praisonaiagents/agent/agent",
         "api/praisonaiagents/agents/agents",
+        "api/praisonaiagents/agents/autoagents",
         "api/praisonaiagents/task/task",
         "api/praisonaiagents/process/process"
       ]

docs/nocode/installation.mdx

@@ -0,0 +1,64 @@
+---
+title: "Installation"
+description: "Guide for installing PraisonAI and its various components including framework support and additional features"
+icon: "download"
+---
+
+## Basic Installation
+```bash
+pip install praisonai
+```
+
+## Framework-specific Installation
+
+### PraisonAI Agents Support (Default)
+
+```bash
+pip install praisonai
+``` 
+
+### CrewAI Support
+```bash
+pip install "praisonai[crewai]"
+```
+
+Includes:
+- CrewAI framework
+- PraisonAI tools integration
+- Task delegation capabilities
+- Sequential and parallel task execution
+
+### AutoGen Support
+```bash
+pip install "praisonai[autogen]"
+```
+
+Includes:
+- AutoGen framework
+- PraisonAI tools integration
+- Multi-agent conversation capabilities
+- Code execution environment
+
+### Both Frameworks
+```bash
+pip install "praisonai[crewai,autogen]"
+```
+
+## Additional Features
+
+```bash
+# UI support
+pip install "praisonai[ui]"
+
+# Chat interface
+pip install "praisonai[chat]"
+
+# Code interface
+pip install "praisonai[code]"
+
+# Realtime voice interaction
+pip install "praisonai[realtime]"
+
+# Call feature
+pip install "praisonai[call]"
+```

docs/nocode/introduction.mdx

@@ -0,0 +1,76 @@
+---
+title: 'Introduction'
+description: 'Get started with PraisonAI, an intelligent AI-powered tool'
+icon: "hand-wave"
+---
+
+<Note>
+  PraisonAI is a low-code, centralized framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications.
+</Note>
+
+# Welcome to PraisonAI
+
+<Frame caption="PraisonAI Framework Overview">
+  ![PraisonAI Overview](/images/praisonai-overview.png)
+</Frame>
+
+## Core Features
+
+<CardGroup cols={2}>
+  <Card title="Multi-Agent Systems" icon="users">
+    Create and orchestrate sophisticated multi-agent systems with ease
+  </Card>
+  <Card title="Framework Integration" icon="puzzle-piece">
+    Seamlessly integrates AutoGen and CrewAI capabilities
+  </Card>
+  <Card title="Low-Code Design" icon="code">
+    Build complex AI systems with minimal coding required
+  </Card>
+  <Card title="Human-AI Interaction" icon="people-arrows">
+    Enhanced support for human-agent collaboration
+  </Card>
+</CardGroup>
+
+## Getting Started
+
+<Steps>
+  <Step title="Installation">
+    Follow our [Installation Guide](./installation) to set up PraisonAI
+  </Step>
+  <Step title="Quick Overview">
+    Check out our [TL;DR](./tldr) for a rapid introduction
+  </Step>
+  <Step title="First Project">
+    Create your first project following our [Quick Start](./quickstart) guide
+  </Step>
+</Steps>
+
+## Key Components
+
+| Component | Description | Benefits |
+|:----------|:------------|:---------|
+| **UI Tools** | Intuitive interface components | Easy system interaction and monitoring |
+| **Model Integration** | Support for various LLM models | Flexibility in AI model selection |
+| **Tool System** | Extensible tool framework | Custom capability integration |
+| **API Layer** | Comprehensive API access | Full programmatic control |
+
+## Explore Further
+
+<CardGroup cols={2}>
+  <Card title="User Interface" icon="desktop" href="./ui/ui">
+    Learn about our intuitive UI components
+  </Card>
+  <Card title="Models" icon="robot" href="./models">
+    Explore supported AI models
+  </Card>
+  <Card title="Tools" icon="wrench" href="./tools">
+    Discover available tools and integrations
+  </Card>
+  <Card title="API Reference" icon="code" href="./api-reference">
+    Access comprehensive API documentation
+  </Card>
+</CardGroup>
+
+<Tip>
+  Start with our no-code interface to learn the basics, then transition to code-based development for more advanced use cases.
+</Tip>

docs/quickstart.mdx

@@ -0,0 +1,339 @@
+---
+title: Quick Start
+description: Build your first AI agent with PraisonAI
+icon: bolt
+---
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+      <Step title="Install PraisonAI">
+        Install the core package:
+        ```bash Terminal
+        pip install praisonaiagents
+        ```
+      </Step>
+
+      <Step title="Configure Environment">
+        ```bash Terminal
+        export OPENAI_API_KEY=your_openai_key
+        ```
+        Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+        Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+      </Step>
+
+      <Step title="Create Agent">
+        Create `app.py`:
+    <CodeGroup>
+      ```python Single Agent
+      from praisonaiagents import Agent, Task, PraisonAIAgents
+
+      # Create an agent
+      researcher = Agent(
+          name="Researcher",
+          role="Senior Research Analyst",
+          goal="Uncover cutting-edge developments in AI",
+          backstory="You are an expert at a technology research group",
+          verbose=True,
+          llm="gpt-4o"
+      )
+
+      # Define a task
+      task = Task(
+          name="research_task",
+          description="Analyze 2024's AI advancements",
+          expected_output="A detailed report",
+          agent=researcher
+      )
+
+      # Run the agents
+      agents = PraisonAIAgents(
+          agents=[researcher],
+          tasks=[task],
+          verbose=False
+      )
+
+      result = agents.start()
+      ```
+
+      ```python Multiple Agents
+      from praisonaiagents import Agent, Task, PraisonAIAgents
+
+      # Create multiple agents
+      researcher = Agent(
+          name="Researcher",
+          role="Senior Research Analyst",
+          goal="Uncover cutting-edge developments in AI",
+          backstory="You are an expert at a technology research group",
+          verbose=True,
+          llm="gpt-4o",
+          markdown=True
+      )
+
+      writer = Agent(
+          name="Writer",
+          role="Tech Content Strategist",
+          goal="Craft compelling content on tech advancements",
+          backstory="You are a content strategist",
+          llm="gpt-4o",
+          markdown=True
+      )
+
+      # Define multiple tasks
+      task1 = Task(
+          name="research_task",
+          description="Analyze 2024's AI advancements",
+          expected_output="A detailed report",
+          agent=researcher
+      )
+
+      task2 = Task(
+          name="writing_task",
+          description="Create a blog post about AI advancements",
+          expected_output="A blog post",
+          agent=writer
+      )
+
+      # Run with hierarchical process
+      agents = PraisonAIAgents(
+          agents=[researcher, writer],
+          tasks=[task1, task2],
+          verbose=False,
+          process="hierarchical",
+          manager_llm="gpt-4o"
+      )
+
+      result = agents.start()
+      ```
+    </CodeGroup>
+      </Step>
+
+      <Step title="Start Agents">
+        Execute your script:
+        ```bash Terminal
+        python app.py
+        ```
+
+        You should see:
+        - Agent initialization
+        - Agents progress
+        - Final results
+        - Generated report
+      </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+      <Step title="Install No Code PraisonAI">
+        Install the No Code PraisonAI Package:
+        ```bash Terminal
+        pip install praisonai
+        ```
+      </Step>
+
+      <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable in your terminal:
+        ```bash Terminal
+        export OPENAI_API_KEY=your_openai_key
+        ```
+      </Step>
+
+      <Step title="Create a file">
+
+        Create a new file `agents.yaml` with the basic setup:
+
+```yaml
+framework: praisonai
+topic: create movie script about cat in mars
+roles:
+  scriptwriter:
+    backstory: Expert in dialogue and script structure, translating concepts into
+      scripts.
+    goal: Write a movie script about a cat in Mars
+    role: Scriptwriter
+    tasks:
+      scriptwriting_task:
+        description: Turn the story concept into a production-ready movie script,
+          including dialogue and scene details.
+        expected_output: Final movie script with dialogue and scene details.
+```
+
+<Note>
+You can automatically create `agents.yaml` file using
+```bash Terminal
+praisonai --init create movie script about cat in mars
+```
+</Note>
+
+      </Step>
+
+      <Step title="Start Agents">
+        Execute your script:
+        ```bash Terminal
+        praisonai agents.yaml
+        ```
+      </Step>
+
+
+    </Steps>
+  </Tab>
+</Tabs>
+
+
+## Creating Custom Tool for Agents
+
+<Info>
+Tools makes the Agent powerful.
+</Info>
+More information about tools can be found in the [Tools](/concepts/tools) section.
+
+<Tabs>
+  <Tab title="Code">
+<Steps>
+<Step title="Install PraisonAI">
+Install the core package and duckduckgo_search package:
+```bash Terminal
+pip install praisonai duckduckgo_search
+```
+</Step>
+<Step title="Create Tools and Agents">
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from duckduckgo_search import DDGS
+from typing import List, Dict
+
+# 1. Tool
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform Internet Search
+    """
+    results = []
+    ddgs = DDGS()
+    for result in ddgs.text(keywords=query, max_results=5):
+        results.append({
+            "title": result.get("title", ""),
+            "url": result.get("href", ""),
+            "snippet": result.get("body", "")
+        })
+    return results
+
+# 2. Agent
+data_agent = Agent(
+    name="DataCollector",
+    role="Search Specialist",
+    goal="Perform internet searches to collect relevant information.",
+    backstory="Expert in finding and organising internet data.",
+    tools=[internet_search_tool],
+    self_reflect=False
+)
+
+# 3. Tasks
+collect_task = Task(
+    description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
+    expected_output="List of search results with titles, URLs, and snippets.",
+    agent=data_agent,
+    name="collect_data",
+)
+
+# 4. Start Agents
+agents = PraisonAIAgents(
+    agents=[data_agent],
+    tasks=[collect_task],
+    process="sequential"
+)
+
+agents.start()
+```
+</Step>
+<Step title="Start Agents">
+Run your script:
+```bash Terminal
+python app.py
+```
+</Step>
+</Steps>
+</Tab>
+
+<Tab title="No Code">
+<Steps>
+<Step title="Install PraisonAI">
+Install the core package and duckduckgo_search package:
+```bash Terminal
+pip install praisonai duckduckgo_search
+```
+</Step>
+<Step title="Create Custom Tool">
+<Info>
+To add additional tools/features you need some coding which can be generated using ChatGPT or any LLM
+</Info>
+Create a new file `tools.py` with the following content:
+```python
+from duckduckgo_search import DDGS
+from typing import List, Dict
+
+# 1. Tool
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform Internet Search
+    """
+    results = []
+    ddgs = DDGS()
+    for result in ddgs.text(keywords=query, max_results=5):
+        results.append({
+            "title": result.get("title", ""),
+            "url": result.get("href", ""),
+            "snippet": result.get("body", "")
+        })
+    return results  
+```
+</Step>
+<Step title="Create Agent">
+
+Create a new file `agents.yaml` with the following content:
+```yaml
+framework: praisonai
+topic: create movie script about cat in mars
+roles:
+  scriptwriter:
+    backstory: Expert in dialogue and script structure, translating concepts into
+      scripts.
+    goal: Write a movie script about a cat in Mars
+    role: Scriptwriter
+    tools:
+      - internet_search_tool # <-- Tool assigned to Agent here
+    tasks:
+      scriptwriting_task:
+        description: Turn the story concept into a production-ready movie script,
+          including dialogue and scene details.
+        expected_output: Final movie script with dialogue and scene details.
+```
+</Step>
+
+<Step title="Start Agents">
+
+Execute your script:
+```bash Terminal
+praisonai agents.yaml
+```
+</Step>
+</Steps>
+</Tab>
+</Tabs>
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card
+    title="Core Concepts"
+    icon="book"
+    href="/concepts/agents"
+  >
+    Learn about agents, tasks, and processes
+  </Card>
+  <Card
+    title="API Reference"
+    icon="code"
+    href="/api/praisonaiagents/index"
+  >
+    Explore detailed API documentation
+  </Card>
+</CardGroup> 

docs/tools/arxiv_tools.mdx

@@ -0,0 +1,429 @@
+---
+title: "arXiv Agent"
+description: "arXiv research paper search and analysis tools for AI agents."
+icon: "book-open"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - `arxiv` package installed
+</Note>
+
+## arXiv Tools
+
+Use arXiv Tools to search and analyze research papers with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required packages:
+    ```bash
+    pip install praisonaiagents arxiv
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create a research agent:
+    ```python
+    research_agent = Agent(
+        name="ResearchAgent",
+        role="Scientific Literature Specialist",
+        goal="Find and analyze relevant scientific papers.",
+        backstory="Expert in academic research and literature review.",
+        tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the research task:
+    ```python
+    research_task = Task(
+        description="Search for recent papers on 'quantum machine learning' and summarize key findings.",
+        expected_output="List of relevant papers with summaries.",
+        agent=research_agent,
+        name="quantum_research"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[research_agent],
+        tasks=[research_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Understanding arXiv Tools
+
+<Card title="What are arXiv Tools?" icon="question">
+  arXiv Tools provide scientific paper search capabilities for AI agents:
+  - Paper search functionality
+  - Author-based search
+  - Category filtering
+  - Abstract retrieval
+  - PDF download options
+</Card>
+
+## Available Functions
+
+```python
+from praisonaiagents.tools import search_arxiv
+from praisonaiagents.tools import get_arxiv_paper
+from praisonaiagents.tools import get_papers_by_author
+from praisonaiagents.tools import get_papers_by_category
+```
+
+## Function Details
+
+### search_arxiv(query: str, max_results: int = 10, sort_by: str = "relevance", sort_order: str = "descending", include_fields: Optional[List[str]] = None)
+
+Search arXiv for papers:
+- Flexible query support
+- Customizable results
+- Multiple sorting options
+- Field selection
+- Error handling
+
+```python
+# Basic search
+papers = search_arxiv("quantum computing")
+
+# Advanced search
+papers = search_arxiv(
+    query="quantum computing",
+    max_results=5,
+    sort_by="submittedDate",
+    sort_order="descending",
+    include_fields=["title", "authors", "summary"]
+)
+
+# Returns: List[Dict[str, Any]]
+# Example output:
+# [
+#     {
+#         "arxiv_id": "2401.00123",
+#         "title": "Quantum Computing: A Survey",
+#         "authors": ["John Doe", "Jane Smith"],
+#         "summary": "This paper reviews...",
+#         "published": "2024-01-01T00:00:00",
+#         "updated": "2024-01-02T00:00:00"
+#     },
+#     ...
+# ]
+```
+
+### get_arxiv_paper(paper_id: str, include_fields: Optional[List[str]] = None)
+
+Get specific paper details:
+- Direct ID lookup
+- Full paper metadata
+- Customizable fields
+- PDF/Abstract links
+
+```python
+# Get paper by ID
+paper = get_arxiv_paper("2401.00123")
+
+# Get specific fields
+paper = get_arxiv_paper(
+    paper_id="2401.00123",
+    include_fields=["title", "authors", "pdf_url"]
+)
+
+# Returns: Dict[str, Any]
+# Example output:
+# {
+#     "arxiv_id": "2401.00123",
+#     "title": "Quantum Computing: A Survey",
+#     "authors": ["John Doe", "Jane Smith"],
+#     "pdf_url": "https://arxiv.org/pdf/2401.00123",
+#     "abstract_url": "https://arxiv.org/abs/2401.00123",
+#     "published": "2024-01-01T00:00:00",
+#     "updated": "2024-01-02T00:00:00"
+# }
+```
+
+### get_papers_by_author(author: str, max_results: int = 10, sort_by: str = "submittedDate", sort_order: str = "descending", include_fields: Optional[List[str]] = None)
+
+Search papers by author:
+- Author-specific search
+- Publication timeline
+- Sort options
+- Field selection
+
+```python
+# Get author's papers
+papers = get_papers_by_author("Yoshua Bengio")
+
+# Recent papers first
+papers = get_papers_by_author(
+    author="Yoshua Bengio",
+    max_results=5,
+    sort_by="submittedDate",
+    sort_order="descending"
+)
+
+# Returns: List[Dict[str, Any]]
+# Example output:
+# [
+#     {
+#         "arxiv_id": "2401.00124",
+#         "title": "Deep Learning Advances",
+#         "authors": ["Yoshua Bengio", "Other Author"],
+#         "published": "2024-01-01T00:00:00"
+#     },
+#     ...
+# ]
+```
+
+### get_papers_by_category(category: str, max_results: int = 10, sort_by: str = "submittedDate", sort_order: str = "descending", include_fields: Optional[List[str]] = None)
+
+Search papers by category:
+- Category-specific search
+- Latest publications
+- Sort options
+- Field selection
+
+```python
+# Get papers in category
+papers = get_papers_by_category("cs.AI")
+
+# Recent machine learning papers
+papers = get_papers_by_category(
+    category="cs.LG",
+    max_results=5,
+    sort_by="submittedDate",
+    sort_order="descending"
+)
+
+# Returns: List[Dict[str, Any]]
+# Example output:
+# [
+#     {
+#         "arxiv_id": "2401.00125",
+#         "title": "New ML Algorithm",
+#         "categories": ["cs.LG", "cs.AI"],
+#         "published": "2024-01-01T00:00:00"
+#     },
+#     ...
+# ]
+```
+
+## Dependencies
+
+The arXiv tools require the following package:
+- arxiv: For accessing the arXiv API
+
+This will be automatically installed when needed.
+
+## Available Fields
+
+When using `include_fields`, you can select from:
+- title: Paper title
+- authors: List of authors
+- summary: Abstract text
+- comment: Author comments
+- journal_ref: Journal reference
+- doi: Digital Object Identifier
+- primary_category: Main arXiv category
+- categories: All arXiv categories
+- links: PDF and abstract URLs
+
+## Common Use Cases
+
+1. Literature Review:
+```python
+# Search multiple related topics
+topics = ["machine learning", "neural networks", "deep learning"]
+all_papers = []
+for topic in topics:
+    papers = search_arxiv(
+        topic,
+        max_results=5,
+        sort_by="relevance"
+    )
+    all_papers.extend(papers)
+```
+
+2. Author Research:
+```python
+# Get recent papers by multiple authors
+authors = ["Yoshua Bengio", "Geoffrey Hinton", "Yann LeCun"]
+author_papers = {}
+for author in authors:
+    papers = get_papers_by_author(
+        author,
+        max_results=3,
+        sort_by="submittedDate"
+    )
+    author_papers[author] = papers
+```
+
+3. Category Monitoring:
+```python
+# Monitor new papers in AI categories
+categories = ["cs.AI", "cs.LG", "cs.CL"]
+new_papers = {}
+for category in categories:
+    papers = get_papers_by_category(
+        category,
+        max_results=5,
+        sort_by="submittedDate"
+    )
+    new_papers[category] = papers
+```
+
+## Examples
+
+### Basic Research Agent
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category
+
+# Create research agent
+research_agent = Agent(
+    name="PaperSearcher",
+    role="Scientific Literature Specialist",
+    goal="Find relevant scientific papers on specified topics.",
+    backstory="Expert in academic research and literature analysis.",
+    tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category],
+    self_reflect=False
+)
+
+# Define research task
+research_task = Task(
+    description="Search for papers on 'transformer models in NLP' from the last year.",
+    expected_output="List of relevant papers with abstracts and key findings.",
+    agent=research_agent,
+    name="nlp_research"
+)
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[research_agent],
+    tasks=[research_task],
+    process="sequential"
+)
+agents.start()
+```
+
+### Advanced Research with Multiple Agents
+
+```python
+# Create research agent
+research_agent = Agent(
+    name="Researcher",
+    role="Literature Specialist",
+    goal="Gather comprehensive scientific literature.",
+    tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category],
+    self_reflect=False
+)
+
+# Create analysis agent
+analysis_agent = Agent(
+    name="Analyzer",
+    role="Research Analyst",
+    goal="Analyze and synthesize research findings.",
+    backstory="Expert in scientific literature analysis.",
+    self_reflect=False
+)
+
+# Define tasks
+research_task = Task(
+    description="Search for papers on quantum computing applications in cryptography.",
+    agent=research_agent,
+    name="quantum_research"
+)
+
+analysis_task = Task(
+    description="Analyze the papers and identify key trends and breakthroughs.",
+    agent=analysis_agent,
+    name="research_analysis"
+)
+
+# Run agents
+agents = PraisonAIAgents(
+    agents=[research_agent, analysis_agent],
+    tasks=[research_task, analysis_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Configuration">
+    Configure agents with clear research focus:
+    ```python
+    Agent(
+        name="Researcher",
+        role="Literature Specialist",
+        goal="Find relevant scientific papers",
+        tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Task Definition">
+    Define specific research objectives:
+    ```python
+    Task(
+        description="Find papers on 'deep learning in healthcare' from top authors",
+        expected_output="Curated list of papers with impact analysis"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### Literature Review
+```python
+# Literature search agent
+searcher = Agent(
+    name="Searcher",
+    role="Literature Specialist",
+    tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category]
+)
+
+# Review agent
+reviewer = Agent(
+    name="Reviewer",
+    role="Research Reviewer"
+)
+
+# Define tasks
+search_task = Task(
+    description="Find papers on AI ethics",
+    agent=searcher
+)
+
+review_task = Task(
+    description="Review and summarize findings",
+    agent=reviewer
+)
+
+# Run workflow
+agents = PraisonAIAgents(
+    agents=[searcher, reviewer],
+    tasks=[search_task, review_task]
+)

docs/tools/calculator_tools.mdx

@@ -0,0 +1,216 @@
+---
+title: "Calculator Agent"
+description: "Mathematical calculation tools for AI agents."
+icon: "calculator"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - `sympy` and `pint` packages installed
+</Note>
+
+## Calculator Tools
+
+Use Calculator Tools to perform mathematical calculations with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required packages:
+    ```bash
+    pip install praisonaiagents sympy pint
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import (
+        evaluate, solve_equation, convert_units,
+        calculate_statistics, calculate_financial
+    )
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create a calculator agent:
+    ```python
+    calculator_agent = Agent(
+        name="MathAssistant",
+        role="Mathematical Computation Specialist",
+        goal="Perform accurate mathematical calculations and solve equations.",
+        backstory="Expert in mathematical computations and problem-solving.",
+        tools=[
+            evaluate, solve_equation, convert_units,
+            calculate_statistics, calculate_financial
+        ],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the calculation task:
+    ```python
+    calc_task = Task(
+        description="Calculate the area of a circle with radius 5",
+        expected_output="The area of the circle in square units",
+        agent=calculator_agent,
+        name="circle_area"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[calculator_agent],
+        tasks=[calc_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Understanding Calculator Tools
+
+<Card title="What are Calculator Tools?" icon="question">
+  Calculator Tools provide mathematical computation capabilities for AI agents:
+  - Expression evaluation
+  - Equation solving
+  - Unit conversions
+  - Support for mathematical functions
+  - Variable substitution
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="Expression Evaluation" icon="function">
+    Evaluate mathematical expressions:
+    ```python
+    result = evaluate("2 + 2 * 3")
+    ```
+  </Card>
+  <Card title="Equation Solving" icon="equals">
+    Solve mathematical equations:
+    ```python
+    result = solve_equation("2*x + 3 = 7")
+    ```
+  </Card>
+  <Card title="Unit Conversions" icon="exchange">
+    Convert between units:
+    ```python
+    result = convert_units(100, "meters", "kilometers")
+    ```
+  </Card>
+  <Card title="Statistical Calculations" icon="chart-line">
+    Calculate statistical metrics:
+    ```python
+    result = calculate_statistics([1, 2, 3, 4, 5])
+    ```
+  </Card>
+  <Card title="Financial Calculations" icon="chart-pie">
+    Calculate financial metrics:
+    ```python
+    result = calculate_financial(1000, 5, 2)
+    ```
+  </Card>
+</CardGroup>
+
+## Advanced Usage
+
+<AccordionGroup>
+  <Accordion title="Expression Evaluation">
+    Evaluate complex mathematical expressions with variables:
+    ```python
+    # Basic arithmetic
+    result = evaluate("2 + 2 * 3")
+    
+    # With mathematical functions
+    result = evaluate("sin(pi/2) + sqrt(16)")
+    
+    # With variables
+    result = evaluate(
+        "x^2 + y",
+        variables={"x": 3, "y": 4}
+    )
+    ```
+  </Accordion>
+  
+  <Accordion title="Equation Solving">
+    Solve different types of equations:
+    ```python
+    # Linear equation
+    result = solve_equation("2*x + 3 = 7")
+    
+    # With custom variable
+    result = solve_equation("y^2 = 4", variable="y")
+    ```
+  </Accordion>
+  
+  <Accordion title="Unit Conversions">
+    Convert between different units:
+    ```python
+    # Length conversion
+    result = convert_units(100, "meters", "kilometers")
+    
+    # Temperature conversion
+    result = convert_units(32, "fahrenheit", "celsius")
+    
+    # Time conversion
+    result = convert_units(2.5, "hours", "minutes")
+    ```
+  </Accordion>
+  
+  <Accordion title="Statistical Calculations">
+    Calculate statistical metrics:
+    ```python
+    result = calculate_statistics([1, 2, 3, 4, 5])
+    ```
+  </Accordion>
+  
+  <Accordion title="Financial Calculations">
+    Calculate financial metrics:
+    ```python
+    result = calculate_financial(1000, 5, 2)
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Error Handling
+
+<Card title="Error Management" icon="shield-check">
+  The tools include comprehensive error handling:
+  ```python
+  # Returns success result
+  {"result": 8.0}
+  
+  # Returns error message
+  {"error": "Invalid expression"}
+  ```
+</Card>
+
+## Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Precision Control" icon="ruler">
+    Control calculation precision:
+    ```python
+    evaluate(
+        "pi",
+        precision=5
+    )
+    ```
+  </Card>
+  <Card title="Safe Execution" icon="shield">
+    Uses safe evaluation environment with:
+    - Restricted builtins
+    - Mathematical functions only
+    - Variable validation
+  </Card>
+</CardGroup>

docs/tools/csv_tools.mdx

@@ -0,0 +1,384 @@
+---
+title: "CSV Agent"
+description: "CSV file processing tools for AI agents."
+icon: "file-csv"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - `pandas` package installed
+</Note>
+
+## CSV Tools
+
+Use CSV Tools to read, write, and manipulate CSV files with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required package:
+    ```bash
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import read_csv, write_csv, merge_csv
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create a CSV processing agent:
+    ```python
+    csv_agent = Agent(
+        name="CSVProcessor",
+        role="CSV Processing Specialist",
+        goal="Process CSV files efficiently and accurately.",
+        backstory="Expert in CSV file manipulation and analysis.",
+        tools=[read_csv, write_csv, merge_csv],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the CSV processing task:
+    ```python
+    csv_task = Task(
+        description="Process and analyze CSV data files.",
+        expected_output="Processed CSV data with analysis.",
+        agent=csv_agent,
+        name="csv_processing"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[csv_agent],
+        tasks=[csv_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Understanding CSV Tools
+
+<Card title="What are CSV Tools?" icon="question">
+  CSV Tools provide CSV processing capabilities for AI agents:
+  - File reading and writing
+  - Data parsing
+  - Column manipulation
+  - Data filtering
+  - Format conversion
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="CSV Agent" icon="user-robot">
+    Create specialized CSV agents:
+    ```python
+    Agent(tools=[read_csv, write_csv, merge_csv])
+    ```
+  </Card>
+  <Card title="CSV Task" icon="list-check">
+    Define CSV tasks:
+    ```python
+    Task(description="csv_operation")
+    ```
+  </Card>
+  <Card title="Process Types" icon="arrows-split-up-and-left">
+    Sequential or parallel processing:
+    ```python
+    process="sequential"
+    ```
+  </Card>
+  <Card title="CSV Options" icon="sliders">
+    Customize CSV parameters:
+    ```python
+    delimiter=",", header=True
+    ```
+  </Card>
+</CardGroup>
+
+## Available Functions
+
+```python
+from praisonaiagents.tools import read_csv
+from praisonaiagents.tools import write_csv
+from praisonaiagents.tools import merge_csv
+```
+
+## Function Details
+
+### read_csv(filepath: str, encoding: str = 'utf-8', delimiter: str = ',', header: Union[int, List[int], None] = 0, usecols: Optional[List[str]] = None, dtype: Optional[Dict[str, str]] = None, parse_dates: Optional[List[str]] = None, na_values: Optional[List[str]] = None, nrows: Optional[int] = None)
+
+Reads a CSV file with advanced options:
+- Supports multiple encodings (utf-8, ascii, etc.)
+- Configurable column delimiter
+- Flexible header row handling
+- Column selection and data type specification
+- Date parsing
+- Custom NA/NaN value handling
+- Row limit option
+
+```python
+# Basic usage
+data = read_csv("data.csv")
+
+# Advanced usage with options
+data = read_csv(
+    "data.csv",
+    encoding='utf-8',
+    delimiter=',',
+    header=0,
+    usecols=['name', 'age', 'city'],
+    dtype={'age': 'int32'},
+    parse_dates=['birth_date'],
+    na_values=['N/A', 'missing'],
+    nrows=1000
+)
+# Returns: List[Dict[str, Any]]
+# Example: [{"name": "Alice", "age": 25, "city": "New York"}, ...]
+```
+
+### write_csv(filepath: str, data: List[Dict[str, Any]], encoding: str = 'utf-8', delimiter: str = ',', index: bool = False, header: bool = True, float_format: Optional[str] = None, date_format: Optional[str] = None, mode: str = 'w')
+
+Writes data to a CSV file with formatting options:
+- Supports different encodings
+- Configurable delimiter
+- Optional row indices
+- Optional headers
+- Custom float and date formatting
+- Append mode support
+
+```python
+# Basic usage
+data = [
+    {"name": "Alice", "age": 25, "city": "New York"},
+    {"name": "Bob", "age": 30, "city": "San Francisco"}
+]
+success = write_csv("output.csv", data)
+
+# Advanced usage with formatting
+success = write_csv(
+    "output.csv",
+    data,
+    encoding='utf-8',
+    delimiter=',',
+    index=False,
+    header=True,
+    float_format='%.2f',
+    date_format='%Y-%m-%d',
+    mode='w'
+)
+# Returns: bool (True if successful)
+```
+
+### merge_csv(files: List[str], output_file: str, how: str = 'inner', on: Optional[Union[str, List[str]]] = None, suffixes: Optional[tuple] = None)
+
+Merges multiple CSV files with flexible join options:
+- Supports different join types (inner, outer, left, right)
+- Merge on single or multiple columns
+- Custom suffixes for overlapping columns
+- Automatic handling of column name conflicts
+
+```python
+# Merge two files on a common column
+success = merge_csv(
+    files=["employees.csv", "salaries.csv"],
+    output_file="merged.csv",
+    how='inner',
+    on='employee_id'
+)
+
+# Advanced merge with multiple columns and custom suffixes
+success = merge_csv(
+    files=["data1.csv", "data2.csv", "data3.csv"],
+    output_file="merged.csv",
+    how='outer',
+    on=['id', 'department'],
+    suffixes=('_2022', '_2023')
+)
+# Returns: bool (True if successful)
+```
+
+## Dependencies
+
+The CSV tools require the following Python packages:
+- pandas: For advanced CSV operations and data manipulation
+
+These will be automatically installed when needed.
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import read_csv, write_csv, merge_csv
+
+agent = Agent(
+    name="CSVProcessor",
+    description="An agent that processes CSV files",
+    tools=[read_csv, write_csv, merge_csv]
+)
+```
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- File not found errors
+- Permission errors
+- Encoding errors
+- Data format errors
+- Missing dependency errors
+
+Errors are logged and returned in a consistent format:
+- Success cases return the expected data type
+- Error cases return a dict with an "error" key containing the error message
+
+## Examples
+
+### Basic CSV Processing Agent
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import read_csv, write_csv, merge_csv
+
+# Create CSV agent
+csv_agent = Agent(
+    name="CSVExpert",
+    role="CSV Processing Specialist",
+    goal="Process CSV files efficiently and accurately.",
+    backstory="Expert in data processing and analysis.",
+    tools=[read_csv, write_csv, merge_csv],
+    self_reflect=False
+)
+
+# Define CSV task
+csv_task = Task(
+    description="Process and analyze customer data.",
+    expected_output="Customer analysis report.",
+    agent=csv_agent,
+    name="customer_analysis"
+)
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[csv_agent],
+    tasks=[csv_task],
+    process="sequential"
+)
+agents.start()
+```
+
+### Advanced CSV Processing with Multiple Agents
+
+```python
+# Create data processing agent
+processor_agent = Agent(
+    name="Processor",
+    role="Data Processor",
+    goal="Process CSV data systematically.",
+    tools=[read_csv, write_csv, merge_csv],
+    self_reflect=False
+)
+
+# Create analysis agent
+analysis_agent = Agent(
+    name="Analyzer",
+    role="Data Analyst",
+    goal="Analyze processed CSV data.",
+    backstory="Expert in data analysis and reporting.",
+    self_reflect=False
+)
+
+# Define tasks
+processing_task = Task(
+    description="Process customer data files.",
+    agent=processor_agent,
+    name="data_processing"
+)
+
+analysis_task = Task(
+    description="Analyze customer trends and patterns.",
+    agent=analysis_agent,
+    name="data_analysis"
+)
+
+# Run agents
+agents = PraisonAIAgents(
+    agents=[processor_agent, analysis_agent],
+    tasks=[processing_task, analysis_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Configuration">
+    Configure agents with clear CSV focus:
+    ```python
+    Agent(
+        name="CSVProcessor",
+        role="CSV Processing Specialist",
+        goal="Process CSV files accurately and efficiently",
+        tools=[read_csv, write_csv, merge_csv]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Task Definition">
+    Define specific CSV operations:
+    ```python
+    Task(
+        description="Process customer data and generate reports",
+        expected_output="Customer analysis report"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### CSV Processing Pipeline
+```python
+# Processing agent
+processor = Agent(
+    name="Processor",
+    role="CSV Processor",
+    tools=[read_csv, write_csv, merge_csv]
+)
+
+# Analysis agent
+analyzer = Agent(
+    name="Analyzer",
+    role="Data Analyst"
+)
+
+# Define tasks
+process_task = Task(
+    description="Process CSV files",
+    agent=processor
+)
+
+analyze_task = Task(
+    description="Analyze processed data",
+    agent=analyzer
+)
+
+# Run workflow
+agents = PraisonAIAgents(
+    agents=[processor, analyzer],
+    tasks=[process_task, analyze_task]
+)

docs/tools/duckdb_tools.mdx

@@ -0,0 +1,401 @@
+---
+title: "DuckDB Agent"
+description: "DuckDB database tools for AI agents."
+icon: "database"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - `duckdb` package installed
+  - Basic understanding of SQL and databases
+</Note>
+
+## DuckDB Tools
+
+Use DuckDB Tools to manage and query databases with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required packages:
+    ```bash
+    pip install praisonaiagents duckdb
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import execute_query, load_csv, export_csv
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create a DuckDB database agent:
+    ```python
+    db_agent = Agent(
+        name="DBProcessor",
+        role="Database Management Specialist",
+        goal="Manage and query databases efficiently.",
+        backstory="Expert in database operations and SQL.",
+        tools=[execute_query, load_csv, export_csv],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the database task:
+    ```python
+    db_task = Task(
+        description="Query and analyze database data.",
+        expected_output="Query results and analysis.",
+        agent=db_agent,
+        name="db_analysis"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[db_agent],
+        tasks=[db_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Available Functions
+
+```python
+from praisonaiagents.tools import execute_query
+from praisonaiagents.tools import load_csv
+from praisonaiagents.tools import export_csv
+```
+
+## Function Details
+
+### execute_query(query: str, params: Optional[Union[tuple, dict]] = None, return_df: bool = True)
+
+Executes SQL queries with advanced features:
+- Supports parameterized queries
+- Returns results as DataFrame records or raw tuples
+- Full SQL query support (SELECT, INSERT, UPDATE, DELETE, etc.)
+- Automatic connection management
+
+```python
+# Basic SELECT query
+results = execute_query("SELECT * FROM employees")
+
+# Parameterized query
+results = execute_query(
+    "SELECT * FROM employees WHERE department = ? AND salary > ?",
+    params=('Engineering', 75000)
+)
+
+# Named parameters
+results = execute_query(
+    "SELECT * FROM employees WHERE department = :dept",
+    params={'dept': 'Engineering'}
+)
+# Returns: List[Dict[str, Any]]
+# Example: [{"id": 1, "name": "Alice", "department": "Engineering"}, ...]
+```
+
+### load_csv(table_name: str, filepath: str, schema: Optional[Dict[str, str]] = None, if_exists: str = 'replace')
+
+Loads CSV files into DuckDB tables:
+- Optional schema definition
+- Flexible table existence handling
+- Automatic type inference
+- Support for large files
+
+```python
+# Basic usage - auto schema inference
+success = load_csv("employees", "employees.csv")
+
+# With custom schema
+schema = {
+    "id": "INTEGER PRIMARY KEY",
+    "name": "VARCHAR",
+    "salary": "DECIMAL(10,2)",
+    "hire_date": "DATE"
+}
+success = load_csv(
+    "employees",
+    "employees.csv",
+    schema=schema,
+    if_exists='replace'
+)
+# Returns: bool (True if successful)
+```
+
+### export_csv(query: str, filepath: str, params: Optional[Union[tuple, dict]] = None)
+
+Exports query results to CSV files:
+- Supports parameterized queries
+- Automatic header generation
+- Configurable output formatting
+- Large result set handling
+
+```python
+# Export simple query results
+success = export_csv(
+    "SELECT * FROM employees",
+    "exported_employees.csv"
+)
+
+# Export filtered data with parameters
+success = export_csv(
+    "SELECT * FROM employees WHERE department = ? AND year = ?",
+    "eng_2023.csv",
+    params=('Engineering', 2023)
+)
+# Returns: bool (True if successful)
+```
+
+## Understanding DuckDB Tools
+
+<Card title="What are DuckDB Tools?" icon="question">
+  DuckDB Tools provide database capabilities for AI agents:
+  - SQL query execution
+  - Data import/export
+  - Schema management
+  - Data analysis
+  - Performance optimization
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="DB Agent" icon="user-robot">
+    Create specialized database agents:
+    ```python
+    Agent(tools=[execute_query, load_csv, export_csv])
+    ```
+  </Card>
+  <Card title="DB Task" icon="list-check">
+    Define database tasks:
+    ```python
+    Task(description="db_operation")
+    ```
+  </Card>
+  <Card title="Process Types" icon="arrows-split-up-and-left">
+    Sequential or parallel processing:
+    ```python
+    process="sequential"
+    ```
+  </Card>
+  <Card title="DB Options" icon="sliders">
+    Customize database parameters:
+    ```python
+    read_only=True, memory=True
+    ```
+  </Card>
+</CardGroup>
+
+## Examples
+
+### Basic Database Agent
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import execute_query, load_csv, export_csv
+
+# Create database agent
+db_agent = Agent(
+    name="DBExpert",
+    role="Database Specialist",
+    goal="Query and analyze data efficiently.",
+    backstory="Expert in database management and SQL.",
+    tools=[execute_query, load_csv, export_csv],
+    self_reflect=False
+)
+
+# Define database task
+db_task = Task(
+    description="Analyze sales performance data.",
+    expected_output="Sales analysis report.",
+    agent=db_agent,
+    name="sales_analysis"
+)
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[db_agent],
+    tasks=[db_task],
+    process="sequential"
+)
+agents.start()
+```
+
+### Advanced Database Operations with Multiple Agents
+
+```python
+# Create query agent
+query_agent = Agent(
+    name="QueryProcessor",
+    role="SQL Query Specialist",
+    goal="Execute SQL queries efficiently.",
+    tools=[execute_query],
+    self_reflect=False
+)
+
+# Create data import/export agent
+data_agent = Agent(
+    name="DataProcessor",
+    role="Data Import/Export Specialist",
+    goal="Handle data import and export operations efficiently.",
+    tools=[load_csv, export_csv],
+    self_reflect=False
+)
+```
+
+## Dependencies
+
+The DuckDB tools require the following Python packages:
+- duckdb: For database operations
+- pandas: For data manipulation and CSV handling
+
+These will be automatically installed when needed.
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import execute_query, load_csv, export_csv
+
+agent = Agent(
+    name="DBProcessor",
+    description="An agent that works with DuckDB databases",
+    tools=[execute_query, load_csv, export_csv]
+)
+```
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- Database connection errors
+- Query syntax errors
+- File I/O errors
+- Schema validation errors
+- Missing dependency errors
+
+Errors are logged and returned in a consistent format:
+- Success cases return the expected data type
+- Error cases return a dict with an "error" key containing the error message
+
+## Common Use Cases
+
+1. Data Analysis:
+```python
+# Load data
+load_csv("sales", "sales_data.csv")
+
+# Analyze with SQL
+results = execute_query("""
+    SELECT 
+        department,
+        COUNT(*) as count,
+        AVG(salary) as avg_salary
+    FROM employees
+    GROUP BY department
+    HAVING count > 5
+    ORDER BY avg_salary DESC
+""")
+```
+
+2. Data Export:
+```python
+# Export filtered and aggregated data
+export_csv("""
+    SELECT 
+        date_trunc('month', date) as month,
+        SUM(amount) as total_sales
+    FROM sales
+    GROUP BY 1
+    ORDER BY 1
+""", "monthly_sales.csv")
+```
+
+3. Data Transformation:
+```python
+# Load and transform data
+load_csv("raw_data", "input.csv")
+execute_query("""
+    CREATE TABLE transformed AS
+    SELECT 
+        id,
+        UPPER(name) as name,
+        ROUND(amount, 2) as amount,
+        DATE_TRUNC('day', timestamp) as date
+    FROM raw_data
+""")
+export_csv("SELECT * FROM transformed", "transformed.csv")
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Configuration">
+    Configure agents with clear database focus:
+    ```python
+    Agent(
+        name="DBProcessor",
+        role="Database Specialist",
+        goal="Process queries accurately and efficiently",
+        tools=[execute_query, load_csv, export_csv]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Task Definition">
+    Define specific database operations:
+    ```python
+    Task(
+        description="Query sales data and generate reports",
+        expected_output="Sales performance report"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### Database Operation Pipeline
+```python
+# Query agent
+querier = Agent(
+    name="Querier",
+    role="SQL Specialist",
+    tools=[execute_query]
+)
+
+# Analysis agent
+analyzer = Agent(
+    name="Analyzer",
+    role="Data Analyst"
+)
+
+# Define tasks
+query_task = Task(
+    description="Execute SQL queries",
+    agent=querier
+)
+
+analyze_task = Task(
+    description="Analyze query results",
+    agent=analyzer
+)
+
+# Run workflow
+agents = PraisonAIAgents(
+    agents=[querier, analyzer],
+    tasks=[query_task, analyze_task]
+)

docs/tools/duckduckgo_tools.mdx

@@ -0,0 +1,248 @@
+---
+title: "DuckDuckGo Agent"
+description: "Internet search tools using DuckDuckGo for AI agents."
+icon: "magnifying-glass"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - `duckduckgo-search` package installed
+</Note>
+
+## DuckDuckGo Tools
+
+Use DuckDuckGo Tools to perform internet searches with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required packages:
+    ```bash
+    pip install praisonaiagents duckduckgo-search
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import duckduckgo
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create a search agent:
+    ```python
+    search_agent = Agent(
+        name="SearchAgent",
+        role="Internet Search Specialist",
+        goal="Perform accurate internet searches and extract relevant information.",
+        backstory="Expert in finding and organizing internet data.",
+        tools=[duckduckgo],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the search task:
+    ```python
+    search_task = Task(
+        description="Search for 'AI trends 2024' and analyze the results.",
+        expected_output="List of key AI trends with sources.",
+        agent=search_agent,
+        name="search_trends"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[search_agent],
+        tasks=[search_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Understanding DuckDuckGo Tools
+
+<Card title="What are DuckDuckGo Tools?" icon="question">
+  DuckDuckGo Tools provide internet search capabilities for AI agents:
+  - Web search functionality
+  - News search
+  - Privacy-focused results
+  - Region-specific searches
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="Search Agent" icon="user-robot">
+    Create specialized search agents:
+    ```python
+    Agent(tools=[duckduckgo])
+    ```
+  </Card>
+  <Card title="Search Task" icon="list-check">
+    Define search tasks:
+    ```python
+    Task(description="search_query")
+    ```
+  </Card>
+  <Card title="Process Types" icon="arrows-split-up-and-left">
+    Sequential or parallel processing:
+    ```python
+    process="sequential"
+    ```
+  </Card>
+  <Card title="Search Options" icon="sliders">
+    Customize search parameters:
+    ```python
+    region="wt-wt", time="w"
+    ```
+  </Card>
+</CardGroup>
+
+## Examples
+
+### Basic Search Agent
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import duckduckgo
+
+# Create search agent
+search_agent = Agent(
+    name="WebSearcher",
+    role="Search Specialist",
+    goal="Find accurate information about specified topics.",
+    backstory="Expert in internet research and data collection.",
+    tools=[duckduckgo],
+    self_reflect=False
+)
+
+# Define search task
+search_task = Task(
+    description="Search for 'Python programming best practices 2024' and summarize the key points.",
+    expected_output="List of best practices with sources.",
+    agent=search_agent,
+    name="search_python"
+)
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[search_agent],
+    tasks=[search_task],
+    process="sequential"
+)
+agents.start()
+```
+
+### Advanced Search with Multiple Agents
+
+```python
+# Create search agent
+search_agent = Agent(
+    name="Researcher",
+    role="Search Specialist",
+    goal="Gather comprehensive information about topics.",
+    tools=[duckduckgo],
+    self_reflect=False
+)
+
+# Create analysis agent
+analysis_agent = Agent(
+    name="Analyzer",
+    role="Data Analyst",
+    goal="Analyze and synthesize search results.",
+    backstory="Expert in data analysis and trend identification.",
+    self_reflect=False
+)
+
+# Define tasks
+search_task = Task(
+    description="Search for latest AI developments in healthcare.",
+    agent=search_agent,
+    name="healthcare_search"
+)
+
+analysis_task = Task(
+    description="Analyze the search results and identify key trends.",
+    agent=analysis_agent,
+    name="trend_analysis"
+)
+
+# Run agents
+agents = PraisonAIAgents(
+    agents=[search_agent, analysis_agent],
+    tasks=[search_task, analysis_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Configuration">
+    Configure agents with clear roles and goals:
+    ```python
+    Agent(
+        name="Researcher",
+        role="Search Specialist",
+        goal="Find accurate and relevant information",
+        tools=[duckduckgo]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Task Definition">
+    Define specific and clear tasks:
+    ```python
+    Task(
+        description="Search for 'AI ethics guidelines 2024' and summarize key points",
+        expected_output="Structured list of guidelines with sources"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### Research and Analysis
+```python
+# Research agent
+researcher = Agent(
+    name="Researcher",
+    role="Search Specialist",
+    tools=[duckduckgo]
+)
+
+# Analysis agent
+analyst = Agent(
+    name="Analyst",
+    role="Information Analyst"
+)
+
+# Define tasks
+research_task = Task(
+    description="Research quantum computing advances",
+    agent=researcher
+)
+
+analysis_task = Task(
+    description="Analyze research findings",
+    agent=analyst
+)
+
+# Run workflow
+agents = PraisonAIAgents(
+    agents=[researcher, analyst],
+    tasks=[research_task, analysis_task]
+)

docs/tools/excel_tools.mdx

@@ -0,0 +1,442 @@
+---
+title: "Excel Agent"
+description: "Excel file processing tools for AI agents."
+icon: "file-excel"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - `openpyxl` package installed
+  - Basic understanding of Excel files
+</Note>
+
+## Excel Tools
+
+Use Excel Tools to process and manipulate Excel files with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required packages:
+    ```bash
+    pip install praisonaiagents openpyxl
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import read_excel, write_excel, merge_excel
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create an Excel processing agent:
+    ```python
+    excel_agent = Agent(
+        name="ExcelProcessor",
+        role="Excel Processing Specialist",
+        goal="Process Excel files efficiently and accurately.",
+        backstory="Expert in Excel file manipulation and analysis.",
+        tools=[read_excel, write_excel, merge_excel],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the Excel processing task:
+    ```python
+    excel_task = Task(
+        description="Process and analyze Excel spreadsheets.",
+        expected_output="Processed Excel data with analysis.",
+        agent=excel_agent,
+        name="excel_processing"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[excel_agent],
+        tasks=[excel_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Understanding Excel Tools
+
+<Card title="What are Excel Tools?" icon="question">
+  Excel Tools provide Excel processing capabilities for AI agents:
+  - File reading and writing
+  - Data extraction
+  - Sheet manipulation
+  - Formula handling
+  - Data analysis
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="Excel Agent" icon="user-robot">
+    Create specialized Excel agents:
+    ```python
+    Agent(tools=[read_excel, write_excel, merge_excel])
+    ```
+  </Card>
+  <Card title="Excel Task" icon="list-check">
+    Define Excel tasks:
+    ```python
+    Task(description="excel_operation")
+    ```
+  </Card>
+  <Card title="Process Types" icon="arrows-split-up-and-left">
+    Sequential or parallel processing:
+    ```python
+    process="sequential"
+    ```
+  </Card>
+  <Card title="Excel Options" icon="sliders">
+    Customize Excel parameters:
+    ```python
+    sheet_name="Sheet1", header=0
+    ```
+  </Card>
+</CardGroup>
+
+## Available Functions
+
+```python
+from praisonaiagents.tools import read_excel
+from praisonaiagents.tools import write_excel
+from praisonaiagents.tools import merge_excel
+```
+
+## Function Details
+
+### read_excel(filepath: str, sheet_name: Optional[Union[str, int, List[Union[str, int]]]] = 0, header: Optional[int] = 0, usecols: Optional[List[str]] = None, skiprows: Optional[Union[int, List[int]]] = None, na_values: Optional[List[str]] = None, dtype: Optional[Dict[str, str]] = None)
+
+Reads Excel files with advanced options:
+- Support for multiple sheets
+- Flexible header handling
+- Column selection
+- Row skipping
+- Custom NA values
+- Data type specification
+
+```python
+# Basic usage - read first sheet
+data = read_excel("data.xlsx")
+
+# Read specific sheets
+data = read_excel(
+    "data.xlsx",
+    sheet_name=['Sheet1', 'Sheet2'],
+    header=0,
+    usecols=['A', 'B', 'C'],
+    skiprows=[0, 1],
+    na_values=['NA', 'missing'],
+    dtype={'age': 'int32', 'salary': 'float64'}
+)
+# Returns: Dict[str, List[Dict[str, Any]]] for multiple sheets
+# Example: {
+#   'Sheet1': [{"name": "Alice", "age": 25}, ...],
+#   'Sheet2': [{"dept": "Engineering", "count": 10}, ...]
+# }
+```
+
+### write_excel(filepath: str, data: Union[Dict[str, List[Dict[str, Any]]], List[Dict[str, Any]]], sheet_name: Optional[str] = None, index: bool = False, header: bool = True, mode: str = 'w')
+
+Writes data to Excel files with formatting:
+- Multiple sheet support
+- Append mode for existing files
+- Optional row indices
+- Optional headers
+- Flexible data structure handling
+
+```python
+# Write single sheet
+data = [
+    {"name": "Alice", "age": 25, "city": "New York"},
+    {"name": "Bob", "age": 30, "city": "San Francisco"}
+]
+success = write_excel("output.xlsx", data, sheet_name="Employees")
+
+# Write multiple sheets
+data = {
+    "Employees": [
+        {"name": "Alice", "age": 25},
+        {"name": "Bob", "age": 30}
+    ],
+    "Departments": [
+        {"name": "Engineering", "count": 50},
+        {"name": "Sales", "count": 30}
+    ]
+}
+success = write_excel(
+    "company.xlsx",
+    data,
+    index=False,
+    header=True,
+    mode='w'
+)
+# Returns: bool (True if successful)
+```
+
+### merge_excel(files: List[str], output_file: str, how: str = 'inner', on: Optional[Union[str, List[str]]] = None, suffixes: Optional[Tuple[str, str]] = None)
+
+Merges multiple Excel files:
+- Flexible join operations
+- Multiple key columns support
+- Custom column suffix handling
+- Preserves data types
+
+```python
+# Merge two files on a common column
+success = merge_excel(
+    files=["employees.xlsx", "salaries.xlsx"],
+    output_file="merged.xlsx",
+    how='inner',
+    on='employee_id'
+)
+
+# Advanced merge with multiple keys and custom suffixes
+success = merge_excel(
+    files=["data2022.xlsx", "data2023.xlsx"],
+    output_file="combined.xlsx",
+    how='outer',
+    on=['id', 'department'],
+    suffixes=('_2022', '_2023')
+)
+# Returns: bool (True if successful)
+```
+
+## Examples
+
+### Basic Excel Processing Agent
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import read_excel, write_excel, merge_excel
+
+# Create Excel agent
+excel_agent = Agent(
+    name="ExcelExpert",
+    role="Excel Processing Specialist",
+    goal="Process Excel files efficiently and accurately.",
+    backstory="Expert in spreadsheet manipulation and analysis.",
+    tools=[read_excel, write_excel, merge_excel],
+    self_reflect=False
+)
+
+# Define Excel task
+excel_task = Task(
+    description="Process and analyze sales data.",
+    expected_output="Sales analysis report.",
+    agent=excel_agent,
+    name="sales_analysis"
+)
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[excel_agent],
+    tasks=[excel_task],
+    process="sequential"
+)
+agents.start()
+```
+
+### Advanced Excel Processing with Multiple Agents
+
+```python
+# Create data processing agent
+processor_agent = Agent(
+    name="Processor",
+    role="Data Processor",
+    goal="Process Excel data systematically.",
+    tools=[read_excel, write_excel, merge_excel],
+    self_reflect=False
+)
+
+# Create analysis agent
+analysis_agent = Agent(
+    name="Analyzer",
+    role="Data Analyst",
+    goal="Analyze processed Excel data.",
+    backstory="Expert in data analysis and reporting.",
+    self_reflect=False
+)
+
+# Define tasks
+processing_task = Task(
+    description="Process sales spreadsheets.",
+    agent=processor_agent,
+    name="data_processing"
+)
+
+analysis_task = Task(
+    description="Analyze sales trends and patterns.",
+    agent=analysis_agent,
+    name="data_analysis"
+)
+
+# Run agents
+agents = PraisonAIAgents(
+    agents=[processor_agent, analysis_agent],
+    tasks=[processing_task, analysis_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Configuration">
+    Configure agents with clear Excel focus:
+    ```python
+    Agent(
+        name="ExcelProcessor",
+        role="Excel Processing Specialist",
+        goal="Process Excel files accurately and efficiently",
+        tools=[read_excel, write_excel, merge_excel]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Task Definition">
+    Define specific Excel operations:
+    ```python
+    Task(
+        description="Process sales data and generate reports"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### Excel Processing Pipeline
+```python
+# Processing agent
+processor = Agent(
+    name="Processor",
+    role="Excel Processor",
+    tools=[read_excel, write_excel, merge_excel]
+)
+
+# Analysis agent
+analyzer = Agent(
+    name="Analyzer",
+    role="Data Analyst"
+)
+
+# Define tasks
+process_task = Task(
+    description="Process Excel files",
+    agent=processor
+)
+
+analyze_task = Task(
+    description="Analyze processed data",
+    agent=analyzer
+)
+
+# Run workflow
+agents = PraisonAIAgents(
+    agents=[processor, analyzer],
+    tasks=[process_task, analyze_task]
+)
+
+```
+
+## Dependencies
+
+The Excel tools require the following Python packages:
+- pandas: For data manipulation
+- openpyxl: For Excel file operations
+
+These will be automatically installed when needed.
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import read_excel, write_excel, merge_excel
+
+agent = Agent(
+    name="ExcelProcessor",
+    description="An agent that processes Excel files",
+    tools=[read_excel, write_excel, merge_excel]
+)
+```
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- File not found errors
+- Permission errors
+- Invalid sheet names
+- Data format errors
+- Missing dependency errors
+
+Errors are logged and returned in a consistent format:
+- Success cases return the expected data type
+- Error cases return a dict with an "error" key containing the error message
+
+## Common Use Cases
+
+1. Data Analysis:
+```python
+# Read and analyze employee data
+data = read_excel(
+    "employees.xlsx",
+    sheet_name="Salaries",
+    usecols=["department", "salary"]
+)
+
+# Write summary to new sheet
+summary = [
+    {"department": dept, "avg_salary": sum(d["salary"] for d in dept_data) / len(dept_data)}
+    for dept, dept_data in groupby(data, key=lambda x: x["department"])
+]
+write_excel("summary.xlsx", summary, sheet_name="Salary Summary")
+```
+
+2. Data Consolidation:
+```python
+# Merge monthly reports
+monthly_files = ["jan.xlsx", "feb.xlsx", "mar.xlsx"]
+merge_excel(
+    files=monthly_files,
+    output_file="q1_report.xlsx",
+    how='outer',
+    on='transaction_id'
+)
+```
+
+3. Multi-sheet Processing:
+```python
+# Read multiple sheets
+data = read_excel(
+    "company_data.xlsx",
+    sheet_name=None  # Read all sheets
+)
+
+# Process each sheet
+processed = {
+    sheet: [process_record(record) for record in records]
+    for sheet, records in data.items()
+}
+
+# Write back to new file
+write_excel("processed_data.xlsx", processed)
+
+```

docs/tools/file_tools.mdx

@@ -0,0 +1,472 @@
+---
+title: "File Agent"
+description: "File system operation tools for AI agents."
+icon: "folder"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - Basic understanding of file operations
+</Note>
+
+## File Tools
+
+Use File Tools to perform file system operations with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required package:
+    ```bash
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import read_file, write_file, list_files, get_file_info, copy_file, move_file, delete_file
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create a file management agent:
+    ```python
+    file_agent = Agent(
+        name="FileManager",
+        role="File System Specialist",
+        goal="Manage files and directories efficiently.",
+        backstory="Expert in file system operations and organization.",
+        tools=[read_file, write_file, list_files, get_file_info, copy_file, move_file, delete_file],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the file management task:
+    ```python
+    file_task = Task(
+        description="Organize files in the downloads directory by file type.",
+        expected_output="Organized directory structure with categorized files.",
+        agent=file_agent,
+        name="organize_files"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[file_agent],
+        tasks=[file_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Understanding File Tools
+
+<Card title="What are File Tools?" icon="question">
+  File Tools provide file system management capabilities for AI agents:
+  - File operations (read, write, copy, move)
+  - Directory management
+  - File information retrieval
+  - File organization
+  - Path manipulation
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="File Agent" icon="user-robot">
+    Create specialized file agents:
+    ```python
+    Agent(tools=[read_file, write_file, list_files, get_file_info, copy_file, move_file, delete_file])
+    ```
+  </Card>
+  <Card title="File Task" icon="list-check">
+    Define file tasks:
+    ```python
+    Task(description="file_operation")
+    ```
+  </Card>
+  <Card title="Process Types" icon="arrows-split-up-and-left">
+    Sequential or parallel processing:
+    ```python
+    process="sequential"
+    ```
+  </Card>
+  <Card title="File Options" icon="sliders">
+    Customize file operations:
+    ```python
+    recursive=True, overwrite=False
+    ```
+  </Card>
+</CardGroup>
+
+## Available Functions
+
+```python
+from praisonaiagents.tools import read_file
+from praisonaiagents.tools import write_file
+from praisonaiagents.tools import list_files
+from praisonaiagents.tools import get_file_info
+from praisonaiagents.tools import copy_file
+from praisonaiagents.tools import move_file
+from praisonaiagents.tools import delete_file
+```
+
+## Function Details
+
+### read_file(filepath: str, encoding: str = 'utf-8')
+
+Reads content from a file:
+- Supports different encodings
+- Error handling for missing files
+- Automatic encoding detection
+
+```python
+# Basic usage
+content = read_file("example.txt")
+
+# With specific encoding
+content = read_file("data.txt", encoding="latin-1")
+
+# Returns: str (file content)
+# Error case: Returns error message as string
+```
+
+### write_file(filepath: str, content: str, encoding: str = 'utf-8')
+
+Writes content to a file:
+- Creates directories if they don't exist
+- Supports different encodings
+- Overwrites existing files
+
+```python
+# Basic usage
+success = write_file("output.txt", "Hello World")
+
+# With specific encoding and nested path
+success = write_file(
+    "path/to/data/output.txt",
+    "Content with special chars: áéíóú",
+    encoding="utf-8"
+)
+# Returns: bool (True if successful)
+```
+
+### list_files(directory: str, pattern: Optional[str] = None)
+
+Lists files in a directory:
+- Optional pattern matching
+- Detailed file information
+- Recursive directory support
+
+```python
+# List all files
+files = list_files("/path/to/directory")
+
+# With pattern matching
+files = list_files("/path/to/directory", pattern="*.txt")
+
+# Returns: List[Dict[str, Union[str, int]]]
+# Example: [
+#   {
+#     'name': 'example.txt',
+#     'path': '/path/to/directory/example.txt',
+#     'size': 1024,
+#     'modified': 1609459200.0,
+#     'created': 1609459100.0
+#   },
+#   ...
+# ]
+```
+
+### get_file_info(filepath: str)
+
+Gets detailed information about a file:
+- File metadata
+- Size and timestamps
+- Type information
+- Path details
+
+```python
+# Get file information
+info = get_file_info("example.txt")
+
+# Returns: Dict[str, Union[str, int]]
+# Example: {
+#   'name': 'example.txt',
+#   'path': '/absolute/path/to/example.txt',
+#   'size': 1024,
+#   'modified': 1609459200.0,
+#   'created': 1609459100.0,
+#   'is_file': True,
+#   'is_dir': False,
+#   'extension': '.txt',
+#   'parent': '/absolute/path/to'
+# }
+```
+
+### copy_file(src: str, dst: str)
+
+Copies a file with metadata:
+- Preserves timestamps
+- Creates destination directories
+- Handles existing files
+
+```python
+# Basic copy
+success = copy_file("source.txt", "destination.txt")
+
+# Copy to new directory
+success = copy_file(
+    "data.txt",
+    "backup/2023/data_backup.txt"
+)
+# Returns: bool (True if successful)
+```
+
+### move_file(src: str, dst: str)
+
+Moves or renames a file:
+- Creates destination directories
+- Handles existing files
+- Cross-device moves
+
+```python
+# Move file
+success = move_file("old/path/file.txt", "new/path/file.txt")
+
+# Rename file
+success = move_file(
+    "document.txt",
+    "document_v2.txt"
+)
+# Returns: bool (True if successful)
+```
+
+### delete_file(filepath: str)
+
+Deletes a file:
+- Safe deletion
+- Error handling
+- Non-recursive (files only)
+
+```python
+# Delete file
+success = delete_file("unwanted.txt")
+# Returns: bool (True if successful)
+```
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import (
+    read_file, write_file, list_files,
+    get_file_info, copy_file, move_file, delete_file
+)
+
+agent = Agent(
+    name="FileManager",
+    description="An agent that manages files",
+    tools=[
+        read_file, write_file, list_files,
+        get_file_info, copy_file, move_file, delete_file
+    ]
+)
+```
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- File not found errors
+- Permission errors
+- Path errors
+- I/O errors
+- Encoding errors
+
+Errors are handled consistently:
+- File operations return bool for success/failure
+- Information functions return error details in result
+- All errors are logged for debugging
+
+## Common Use Cases
+
+1. File Organization:
+```python
+# List and organize files by extension
+files = list_files("downloads", pattern="*.*")
+for file in files:
+    ext = file['extension']
+    if ext:
+        dst = f"organized/{ext[1:]}/{file['name']}"
+        move_file(file['path'], dst)
+```
+
+2. File Backup:
+```python
+# Create timestamped backups
+import time
+files = list_files("important_data")
+backup_time = time.strftime("%Y%m%d_%H%M%S")
+for file in files:
+    dst = f"backup/{backup_time}/{file['name']}"
+    copy_file(file['path'], dst)
+```
+
+3. File Processing:
+```python
+# Read, process, and write files
+content = read_file("input.txt")
+processed = process_content(content)  # Your processing function
+write_file("output.txt", processed)
+```
+
+## Examples
+
+### Basic File Management Agent
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import read_file, write_file, list_files, get_file_info
+
+# Create file management agent
+file_agent = Agent(
+    name="FileExpert",
+    role="File Manager",
+    goal="Process and organize files efficiently.",
+    backstory="Expert in file system operations and organization.",
+    tools=[read_file, write_file, list_files, get_file_info],
+    self_reflect=False
+)
+
+# Define file task
+file_task = Task(
+    description="Organize and process documents.",
+    expected_output="Organized file structure.",
+    agent=file_agent,
+    name="file_organization"
+)
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[file_agent],
+    tasks=[file_task],
+    process="sequential"
+)
+agents.start()
+```
+
+### Advanced File Operations with Multiple Agents
+
+```python
+# Create file IO agent
+io_agent = Agent(
+    name="FileIO",
+    role="File IO Specialist",
+    goal="Handle file read/write operations efficiently.",
+    tools=[read_file, write_file],
+    self_reflect=False
+)
+
+# Create file management agent
+management_agent = Agent(
+    name="FileManager",
+    role="File Management Specialist",
+    goal="Handle file organization and movement.",
+    tools=[list_files, get_file_info, copy_file, move_file, delete_file],
+    self_reflect=False
+)
+
+# Define tasks
+io_task = Task(
+    description="Read and write files.",
+    expected_output="File operations completed successfully.",
+    agent=io_agent,
+    name="file_io"
+)
+
+management_task = Task(
+    description="Organize and move files.",
+    expected_output="File management completed successfully.",
+    agent=management_agent,
+    name="file_management"
+)
+
+# Run agents
+agents = PraisonAIAgents(
+    agents=[io_agent, management_agent],
+    tasks=[io_task, management_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Configuration">
+    Configure agents with clear file management focus:
+    ```python
+    Agent(
+        name="FileManager",
+        role="File System Specialist",
+        goal="Manage files efficiently and safely",
+        tools=[read_file, write_file, list_files, get_file_info, copy_file, move_file, delete_file]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Task Definition">
+    Define specific file operations:
+    ```python
+    Task(
+        description="Clean up downloads folder and organize by file type",
+        expected_output="Organized directory structure"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### File Organization Pipeline
+```python
+# Organization agent
+organizer = Agent(
+    name="Organizer",
+    role="File Organizer",
+    tools=[read_file, write_file, list_files, get_file_info, copy_file, move_file, delete_file]
+)
+
+# Cleanup agent
+cleaner = Agent(
+    name="Cleaner",
+    role="File Cleaner"
+)
+
+# Define tasks
+organize_task = Task(
+    description="Organize files by type",
+    agent=organizer
+)
+
+cleanup_task = Task(
+    description="Remove temporary files",
+    agent=cleaner
+)
+
+# Run workflow
+agents = PraisonAIAgents(
+    agents=[organizer, cleaner],
+    tasks=[organize_task, cleanup_task]
+)

docs/tools/json_tools.mdx

@@ -0,0 +1,587 @@
+---
+title: "JSON Agent"
+description: "JSON data processing tools for AI agents."
+icon: "brackets-curly"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - Basic understanding of JSON format
+</Note>
+
+## JSON Tools
+
+Use JSON Tools to process and manipulate JSON files with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required package:
+    ```bash
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import (
+        read_json, write_json, merge_json,
+        validate_json, analyze_json, transform_json
+    )
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create a JSON processing agent:
+    ```python
+    json_agent = Agent(
+        name="JSONProcessor",
+        role="JSON Processing Specialist",
+        goal="Process JSON files efficiently and accurately.",
+        backstory="Expert in JSON file manipulation and validation.",
+        tools=[
+            read_json, write_json, merge_json,
+            validate_json, analyze_json, transform_json
+        ],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the JSON processing task:
+    ```python
+    json_task = Task(
+        description="Parse and validate API response data.",
+        expected_output="Validated and processed JSON data.",
+        agent=json_agent,
+        name="json_processing"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[json_agent],
+        tasks=[json_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Understanding JSON Tools
+
+<Card title="What are JSON Tools?" icon="question">
+  JSON Tools provide JSON processing capabilities for AI agents:
+  - File reading and writing
+  - Data validation
+  - Schema validation
+  - Data transformation
+  - Structure analysis
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="JSON Agent" icon="user-robot">
+    Create specialized JSON agents:
+    ```python
+    Agent(tools=[read_json, write_json, merge_json, validate_json, analyze_json, transform_json])
+    ```
+  </Card>
+  <Card title="JSON Task" icon="list-check">
+    Define JSON tasks:
+    ```python
+    Task(description="json_operation")
+    ```
+  </Card>
+  <Card title="Process Types" icon="arrows-split-up-and-left">
+    Sequential or parallel processing:
+    ```python
+    process="sequential"
+    ```
+  </Card>
+  <Card title="JSON Options" icon="sliders">
+    Customize JSON operations:
+    ```python
+    indent=2, sort_keys=True
+    ```
+  </Card>
+</CardGroup>
+
+## Available Functions
+
+```python
+from praisonaiagents.tools import read_json
+from praisonaiagents.tools import write_json
+from praisonaiagents.tools import merge_json
+from praisonaiagents.tools import validate_json
+from praisonaiagents.tools import analyze_json
+from praisonaiagents.tools import transform_json
+```
+
+## Function Details
+
+### read_json(filepath: str, encoding: str = 'utf-8', validate_schema: Optional[Dict[str, Any]] = None)
+
+Reads JSON files with schema validation:
+- Optional schema validation
+- Custom encoding support
+- Error handling
+
+```python
+# Basic usage
+data = read_json("config.json")
+
+# With schema validation
+schema = {
+    "type": "object",
+    "properties": {
+        "name": {"type": "string"},
+        "age": {"type": "integer"}
+    }
+}
+data = read_json(
+    "user.json",
+    encoding="utf-8",
+    validate_schema=schema
+)
+
+# Returns: Dict[str, Any] (JSON data)
+# Error case: Returns dict with 'error' key
+```
+
+### write_json(data: Union[Dict[str, Any], List[Any]], filepath: str, encoding: str = 'utf-8', indent: int = 2, sort_keys: bool = False, ensure_ascii: bool = False)
+
+Writes data to JSON files:
+- Pretty printing with indentation
+- Optional key sorting
+- Unicode support
+- Directory creation
+
+```python
+# Basic usage
+data = {"name": "Alice", "age": 30}
+success = write_json(data, "output.json")
+
+# With formatting options
+data = {
+    "users": [
+        {"name": "Alice", "age": 30},
+        {"name": "Bob", "age": 25}
+    ]
+}
+success = write_json(
+    data,
+    "users.json",
+    indent=4,
+    sort_keys=True,
+    ensure_ascii=False
+)
+# Returns: bool (True if successful)
+```
+
+### merge_json(files: List[str], output_file: str, merge_arrays: bool = True, overwrite_duplicates: bool = True)
+
+Merges multiple JSON files:
+- Deep merging of objects
+- Array handling options
+- Duplicate key handling
+- Nested structure support
+
+```python
+# Merge configuration files
+success = merge_json(
+    files=["config1.json", "config2.json"],
+    output_file="merged_config.json"
+)
+
+# Advanced merge with options
+success = merge_json(
+    files=["base.json", "override.json"],
+    output_file="final.json",
+    merge_arrays=True,
+    overwrite_duplicates=False
+)
+# Returns: bool (True if successful)
+```
+
+### validate_json(data: Union[Dict[str, Any], str], schema: Dict[str, Any])
+
+Validates JSON against a schema:
+- JSON Schema support
+- Detailed error messages
+- File or data validation
+
+```python
+# Validate data
+schema = {
+    "type": "object",
+    "required": ["name", "email"],
+    "properties": {
+        "name": {"type": "string"},
+        "email": {"type": "string", "format": "email"},
+        "age": {"type": "integer", "minimum": 0}
+    }
+}
+
+# Validate data directly
+data = {"name": "Alice", "email": "[email protected]", "age": 25}
+is_valid, error = validate_json(data, schema)
+
+# Validate file
+is_valid, error = validate_json("user.json", schema)
+# Returns: Tuple[bool, Optional[str]]
+```
+
+### analyze_json(data: Union[Dict[str, Any], str], max_depth: int = 10)
+
+Analyzes JSON structure:
+- Type information
+- Size metrics
+- Nested structure analysis
+- Sample data
+
+```python
+# Analyze file
+analysis = analyze_json("data.json", max_depth=5)
+
+# Analyze data structure
+data = {
+    "users": [
+        {"name": "Alice", "scores": [95, 87, 92]},
+        {"name": "Bob", "scores": [88, 85, 90]}
+    ]
+}
+analysis = analyze_json(data)
+
+# Returns: Dict[str, Any]
+# Example output:
+# {
+#   'analysis_time': '2023-01-01T12:00:00',
+#   'structure': {
+#     'type': 'dict',
+#     'size': 1,
+#     'keys': ['users'],
+#     'children': {
+#       'users': {
+#         'type': 'list',
+#         'length': 2,
+#         'element_types': ['dict'],
+#         'sample_elements': [...]
+#       }
+#     }
+#   }
+# }
+```
+
+### transform_json(data: Union[Dict[str, Any], str], transformations: List[Dict[str, Any]])
+
+Transforms JSON data:
+- Multiple operations
+- Path-based modifications
+- Nested transformations
+- Operation types: set, delete, rename, move
+
+```python
+# Transform data
+transformations = [
+    {
+        "operation": "set",
+        "path": "user.settings.theme",
+        "value": "dark"
+    },
+    {
+        "operation": "move",
+        "path": "old.config",
+        "value": "new.config"
+    },
+    {
+        "operation": "delete",
+        "path": "temporary.data"
+    }
+]
+
+# Transform file
+result = transform_json("config.json", transformations)
+
+# Transform data directly
+data = {"user": {"name": "Alice"}}
+result = transform_json(data, transformations)
+# Returns: Dict[str, Any] (transformed data)
+```
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import (
+    read_json, write_json, merge_json,
+    validate_json, analyze_json, transform_json
+)
+
+agent = Agent(
+    name="JSONProcessor",
+    description="An agent that processes JSON data",
+    tools=[
+        read_json, write_json, merge_json,
+        validate_json, analyze_json, transform_json
+    ]
+)
+```
+
+## Dependencies
+
+The JSON tools require the following Python packages:
+- jsonschema: For JSON schema validation
+
+These will be automatically installed when needed.
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- File I/O errors
+- JSON parsing errors
+- Schema validation errors
+- Transformation errors
+
+Errors are handled consistently:
+- File operations return bool for success/failure
+- Data operations return error details in result
+- All errors are logged for debugging
+
+## Common Use Cases
+
+1. Configuration Management:
+```python
+# Merge multiple config files
+success = merge_json(
+    ["base_config.json", "env_config.json", "user_config.json"],
+    "final_config.json",
+    merge_arrays=True,
+    overwrite_duplicates=True
+)
+
+# Validate configuration
+schema = {
+    "type": "object",
+    "required": ["database", "api"],
+    "properties": {
+        "database": {
+            "type": "object",
+            "required": ["host", "port"]
+        },
+        "api": {
+            "type": "object",
+            "required": ["key"]
+        }
+    }
+}
+is_valid, error = validate_json("final_config.json", schema)
+```
+
+2. Data Analysis:
+```python
+# Analyze data structure
+analysis = analyze_json("large_dataset.json", max_depth=3)
+print(f"Dataset size: {analysis['structure']['size']} entries")
+print(f"Available fields: {analysis['structure']['keys']}")
+```
+
+3. Data Transformation:
+```python
+# Transform data format
+transformations = [
+    {"operation": "rename", "path": "firstName", "value": "first_name"},
+    {"operation": "rename", "path": "lastName", "value": "last_name"},
+    {"operation": "move", "path": "address.zip", "value": "address.postal_code"},
+    {"operation": "delete", "path": "temporary_data"}
+]
+result = transform_json("user_data.json", transformations)
+write_json(result, "transformed_data.json")
+```
+
+## Understanding JSON Tools
+
+<Card title="What are JSON Tools?" icon="question">
+  JSON Tools provide JSON processing capabilities for AI agents:
+  - File reading and writing
+  - Data validation
+  - Schema validation
+  - Data transformation
+  - Structure analysis
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="JSON Agent" icon="user-robot">
+    Create specialized JSON agents:
+    ```python
+    Agent(tools=[read_json, write_json, merge_json, validate_json, analyze_json, transform_json])
+    ```
+  </Card>
+  <Card title="JSON Task" icon="list-check">
+    Define JSON tasks:
+    ```python
+    Task(description="json_operation")
+    ```
+  </Card>
+  <Card title="Process Types" icon="arrows-split-up-and-left">
+    Sequential or parallel processing:
+    ```python
+    process="sequential"
+    ```
+  </Card>
+  <Card title="JSON Options" icon="sliders">
+    Customize JSON operations:
+    ```python
+    indent=2, sort_keys=True
+    ```
+  </Card>
+</CardGroup>
+
+## Examples
+
+### Basic JSON Processing Agent
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import read_json, write_json, validate_json
+
+# Create JSON processing agent
+json_agent = Agent(
+    name="JSONExpert",
+    role="JSON Processor",
+    goal="Process and validate JSON data efficiently.",
+    backstory="Expert in JSON processing and validation.",
+    tools=[read_json, write_json, validate_json],
+    self_reflect=False
+)
+
+# Define JSON task
+json_task = Task(
+    description="Process and validate configuration files.",
+    expected_output="Validated JSON configuration.",
+    agent=json_agent,
+    name="config_validation"
+)
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[json_agent],
+    tasks=[json_task],
+    process="sequential"
+)
+agents.start()
+```
+
+### Advanced JSON Operations with Multiple Agents
+
+```python
+# Create JSON validation agent
+validation_agent = Agent(
+    name="Validator",
+    role="JSON Validation Specialist",
+    goal="Ensure JSON data integrity and schema compliance.",
+    tools=[validate_json, analyze_json],
+    self_reflect=False
+)
+
+# Create JSON transformation agent
+transform_agent = Agent(
+    name="Transformer",
+    role="JSON Transformation Specialist",
+    goal="Transform and merge JSON data structures.",
+    tools=[transform_json, merge_json],
+    self_reflect=False
+)
+
+# Define tasks
+validation_task = Task(
+    description="Validate JSON data",
+    agent=validation_agent,
+    name="json_validation"
+)   
+
+transform_task = Task(
+    description="Transform JSON data",
+    agent=transform_agent,
+    name="json_transformation"
+)   
+
+# Run agents
+agents = PraisonAIAgents(
+    agents=[validation_agent, transform_agent],
+    tasks=[validation_task, transform_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Configuration">
+    Configure agents with clear JSON focus:
+    ```python
+    Agent(
+        name="JSONProcessor",
+        role="JSON Processing Specialist",
+        goal="Process JSON files accurately and safely",
+        tools=[read_json, write_json, merge_json, validate_json, analyze_json, transform_json]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Task Definition">
+    Define specific JSON operations:
+    ```python
+    Task(
+        description="Parse and validate API response data",
+        expected_output="Validated data structure"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### JSON Processing Pipeline
+```python
+# Processing agent
+processor = Agent(
+    name="Processor",
+    role="JSON Processor",
+    tools=[read_json, write_json, merge_json, validate_json, analyze_json, transform_json]
+)
+
+# Validation agent
+validator = Agent(
+    name="Validator",
+    role="Data Validator"
+)
+
+# Define tasks
+process_task = Task(
+    description="Process JSON files",
+    agent=processor
+)
+
+validate_task = Task(
+    description="Validate processed data",
+    agent=validator
+)
+
+# Run workflow
+agents = PraisonAIAgents(
+    agents=[processor, validator],
+    tasks=[process_task, validate_task]
+)

docs/tools/newspaper_tools.mdx

@@ -0,0 +1,464 @@
+---
+title: "Newspaper Agent"
+description: "News article extraction tools for AI agents."
+icon: "newspaper"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - `newspaper3k` package installed
+</Note>
+
+## Newspaper Tools
+
+Use Newspaper Tools to extract and analyze news articles with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required packages:
+    ```bash
+    pip install praisonaiagents newspaper3k
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import get_article, get_news_sources, get_articles_from_source, get_trending_topics
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create a news agent:
+    ```python
+    news_agent = Agent(
+        name="NewsAgent",
+        role="News Analyst",
+        goal="Collect and analyze news articles from various sources.",
+        backstory="Expert in news gathering and content analysis.",
+        tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the news task:
+    ```python
+    news_task = Task(
+        description="Analyze news articles about 'AI developments' from major tech news sources.",
+        expected_output="Summary of key AI developments with source articles.",
+        agent=news_agent,
+        name="ai_news"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[news_agent],
+        tasks=[news_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Understanding Newspaper Tools
+
+<Card title="What are Newspaper Tools?" icon="question">
+  Newspaper Tools provide news article processing capabilities for AI agents:
+  - Article extraction
+  - Source management
+  - Content analysis
+  - Trend detection
+  - Multi-language support
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="News Agent" icon="user-robot">
+    Create specialized news agents:
+    ```python
+    Agent(tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics])
+    ```
+  </Card>
+  <Card title="News Task" icon="list-check">
+    Define news tasks:
+    ```python
+    Task(description="news_query")
+    ```
+  </Card>
+  <Card title="Process Types" icon="arrows-split-up-and-left">
+    Sequential or parallel processing:
+    ```python
+    process="sequential"
+    ```
+  </Card>
+  <Card title="Article Options" icon="sliders">
+    Customize article retrieval:
+    ```python
+    language="en", limit=10
+    ```
+  </Card>
+</CardGroup>
+
+## Available Functions
+
+```python
+from praisonaiagents.tools import get_article
+from praisonaiagents.tools import get_news_sources
+from praisonaiagents.tools import get_articles_from_source
+from praisonaiagents.tools import get_trending_topics
+```
+
+## Function Details
+
+### get_article(url: str, language: str = 'en')
+
+Extracts and parses news articles:
+- Full text extraction
+- Author detection
+- Date parsing
+- Image extraction
+- NLP processing
+
+```python
+# Basic usage
+article = get_article("https://example.com/article")
+
+# With language specification
+article = get_article(
+    "https://lemonde.fr/article",
+    language="fr"
+)
+
+# Returns: Dict[str, Any]
+# Example output:
+# {
+#   "url": "https://example.com/article",
+#   "title": "Breaking News Story",
+#   "text": "Full article text...",
+#   "authors": ["John Doe", "Jane Smith"],
+#   "publish_date": "2025-01-07T05:31:38",
+#   "top_image": "https://example.com/image.jpg",
+#   "images": ["https://example.com/image1.jpg", ...],
+#   "movies": ["https://example.com/video1.mp4", ...],
+#   "source_domain": "example.com",
+#   "keywords": ["technology", "ai", ...],
+#   "summary": "Article summary..."
+# }
+```
+
+### get_news_sources(category: Optional[str] = None, language: str = 'en', country: Optional[str] = None)
+
+Gets news sources by category:
+- Category filtering
+- Language support
+- Country filtering
+- Domain information
+
+```python
+# Get all sources
+sources = get_news_sources()
+
+# Get technology news sources
+tech_sources = get_news_sources(
+    category="technology",
+    language="en",
+    country="us"
+)
+
+# Returns: List[Dict[str, str]]
+# Example output:
+# [
+#   {
+#     "url": "https://techcrunch.com",
+#     "domain": "techcrunch.com",
+#     "name": "TechCrunch",
+#     "category": "technology"
+#   },
+#   ...
+# ]
+```
+
+### get_articles_from_source(source_url: str, limit: int = 10, language: str = 'en')
+
+Gets recent articles from a source:
+- Configurable limit
+- Language support
+- Full article parsing
+- Error handling
+
+```python
+# Get latest articles
+articles = get_articles_from_source(
+    "https://techcrunch.com",
+    limit=5
+)
+
+# Get articles in specific language
+articles = get_articles_from_source(
+    "https://lemonde.fr",
+    limit=10,
+    language="fr"
+)
+
+# Returns: List[Dict[str, Any]]
+# Each article has the same structure as get_article()
+```
+
+### get_trending_topics(sources: Optional[List[str]] = None, limit: int = 10, language: str = 'en')
+
+Analyzes trending topics:
+- Cross-source analysis
+- Keyword extraction
+- Customizable sources
+- Topic ranking
+
+```python
+# Get trending topics from default sources
+topics = get_trending_topics(limit=5)
+
+# Get topics from specific sources
+topics = get_trending_topics(
+    sources=[
+        "https://techcrunch.com",
+        "https://wired.com"
+    ],
+    limit=10,
+    language="en"
+)
+
+# Returns: List[str]
+# Example: ["artificial intelligence", "climate change", ...]
+```
+
+## Examples
+
+### Basic News Agent
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import get_article, get_news_sources, get_articles_from_source, get_trending_topics
+
+# Create news agent
+news_agent = Agent(
+    name="NewsExpert",
+    role="News Analyst",
+    goal="Gather and analyze news content efficiently.",
+    backstory="Expert in news analysis and content curation.",
+    tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics],
+    self_reflect=False
+)
+
+# Define news task
+news_task = Task(
+    description="Analyze tech news trends.",
+    expected_output="Tech news analysis report.",
+    agent=news_agent,
+    name="tech_news_analysis"
+)
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[news_agent],
+    tasks=[news_task],
+    process="sequential"
+)
+agents.start()
+```
+
+### Advanced News Analysis with Multiple Agents
+
+```python
+# Create news collection agent
+collector_agent = Agent(
+    name="Collector",
+    role="News Collector",
+    goal="Gather comprehensive news coverage.",
+    tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics],
+    self_reflect=False
+)
+
+# Create analysis agent
+analysis_agent = Agent(
+    name="Analyzer",
+    role="Content Analyst",
+    goal="Analyze news content and identify trends.",
+    backstory="Expert in news analysis and trend identification.",
+    self_reflect=False
+)
+
+# Define tasks
+collection_task = Task(
+    description="Collect news articles about renewable energy developments.",
+    agent=collector_agent,
+    name="energy_news"
+)
+
+analysis_task = Task(
+    description="Analyze the articles and identify key trends and breakthroughs.",
+    agent=analysis_agent,
+    name="news_analysis"
+)
+
+# Run agents
+agents = PraisonAIAgents(
+    agents=[collector_agent, analysis_agent],
+    tasks=[collection_task, analysis_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Configuration">
+    Configure agents with clear news focus:
+    ```python
+    Agent(
+        name="NewsAnalyst",
+        role="News Specialist",
+        goal="Analyze news content effectively",
+        tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Task Definition">
+    Define specific news objectives:
+    ```python
+    Task(
+        description="Analyze tech news from major sources for AI developments",
+        expected_output="Trend analysis with key findings"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### News Monitoring
+```python
+# News monitor agent
+monitor = Agent(
+    name="Monitor",
+    role="News Monitor",
+    tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics]
+)
+
+# Analysis agent
+analyst = Agent(
+    name="Analyst",
+    role="News Analyst"
+)
+
+# Define tasks
+monitor_task = Task(
+    description="Monitor tech news sources",
+    agent=monitor
+)
+
+analysis_task = Task(
+    description="Analyze news trends",
+    agent=analyst
+)
+
+# Run workflow
+agents = PraisonAIAgents(
+    agents=[monitor, analyst],
+    tasks=[monitor_task, analysis_task]
+)
+
+```
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import (
+    get_article, get_news_sources,
+    get_articles_from_source, get_trending_topics
+)
+
+agent = Agent(
+    name="NewsAnalyzer",
+    description="An agent that analyzes news articles",
+    tools=[
+        get_article, get_news_sources,
+        get_articles_from_source, get_trending_topics
+    ]
+)
+```
+
+## Dependencies
+
+The newspaper tools require the following Python packages:
+- newspaper3k: For article extraction and parsing
+- nltk: For NLP processing (automatically installed with newspaper3k)
+
+These will be automatically installed when needed.
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- Network errors
+- Parsing errors
+- Language errors
+- Source availability errors
+
+Errors are handled consistently:
+- Success cases return the expected data type
+- Error cases return a dict with an "error" key
+- All errors are logged for debugging
+
+## Common Use Cases
+
+1. News Monitoring:
+```python
+# Monitor tech news
+sources = get_news_sources(category="technology")
+for source in sources:
+    articles = get_articles_from_source(
+        source["url"],
+        limit=5
+    )
+    for article in articles:
+        if "ai" in article.get("keywords", []):
+            print(f"AI article found: {article['title']}")
+```
+
+2. Trend Analysis:
+```python
+# Analyze trending topics
+topics = get_trending_topics(limit=10)
+print("Current hot topics:")
+for i, topic in enumerate(topics, 1):
+    print(f"{i}. {topic}")
+```
+
+3. Content Aggregation:
+```python
+# Aggregate news from multiple sources
+def aggregate_news(categories):
+    all_articles = []
+    for category in categories:
+        sources = get_news_sources(category=category)
+        for source in sources[:3]:  # Top 3 sources per category
+            articles = get_articles_from_source(
+                source["url"],
+                limit=3
+            )
+            all_articles.extend(articles)
+    return all_articles
+
+news = aggregate_news(["technology", "business", "science"])

docs/tools/pandas_tools.mdx

@@ -0,0 +1,584 @@
+---
+title: "Pandas Agent"
+description: "Pandas data manipulation tools for AI agents."
+icon: "table"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - `pandas` package installed
+</Note>
+
+## Pandas Tools
+
+Use Pandas Tools to manipulate and analyze data with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required packages:
+    ```bash
+    pip install praisonaiagents pandas
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create a data analysis agent:
+    ```python
+    data_agent = Agent(
+        name="DataAnalyst",
+        role="Data Analysis Specialist",
+        goal="Analyze and transform data efficiently.",
+        backstory="Expert in data manipulation and statistical analysis.",
+        tools=[read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the analysis task:
+    ```python
+    analysis_task = Task(
+        description="Analyze sales data to identify trends and patterns.",
+        expected_output="Statistical summary and key insights.",
+        agent=data_agent,
+        name="sales_analysis"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[data_agent],
+        tasks=[analysis_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Understanding Pandas Tools
+
+<Card title="What are Pandas Tools?" icon="question">
+  Pandas Tools provide data analysis capabilities for AI agents:
+  - Data filtering and selection
+  - Statistical analysis
+  - Data transformation
+  - Group operations
+  - Pivot table creation
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="Data Agent" icon="user-robot">
+    Create specialized data agents:
+    ```python
+    Agent(tools=[read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table])
+    ```
+  </Card>
+  <Card title="Analysis Task" icon="list-check">
+    Define analysis tasks:
+    ```python
+    Task(description="analysis_query")
+    ```
+  </Card>
+  <Card title="Process Types" icon="arrows-split-up-and-left">
+    Sequential or parallel processing:
+    ```python
+    process="sequential"
+    ```
+  </Card>
+  <Card title="Analysis Options" icon="sliders">
+    Customize analysis parameters:
+    ```python
+    group_by="category", agg_func="mean"
+    ```
+  </Card>
+</CardGroup>
+
+## Available Functions
+
+```python
+from praisonaiagents.tools import read_csv
+from praisonaiagents.tools import read_excel
+from praisonaiagents.tools import write_csv
+from praisonaiagents.tools import write_excel
+from praisonaiagents.tools import filter_data
+from praisonaiagents.tools import get_summary
+from praisonaiagents.tools import group_by
+from praisonaiagents.tools import pivot_table
+```
+
+## Function Details
+
+### read_csv(filepath: str, **kwargs)
+
+Reads CSV files into pandas DataFrames:
+- Flexible data import
+- Error handling
+- Additional pandas options
+
+```python
+# Basic usage
+df = read_csv("data.csv")
+
+# With additional options
+df = read_csv(
+    "data.csv",
+    encoding="utf-8",
+    sep=",",
+    header=0,
+    usecols=["name", "age", "salary"]
+)
+
+# Returns: pd.DataFrame
+# Error case: Returns dict with 'error' key
+```
+
+### read_excel(filepath: str, **kwargs)
+
+Reads Excel files into pandas DataFrames:
+- Multiple sheet support
+- Error handling
+- Additional pandas options
+
+```python
+# Basic usage
+df = read_excel("data.xlsx")
+
+# With sheet specification
+df = read_excel(
+    "data.xlsx",
+    sheet_name="Sheet1",
+    header=0,
+    usecols="A:D"
+)
+
+# Returns: pd.DataFrame
+# Error case: Returns dict with 'error' key
+```
+
+### write_csv(df: pd.DataFrame, filepath: str, **kwargs)
+
+Writes DataFrames to CSV files:
+- Directory creation
+- Error handling
+- Formatting options
+
+```python
+# Basic usage
+success = write_csv(df, "output.csv")
+
+# With formatting options
+success = write_csv(
+    df,
+    "output.csv",
+    index=False,
+    encoding="utf-8",
+    sep=",",
+    date_format="%Y-%m-%d"
+)
+# Returns: bool (True if successful)
+```
+
+### write_excel(df: pd.DataFrame, filepath: str, **kwargs)
+
+Writes DataFrames to Excel files:
+- Directory creation
+- Error handling
+- Excel-specific options
+
+```python
+# Basic usage
+success = write_excel(df, "output.xlsx")
+
+# With formatting options
+success = write_excel(
+    df,
+    "output.xlsx",
+    sheet_name="Data",
+    index=False,
+    freeze_panes=(1,0)
+)
+# Returns: bool (True if successful)
+```
+
+### filter_data(df: pd.DataFrame, query: str)
+
+Filters DataFrames using query strings:
+- SQL-like syntax
+- Complex conditions
+- Efficient filtering
+
+```python
+# Basic filtering
+filtered_df = filter_data(df, "age > 25")
+
+# Complex conditions
+filtered_df = filter_data(
+    df,
+    "age > 25 and salary >= 50000 and city == 'New York'"
+)
+
+# Returns: pd.DataFrame
+# Error case: Returns dict with 'error' key
+```
+
+### get_summary(df: pd.DataFrame)
+
+Generates comprehensive data summaries:
+- Basic statistics
+- Data types
+- Memory usage
+- Null counts
+
+```python
+# Get data summary
+summary = get_summary(df)
+
+# Returns: Dict[str, Any]
+# Example output:
+# {
+#   "shape": [1000, 5],
+#   "columns": ["name", "age", "salary", "city"],
+#   "dtypes": {
+#     "name": "object",
+#     "age": "int64",
+#     "salary": "float64",
+#     "city": "object"
+#   },
+#   "null_counts": {
+#     "name": 0,
+#     "age": 2,
+#     "salary": 1,
+#     "city": 0
+#   },
+#   "numeric_summary": {
+#     "age": {
+#       "count": 998,
+#       "mean": 35.5,
+#       "std": 12.3,
+#       "min": 18,
+#       "25%": 28,
+#       "50%": 35,
+#       "75%": 45,
+#       "max": 65
+#     },
+#     ...
+#   },
+#   "memory_usage": 40960
+# }
+```
+
+### group_by(df: pd.DataFrame, columns: Union[str, List[str]], agg_dict: Dict[str, Union[str, List[str]]])
+
+Groups and aggregates data:
+- Multiple grouping columns
+- Multiple aggregations
+- Custom functions
+
+```python
+# Basic grouping
+grouped_df = group_by(
+    df,
+    columns="city",
+    agg_dict={"salary": "mean"}
+)
+
+# Complex grouping
+grouped_df = group_by(
+    df,
+    columns=["city", "department"],
+    agg_dict={
+        "salary": ["mean", "sum", "count"],
+        "age": ["min", "max", "mean"]
+    }
+)
+
+# Returns: pd.DataFrame
+# Error case: Returns dict with 'error' key
+```
+
+### pivot_table(df: pd.DataFrame, index: Union[str, List[str]], columns: Optional[Union[str, List[str]]] = None, values: Optional[Union[str, List[str]]] = None, aggfunc: str = "mean")
+
+Creates pivot tables:
+- Multiple index levels
+- Column pivoting
+- Custom aggregations
+- Flexible reshaping
+
+```python
+# Basic pivot
+pivot_df = pivot_table(
+    df,
+    index="city",
+    columns="department",
+    values="salary"
+)
+
+# Complex pivot
+pivot_df = pivot_table(
+    df,
+    index=["city", "department"],
+    columns="year",
+    values=["salary", "headcount"],
+    aggfunc={
+        "salary": "mean",
+        "headcount": "sum"
+    }
+)
+
+# Returns: pd.DataFrame
+# Error case: Returns dict with 'error' key
+```
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import (
+    read_csv, read_excel, write_csv, write_excel,
+    filter_data, get_summary, group_by, pivot_table
+)
+
+agent = Agent(
+    name="DataAnalyzer",
+    description="An agent that analyzes data using pandas",
+    tools=[
+        read_csv, read_excel, write_csv, write_excel,
+        filter_data, get_summary, group_by, pivot_table
+    ]
+)
+```
+
+## Dependencies
+
+The pandas tools require the following Python packages:
+- pandas: For data manipulation and analysis
+- numpy: For numerical operations (automatically installed with pandas)
+- openpyxl: For Excel file support (optional)
+
+These will be automatically installed when needed.
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- File I/O errors
+- Data format errors
+- Query syntax errors
+- Memory errors
+
+Errors are handled consistently:
+- File operations return bool for success/failure
+- Data operations return error details in result
+- All errors are logged for debugging
+
+## Common Use Cases
+
+1. Data Analysis:
+```python
+# Load and analyze sales data
+df = read_csv("sales_data.csv")
+summary = get_summary(df)
+print(f"Total records: {summary['shape'][0]}")
+print(f"Missing values: {summary['null_counts']}")
+
+# Filter high-value transactions
+high_value = filter_data(df, "amount > 10000")
+write_excel(high_value, "high_value_sales.xlsx")
+```
+
+2. Sales Reporting:
+```python
+# Create sales summary by region and product
+sales_summary = group_by(
+    df,
+    columns=["region", "product"],
+    agg_dict={
+        "amount": ["sum", "mean", "count"],
+        "profit": ["sum", "mean"]
+    }
+)
+
+# Create pivot table for quarterly analysis
+quarterly_sales = pivot_table(
+    df,
+    index="product",
+    columns="quarter",
+    values="amount",
+    aggfunc="sum"
+)
+```
+
+3. Data Transformation:
+```python
+# Load and clean customer data
+customers = read_excel("customers.xlsx")
+filtered = filter_data(
+    customers,
+    "age >= 18 and status == 'active'"
+)
+
+# Group by demographics
+demographics = group_by(
+    filtered,
+    columns=["age_group", "region"],
+    agg_dict={
+        "spending": ["mean", "sum"],
+        "visits": "count"
+    }
+)
+write_csv(demographics, "demographic_analysis.csv")
+```
+
+## Examples
+
+### Basic Data Analysis Agent
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import read_csv, filter_data, get_summary, group_by
+
+# Create data analysis agent
+data_agent = Agent(
+    name="DataExpert",
+    role="Data Analyst",
+    goal="Process and analyze data efficiently.",
+    backstory="Expert in data analysis and statistical methods.",
+    tools=[read_csv, filter_data, get_summary, group_by],
+    self_reflect=False
+)
+
+# Define analysis task
+analysis_task = Task(
+    description="Analyze sales performance data.",
+    expected_output="Sales analysis report.",
+    agent=data_agent,
+    name="sales_analysis"
+)
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[data_agent],
+    tasks=[analysis_task],
+    process="sequential"
+)
+agents.start()
+```
+
+### Advanced Data Operations with Multiple Agents
+
+```python
+# Create data import/export agent
+io_agent = Agent(
+    name="DataIO",
+    role="Data IO Specialist",
+    goal="Handle data import and export operations.",
+    tools=[read_csv, read_excel, write_csv, write_excel],
+    self_reflect=False
+)
+
+# Create analysis agent
+analysis_agent = Agent(
+    name="Analyzer",
+    role="Data Analysis Specialist",
+    goal="Perform complex data analysis.",
+    tools=[filter_data, get_summary, group_by, pivot_table],
+    self_reflect=False
+)
+
+# Define tasks
+io_task = Task(
+    description="Import and export data",
+    agent=io_agent,
+    name="data_io"
+)
+
+analysis_task = Task(
+    description="Analyze data",
+    agent=analysis_agent,
+    name="data_analysis"
+)   
+
+# Run agents
+agents = PraisonAIAgents(
+    agents=[io_agent, analysis_agent],
+    tasks=[io_task, analysis_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Configuration">
+    Configure agents with clear analysis focus:
+    ```python
+    Agent(
+        name="DataScientist",
+        role="Data Analysis Specialist",
+        goal="Extract meaningful insights from data",
+        tools=[read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Task Definition">
+    Define specific analysis objectives:
+    ```python
+    Task(
+        description="Analyze sales trends and forecast future growth",
+        expected_output="Growth analysis with forecasts"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### Data Analysis Pipeline
+```python
+# Data preparation agent
+prep_agent = Agent(
+    name="Preparer",
+    role="Data Preparation",
+    tools=[read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table]
+)
+
+# Analysis agent
+analyst = Agent(
+    name="Analyst",
+    role="Data Analyst"
+)
+
+# Define tasks
+prep_task = Task(
+    description="Clean and prepare data",
+    agent=prep_agent
+)
+
+analysis_task = Task(
+    description="Analyze prepared data",
+    agent=analyst
+)
+
+# Run workflow
+agents = PraisonAIAgents(
+    agents=[prep_agent, analyst],
+    tasks=[prep_task, analysis_task]
+)

docs/tools/python_tools.mdx

@@ -0,0 +1,528 @@
+---
+title: "Python Agent"
+description: "Python code execution tools for AI agents."
+icon: "code"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - Basic understanding of Python programming
+</Note>
+
+## Python Tools
+
+Use Python Tools to execute and manage Python code with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required package:
+    ```bash
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import (
+        execute_code, analyze_code, format_code,
+        lint_code, disassemble_code
+    )
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create a Python execution agent:
+    ```python
+    python_agent = Agent(
+        name="PythonExecutor",
+        role="Python Code Specialist",
+        goal="Execute Python code efficiently and safely.",
+        backstory="Expert in Python programming and code execution.",
+        tools=[
+            execute_code, analyze_code, format_code,
+            lint_code, disassemble_code
+        ],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the Python execution task:
+    ```python
+    python_task = Task(
+        description="Execute and manage Python code.",
+        expected_output="Code execution results.",
+        agent=python_agent,
+        name="code_execution"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[python_agent],
+        tasks=[python_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Available Functions
+
+```python
+from praisonaiagents.tools import execute_code
+from praisonaiagents.tools import analyze_code
+from praisonaiagents.tools import format_code
+from praisonaiagents.tools import lint_code
+from praisonaiagents.tools import disassemble_code
+```
+
+## Function Details
+
+### execute_code(code: str, globals_dict: Optional[Dict[str, Any]] = None, locals_dict: Optional[Dict[str, Any]] = None, timeout: int = 30, max_output_size: int = 10000)
+
+Safely executes Python code:
+- Isolated execution environment
+- Output capture
+- Error handling
+- Timeout protection
+- Output size limits
+
+```python
+# Basic execution
+result = execute_code("print('Hello, World!')")
+
+# With custom environment
+result = execute_code(
+    """
+    x = 10
+    y = 20
+    print(f'Sum: {x + y}')
+    """,
+    globals_dict={'__builtins__': __builtins__},
+    timeout=5
+)
+
+# Returns: Dict[str, Any]
+# Example output:
+# {
+#     'result': None,
+#     'stdout': 'Hello, World!\n',
+#     'stderr': '',
+#     'success': True
+# }
+```
+
+### analyze_code(code: str)
+
+Analyzes Python code structure:
+- Import statements
+- Function definitions
+- Class definitions
+- Variable usage
+- Code complexity
+
+```python
+# Analyze code structure
+analysis = analyze_code("""
+def greet(name):
+    return f"Hello, {name}!"
+
+class Person:
+    def __init__(self, name):
+        self.name = name
+""")
+
+# Returns: Dict[str, Any]
+# Example output:
+# {
+#     'imports': [],
+#     'functions': [
+#         {
+#             'name': 'greet',
+#             'args': ['name'],
+#             'decorators': []
+#         }
+#     ],
+#     'classes': [
+#         {
+#             'name': 'Person',
+#             'bases': [],
+#             'decorators': []
+#         }
+#     ],
+#     'variables': ['name'],
+#     'complexity': {
+#         'lines': 6,
+#         'functions': 2,
+#         'classes': 1,
+#         'branches': 0
+#     }
+# }
+```
+
+### format_code(code: str, style: str = 'black', line_length: int = 88)
+
+Formats Python code:
+- Multiple style options
+- Line length control
+- PEP 8 compliance
+- Consistent formatting
+
+```python
+# Format with black
+formatted = format_code("""
+def messy_function(x,y,   z):
+    if x>0:
+     return y+z
+    else:
+     return y-z
+""")
+
+# Format with PEP 8
+formatted = format_code(
+    """
+    def messy_function(x,y,   z):
+        if x>0:
+         return y+z
+        else:
+         return y-z
+    """,
+    style='pep8',
+    line_length=79
+)
+
+# Returns: str
+# Example output:
+# def messy_function(x, y, z):
+#     if x > 0:
+#         return y + z
+#     else:
+#         return y - z
+```
+
+### lint_code(code: str)
+
+Lints Python code for issues:
+- Code quality checks
+- Style violations
+- Potential bugs
+- Best practices
+
+```python
+# Lint code for issues
+results = lint_code("""
+def bad_function():
+    unused_var = 42
+    return 'result'
+""")
+
+# Returns: Dict[str, List[Dict[str, Any]]]
+# Example output:
+# {
+#     'errors': [],
+#     'warnings': [
+#         {
+#             'type': 'warning',
+#             'module': 'bad_function',
+#             'obj': 'unused_var',
+#             'line': 2,
+#             'column': 4,
+#             'path': '<string>',
+#             'symbol': 'unused-variable',
+#             'message': 'Unused variable "unused_var"',
+#             'message-id': 'W0612'
+#         }
+#     ],
+#     'conventions': []
+# }
+```
+
+### disassemble_code(code: str)
+
+Disassembles Python code to bytecode:
+- Bytecode inspection
+- Performance analysis
+- Code optimization
+- Debugging support
+
+```python
+# Disassemble code to bytecode
+bytecode = disassemble_code("""
+def add(a, b):
+    return a + b
+""")
+
+# Returns: str
+# Example output:
+#  1           0 LOAD_CONST               0 (<code object add at ...>)
+#              2 LOAD_CONST               1 ('add')
+#              4 MAKE_FUNCTION            0
+#              6 STORE_NAME               0 (add)
+#              8 LOAD_CONST               2 (None)
+#             10 RETURN_VALUE
+```
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import (
+    execute_code, analyze_code, format_code,
+    lint_code, disassemble_code
+)
+
+agent = Agent(
+    name="PythonDeveloper",
+    description="An agent that helps with Python development",
+    tools=[
+        execute_code, analyze_code, format_code,
+        lint_code, disassemble_code
+    ]
+)
+```
+
+## Dependencies
+
+The Python tools require the following packages:
+- black: For code formatting (black style)
+- autopep8: For code formatting (PEP 8 style)
+- pylint: For code linting
+
+These will be automatically installed when needed.
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- Code execution errors
+- Syntax errors
+- Import errors
+- Timeout errors
+- Memory errors
+
+Errors are handled consistently:
+- Success cases return expected data type
+- Error cases return None or error details
+- All errors are logged for debugging
+
+## Common Use Cases
+
+1. Code Testing:
+```python
+# Test code execution
+test_code = """
+def factorial(n):
+    return 1 if n <= 1 else n * factorial(n - 1)
+
+result = factorial(5)
+print(f"Factorial: {result}")
+"""
+result = execute_code(test_code)
+print(f"Output: {result['stdout']}")
+```
+
+2. Code Quality:
+```python
+# Check code quality
+code = """
+def process_data(data):
+    processed = []
+    for item in data:
+        if item > 0:
+            processed.append(item * 2)
+    return processed
+"""
+analysis = analyze_code(code)
+lint_results = lint_code(code)
+formatted = format_code(code)
+```
+
+3. Code Analysis:
+```python
+# Analyze code structure
+code = """
+class DataProcessor:
+    def __init__(self, data):
+        self.data = data
+    
+    def process(self):
+        return [x * 2 for x in self.data]
+"""
+structure = analyze_code(code)
+bytecode = disassemble_code(code)
+print(f"Classes: {structure['classes']}")
+print(f"Functions: {structure['functions']}")
+```
+
+## Understanding Python Tools
+
+<Card title="What are Python Tools?" icon="question">
+  Python Tools provide code execution capabilities for AI agents:
+  - Code execution
+  - Module management
+  - Error handling
+  - Output capture
+  - Environment control
+</Card>
+
+## Examples
+
+### Basic Python Execution Agent
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import (
+    execute_code, analyze_code, format_code,
+    lint_code, disassemble_code
+)
+
+# Create Python agent
+python_agent = Agent(
+    name="PythonExpert",
+    role="Code Execution Specialist",
+    goal="Execute Python code efficiently and safely.",
+    backstory="Expert in Python programming and execution.",
+    tools=[
+        execute_code, analyze_code, format_code,
+        lint_code, disassemble_code
+    ],
+    self_reflect=False
+)
+
+# Define Python task
+python_task = Task(
+    description="Execute data processing scripts.",
+    expected_output="Processing results.",
+    agent=python_agent,
+    name="data_processing"
+)
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[python_agent],
+    tasks=[python_task],
+    process="sequential"
+)
+agents.start()
+```
+
+### Advanced Python Operations with Multiple Agents
+
+```python
+# Create execution agent
+executor_agent = Agent(
+    name="CodeExecutor",
+    role="Code Execution Specialist",
+    goal="Execute Python code efficiently.",
+    tools=[
+        execute_code, analyze_code, format_code,
+        lint_code, disassemble_code
+    ],
+    self_reflect=False
+)
+
+# Create monitoring agent
+monitor_agent = Agent(
+    name="CodeMonitor",
+    role="Execution Monitor",
+    goal="Monitor code execution and handle errors.",
+    backstory="Expert in code monitoring and error handling.",
+    self_reflect=False
+)
+
+# Define tasks
+execution_task = Task(
+    description="Execute Python scripts.",
+    agent=executor_agent,
+    name="code_execution"
+)
+
+monitoring_task = Task(
+    description="Monitor execution and handle errors.",
+    agent=monitor_agent,
+    name="execution_monitoring"
+)
+
+# Run agents
+agents = PraisonAIAgents(
+    agents=[executor_agent, monitor_agent],
+    tasks=[execution_task, monitoring_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Configuration">
+    Configure agents with clear Python focus:
+    ```python
+    Agent(
+        name="PythonExecutor",
+        role="Code Execution Specialist",
+        goal="Execute code safely and efficiently",
+        tools=[
+            execute_code, analyze_code, format_code,
+            lint_code, disassemble_code
+        ]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Task Definition">
+    Define specific Python operations:
+    ```python
+    Task(
+        description="Execute data processing scripts",
+        expected_output="Processing results"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### Python Execution Pipeline
+```python
+# Execution agent
+executor = Agent(
+    name="Executor",
+    role="Code Executor",
+    tools=[
+        execute_code, analyze_code, format_code,
+        lint_code, disassemble_code
+    ]
+)
+
+# Monitor agent
+monitor = Agent(
+    name="Monitor",
+    role="Execution Monitor"
+)
+
+# Define tasks
+execute_task = Task(
+    description="Execute Python code",
+    agent=executor
+)
+
+monitor_task = Task(
+    description="Monitor execution",
+    agent=monitor
+)
+
+# Run workflow
+agents = PraisonAIAgents(
+    agents=[executor, monitor],
+    tasks=[execute_task, monitor_task]
+)

docs/tools/shell_tools.mdx

@@ -0,0 +1,488 @@
+---
+title: "Shell Agent"
+description: "Shell command execution tools for AI agents."
+icon: "terminal"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - Basic understanding of shell commands
+</Note>
+
+## Shell Tools
+
+Use Shell Tools to execute shell commands with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required package:
+    ```bash
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create a shell command agent:
+    ```python
+    shell_agent = Agent(
+        name="ShellCommander",
+        role="Shell Command Specialist",
+        goal="Execute shell commands efficiently and safely.",
+        backstory="Expert in command-line operations and automation.",
+        tools=[execute_command, list_processes, kill_process, get_system_info],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the shell task:
+    ```python
+    shell_task = Task(
+        description="List and organize files in the current directory.",
+        expected_output="Organized file structure with detailed listing.",
+        agent=shell_agent,
+        name="file_organization"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[shell_agent],
+        tasks=[shell_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Understanding Shell Tools
+
+<Card title="What are Shell Tools?" icon="question">
+  Shell Tools provide command-line capabilities for AI agents:
+  - Command execution
+  - Process management
+  - Output handling
+  - Error handling
+  - Environment management
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="Shell Agent" icon="user-robot">
+    Create specialized shell agents:
+    ```python
+    Agent(tools=[execute_command, list_processes, kill_process, get_system_info])
+    ```
+  </Card>
+  <Card title="Shell Task" icon="list-check">
+    Define shell tasks:
+    ```python
+    Task(description="shell_operation")
+    ```
+  </Card>
+  <Card title="Process Types" icon="arrows-split-up-and-left">
+    Sequential or parallel processing:
+    ```python
+    process="sequential"
+    ```
+  </Card>
+  <Card title="Shell Options" icon="sliders">
+    Customize shell operations:
+    ```python
+    timeout=30, shell=True
+    ```
+  </Card>
+</CardGroup>
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info
+
+# Create shell agent
+shell_agent = Agent(
+    name="CommandExecutor",
+    role="Shell Command Specialist",
+    goal="Execute shell commands efficiently and safely.",
+    backstory="Expert in command-line operations and scripting.",
+    tools=[execute_command, list_processes, kill_process, get_system_info],
+    self_reflect=False
+)
+
+# Define shell task
+shell_task = Task(
+    description="Clean up temporary files and organize downloads.",
+    expected_output="Cleaned and organized file system.",
+    agent=shell_agent,
+    name="system_cleanup"
+)
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[shell_agent],
+    tasks=[shell_task],
+    process="sequential"
+)
+agents.start()
+```
+
+### Advanced Shell Management with Multiple Agents
+
+```python
+# Create command agent
+command_agent = Agent(
+    name="Commander",
+    role="Command Executor",
+    goal="Execute shell commands systematically.",
+    tools=[execute_command, list_processes, kill_process, get_system_info],
+    self_reflect=False
+)
+
+# Create monitoring agent
+monitor_agent = Agent(
+    name="Monitor",
+    role="Process Monitor",
+    goal="Monitor and manage running processes.",
+    backstory="Expert in system monitoring and process control.",
+    self_reflect=False
+)
+
+# Define tasks
+command_task = Task(
+    description="Execute system maintenance commands.",
+    agent=command_agent,
+    name="system_maintenance"
+)
+
+monitor_task = Task(
+    description="Monitor system resources and processes.",
+    agent=monitor_agent,
+    name="process_monitoring"
+)
+
+# Run agents
+agents = PraisonAIAgents(
+    agents=[command_agent, monitor_agent],
+    tasks=[command_task, monitor_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Available Functions
+
+```python
+from praisonaiagents.tools import execute_command
+from praisonaiagents.tools import list_processes
+from praisonaiagents.tools import kill_process
+from praisonaiagents.tools import get_system_info
+```
+
+## Function Details
+
+### execute_command(command: str, cwd: Optional[str] = None, timeout: int = 30, shell: bool = False, env: Optional[Dict[str, str]] = None, max_output_size: int = 10000)
+
+Safely executes shell commands:
+- Timeout protection
+- Output capture
+- Error handling
+- Environment control
+- Working directory control
+
+```python
+# Basic command execution
+result = execute_command("ls -la")
+
+# Advanced execution
+result = execute_command(
+    "python script.py",
+    cwd="/path/to/scripts",
+    timeout=60,
+    env={"PYTHONPATH": "/custom/path"},
+    shell=True
+)
+
+# Returns: Dict[str, Union[str, int, bool]]
+# Example output:
+# {
+#     'stdout': 'command output...',
+#     'stderr': 'error output if any',
+#     'exit_code': 0,
+#     'success': True,
+#     'execution_time': 0.123
+# }
+```
+
+### list_processes()
+
+Lists running system processes:
+- Process details
+- Resource usage
+- User information
+- Performance metrics
+
+```python
+# Get list of running processes
+processes = list_processes()
+
+# Sort by CPU usage
+cpu_intensive = sorted(
+    processes,
+    key=lambda x: x['cpu_percent'],
+    reverse=True
+)[:5]
+
+# Returns: List[Dict[str, Union[int, str, float]]]
+# Example output:
+# [
+#     {
+#         'pid': 1234,
+#         'name': 'python',
+#         'username': 'user',
+#         'memory_percent': 2.5,
+#         'cpu_percent': 15.3
+#     },
+#     ...
+# ]
+```
+
+### kill_process(pid: int, force: bool = False)
+
+Terminates system processes:
+- Graceful termination
+- Force kill option
+- Error handling
+- Access control
+
+```python
+# Graceful termination
+result = kill_process(1234)
+
+# Force kill
+result = kill_process(
+    pid=1234,
+    force=True  # Use SIGKILL
+)
+
+# Returns: Dict[str, Union[bool, str]]
+# Example output:
+# {
+#     'success': True,
+#     'message': 'Process 1234 killed successfully'
+# }
+# or
+# {
+#     'success': False,
+#     'message': 'Access denied to kill process 1234'
+# }
+```
+
+### get_system_info()
+
+Retrieves system information:
+- CPU statistics
+- Memory usage
+- Disk space
+- Platform details
+- Boot time
+
+```python
+# Get system information
+info = get_system_info()
+
+# Access specific metrics
+print(f"CPU Usage: {info['cpu']['percent']}%")
+print(f"Memory Free: {info['memory']['free']} bytes")
+
+# Returns: Dict[str, Union[float, int, str, Dict]]
+# Example output:
+# {
+#     'cpu': {
+#         'percent': 45.2,
+#         'cores': 8,
+#         'physical_cores': 4
+#     },
+#     'memory': {
+#         'total': 16000000000,
+#         'available': 8000000000,
+#         'percent': 50.0,
+#         'used': 8000000000,
+#         'free': 4000000000
+#     },
+#     'disk': {
+#         'total': 500000000000,
+#         'used': 250000000000,
+#         'free': 250000000000,
+#         'percent': 50.0
+#     },
+#     'boot_time': 1641544800,
+#     'platform': 'Darwin'
+# }
+```
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import (
+    execute_command, list_processes,
+    kill_process, get_system_info
+)
+
+agent = Agent(
+    name="SystemManager",
+    description="An agent that manages system processes and executes commands",
+    tools=[
+        execute_command, list_processes,
+        kill_process, get_system_info
+    ]
+)
+```
+
+## Dependencies
+
+The shell tools require the following package:
+- psutil: For system and process information
+
+This will be automatically installed when needed.
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- Command execution errors
+- Process access errors
+- Permission errors
+- Timeout errors
+- Resource errors
+
+Errors are handled consistently:
+- Success cases return expected data type
+- Error cases return error details in result
+- All errors are logged for debugging
+
+## Common Use Cases
+
+1. System Monitoring:
+```python
+# Monitor system resources
+info = get_system_info()
+if info['cpu']['percent'] > 90:
+    print("High CPU usage detected!")
+if info['memory']['percent'] > 80:
+    print("Low memory warning!")
+
+# List resource-intensive processes
+processes = sorted(
+    list_processes(),
+    key=lambda x: x['memory_percent'],
+    reverse=True
+)[:5]
+print("Top memory users:", processes)
+```
+
+2. Process Management:
+```python
+# Find and kill zombie processes
+for process in list_processes():
+    if process['name'] == 'zombie_process':
+        result = kill_process(
+            process['pid'],
+            force=True
+        )
+        print(f"Kill result: {result['message']}")
+```
+
+3. Command Execution:
+```python
+# Run a series of maintenance commands
+commands = [
+    "apt-get update",
+    "apt-get upgrade -y",
+    "apt-get autoremove -y"
+]
+for cmd in commands:
+    result = execute_command(
+        cmd,
+        timeout=300,
+        shell=True
+    )
+    if result['success']:
+        print(f"Command succeeded: {cmd}")
+    else:
+        print(f"Command failed: {result['stderr']}")
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Configuration">
+    Configure agents with clear shell focus:
+    ```python
+    Agent(
+        name="ShellManager",
+        role="Command Line Specialist",
+        goal="Execute shell commands safely and efficiently",
+        tools=[execute_command, list_processes, kill_process, get_system_info]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Task Definition">
+    Define specific shell operations:
+    ```python
+    Task(
+        description="Clean up system temp files and optimize storage",
+        expected_output="Optimized system storage"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### Shell Command Pipeline
+```python
+# Command agent
+commander = Agent(
+    name="Commander",
+    role="Shell Commander",
+    tools=[execute_command]
+)
+
+# Monitor agent
+monitor = Agent(
+    name="Monitor",
+    role="Process Monitor"
+)
+
+# Define tasks
+command_task = Task(
+    description="Execute maintenance commands",
+    agent=commander
+)
+
+monitor_task = Task(
+    description="Monitor command execution",
+    agent=monitor
+)
+
+# Run workflow
+agents = PraisonAIAgents(
+    agents=[commander, monitor],
+    tasks=[command_task, monitor_task]
+)

docs/tools/spider_tools.mdx

@@ -0,0 +1,251 @@
+---
+title: "Spider Agent"
+description: "Web scraping tools for AI agents."
+icon: "spider"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - `scrapy` package installed
+</Note>
+
+## Spider Tools
+
+Use Spider Tools to crawl and scrape web content with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required packages:
+    ```bash
+    pip install praisonaiagents scrapy
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import scrape_page, extract_links, crawl, extract_text
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create a web scraping agent:
+    ```python
+    spider_agent = Agent(
+        name="WebSpider",
+        role="Web Scraping Specialist",
+        goal="Extract and analyze web content efficiently.",
+        backstory="Expert in web scraping and content extraction.",
+        tools=[scrape_page, extract_links, crawl, extract_text],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the scraping task:
+    ```python
+    scraping_task = Task(
+        description="Scrape product information from an e-commerce website.",
+        expected_output="Structured product data with prices and descriptions.",
+        agent=spider_agent,
+        name="product_scraping"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[spider_agent],
+        tasks=[scraping_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Understanding Spider Tools
+
+<Card title="What are Spider Tools?" icon="question">
+  Spider Tools provide web scraping capabilities for AI agents:
+  - Page scraping and downloading
+  - Content extraction and filtering
+  - Link discovery and crawling
+  - HTML parsing and cleaning
+  - Data structuring and formatting
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="Spider Agent" icon="user-robot">
+    Create specialized scraping agents:
+    ```python
+    Agent(tools=[scrape_page, extract_content, crawl_links, parse_html, structure_data])
+    ```
+  </Card>
+  <Card title="Spider Task" icon="list-check">
+    Define scraping tasks:
+    ```python
+    Task(description="scraping_query")
+    ```
+  </Card>
+  <Card title="Process Types" icon="arrows-split-up-and-left">
+    Sequential or parallel processing:
+    ```python
+    process="sequential"
+    ```
+  </Card>
+  <Card title="Scraping Options" icon="sliders">
+    Customize scraping parameters:
+    ```python
+    max_depth=2, delay=1
+    ```
+  </Card>
+</CardGroup>
+
+## Examples
+
+### Basic Web Scraping Agent
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import scrape_page, extract_links, crawl, extract_text
+
+# Create search agent
+agent = Agent(
+    name="WebCrawler",
+    role="Web Scraping Specialist",
+    goal="Extract and analyze web content efficiently.",
+    backstory="Expert in web scraping and content extraction.",
+    tools=[scrape_page, extract_links, crawl, extract_text],
+    self_reflect=False
+)
+
+# Define task
+task = Task(
+    description="Scrape and analyze the content from 'https://example.com'",
+    expected_output="Extracted content with links and text analysis",
+    agent=agent,
+    name="web_scraping"
+)
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[agent],
+    tasks=[task],
+    process="sequential"
+)
+agents.start()
+```
+
+### Advanced Scraping with Multiple Agents
+
+```python
+# Create scraping agent
+scraper_agent = Agent(
+    name="Scraper",
+    role="Content Scraper",
+    goal="Extract web content systematically.",
+    tools=[scrape_page, crawl_links, parse_html],
+    self_reflect=False
+)
+
+# Create analysis agent
+analysis_agent = Agent(
+    name="Analyzer",
+    role="Content Analyst",
+    goal="Analyze and structure scraped content.",
+    backstory="Expert in data analysis and organization.",
+    tools=[extract_content, structure_data],
+    self_reflect=False
+)
+
+# Define tasks
+scraping_task = Task(
+    description="Scrape product data from multiple pages.",
+    agent=scraper_agent,
+    name="product_scraping"
+)
+
+analysis_task = Task(
+    description="Analyze and structure the scraped product data.",
+    agent=analysis_agent,
+    name="data_analysis"
+)
+
+# Run agents
+agents = PraisonAIAgents(
+    agents=[scraper_agent, analysis_agent],
+    tasks=[scraping_task, analysis_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Configuration">
+    Configure agents with clear scraping focus:
+    ```python
+    Agent(
+        name="WebScraper",
+        role="Content Extraction Specialist",
+        goal="Extract web content ethically and efficiently",
+        tools=[scrape_page, extract_content, crawl_links, parse_html, structure_data]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Task Definition">
+    Define specific scraping objectives:
+    ```python
+    Task(
+        description="Extract product prices and descriptions from e-commerce site",
+        expected_output="Structured product database"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### Web Scraping Pipeline
+```python
+# Scraping agent
+scraper = Agent(
+    name="Scraper",
+    role="Web Scraper",
+    tools=[scrape_page, crawl_links, parse_html]
+)
+
+# Processing agent
+processor = Agent(
+    name="Processor",
+    role="Data Processor",
+    tools=[extract_content, structure_data]
+)
+
+# Define tasks
+scrape_task = Task(
+    description="Scrape website content",
+    agent=scraper
+)
+
+process_task = Task(
+    description="Process scraped content",
+    agent=processor
+)
+
+# Run workflow
+agents = PraisonAIAgents(
+    agents=[scraper, processor],
+    tasks=[scrape_task, process_task]
+)

docs/tools/wikipedia_tools.mdx

@@ -0,0 +1,253 @@
+---
+title: "Wikipedia Agent"
+description: "Wikipedia data retrieval tools for AI agents."
+icon: "book"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - `wikipedia` package installed
+  - Basic understanding of Wikipedia APIs
+</Note>
+
+## Wikipedia Tools
+
+Use Wikipedia Tools to retrieve and analyze Wikipedia content with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required packages:
+    ```bash
+    pip install praisonaiagents wikipedia
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create a Wikipedia research agent:
+    ```python
+    wiki_agent = Agent(
+        name="WikiResearcher",
+        role="Wikipedia Research Specialist",
+        goal="Research and analyze Wikipedia content efficiently.",
+        backstory="Expert in information retrieval and content analysis.",
+        tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the research task:
+    ```python
+    research_task = Task(
+        description="Research historical events and gather information.",
+        expected_output="Comprehensive research summary with citations.",
+        agent=wiki_agent,
+        name="historical_research"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[wiki_agent],
+        tasks=[research_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Understanding Wikipedia Tools
+
+<Card title="What are Wikipedia Tools?" icon="question">
+  Wikipedia Tools provide research capabilities for AI agents:
+  - Article search and retrieval
+  - Content summary generation
+  - Full page information access
+  - Random article discovery
+  - Multi-language support
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="Wiki Agent" icon="user-robot">
+    Create specialized research agents:
+    ```python
+    Agent(tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language])
+    ```
+  </Card>
+  <Card title="Wiki Task" icon="list-check">
+    Define research tasks:
+    ```python
+    Task(description="wiki_query")
+    ```
+  </Card>
+  <Card title="Process Types" icon="arrows-split-up-and-left">
+    Sequential or parallel processing:
+    ```python
+    process="sequential"
+    ```
+  </Card>
+  <Card title="Wiki Options" icon="sliders">
+    Customize search parameters:
+    ```python
+    language="en", sentences=3
+    ```
+  </Card>
+</CardGroup>
+
+## Examples
+
+### Basic Wikipedia Research Agent
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language
+
+# Create Wikipedia agent
+wiki_agent = Agent(
+    name="WikiExpert",
+    role="Research Specialist",
+    goal="Research topics efficiently and accurately.",
+    backstory="Expert in information gathering and analysis.",
+    tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
+    self_reflect=False
+)
+
+# Define research task
+research_task = Task(
+    description="Research scientific discoveries and breakthroughs.",
+    expected_output="Detailed research report with references.",
+    agent=wiki_agent,
+    name="science_research"
+)
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[wiki_agent],
+    tasks=[research_task],
+    process="sequential"
+)
+agents.start()
+
+```
+
+### Advanced Research with Multiple Agents
+
+```python
+# Create research agent
+researcher_agent = Agent(
+    name="Researcher",
+    role="Content Researcher",
+    goal="Research topics systematically.",
+    tools=[wiki_search, wiki_summary, wiki_page],
+    self_reflect=False
+)
+
+# Create analysis agent
+analysis_agent = Agent(
+    name="Analyzer",
+    role="Content Analyst",
+    goal="Analyze and summarize research findings.",
+    backstory="Expert in content analysis and synthesis.",
+    tools=[wiki_summary, wiki_page],
+    self_reflect=False
+)
+
+# Define tasks
+research_task = Task(
+    description="Research technological advancements.",
+    agent=researcher_agent,
+    name="tech_research"
+)
+
+analysis_task = Task(
+    description="Analyze and synthesize research findings.",
+    agent=analysis_agent,
+    name="content_analysis"
+)
+
+# Run agents
+agents = PraisonAIAgents(
+    agents=[researcher_agent, analysis_agent],
+    tasks=[research_task, analysis_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Configuration">
+    Configure agents with clear research focus:
+    ```python
+    Agent(
+        name="WikiResearcher",
+        role="Research Specialist",
+        goal="Research topics accurately and efficiently",
+        tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Task Definition">
+    Define specific research objectives:
+    ```python
+    Task(
+        description="Research historical events and gather sources",
+        expected_output="Detailed research summary"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### Research Pipeline
+```python
+# Research agent
+researcher = Agent(
+    name="Researcher",
+    role="Wiki Researcher",
+    tools=[wiki_search, wiki_summary, wiki_page]
+)
+
+# Analysis agent
+analyzer = Agent(
+    name="Analyzer",
+    role="Content Analyzer",
+    tools=[wiki_summary, wiki_page]
+)
+
+# Define tasks
+research_task = Task(
+    description="Research topic",
+    agent=researcher
+)
+
+analyze_task = Task(
+    description="Analyze findings",
+    agent=analyzer
+)
+
+# Run workflow
+agents = PraisonAIAgents(
+    agents=[researcher, analyzer],
+    tasks=[research_task, analyze_task]
+)

docs/tools/xml_tools.mdx

@@ -0,0 +1,251 @@
+---
+title: "XML Agent"
+description: "XML data processing tools for AI agents."
+icon: "code"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - `lxml` package installed
+</Note>
+
+## XML Tools
+
+Use XML Tools to read, write, and manipulate XML data with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required package:
+    ```bash
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import read_xml, write_xml, transform_xml, validate_xml, xml_to_dict, dict_to_xml
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create an XML processing agent:
+    ```python
+    xml_agent = Agent(
+        name="XMLProcessor",
+        role="XML Processing Specialist",
+        goal="Process XML files efficiently and accurately.",
+        backstory="Expert in XML file manipulation and validation.",
+        tools=[read_xml, write_xml, transform_xml, validate_xml, xml_to_dict, dict_to_xml],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the XML processing task:
+    ```python
+    xml_task = Task(
+        description="Parse and validate XML configuration files.",
+        expected_output="Validated and processed XML data.",
+        agent=xml_agent,
+        name="xml_processing"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[xml_agent],
+        tasks=[xml_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Understanding XML Tools
+
+<Card title="What are XML Tools?" icon="question">
+  XML Tools provide XML processing capabilities for AI agents:
+  - XML parsing and reading
+  - File writing and validation
+  - Data transformation
+  - Dictionary conversion
+  - Structure manipulation
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="XML Agent" icon="user-robot">
+    Create specialized XML agents:
+    ```python
+    Agent(tools=[read_xml, write_xml, transform_xml, validate_xml, xml_to_dict, dict_to_xml])
+    ```
+  </Card>
+  <Card title="XML Task" icon="list-check">
+    Define XML tasks:
+    ```python
+    Task(description="xml_operation")
+    ```
+  </Card>
+  <Card title="Process Types" icon="arrows-split-up-and-left">
+    Sequential or parallel processing:
+    ```python
+    process="sequential"
+    ```
+  </Card>
+  <Card title="XML Options" icon="sliders">
+    Customize XML parameters:
+    ```python
+    encoding="utf-8", pretty_print=True
+    ```
+  </Card>
+</CardGroup>
+
+## Examples
+
+### Basic XML Processing Agent
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import read_xml, write_xml, transform_xml, validate_xml, xml_to_dict, dict_to_xml
+
+# Create XML agent
+xml_agent = Agent(
+    name="XMLExpert",
+    role="XML Processing Specialist",
+    goal="Process XML files efficiently and accurately.",
+    backstory="Expert in XML file handling and validation.",
+    tools=[read_xml, write_xml, transform_xml, validate_xml, xml_to_dict, dict_to_xml],
+    self_reflect=False
+)
+
+# Define XML task
+xml_task = Task(
+    description="Parse and validate XML configurations.",
+    expected_output="Processed and validated XML data.",
+    agent=xml_agent,
+    name="config_processing"
+)
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[xml_agent],
+    tasks=[xml_task],
+    process="sequential"
+)
+agents.start()
+```
+
+### Advanced XML Processing with Multiple Agents
+
+```python
+# Create XML processing agent
+processor_agent = Agent(
+    name="Processor",
+    role="XML Processor",
+    goal="Process XML files systematically.",
+    tools=[read_xml, write_xml, transform_xml],
+    self_reflect=False
+)
+
+# Create validation agent
+validator_agent = Agent(
+    name="Validator",
+    role="Data Validator",
+    goal="Validate XML structure and content.",
+    backstory="Expert in data validation and verification.",
+    tools=[validate_xml, xml_to_dict, dict_to_xml],
+    self_reflect=False
+)
+
+# Define tasks
+processing_task = Task(
+    description="Process and transform XML configurations.",
+    agent=processor_agent,
+    name="xml_processing"
+)
+
+validation_task = Task(
+    description="Validate processed XML data.",
+    agent=validator_agent,
+    name="data_validation"
+)
+
+# Run agents
+agents = PraisonAIAgents(
+    agents=[processor_agent, validator_agent],
+    tasks=[processing_task, validation_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Configuration">
+    Configure agents with clear XML focus:
+    ```python
+    Agent(
+        name="XMLProcessor",
+        role="XML Processing Specialist",
+        goal="Process XML files accurately and safely",
+        tools=[read_xml, write_xml, transform_xml, validate_xml, xml_to_dict, dict_to_xml]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Task Definition">
+    Define specific XML operations:
+    ```python
+    Task(
+        description="Parse and validate XML configurations",
+        expected_output="Validated data structure"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### XML Processing Pipeline
+```python
+# Processing agent
+processor = Agent(
+    name="Processor",
+    role="XML Processor",
+    tools=[read_xml, write_xml, transform_xml]
+)
+
+# Validation agent
+validator = Agent(
+    name="Validator",
+    role="Data Validator",
+    tools=[validate_xml, xml_to_dict, dict_to_xml]
+)
+
+# Define tasks
+process_task = Task(
+    description="Process XML files",
+    agent=processor
+)
+
+validate_task = Task(
+    description="Validate processed data",
+    agent=validator
+)
+
+# Run workflow
+agents = PraisonAIAgents(
+    agents=[processor, validator],
+    tasks=[process_task, validate_task]
+)

docs/tools/yaml_tools.mdx

@@ -0,0 +1,252 @@
+---
+title: "YAML Agent"
+description: "YAML data processing tools for AI agents."
+icon: "code"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - `pyyaml` package installed
+  - Basic understanding of YAML format
+</Note>
+
+## YAML Tools
+
+Use YAML Tools to process and manipulate YAML files with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required packages:
+    ```bash
+    pip install praisonaiagents pyyaml
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import read_yaml, write_yaml, validate_yaml, merge_yaml, convert_yaml
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create a YAML processing agent:
+    ```python
+    yaml_agent = Agent(
+        name="YAMLProcessor",
+        role="YAML Processing Specialist",
+        goal="Process YAML files efficiently and accurately.",
+        backstory="Expert in YAML file manipulation and validation.",
+        tools=[read_yaml, write_yaml, validate_yaml, merge_yaml, convert_yaml],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the YAML processing task:
+    ```python
+    yaml_task = Task(
+        description="Parse and validate configuration files.",
+        expected_output="Validated and processed YAML configurations.",
+        agent=yaml_agent,
+        name="yaml_processing"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[yaml_agent],
+        tasks=[yaml_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Understanding YAML Tools
+
+<Card title="What are YAML Tools?" icon="question">
+  YAML Tools provide YAML processing capabilities for AI agents:
+  - Reading YAML files
+  - Writing YAML data
+  - Validating YAML structure
+  - Merging YAML files
+  - Converting between formats
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="YAML Agent" icon="user-robot">
+    Create specialized YAML agents:
+    ```python
+    Agent(tools=[read_yaml, write_yaml, validate_yaml, merge_yaml, convert_yaml])
+    ```
+  </Card>
+  <Card title="YAML Task" icon="list-check">
+    Define YAML tasks:
+    ```python
+    Task(description="yaml_operation")
+    ```
+  </Card>
+  <Card title="Process Types" icon="arrows-split-up-and-left">
+    Sequential or parallel processing:
+    ```python
+    process="sequential"
+    ```
+  </Card>
+  <Card title="YAML Options" icon="sliders">
+    Customize YAML parameters:
+    ```python
+    safe_load=True, encoding="utf-8"
+    ```
+  </Card>
+</CardGroup>
+
+## Examples
+
+### Basic YAML Processing Agent
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import read_yaml, write_yaml, validate_yaml, merge_yaml, convert_yaml
+
+# Create YAML agent
+yaml_agent = Agent(
+    name="YAMLExpert",
+    role="YAML Processing Specialist",
+    goal="Process YAML files efficiently and accurately.",
+    backstory="Expert in YAML file handling and validation.",
+    tools=[read_yaml, write_yaml, validate_yaml, merge_yaml, convert_yaml],
+    self_reflect=False
+)
+
+# Define YAML task
+yaml_task = Task(
+    description="Parse and validate configuration files.",
+    expected_output="Processed and validated YAML data.",
+    agent=yaml_agent,
+    name="config_processing"
+)
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[yaml_agent],
+    tasks=[yaml_task],
+    process="sequential"
+)
+agents.start()
+```
+
+### Advanced YAML Processing with Multiple Agents
+
+```python
+# Create YAML processing agent
+processor_agent = Agent(
+    name="Processor",
+    role="YAML Processor",
+    goal="Process YAML files systematically.",
+    tools=[read_yaml, write_yaml, convert_yaml],
+    self_reflect=False
+)
+
+# Create validation agent
+validator_agent = Agent(
+    name="Validator",
+    role="Data Validator",
+    goal="Validate YAML structure and content.",
+    backstory="Expert in data validation and verification.",
+    tools=[validate_yaml, merge_yaml],
+    self_reflect=False
+)
+
+# Define tasks
+processing_task = Task(
+    description="Process and transform YAML configurations.",
+    agent=processor_agent,
+    name="yaml_processing"
+)
+
+validation_task = Task(
+    description="Validate processed YAML data.",
+    agent=validator_agent,
+    name="data_validation"
+)
+
+# Run agents
+agents = PraisonAIAgents(
+    agents=[processor_agent, validator_agent],
+    tasks=[processing_task, validation_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Configuration">
+    Configure agents with clear YAML focus:
+    ```python
+    Agent(
+        name="YAMLProcessor",
+        role="YAML Processing Specialist",
+        goal="Process YAML files accurately and safely",
+        tools=[read_yaml, write_yaml, validate_yaml, merge_yaml, convert_yaml]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Task Definition">
+    Define specific YAML operations:
+    ```python
+    Task(
+        description="Parse and validate deployment configurations",
+        expected_output="Validated configuration set"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### YAML Processing Pipeline
+```python
+# Processing agent
+processor = Agent(
+    name="Processor",
+    role="YAML Processor",
+    tools=[read_yaml, write_yaml, convert_yaml]
+)
+
+# Validation agent
+validator = Agent(
+    name="Validator",
+    role="Data Validator",
+    tools=[validate_yaml, merge_yaml]
+)
+
+# Define tasks
+process_task = Task(
+    description="Process YAML files",
+    agent=processor
+)
+
+validate_task = Task(
+    description="Validate processed data",
+    agent=validator
+)
+
+# Run workflow
+agents = PraisonAIAgents(
+    agents=[processor, validator],
+    tasks=[process_task, validate_task]
+)

docs/tools/yfinance_tools.mdx

@@ -0,0 +1,252 @@
+---
+title: "YFinance Agent"
+description: "Yahoo Finance data retrieval tools for AI agents."
+icon: "chart-line"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - `yfinance` package installed
+  - Basic understanding of financial data
+</Note>
+
+## YFinance Tools
+
+Use YFinance Tools to retrieve and analyze financial data with AI agents.
+
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required packages:
+    ```bash
+    pip install praisonaiagents yfinance
+    ```
+  </Step>
+
+  <Step title="Import Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import get_stock_price, get_stock_info, get_historical_data
+    ```
+  </Step>
+
+  <Step title="Create Agent">
+    Create a financial data agent:
+    ```python
+    finance_agent = Agent(
+        name="FinanceAnalyst",
+        role="Financial Data Specialist",
+        goal="Retrieve and analyze financial data efficiently.",
+        backstory="Expert in financial data analysis and market research.",
+        tools=[get_stock_price, get_stock_info, get_historical_data],
+        self_reflect=False
+    )
+    ```
+  </Step>
+
+  <Step title="Define Task">
+    Define the financial analysis task:
+    ```python
+    finance_task = Task(
+        description="Analyze stock performance and market trends.",
+        expected_output="Detailed financial analysis with market insights.",
+        agent=finance_agent,
+        name="market_analysis"
+    )
+    ```
+  </Step>
+
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[finance_agent],
+        tasks=[finance_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Understanding YFinance Tools
+
+<Card title="What are YFinance Tools?" icon="question">
+  YFinance Tools provide financial data capabilities for AI agents:
+  - Real-time stock prices
+  - Detailed company information
+  - Historical market data
+  - Financial metrics and ratios
+  - Market performance analysis
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="Finance Agent" icon="user-robot">
+    Create specialized finance agents:
+    ```python
+    Agent(tools=[get_stock_price, get_stock_info, get_historical_data])
+    ```
+  </Card>
+  <Card title="Finance Task" icon="list-check">
+    Define finance tasks:
+    ```python
+    Task(description="finance_query")
+    ```
+  </Card>
+  <Card title="Process Types" icon="arrows-split-up-and-left">
+    Sequential or parallel processing:
+    ```python
+    process="sequential"
+    ```
+  </Card>
+  <Card title="Finance Options" icon="sliders">
+    Customize data parameters:
+    ```python
+    period="1y", interval="1d"
+    ```
+  </Card>
+</CardGroup>
+
+## Examples
+
+### Basic Financial Data Agent
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import get_stock_price, get_stock_info, get_historical_data
+
+# Create finance agent
+finance_agent = Agent(
+    name="MarketAnalyst",
+    role="Financial Data Specialist",
+    goal="Analyze market data efficiently and accurately.",
+    backstory="Expert in financial analysis and market research.",
+    tools=[get_stock_price, get_stock_info, get_historical_data],
+    self_reflect=False
+)
+
+# Define finance task
+finance_task = Task(
+    description="Analyze tech sector performance and trends.",
+    expected_output="Comprehensive market analysis report.",
+    agent=finance_agent,
+    name="sector_analysis"
+)
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[finance_agent],
+    tasks=[finance_task],
+    process="sequential"
+)
+agents.start()
+```
+
+### Advanced Market Analysis with Multiple Agents
+
+```python
+# Create data retrieval agent
+data_agent = Agent(
+    name="DataCollector",
+    role="Market Data Collector",
+    goal="Retrieve financial data systematically.",
+    tools=[get_stock_price, get_historical_data],
+    self_reflect=False
+)
+
+# Create analysis agent
+analysis_agent = Agent(
+    name="Analyst",
+    role="Market Analyst",
+    goal="Analyze market trends and patterns.",
+    backstory="Expert in financial market analysis.",
+    tools=[get_stock_info],
+    self_reflect=False
+)
+
+# Define tasks
+data_task = Task(
+    description="Collect historical market data for analysis.",
+    agent=data_agent,
+    name="data_collection"
+)
+
+analysis_task = Task(
+    description="Analyze collected market data for insights.",
+    agent=analysis_agent,
+    name="trend_analysis"
+)
+
+# Run agents
+agents = PraisonAIAgents(
+    agents=[data_agent, analysis_agent],
+    tasks=[data_task, analysis_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Configuration">
+    Configure agents with clear finance focus:
+    ```python
+    Agent(
+        name="FinanceAnalyst",
+        role="Market Analysis Specialist",
+        goal="Analyze financial data accurately and efficiently",
+        tools=[get_stock_price, get_stock_info, get_historical_data]
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Task Definition">
+    Define specific analysis objectives:
+    ```python
+    Task(
+        description="Analyze market trends and generate insights",
+        expected_output="Detailed market analysis"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### Market Analysis Pipeline
+```python
+# Data agent
+collector = Agent(
+    name="Collector",
+    role="Data Collector",
+    tools=[get_stock_price, get_historical_data]
+)
+
+# Analysis agent
+analyst = Agent(
+    name="Analyst",
+    role="Market Analyst",
+    tools=[get_stock_info]
+)
+
+# Define tasks
+collect_task = Task(
+    description="Collect market data",
+    agent=collector
+)
+
+analyze_task = Task(
+    description="Analyze market trends",
+    agent=analyst
+)
+
+# Run workflow
+agents = PraisonAIAgents(
+    agents=[collector, analyst],
+    tasks=[collect_task, analyze_task]
+)

praisonai.rb

@@ -3,7 +3,7 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.31.tar.gz"
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.39.tar.gz"
     sha256 "1828fb9227d10f991522c3f24f061943a254b667196b40b1a3e4a54a8d30ce32"  # Replace with actual SHA256 checksum
     license "MIT"
   

praisonai/deploy.py

@@ -56,7 +56,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.0.31 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.0.39 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')