Merge pull request #298 from MervinPraison/develop

Update PraisonAI and praisonaiagents to versions 2.0.46 and 0.0.36 re…

.gitignore

@@ -65,4 +65,5 @@ outputs
 agents/praisonaiagents/build
 agents/praisonaiagents/dist
 agents/praisonaiagents/praisonaiagents.egg-info
-.codegpt
+.codegpt
+.praison

Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.35 gunicorn markdown
+RUN pip install flask praisonai==2.0.46 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

agents.yaml

@@ -1,6 +1,7 @@
 framework: praisonai
 process: sequential
 topic: create movie script about cat in mars
+memory: false
 roles:
   researcher:
     backstory: Skilled in research, with a focus on gathering accurate and relevant

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/autonomous-agent.py

@@ -0,0 +1,114 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import time
+
+def get_environment_state():
+    """Simulates getting current environment state"""
+    current_time = int(time.time())
+    states = ["normal", "critical", "optimal"]
+    state = states[current_time % 3]
+    print(f"Environment state: {state}")
+    return state
+
+def perform_action(state: str):
+    """Simulates performing an action based on state"""
+    actions = {
+        "normal": "maintain",
+        "critical": "fix",
+        "optimal": "enhance"
+    }
+    action = actions.get(state, "observe")
+    print(f"Performing action: {action} for state: {state}")
+    return action
+
+def get_feedback():
+    """Simulates environment feedback"""
+    current_time = int(time.time())
+    feedback = "positive" if current_time % 2 == 0 else "negative"
+    print(f"Feedback received: {feedback}")
+    return feedback
+
+# Create specialized agents
+llm_caller = Agent(
+    name="Environment Monitor",
+    role="State analyzer",
+    goal="Monitor environment and analyze state",
+    instructions="Check environment state and provide analysis",
+    tools=[get_environment_state]
+)
+
+action_agent = Agent(
+    name="Action Executor",
+    role="Action performer",
+    goal="Execute appropriate actions based on state",
+    instructions="Determine and perform actions based on environment state",
+    tools=[perform_action]
+)
+
+feedback_agent = Agent(
+    name="Feedback Processor",
+    role="Feedback analyzer",
+    goal="Process environment feedback and adapt strategy",
+    instructions="Analyze feedback and provide adaptation recommendations",
+    tools=[get_feedback]
+)
+
+# Create tasks for autonomous workflow
+monitor_task = Task(
+    name="monitor_environment",
+    description="Monitor and analyze environment state",
+    expected_output="Current environment state analysis",
+    agent=llm_caller,
+    is_start=True,
+    task_type="decision",
+    next_tasks=["execute_action"],
+    condition={
+        "normal": ["execute_action"],
+        "critical": ["execute_action"],
+        "optimal": "exit"
+    }
+)
+
+action_task = Task(
+    name="execute_action",
+    description="Execute appropriate action based on state",
+    expected_output="Action execution result",
+    agent=action_agent,
+    next_tasks=["process_feedback"]
+)
+
+feedback_task = Task(
+    name="process_feedback",
+    description="Process feedback and adapt strategy",
+    expected_output="Strategy adaptation based on feedback",
+    agent=feedback_agent,
+    next_tasks=["monitor_environment"],  # Create feedback loop
+    context=[monitor_task, action_task]  # Access to previous states and actions
+)
+
+# Create workflow manager
+workflow = PraisonAIAgents(
+    agents=[llm_caller, action_agent, feedback_agent],
+    tasks=[monitor_task, action_task, feedback_task],
+    process="workflow",
+    verbose=True
+)
+
+def main():
+    print("\nStarting Autonomous Agent Workflow...")
+    print("=" * 50)
+    
+    # Run autonomous workflow
+    results = workflow.start()
+    
+    # Print results
+    print("\nAutonomous Agent Results:")
+    print("=" * 50)
+    for task_id, result in results["task_results"].items():
+        if result:
+            task_name = result.description
+            print(f"\nTask: {task_name}")
+            print(f"Result: {result.raw}")
+            print("-" * 50)
+
+if __name__ == "__main__":
+    main()

agents/code_review.py

@@ -0,0 +1,108 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import time
+from typing import List, Dict
+
+def analyze_code_changes():
+    """Simulates code analysis"""
+    issues = [
+        {"type": "style", "severity": "low", "file": "main.py"},
+        {"type": "security", "severity": "high", "file": "auth.py"},
+        {"type": "performance", "severity": "medium", "file": "data.py"}
+    ]
+    return issues[int(time.time()) % 3]
+
+def suggest_fixes(issue: Dict):
+    """Simulates fix suggestions"""
+    fixes = {
+        "style": "Apply PEP 8 formatting",
+        "security": "Implement input validation",
+        "performance": "Use list comprehension"
+    }
+    return fixes.get(issue["type"], "Review manually")
+
+def apply_automated_fix(fix: str):
+    """Simulates applying automated fixes"""
+    success = int(time.time()) % 2 == 0
+    return "fixed" if success else "manual_review"
+
+# Create specialized agents
+analyzer = Agent(
+    name="Code Analyzer",
+    role="Code analysis",
+    goal="Analyze code changes and identify issues",
+    instructions="Review code changes and report issues",
+    tools=[analyze_code_changes]
+)
+
+fix_suggester = Agent(
+    name="Fix Suggester",
+    role="Solution provider",
+    goal="Suggest fixes for identified issues",
+    instructions="Provide appropriate fix suggestions",
+    tools=[suggest_fixes]
+)
+
+fix_applier = Agent(
+    name="Fix Applier",
+    role="Fix implementation",
+    goal="Apply suggested fixes automatically when possible",
+    instructions="Implement suggested fixes and report results",
+    tools=[apply_automated_fix]
+)
+
+# Create workflow tasks
+analysis_task = Task(
+    name="analyze_code",
+    description="Analyze code changes for issues",
+    expected_output="Identified code issues",
+    agent=analyzer,
+    is_start=True,
+    next_tasks=["suggest_fixes"]
+)
+
+suggestion_task = Task(
+    name="suggest_fixes",
+    description="Suggest fixes for identified issues",
+    expected_output="Fix suggestions",
+    agent=fix_suggester,
+    next_tasks=["apply_fixes"]
+)
+
+fix_task = Task(
+    name="apply_fixes",
+    description="Apply suggested fixes",
+    expected_output="Fix application status",
+    agent=fix_applier,
+    task_type="decision",
+    condition={
+        "fixed": "",
+        "manual_review": ["suggest_fixes"]  # Loop back for manual review
+    }
+)
+
+# Create workflow
+workflow = PraisonAIAgents(
+    agents=[analyzer, fix_suggester, fix_applier],
+    tasks=[analysis_task, suggestion_task, fix_task],
+    process="workflow",
+    verbose=True
+)
+
+def main():
+    print("\nStarting Code Review Workflow...")
+    print("=" * 50)
+    
+    # Run workflow
+    results = workflow.start()
+    
+    # Print results
+    print("\nCode Review Results:")
+    print("=" * 50)
+    for task_id, result in results["task_results"].items():
+        if result:
+            print(f"\nTask: {task_id}")
+            print(f"Result: {result.raw}")
+            print("-" * 50)
+
+if __name__ == "__main__":
+    main()

agents/data-analyst-agent.py

@@ -0,0 +1,10 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table
+import os
+
+agent = Agent(instructions="You are a Data Analyst Agent", tools=[read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table])
+agent.start(f"""
+    Read the data from the csv file {os.path.join(os.path.dirname(__file__), "tesla-stock-price.csv")}
+    Analyse the data and give me the insights
+    read_csv to read the file
+""")

agents/evaluator-optimiser.py

@@ -0,0 +1,70 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create generator and evaluator agents
+generator = Agent(
+    name="Generator",
+    role="Solution generator",
+    goal="Generate initial solutions and incorporate feedback",
+    instructions=(
+        "1. Look at the context from previous tasks.\n"
+        "2. If you see that you have already produced 2 points, then add another 2 new points "
+        "   so that the total becomes 10.\n"
+        "3. Otherwise, just produce the first 2 points.\n"
+        "4. Return only the final list of points, with no extra explanation."
+    )
+)
+
+evaluator = Agent(
+    name="Evaluator",
+    role="Solution evaluator",
+    goal="Evaluate solutions and provide improvement feedback",
+    instructions=(
+        "1. Count how many lines in the response start with a number and a period (like '1. ' or '2. ').\n"
+        "2. If there are 10 or more, respond with 'done'.\n"
+        "3. Otherwise, respond with 'more'.\n"
+        "4. Return only the single word: 'done' or 'more'."
+    )
+)
+
+
+# Create tasks for the feedback loop
+generate_task = Task(
+    name="generate",
+    description="Write 2 points about AI incuding if anything exiting from previous points",
+    expected_output="2 points",
+    agent=generator,
+    is_start=True,
+    task_type="decision",
+    next_tasks=["evaluate"]
+)
+
+evaluate_task = Task(
+    name="evaluate",
+    description="Check if there are 10 points about AI",
+    expected_output="more or done",
+    agent=evaluator,
+    next_tasks=["generate"],
+    context=[generate_task],
+    task_type="decision",
+    condition={
+        "more": ["generate"],  # Continue to generate
+        "done": [""]  # Exit when optimization complete
+    }
+)
+
+# Create workflow manager
+workflow = PraisonAIAgents(
+    agents=[generator, evaluator],
+    tasks=[generate_task, evaluate_task],
+    process="workflow",
+    verbose=True
+)
+
+# Run optimization workflow
+results = workflow.start()
+
+# Print results
+print("\nEvaluator-Optimizer Results:")
+for task_id, result in results["task_results"].items():
+    if result:
+        print(f"Task {task_id}: {result.raw}")

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/finance-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import get_stock_price, get_stock_info, get_historical_data
+
+agent = Agent(instructions="You are a Research Agent", tools=[get_stock_price, get_stock_info, get_historical_data])
+agent.start("Understand current stock price and historical data of Apple and Google. Tell me if I can invest in them")

agents/image-agent.py

@@ -0,0 +1,47 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Image Analysis Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")
+
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Image Analysis Agent
+image_agent = Agent(
+    name="ImageAnalyst",
+    role="Image Analysis 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
+)
+
+# 1. Task with Image URL
+task1 = 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=image_agent,
+    images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
+)
+
+# 2. Task with Local Image File
+task2 = Task(
+    name="analyze_local_image",
+    description="What objects can you see in this image? Describe their arrangement.",
+    expected_output="Detailed description of objects and their spatial relationships",
+    agent=image_agent,
+    images=["image.jpg"] 
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+    agents=[image_agent],
+    tasks=[task1, task2],
+    process="sequential",
+    verbose=1
+)
+
+# Run all tasks
+agents.start()

agents/image-to-text-agent.py

@@ -0,0 +1,47 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Image Analysis Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")
+
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Image Analysis Agent
+image_agent = Agent(
+    name="ImageAnalyst",
+    role="Image Analysis 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
+)
+
+# 1. Task with Image URL
+task1 = 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=image_agent,
+    images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
+)
+
+# 2. Task with Local Image File
+task2 = Task(
+    name="analyze_local_image",
+    description="What objects can you see in this image? Describe their arrangement.",
+    expected_output="Detailed description of objects and their spatial relationships",
+    agent=image_agent,
+    images=["image.jpg"] 
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+    agents=[image_agent],
+    tasks=[task1, task2],
+    process="sequential",
+    verbose=1
+)
+
+# Run all tasks
+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/markdown-agent.py

@@ -0,0 +1,4 @@
+from praisonaiagents import Agent
+
+agent = Agent(instructions="You are a Markdown Agent, output in markdown format")
+agent.start("Write a blog post about AI")

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/multimodal.py

@@ -51,9 +51,4 @@ agents = PraisonAIAgents(
 )
 
 # Run all tasks
-result = agents.start()
-
-# Print results
-for task_id, task_result in result["task_results"].items():
-    print(f"\nTask {task_id} Result:")
-    print(task_result.raw)
+agents.start()

agents/orchestrator-workers.py

@@ -0,0 +1,110 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import time
+
+def get_time_check():
+    current_time = int(time.time())
+    if current_time % 3 == 0:
+        result = 1
+    elif current_time % 3 == 1:
+        result = 2
+    else:
+        result = 3
+    print(f"Time: {current_time}, Result: {result}")
+    return result
+
+# Create orchestrator and worker agents
+router = Agent(
+    name="Router",
+    role="Task router",
+    goal="Distribute tasks to based on response from get_time_check",
+    tools=[get_time_check]
+)
+
+worker1 = Agent(
+    name="Worker 1",
+    role="Specialized worker",
+    goal="Handle specific subtask type 1",
+)
+
+worker2 = Agent(
+    name="Worker 2",
+    role="Specialized worker",
+    goal="Handle specific subtask type 2",
+)
+
+worker3 = Agent(
+    name="Worker 3",
+    role="Specialized worker",
+    goal="Handle specific subtask type 3",
+)
+
+synthesizer = Agent(
+    name="Synthesizer",
+    role="Result synthesizer",
+    goal="Combine and synthesize worker outputs",
+)
+
+# Create orchestrated workflow tasks
+router_task = Task(
+    name="route_task",
+    description="Analyze input from get_time_check and route to appropriate workers",
+    expected_output="Task routing decision, 1 , 2 or 3",
+    agent=router,
+    is_start=True,
+    task_type="decision",
+    next_tasks=["worker1_task", "worker2_task", "worker3_task"],
+    condition={
+        "1": ["worker1_task"],
+        "2": ["worker2_task"],
+        "3": ["worker3_task"]
+    }
+)
+
+worker1_task = Task(
+    name="worker1_task",
+    description="Process type 1 operation",
+    expected_output="Worker 1 result",
+    agent=worker1,
+    next_tasks=["synthesize_task"]
+)
+
+worker2_task = Task(
+    name="worker2_task",
+    description="Process type 2 operation",
+    expected_output="Worker 2 result",
+    agent=worker2,
+    next_tasks=["synthesize_task"]
+)
+
+worker3_task = Task(
+    name="worker3_task",
+    description="Process type 3 operation",
+    expected_output="Worker 3 result",
+    agent=worker3,
+    next_tasks=["synthesize_task"]
+)
+
+synthesize_task = Task(
+    name="synthesize_task",
+    description="Synthesize worker results into final output",
+    expected_output="Final synthesized result",
+    agent=synthesizer,
+    context=[worker1_task, worker2_task, worker3_task]
+)
+
+# Create workflow manager
+workflow = PraisonAIAgents(
+    agents=[router, worker1, worker2, worker3, synthesizer],
+    tasks=[router_task, worker1_task, worker2_task, worker3_task, synthesize_task],
+    process="workflow",
+    verbose=True
+)
+
+# Run orchestrated workflow
+results = workflow.start()
+
+# Print results
+print("\nOrchestrator-Workers Results:")
+for task_id, result in results["task_results"].items():
+    if result:
+        print(f"Task {task_id}: {result.raw}")

agents/parallelisation.py

@@ -0,0 +1,95 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from datetime import datetime
+import asyncio
+
+def process_time():
+    """Simulate processing"""
+    current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    print(f"Processing at: {current_time}")
+    return f"Processed at {current_time}"
+
+# Create parallel processing agents
+agent1 = Agent(
+    name="Processor 1",
+    role="Time collector",
+    goal="Get the time and return it",
+    tools=[process_time]
+)
+
+agent2 = Agent(
+    name="Processor 2",
+    role="Time collector",
+    goal="Get the time and return it",
+    tools=[process_time]
+)
+
+agent3 = Agent(
+    name="Processor 3",
+    role="Time collector",
+    goal="Get the time and return it",
+    tools=[process_time]
+)
+
+aggregator = Agent(
+    name="Aggregator",
+    role="Result aggregator",
+    goal="Collect all the processed time from all tasks"
+)
+
+# Create parallel tasks with memory disabled
+task1 = Task(
+    name="process_1",
+    description="Use process_time tool to get the time",
+    expected_output="processed time",
+    agent=agent1,
+    is_start=True,
+    async_execution=True
+)
+
+task2 = Task(
+    name="process_2",
+    description="Use process_time tool to get the time",
+    expected_output="processed time",
+    agent=agent2,
+    is_start=True,
+    async_execution=True
+)
+
+task3 = Task(
+    name="process_3",
+    description="Use process_time tool to get the time",
+    expected_output="processed time",
+    agent=agent3,
+    is_start=True,
+    async_execution=True
+)
+
+aggregate_task = Task(
+    name="aggregate",
+    description="Collect all the processed time from all tasks",
+    expected_output="Output all the processed time from all tasks and just the time",
+    agent=aggregator,
+    context=[task1, task2, task3]
+)
+
+async def main():
+
+    # Create workflow manager
+    workflow = PraisonAIAgents(
+        agents=[agent1, agent2, agent3, aggregator],
+        tasks=[task1, task2, task3, aggregate_task],
+        process="workflow"
+    )
+
+    # Run parallel workflow
+    results = await workflow.astart()
+
+    # Print results
+    print("\nParallel Processing Results:")
+    for task_id, result in results["task_results"].items():
+        if result:
+            print(f"Task {task_id}: {result.raw}")
+
+# Run the async main function
+if __name__ == "__main__":
+    asyncio.run(main())

agents/pdf_agents_example.py

@@ -0,0 +1,41 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create PDF Analysis Agent
+pdf_agent = Agent(
+    name="PDFAnalyst",
+    role="PDF Document Specialist",
+    goal="Analyze PDF documents to extract meaningful information",
+    backstory="""You are an expert in PDF document analysis and text extraction.
+    You excel at understanding document structure, extracting content, and analyzing textual information.""",
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# 1. Task with PDF URL
+task1 = Task(
+    name="analyze_pdf_url",
+    description="Extract and analyze content from this PDF document.",
+    expected_output="Detailed analysis of the PDF content and structure",
+    agent=pdf_agent,
+    input=["https://example.com/document.pdf"]
+)
+
+# 2. Task with Local PDF File
+task2 = Task(
+    name="analyze_local_pdf",
+    description="What information can you extract from this PDF? Analyze its content.",
+    expected_output="Detailed analysis of the PDF content and structure",
+    agent=pdf_agent,
+    input=["document.pdf"] 
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+    agents=[pdf_agent],
+    tasks=[task1, task2],
+    process="sequential",
+    verbose=1
+)
+
+# Run all tasks
+agents.start()

agents/planning-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Planning Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")

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

@@ -16,8 +16,10 @@ from ..main import (
     display_self_reflection,
     ReflectionOutput,
     client,
-    error_logs
+    error_logs,
+    adisplay_instruction
 )
+import inspect
 
 if TYPE_CHECKING:
     from ..task.task import Task
@@ -67,9 +69,24 @@ 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 +112,7 @@ 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 +163,7 @@ 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 +179,7 @@ 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 +266,31 @@ 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 +382,7 @@ 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:
@@ -444,7 +467,15 @@ 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:
@@ -503,7 +534,7 @@ Your Goal: {self.goal}
                     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
 
@@ -608,6 +639,22 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                             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:
@@ -711,4 +758,12 @@ 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() 
+
+    def start(self, prompt: str, **kwargs):
+        """Start the agent with a prompt. This is a convenience method that wraps chat()."""
+        return self.chat(prompt, **kwargs) 

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
@@ -139,10 +211,15 @@ class PraisonAIAgents:
 
         executor_agent = task.agent
 
+        # Ensure tools are available from both task and agent
+        tools = task.tools or []
+        if executor_agent and executor_agent.tools:
+            tools.extend(executor_agent.tools)
+
         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 +234,8 @@ 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):
@@ -195,14 +272,14 @@ 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=tools,
                 output_json=task.output_json,
                 output_pydantic=task.output_pydantic
             )
         else:
             agent_output = await executor_agent.achat(
                 task_prompt,
-                tools=task.tools,
+                tools=tools,
                 output_json=task.output_json,
                 output_pydantic=task.output_pydantic
             )
@@ -223,8 +300,8 @@ 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)
@@ -234,8 +311,8 @@ 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
@@ -250,37 +327,53 @@ 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"""
@@ -292,11 +385,25 @@ Here are the results of previous tasks that might be useful:\n
         )
         
         if self.process == "workflow":
+            # Collect all tasks that should run in parallel
+            parallel_tasks = []
             async for task_id in process.aworkflow():
-                if self.tasks[task_id].async_execution:
-                    await self.arun_task(task_id)
-                else:
-                    self.run_task(task_id)
+                if self.tasks[task_id].async_execution and self.tasks[task_id].is_start:
+                    parallel_tasks.append(task_id)
+                elif parallel_tasks:
+                    # Execute collected parallel tasks
+                    await asyncio.gather(*[self.arun_task(t) for t in parallel_tasks])
+                    parallel_tasks = []
+                    # Run the current non-parallel task
+                    if self.tasks[task_id].async_execution:
+                        await self.arun_task(task_id)
+                    else:
+                        self.run_task(task_id)
+            
+            # Execute any remaining parallel tasks
+            if parallel_tasks:
+                await asyncio.gather(*[self.arun_task(t) for t in parallel_tasks])
+                
         elif self.process == "sequential":
             async for task_id in process.asequential():
                 if self.tasks[task_id].async_execution:
@@ -328,7 +435,7 @@ 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}")
 
@@ -339,6 +446,15 @@ 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:
@@ -371,11 +487,20 @@ 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):
@@ -425,6 +550,17 @@ Here are the results of previous tasks that might be useful:\n
             )
 
         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],
@@ -440,8 +576,8 @@ 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)
@@ -451,8 +587,8 @@ 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
@@ -467,37 +603,63 @@ 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:
+                        if asyncio.get_event_loop().is_running():
+                            asyncio.create_task(task.execute_callback(task_output))
+                        else:
+                            loop = asyncio.new_event_loop()
+                            asyncio.set_event_loop(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):
+                                if asyncio.get_event_loop().is_running():
+                                    asyncio.create_task(task.callback(task_output))
+                                else:
+                                    loop = asyncio.new_event_loop()
+                                    asyncio.set_event_loop(loop)
+                                    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/process/process.py

@@ -10,14 +10,16 @@ class LoopItems(BaseModel):
     items: List[Any]
 
 class Process:
-    def __init__(self, tasks: Dict[str, Task], agents: List[Agent], manager_llm: Optional[str] = None, verbose: bool = False):
+    def __init__(self, tasks: Dict[str, Task], agents: List[Agent], manager_llm: Optional[str] = None, verbose: bool = False, max_iter: int = 10):
         self.tasks = tasks
         self.agents = agents
         self.manager_llm = manager_llm
         self.verbose = verbose
+        self.max_iter = max_iter
 
     async def aworkflow(self) -> AsyncGenerator[str, None]:
         """Async version of workflow method"""
+        current_iter = 0  # Track how many times we've looped
         # Build workflow relationships first
         for task in self.tasks.values():
             if task.next_tasks:
@@ -41,7 +43,12 @@ class Process:
         visited_tasks = set()
         loop_data = {}  # Store loop-specific data
         
-        while current_task and current_task.id not in visited_tasks:
+        while current_task:
+            current_iter += 1
+            if current_iter > self.max_iter:
+                logging.info(f"Max iteration limit {self.max_iter} reached, ending workflow.")
+                break
+
             task_id = current_task.id
             logging.info(f"Executing workflow task: {current_task.name if current_task.name else task_id}")
             
@@ -111,6 +118,11 @@ Return a JSON object with an 'items' array containing the items to process.
             yield task_id
             visited_tasks.add(task_id)
             
+            # Reset completed task to "not started" so it can run again
+            if self.tasks[task_id].status == "completed":
+                logging.debug(f"Task {task_id} was completed, resetting to 'not started' for next iteration.")
+                self.tasks[task_id].status = "not started"
+            
             # Handle loop progression
             if current_task.task_type == "loop":
                 loop_key = f"loop_{current_task.name}"
@@ -130,22 +142,28 @@ Return a JSON object with an 'items' array containing the items to process.
                     
             # Determine next task based on result
             next_task = None
-            if current_task.result:
+            if current_task and current_task.result:
                 if current_task.task_type in ["decision", "loop"]:
                     result = current_task.result.raw.lower()
                     # Check conditions
                     for condition, tasks in current_task.condition.items():
-                        if condition.lower() in result and tasks:
-                            next_task_name = tasks[0]
+                        if condition.lower() in result:
+                            # Handle both list and direct string values
+                            task_value = tasks[0] if isinstance(tasks, list) else tasks
+                            if not task_value or task_value == "exit":  # If empty or explicit exit
+                                logging.info("Workflow exit condition met, ending workflow")
+                                current_task = None
+                                break
+                            next_task_name = task_value
                             next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
                             # For loops, allow revisiting the same task
                             if next_task and next_task.id == current_task.id:
                                 visited_tasks.discard(current_task.id)
                             break
             
-                if not next_task and current_task.next_tasks:
-                    next_task_name = current_task.next_tasks[0]
-                    next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+            if not next_task and current_task and current_task.next_tasks:
+                next_task_name = current_task.next_tasks[0]
+                next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
             
             current_task = next_task
             if not current_task:
@@ -285,6 +303,7 @@ Provide a JSON with the structure:
 
     def workflow(self):
         """Synchronous version of workflow method"""
+        current_iter = 0  # Track how many times we've looped
         # Build workflow relationships first
         for task in self.tasks.values():
             if task.next_tasks:
@@ -308,7 +327,12 @@ Provide a JSON with the structure:
         visited_tasks = set()
         loop_data = {}  # Store loop-specific data
         
-        while current_task and current_task.id not in visited_tasks:
+        while current_task:
+            current_iter += 1
+            if current_iter > self.max_iter:
+                logging.info(f"Max iteration limit {self.max_iter} reached, ending workflow.")
+                break
+
             task_id = current_task.id
             logging.info(f"Executing workflow task: {current_task.name if current_task.name else task_id}")
             
@@ -372,6 +396,11 @@ Return a JSON object with an 'items' array containing the items to process.
             yield task_id
             visited_tasks.add(task_id)
             
+            # Reset completed task to "not started" so it can run again
+            if self.tasks[task_id].status == "completed":
+                logging.debug(f"Task {task_id} was completed, resetting to 'not started' for next iteration.")
+                self.tasks[task_id].status = "not started"
+            
             # Handle loop progression
             if current_task.task_type == "loop":
                 loop_key = f"loop_{current_task.name}"
@@ -391,22 +420,28 @@ Return a JSON object with an 'items' array containing the items to process.
                     
             # Determine next task based on result
             next_task = None
-            if current_task.result:
+            if current_task and current_task.result:
                 if current_task.task_type in ["decision", "loop"]:
                     result = current_task.result.raw.lower()
                     # Check conditions
                     for condition, tasks in current_task.condition.items():
-                        if condition.lower() in result and tasks:
-                            next_task_name = tasks[0]
+                        if condition.lower() in result:
+                            # Handle both list and direct string values
+                            task_value = tasks[0] if isinstance(tasks, list) else tasks
+                            if not task_value or task_value == "exit":  # If empty or explicit exit
+                                logging.info("Workflow exit condition met, ending workflow")
+                                current_task = None
+                                break
+                            next_task_name = task_value
                             next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
                             # For loops, allow revisiting the same task
                             if next_task and next_task.id == current_task.id:
                                 visited_tasks.discard(current_task.id)
                             break
             
-                if not next_task and current_task.next_tasks:
-                    next_task_name = current_task.next_tasks[0]
-                    next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+            if not next_task and current_task and current_task.next_tasks:
+                next_task_name = current_task.next_tasks[0]
+                next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
             
             current_task = next_task
             if not current_task:

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/pandas_tools.py

@@ -160,6 +160,9 @@ class PandasTools:
             Dict: Summary statistics and information
         """
         try:
+            if not isinstance(df, pd.DataFrame):
+                raise TypeError(f"Expected pandas DataFrame, got {type(df).__name__}")
+                
             numeric_summary = df.describe().to_dict()
             # Convert numpy types to native Python types
             for col in numeric_summary:

agents/praisonaiagents/tools/yfinance_tools.py

@@ -145,7 +145,15 @@ class YFinanceTools:
                 return [{"error": "yfinance package not available"}]
 
             hist = ticker.history(period=period, interval=interval, start=start, end=end)
-            return hist.reset_index().to_dict('records')
+            data = hist.reset_index().to_dict('records')
+            
+            # Convert timestamps to ISO format strings
+            for record in data:
+                if 'Date' in record and hasattr(record['Date'], 'isoformat'):
+                    record['Date'] = record['Date'].isoformat()
+                if 'Datetime' in record and hasattr(record['Datetime'], 'isoformat'):
+                    record['Datetime'] = record['Datetime'].isoformat()
+            return data
         except Exception as e:
             error_msg = f"Error fetching historical data: {str(e)}"
             logging.error(error_msg)

agents/programming-agent.py

@@ -0,0 +1,19 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import execute_code, analyze_code, format_code, lint_code, disassemble_code # Code Tools
+from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info # Shell Tools
+from praisonaiagents.tools import duckduckgo # Web Search Tool
+
+agent = Agent(
+    instructions="You are a Programming Agent", self_reflect=True, min_reflect=5, max_reflect=10, 
+    tools=[execute_code, analyze_code, format_code, lint_code, disassemble_code, execute_command, list_processes, kill_process, get_system_info, duckduckgo]
+)
+agent.start(
+    "Write a python script using yfinance to find the stock price of Tesla"
+    "First check if required packages are installed"
+    "Run it using execute_code"
+    "execute_command if you want to run any terminal command"
+    "search internet using duckduckgo if you want to know update python package information"
+    "Analyse the output using analyze_code and fix error if required"
+    "if no package is installed, install it"
+    "then run the code"
+)

agents/prompt_chaining copy.py

@@ -0,0 +1,91 @@
+from praisonaiagents.agent import Agent
+from praisonaiagents.task import Task
+from praisonaiagents.agents import PraisonAIAgents
+from typing import List, Dict
+import time
+
+def get_time_check():
+    current_time = int(time.time())
+    result = "even" if current_time % 2 == 0 else "odd"
+    print(f"Time check: {current_time} is {result}")
+    return result
+
+def create_prompt_chain():
+    # Create agents for each step in the chain
+    agent1 = Agent(
+        name="Time Checker",
+        role="Time checker",
+        goal="Check if the time is even or odd",
+        instructions="Check if the time is even or odd",
+        tools=[get_time_check]
+    )
+
+    agent2 = Agent(
+        name="Advanced Analyzer",
+        role="Advanced data analyzer",
+        goal="Perform in-depth analysis of processed data",
+        instructions="Analyze the processed data in detail"
+    )
+
+    agent3 = Agent(
+        name="Final Processor",
+        role="Final data processor",
+        goal="Generate final output based on analysis",
+        instructions="Create final output based on analyzed data"
+    )
+
+    # Create tasks for each step
+    initial_task = Task(
+        name="time_check",
+        description="Getting time check and checking if it is even or odd",
+        expected_output="Getting time check and checking if it is even or odd",
+        agent=agent1,
+        is_start=True,  # Mark as the starting task
+        task_type="decision",  # This task will make a decision
+        next_tasks=["advanced_analysis"],  # Next task if condition passes
+        condition={
+            "even": ["advanced_analysis"],  # If passes, go to advanced analysis
+            "odd": ["final_processing"]  # If fails, exit the chain
+        }
+    )
+
+    analysis_task = Task(
+        name="advanced_analysis",
+        description="Perform advanced analysis on the processed data",
+        expected_output="Analyzed data ready for final processing",
+        agent=agent2,
+        next_tasks=["final_processing"]
+    )
+
+    final_task = Task(
+        name="final_processing",
+        description="Generate final output",
+        expected_output="Final processed result",
+        agent=agent3
+    )
+
+    # Create the workflow manager
+    workflow = PraisonAIAgents(
+        agents=[agent1, agent2, agent3],
+        tasks=[initial_task, analysis_task, final_task],
+        process="workflow",  # Use workflow process type
+        verbose=True
+    )
+
+    return workflow
+
+def main():
+    # Create and run the prompt chain
+    workflow = create_prompt_chain()
+    
+    # Run the workflow
+    results = workflow.start()
+    
+    # Print results
+    print("\nWorkflow Results:")
+    for task_id, result in results["task_results"].items():
+        if result:
+            print(f"Task {task_id}: {result.raw}")
+
+if __name__ == "__main__":
+    main()

agents/prompt_chaining.py

@@ -0,0 +1,78 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import time
+
+def get_time_check():
+    current_time = int(time.time())
+    result = "even" if current_time % 2 == 0 else "odd"
+    print(f"Time check: {current_time} is {result}")
+    return result
+
+# Create agents for each step in the chain
+agent1 = Agent(
+    name="Time Checker",
+    role="Time checker",
+    goal="Check if the time is even or odd",
+    instructions="Check if the time is even or odd",
+    tools=[get_time_check]
+)
+
+agent2 = Agent(
+    name="Advanced Analyzer",
+    role="Advanced data analyzer",
+    goal="Perform in-depth analysis of processed data",
+    instructions="Analyze the processed data in detail"
+)
+
+agent3 = Agent(
+    name="Final Processor",
+    role="Final data processor",
+    goal="Generate final output based on analysis",
+    instructions="Create final output based on analyzed data"
+)
+
+# Create tasks for each step
+initial_task = Task(
+    name="time_check",
+    description="Getting time check and checking if it is even or odd",
+    expected_output="Getting time check and checking if it is even or odd",
+    agent=agent1,
+    is_start=True,  # Mark as the starting task
+    task_type="decision",  # This task will make a decision
+    next_tasks=["advanced_analysis"],  # Next task if condition passes
+    condition={
+        "even": ["advanced_analysis"],  # If passes, go to advanced analysis
+        "odd": "exit"  # If fails, exit the chain
+    }
+)
+
+analysis_task = Task(
+    name="advanced_analysis",
+    description="Perform advanced analysis on the processed data",
+    expected_output="Analyzed data ready for final processing",
+    agent=agent2,
+    next_tasks=["final_processing"]
+)
+
+final_task = Task(
+    name="final_processing",
+    description="Generate final output",
+    expected_output="Final processed result",
+    agent=agent3
+)
+
+# Create the workflow manager
+workflow = PraisonAIAgents(
+    agents=[agent1, agent2, agent3],
+    tasks=[initial_task, analysis_task, final_task],
+    process="workflow",  # Use workflow process type
+    verbose=True
+)
+
+# Run the workflow
+results = workflow.start()
+
+# Print results
+print("\nWorkflow Results:")
+for task_id, result in results["task_results"].items():
+    if result:
+        print(f"Task {task_id}: {result.raw}")

agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.26"
+version = "0.0.36"
 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/recommendation-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Recommendation Agent", tools=[duckduckgo])
+agent.start("Recommend me a good movie to watch in 2025")

agents/research-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+agent.start("Research about AI 2024")

agents/routing copy.py

@@ -0,0 +1,82 @@
+from praisonaiagents.agent import Agent
+from praisonaiagents.task import Task
+from praisonaiagents.agents import PraisonAIAgents
+import time
+
+current_time = int(time.time())
+result = "even" if current_time % 2 == 0 else "odd"
+print(f"Time check: {current_time} is {result}")
+
+# Create specialized agents
+router = Agent(
+    name="Router",
+    role="Input Router",
+    goal="Evaluate input and determine routing path",
+    instructions="Analyze input and decide whether to proceed or exit",
+    tools=[get_time_check]
+)
+
+processor1 = Agent(
+    name="Processor 1",
+    role="Secondary Processor",
+    goal="Process valid inputs that passed initial check",
+    instructions="Process data that passed the routing check"
+)
+
+processor2 = Agent(
+    name="Processor 2",
+    role="Final Processor",
+    goal="Perform final processing on validated data",
+    instructions="Generate final output for processed data"
+)
+
+# Create tasks with routing logic
+routing_task = Task(
+    name="initial_routing",
+    description="check the time and return according to what is returned",
+    expected_output="pass or fail based on what is returned",
+    agent=router,
+    is_start=True,
+    task_type="decision",
+    condition={
+        "pass": ["process_valid"],
+        "fail": "exit"
+    }
+)
+
+processing_task = Task(
+    name="process_valid",
+    description="Process validated input",
+    expected_output="Processed data ready for final step",
+    agent=processor1,
+    next_tasks=["final_process"]
+)
+
+final_task = Task(
+    name="final_process",
+    description="Generate final output",
+    expected_output="Final processed result",
+    agent=processor2
+)
+
+# Create and run workflow
+workflow = PraisonAIAgents(
+    agents=[router, processor1, processor2],
+    tasks=[routing_task, processing_task, final_task],
+    process="workflow",
+    verbose=True
+)
+
+print("\nStarting Routing Workflow...")
+print("=" * 50)
+
+results = workflow.start()
+
+print("\nWorkflow Results:")
+print("=" * 50)
+for task_id, result in results["task_results"].items():
+    if result:
+        task_name = result.description
+        print(f"\nTask: {task_name}")
+        print(f"Result: {result.raw}")
+        print("-" * 50)

agents/routing.py

@@ -0,0 +1,83 @@
+from praisonaiagents.agent import Agent
+from praisonaiagents.task import Task
+from praisonaiagents.agents import PraisonAIAgents
+import time
+
+def get_time_check():
+    current_time = int(time.time())
+    result = "even" if current_time % 2 == 0 else "odd"
+    print(f"Time check: {current_time} is {result}")
+    return result
+
+# Create specialized agents
+router = Agent(
+    name="Router",
+    role="Input Router",
+    goal="Evaluate input and determine routing path",
+    instructions="Analyze input and decide whether to proceed or exit",
+    tools=[get_time_check]
+)
+
+processor1 = Agent(
+    name="Processor 1",
+    role="Secondary Processor",
+    goal="Process valid inputs that passed initial check",
+    instructions="Process data that passed the routing check"
+)
+
+processor2 = Agent(
+    name="Processor 2",
+    role="Final Processor",
+    goal="Perform final processing on validated data",
+    instructions="Generate final output for processed data"
+)
+
+# Create tasks with routing logic
+routing_task = Task(
+    name="initial_routing",
+    description="check the time and return according to what is returned",
+    expected_output="pass or fail based on what is returned",
+    agent=router,
+    is_start=True,
+    task_type="decision",
+    condition={
+        "pass": ["process_valid"],
+        "fail": ["process_invalid"]
+    }
+)
+
+processing_task = Task(
+    name="process_valid",
+    description="Process validated input",
+    expected_output="Processed data ready for final step",
+    agent=processor1,
+)
+
+final_task = Task(
+    name="process_invalid",
+    description="Generate final output",
+    expected_output="Final processed result",
+    agent=processor2
+)
+
+# Create and run workflow
+workflow = PraisonAIAgents(
+    agents=[router, processor1, processor2],
+    tasks=[routing_task, processing_task, final_task],
+    process="workflow",
+    verbose=True
+)
+
+print("\nStarting Routing Workflow...")
+print("=" * 50)
+
+results = workflow.start()
+
+print("\nWorkflow Results:")
+print("=" * 50)
+for task_id, result in results["task_results"].items():
+    if result:
+        task_name = result.description
+        print(f"\nTask: {task_name}")
+        print(f"Result: {result.raw}")
+        print("-" * 50)

agents/shopping-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Shopping Agent", tools=[duckduckgo])
+agent.start("I want to buy iPhone 16 Pro Max, check 5 stores and give me price in table")

agents/single-agent.py

@@ -0,0 +1,4 @@
+from praisonaiagents import Agent
+
+agent = Agent(instructions="You are a Markdown Agent, output in markdown format")
+agent.start("Write a blog post about AI")

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/tesla-stock-price.csv

@@ -0,0 +1,758 @@
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agents/tools-class.py

@@ -0,0 +1,115 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import os
+import requests
+from typing import Any, Dict, List, Optional
+from pydantic import BaseModel, Field
+
+class EXASearchTool(BaseModel):
+    """Wrapper for EXA Search API."""
+    search_url: str = "https://api.exa.ai/search"
+    headers: Dict = {
+        "accept": "application/json",
+        "content-type": "application/json",
+    }
+    max_results: Optional[int] = None
+
+    def run(self, query: str) -> str:
+        """Run query through EXA and return concatenated results.
+        
+        Args:
+            query (str): The search query to use
+            
+        Returns:
+            str: The concatenated search results
+        """
+        payload = {
+            "query": query,
+            "type": "magic",
+        }
+
+        headers = self.headers.copy()
+        headers["x-api-key"] = os.environ['EXA_API_KEY']
+
+        response = requests.post(self.search_url, json=payload, headers=headers)
+        results = response.json()
+        
+        if 'results' in results:
+            return self._parse_results(results['results'])
+        return ""
+
+    def results(self, query: str, max_results: Optional[int] = None) -> List[Dict[str, Any]]:
+        """Run query through EXA and return metadata.
+        
+        Args:
+            query (str): The search query to use
+            max_results (Optional[int]): Maximum number of results to return
+            
+        Returns:
+            List[Dict[str, Any]]: List of result dictionaries
+        """
+        payload = {
+            "query": query,
+            "type": "magic",
+        }
+
+        headers = self.headers.copy()
+        headers["x-api-key"] = os.environ['EXA_API_KEY']
+
+        response = requests.post(self.search_url, json=payload, headers=headers)
+        results = response.json()
+        
+        if 'results' in results:
+            return results['results'][:max_results] if max_results else results['results']
+        return []
+
+    def _parse_results(self, results: List[Dict[str, Any]]) -> str:
+        """Parse results into a readable string format.
+        
+        Args:
+            results (List[Dict[str, Any]]): List of result dictionaries
+            
+        Returns:
+            str: Formatted string of results
+        """
+        strings = []
+        for result in results:
+            try:
+                strings.append('\n'.join([
+                    f"Title: {result['title']}",
+                    f"Score: {result['score']}",
+                    f"Url: {result['url']}",
+                    f"ID: {result['id']}",
+                    "---"
+                ]))
+            except KeyError:
+                continue
+
+        content = '\n'.join(strings)
+        return f"\nSearch results: {content}\n"
+
+# Create an agent with the tool
+agent = Agent(
+    name="SearchAgent",
+    role="Research Assistant",
+    goal="Search for information about 'AI Agents Framework'",
+    backstory="I am an AI assistant that can search GitHub.",
+    tools=[EXASearchTool],
+    self_reflect=False
+)
+
+# Create tasks to demonstrate both tools
+task = Task(
+    name="search_task",
+    description="Search for information about 'AI Agents Framework'",
+    expected_output="Information about AI Agents Framework",
+    agent=agent
+)
+
+# Create and start the workflow
+agents = PraisonAIAgents(
+    agents=[agent],
+    tasks=[task],
+    verbose=True
+)
+
+agents.start()

agents/video-agent.py

@@ -0,0 +1,36 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Video Analysis Agent
+video_agent = Agent(
+    name="VideoAnalyst",
+    role="Video Analysis 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
+)
+
+# Task with Video File
+task1 = Task(
+    name="analyze_video",
+    description="""Watch this video and provide:
+    1. A summary of the main events
+    2. Key objects and people visible
+    3. Any text or important information shown
+    4. The overall context and setting""",
+    expected_output="Comprehensive analysis of the video content",
+    agent=video_agent,
+    images=["video.mp4"]  
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+    agents=[video_agent],
+    tasks=[task1],
+    process="sequential",
+    verbose=1
+)
+
+# Run all tasks
+agents.start()

agents/websearch-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Web Search Agent", tools=[duckduckgo])
+agent.start("Search about AI 2024")

agents/wikipedia-agent.py

@@ -0,0 +1,16 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language
+
+agent = Agent(
+    instructions="You are a Wikipedia Agent", 
+    tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
+    self_reflect=True,
+    min_reflect=3,
+    max_reflect=5,
+)
+agent.start(
+    "What is the history of AI?"
+    "First search the history of AI"
+    "Read the page of the history of AI"
+    "Get the summary of the page"
+)

docs/agents/data-analyst.mdx

@@ -0,0 +1,120 @@
+---
+title: "Data Analyst Agent"
+sidebarTitle: "Data Analyst"
+description: "Learn how to create AI agents for data analysis and insights generation."
+icon: "chart-line"
+---
+
+```mermaid
+flowchart LR
+    In[Data Source] --> Reader[Data Reader]
+    Reader --> Analyzer[Data Analyzer]
+    Analyzer --> Generator[Insights Generator]
+    Generator --> Out[Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Reader fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Data Analyst Agent can read data from various sources, analyze it, and generate insights.
+
+## Quick Start
+
+<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="Create Script">
+        Create a new file `data_analysis.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import (
+            read_csv, read_excel, write_csv, write_excel,
+            filter_data, get_summary, group_by, pivot_table
+        )
+
+        agent = Agent(
+            instructions="You are a Data Analyst Agent",
+            tools=[
+                read_csv, read_excel, write_csv, write_excel,
+                filter_data, get_summary, group_by, pivot_table
+            ]
+        )
+
+        # Start analysis with a specific task
+        agent.start(f"""
+            Read the data from the csv file {os.path.join(os.path.dirname(__file__), "tesla-stock-price.csv")}
+            Analyse the data and give me the insights
+            read_csv to read the file
+        """)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Data Analysis Workflow
+
+The Data Analyst Agent is designed to perform comprehensive data analysis tasks using a suite of specialized tools. Here's how it works:
+
+1. **Data Reading**: The agent can read data from various sources:
+   - CSV files using `read_csv`
+   - Excel files using `read_excel`
+
+2. **Data Analysis**: Multiple analysis tools are available:
+   - `filter_data`: Filter datasets based on conditions
+   - `get_summary`: Generate statistical summaries
+   - `group_by`: Group data by specific columns
+   - `pivot_table`: Create pivot tables for analysis
+
+3. **Data Export**: Results can be exported to:
+   - CSV format using `write_csv`
+   - Excel format using `write_excel`
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Multiple Data Sources" icon="table">
+    Support for both CSV and Excel file formats.
+  </Card>
+  <Card title="Analysis Tools" icon="calculator">
+    Comprehensive suite of analysis tools including filtering, summarization, and pivoting.
+  </Card>
+  <Card title="Data Export" icon="file-export">
+    Export capabilities to various formats.
+  </Card>
+  <Card title="Automated Insights" icon="lightbulb">
+    Automatic generation of data insights and patterns.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Analyzing stock data
+agent.start("""
+    1. Read 'stock_data.csv'
+    2. Filter data for the last 30 days
+    3. Calculate daily returns
+    4. Generate summary statistics
+    5. Export results to 'analysis_results.xlsx'
+""")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex analysis workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving analysis accuracy
+- Check out other specialized agents like the [Finance Agent](/agents/finance) for specific use cases

docs/agents/finance.mdx

@@ -0,0 +1,97 @@
+---
+title: "Finance Agent"
+sidebarTitle: "Finance"
+description: "Learn how to create AI agents for financial analysis and investment recommendations."
+icon: "money-bill-trend-up"
+---
+
+```mermaid
+flowchart LR
+    In[Request] --> Price[Stock Price]
+    Price --> Info[Stock Info]
+    Info --> History[Historical Data]
+    History --> Out[Investment Advice]
+    
+    style In fill:#8B0000,color:#fff
+    style Price fill:#2E8B57,color:#fff
+    style Info fill:#2E8B57,color:#fff
+    style History fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Finance Agent can analyze stock market data and provide investment recommendations.
+
+## Quick Start
+
+<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="Create Script">
+        Create a new file `finance_analysis.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import get_stock_price, get_stock_info, get_historical_data
+
+        agent = Agent(
+            instructions="You are a Research Agent",
+            tools=[get_stock_price, get_stock_info, get_historical_data]
+        )
+        agent.start("Understand current stock price and historical data of Apple and Google. Tell me if I can invest in them")
+        ```
+    </Step>
+</Steps>
+
+## Understanding Financial Analysis
+
+The Finance Agent specializes in stock market analysis and investment recommendations using three key components:
+
+1. **Real-time Price Data**: Uses `get_stock_price` to fetch current market prices
+2. **Company Information**: Retrieves company details using `get_stock_info`
+3. **Historical Analysis**: Analyzes trends with `get_historical_data`
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Real-time Data" icon="clock">
+    Access to current stock market prices and information.
+  </Card>
+  <Card title="Company Analysis" icon="building">
+    Detailed company information and fundamentals.
+  </Card>
+  <Card title="Historical Trends" icon="chart-line">
+    Analysis of historical price movements and patterns.
+  </Card>
+  <Card title="Investment Advice" icon="money-bill">
+    AI-powered investment recommendations.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Analyzing specific stocks
+agent.start("""
+    1. Get current prices for AAPL and GOOGL
+    2. Retrieve company information
+    3. Analyze 6-month historical data
+    4. Provide investment recommendations
+""")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex financial analysis
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving recommendation accuracy
+- Check out the [Data Analyst Agent](/agents/data-analyst) for detailed data analysis capabilities

docs/agents/image-to-text.mdx

@@ -0,0 +1,140 @@
+---
+title: "Image to Text Agent"
+sidebarTitle: "Image to Text"
+description: "Learn how to create AI agents for converting images to textual descriptions and extracting text from images."
+icon: "image-to-text"
+---
+
+```mermaid
+flowchart LR
+    In[Image Input] --> OCR[Text Extractor]
+    OCR --> Analyzer[Content Analyzer]
+    Analyzer --> Generator[Description Generator]
+    Generator --> Out[Text Output]
+    
+    style In fill:#8B0000,color:#fff
+    style OCR fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Image-to-Text Agent can extract text from images and generate comprehensive descriptions.
+
+## Quick Start
+
+<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="Create Script">
+        Create a new file `image_to_text.py`:
+        ```python
+        from praisonaiagents import Agent, Task, PraisonAIAgents
+
+        # Create Image-to-Text Agent
+        image_text_agent = Agent(
+            name="ImageTextConverter",
+            role="Image Text Extraction Specialist",
+            goal="Convert image content to textual descriptions and extract text",
+            backstory="""You are an expert in OCR and image understanding.
+            You excel at extracting text from images and generating detailed descriptions.""",
+            llm="gpt-4o-mini",
+            self_reflect=False
+        )
+
+        # Create text extraction task
+        extraction_task = Task(
+            name="extract_text",
+            description="Extract all text from this image and describe its layout.",
+            expected_output="Extracted text and layout description",
+            agent=image_text_agent,
+            images=["document.jpg"]
+        )
+
+        # Create description task
+        description_task = Task(
+            name="generate_description",
+            description="Generate a detailed description of the image content.",
+            expected_output="Comprehensive description of visual elements",
+            agent=image_text_agent,
+            images=["scene.jpg"]
+        )
+
+        # Create PraisonAIAgents instance
+        agents = PraisonAIAgents(
+            agents=[image_text_agent],
+            tasks=[extraction_task, description_task],
+            process="sequential",
+            verbose=1
+        )
+
+        # Run analysis
+        agents.start()
+        ```
+    </Step>
+</Steps>
+
+## Understanding Image-to-Text Conversion
+
+The Image-to-Text Agent combines multiple capabilities to convert visual content into textual form:
+
+1. **OCR Processing**: Extracts text from images using optical character recognition
+2. **Layout Analysis**: Understands the spatial arrangement of text and visual elements
+3. **Content Description**: Generates natural language descriptions of image content
+4. **Text Formatting**: Preserves text formatting and structure where possible
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Text Extraction" icon="text">
+    Advanced OCR capabilities for text extraction.
+  </Card>
+  <Card title="Layout Understanding" icon="table-layout">
+    Analysis of text and content layout.
+  </Card>
+  <Card title="Content Description" icon="pen-to-square">
+    Detailed descriptions of visual content.
+  </Card>
+  <Card title="Format Preservation" icon="paragraph">
+    Maintains text formatting and structure.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Processing a document image
+document_task = Task(
+    name="process_document",
+    description="Extract text and analyze document layout",
+    expected_output="Extracted text with layout information",
+    agent=image_text_agent,
+    images=["business_document.jpg"]
+)
+
+# Run single task
+agents = PraisonAIAgents(
+    agents=[image_text_agent],
+    tasks=[document_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex document processing
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving text extraction accuracy
+- Check out the [Image Agent](/agents/image) for pure image analysis capabilities

docs/agents/image.mdx

@@ -0,0 +1,139 @@
+---
+title: "Image Analysis Agent"
+sidebarTitle: "Image Analysis"
+description: "Learn how to create AI agents for image analysis and visual content understanding."
+icon: "image"
+---
+
+```mermaid
+flowchart LR
+    In[Image Input] --> Analyzer[Image Analyzer]
+    Analyzer --> Detector[Object Detector]
+    Detector --> Generator[Description Generator]
+    Generator --> Out[Analysis Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Detector fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Image Analysis Agent can process and analyze visual content, detect objects, and generate detailed descriptions.
+
+## Quick Start
+
+<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="Create Script">
+        Create a new file `image_analysis.py`:
+        ```python
+        from praisonaiagents import Agent, Task, PraisonAIAgents
+
+        # Create Image Analysis Agent
+        image_agent = Agent(
+            name="ImageAnalyst",
+            role="Image Analysis 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 for different types of analysis
+        task1 = 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=image_agent,
+            images=["https://example.com/landmark.jpg"]
+        )
+
+        task2 = Task(
+            name="analyze_local_image",
+            description="What objects can you see in this image? Describe their arrangement.",
+            expected_output="Detailed description of objects and their spatial relationships",
+            agent=image_agent,
+            images=["local_image.jpg"]
+        )
+
+        # Create PraisonAIAgents instance
+        agents = PraisonAIAgents(
+            agents=[image_agent],
+            tasks=[task1, task2],
+            process="sequential",
+            verbose=1
+        )
+
+        # Run analysis
+        agents.start()
+        ```
+    </Step>
+</Steps>
+
+## Understanding Image Analysis
+
+The Image Analysis Agent is designed to process and understand visual content through several key capabilities:
+
+1. **Image Input**: Supports both URL and local file inputs
+2. **Object Detection**: Identifies and locates objects within images
+3. **Architectural Analysis**: Specialized in analyzing architectural features
+4. **Spatial Understanding**: Describes spatial relationships between objects
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Multiple Input Sources" icon="images">
+    Support for both URL and local image files.
+  </Card>
+  <Card title="Object Detection" icon="magnifying-glass">
+    Advanced object detection and recognition.
+  </Card>
+  <Card title="Architectural Analysis" icon="building">
+    Specialized analysis of architectural features.
+  </Card>
+  <Card title="Detailed Descriptions" icon="pen">
+    Generation of comprehensive visual descriptions.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Analyzing a landmark image
+landmark_task = Task(
+    name="analyze_famous_landmark",
+    description="Analyze this historical landmark",
+    expected_output="Architectural details and historical significance",
+    agent=image_agent,
+    images=["https://example.com/landmark.jpg"]
+)
+
+# Run single task
+agents = PraisonAIAgents(
+    agents=[image_agent],
+    tasks=[landmark_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex image analysis workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving analysis accuracy
+- Check out the [Video Agent](/agents/video) for video content analysis

docs/agents/markdown.mdx

@@ -0,0 +1,120 @@
+---
+title: "Markdown Agent"
+sidebarTitle: "Markdown"
+description: "Learn how to create AI agents for generating and formatting content in Markdown."
+icon: "markdown"
+---
+
+```mermaid
+flowchart LR
+    In[Content Request] --> Generator[Content Generator]
+    Generator --> Formatter[Markdown Formatter]
+    Formatter --> Validator[Format Validator]
+    Validator --> Out[Markdown Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Formatter fill:#2E8B57,color:#fff
+    style Validator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Markdown Agent can generate and format content in Markdown syntax.
+
+## Quick Start
+
+<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="Create Script">
+        Create a new file `markdown_generator.py`:
+        ```python
+        from praisonaiagents import Agent
+
+        # Create Markdown Agent
+        markdown_agent = Agent(
+            name="MarkdownWriter",
+            role="Markdown Content Specialist",
+            goal="Generate well-formatted content in Markdown syntax",
+            instructions="You are a Markdown Agent, output in markdown format",
+            llm="gpt-4o-mini",
+            self_reflect=False
+        )
+
+        # Generate content
+        response = markdown_agent.start(
+            "Write a blog post about artificial intelligence"
+        )
+        
+        # Save to file
+        with open('blog_post.md', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Markdown Generation
+
+The Markdown Agent specializes in creating properly formatted Markdown content:
+
+1. **Content Generation**: Creates original content based on prompts
+2. **Markdown Formatting**: Applies proper Markdown syntax
+3. **Structure Validation**: Ensures correct formatting
+4. **Document Organization**: Creates well-structured documents
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Content Creation" icon="pen-to-square">
+    Generates original, well-structured content.
+  </Card>
+  <Card title="Markdown Syntax" icon="code">
+    Proper implementation of Markdown formatting.
+  </Card>
+  <Card title="Document Structure" icon="sitemap">
+    Organized document hierarchy and sections.
+  </Card>
+  <Card title="Format Validation" icon="check">
+    Ensures correct Markdown syntax usage.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Generate a technical documentation
+agent = Agent(
+    instructions="You are a Markdown Agent, output in markdown format"
+)
+
+# Generate API documentation
+response = agent.start("""
+    Create technical documentation for a REST API with:
+    - Introduction
+    - Authentication
+    - Endpoints
+    - Examples
+""")
+
+# Save documentation
+with open('api_docs.md', 'w') as f:
+    f.write(response)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex document generation
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving content quality
+- Check out the [Documentation Agent](/agents/documentation) for specialized documentation generation

docs/agents/planning.mdx

@@ -0,0 +1,109 @@
+---
+title: "Planning Agent"
+sidebarTitle: "Planning"
+description: "Learn how to create AI agents for trip planning and itinerary generation."
+icon: "calendar"
+---
+
+```mermaid
+flowchart LR
+    In[Request] --> Search[Web Search]
+    Search --> Analyzer[Options Analyzer]
+    Analyzer --> Generator[Plan Generator]
+    Generator --> Out[Itinerary]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Planning Agent can search for travel options and create detailed itineraries.
+
+## Quick Start
+
+<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="Create Script">
+        Create a new file `travel_planner.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        # Create Planning Agent
+        planning_agent = Agent(
+            name="TravelPlanner",
+            role="Travel Planning Specialist",
+            goal="Create comprehensive travel plans and itineraries",
+            instructions="You are a Planning Agent",
+            tools=[duckduckgo]
+        )
+
+        # Generate travel plan
+        response = planning_agent.start(
+            "I want to go to London next week, find me a good hotel and flight"
+        )
+        
+        # Save plan
+        with open('travel_plan.txt', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Travel Planning
+
+The Planning Agent combines multiple capabilities to create comprehensive travel plans:
+
+1. **Web Search**: Uses DuckDuckGo to find current travel options
+2. **Options Analysis**: Evaluates and compares different choices
+3. **Plan Generation**: Creates detailed itineraries
+4. **Recommendations**: Provides personalized suggestions
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Flight Search" icon="plane">
+    Finds and compares flight options.
+  </Card>
+  <Card title="Hotel Booking" icon="hotel">
+    Recommends suitable accommodations.
+  </Card>
+  <Card title="Itinerary Creation" icon="calendar-days">
+    Generates detailed travel schedules.
+  </Card>
+  <Card title="Real-time Data" icon="clock">
+    Uses current pricing and availability.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Create a weekend trip plan
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Planning Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex travel planning
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving recommendations
+- Check out the [Research Agent](/agents/research) for detailed destination research

docs/agents/programming.mdx

@@ -0,0 +1,125 @@
+---
+title: "Programming Agent"
+sidebarTitle: "Programming"
+description: "Learn how to create AI agents for code development, analysis, and execution."
+icon: "code"
+---
+
+```mermaid
+flowchart LR
+    In[Code Request] --> Analyzer[Code Analyzer]
+    Analyzer --> Generator[Code Generator]
+    Generator --> Executor[Code Executor]
+    Executor --> Debugger[Code Debugger]
+    Debugger --> Out[Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Executor fill:#2E8B57,color:#fff
+    style Debugger fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Programming Agent can analyze, generate, execute, and debug code.
+
+## Quick Start
+
+<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="Create Script">
+        Create a new file `code_assistant.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import execute_code, analyze_code, format_code, lint_code, disassemble_code # Code Tools
+        from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info # Shell Tools
+        from praisonaiagents.tools import duckduckgo # Web Search Tool
+
+        agent = Agent(
+            instructions="You are a Programming Agent", self_reflect=True, min_reflect=5, max_reflect=10, 
+            tools=[execute_code, analyze_code, format_code, lint_code, disassemble_code, execute_command, list_processes, kill_process, get_system_info, duckduckgo]
+        )
+        agent.start(
+            "Write a python script using yfinance to find the stock price of Tesla"
+            "First check if required packages are installed"
+            "Run it using execute_code"
+            "execute_command if you want to run any terminal command"
+            "search internet using duckduckgo if you want to know update python package information"
+            "Analyse the output using analyze_code and fix error if required"
+            "if no package is installed, install it"
+            "then run the code"
+        )
+        ```
+    </Step>
+</Steps>
+
+## Understanding Code Development
+
+The Programming Agent combines multiple tools for comprehensive code development:
+
+1. **Code Tools**:
+   - `execute_code`: Run Python code
+   - `analyze_code`: Analyze code structure
+   - `format_code`: Format code to standards
+   - `lint_code`: Check code quality
+   - `disassemble_code`: View bytecode
+
+2. **Shell Tools**:
+   - `execute_command`: Run terminal commands
+   - `list_processes`: View running processes
+   - `kill_process`: Terminate processes
+   - `get_system_info`: System information
+
+3. **Web Tools**:
+   - `duckduckgo`: Search for programming resources
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Code Execution" icon="play">
+    Run and test code directly.
+  </Card>
+  <Card title="Code Analysis" icon="magnifying-glass-code">
+    Analyze and improve code quality.
+  </Card>
+  <Card title="System Integration" icon="terminal">
+    Execute system commands and manage processes.
+  </Card>
+  <Card title="Package Management" icon="box">
+    Handle dependencies and installations.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Create and run a data analysis script
+response = agent.start("""
+    1. Create a script that:
+       - Uses pandas for data analysis
+       - Reads a CSV file
+       - Performs basic statistics
+       - Creates a visualization
+    2. Check and install required packages
+    3. Execute and analyze the results
+""")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex development workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving code quality
+- Check out the [Data Analyst Agent](/agents/data-analyst) for specialized data analysis

docs/agents/recommendation.mdx

@@ -0,0 +1,109 @@
+---
+title: "Recommendation Agent"
+sidebarTitle: "Recommendation"
+description: "Learn how to create AI agents for personalized recommendations across various domains."
+icon: "thumbs-up"
+---
+
+```mermaid
+flowchart LR
+    In[User Preferences] --> Search[Content Search]
+    Search --> Analyzer[Preference Analyzer]
+    Analyzer --> Generator[Recommendation Generator]
+    Generator --> Out[Recommendations]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Recommendation Agent can analyze preferences and generate personalized recommendations.
+
+## Quick Start
+
+<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="Create Script">
+        Create a new file `recommendation_system.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        # Create Recommendation Agent
+        recommendation_agent = Agent(
+            name="RecommendationEngine",
+            role="Recommendation Specialist",
+            goal="Generate personalized recommendations based on preferences",
+            instructions="You are a Recommendation Agent",
+            tools=[duckduckgo]
+        )
+
+        # Get recommendations
+        response = recommendation_agent.start(
+            "Recommend me a good movie to watch in 2025"
+        )
+        
+        # Save recommendations
+        with open('recommendations.txt', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Recommendation System
+
+The Recommendation Agent uses multiple approaches to generate personalized suggestions:
+
+1. **Content Search**: Uses DuckDuckGo to find current options
+2. **Preference Analysis**: Understands user preferences
+3. **Recommendation Generation**: Creates personalized suggestions
+4. **Content Filtering**: Filters based on relevance and quality
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Personalization" icon="user">
+    Tailored recommendations based on preferences.
+  </Card>
+  <Card title="Real-time Data" icon="clock">
+    Up-to-date content and information.
+  </Card>
+  <Card title="Multi-domain" icon="layer-group">
+    Recommendations across various categories.
+  </Card>
+  <Card title="Content Filtering" icon="filter">
+    Quality-based content selection.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Get personalized recommendations
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Recommendation Agent", tools=[duckduckgo])
+agent.start("Recommend me a good movie to watch in 2025")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex recommendation systems
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving recommendation accuracy
+- Check out the [Research Agent](/agents/research) for detailed content research

docs/agents/research.mdx

@@ -0,0 +1,108 @@
+---
+title: "Research Agent"
+sidebarTitle: "Research"
+description: "Learn how to create AI agents for conducting comprehensive research and analysis."
+icon: "magnifying-glass-chart"
+---
+
+```mermaid
+flowchart LR
+    In[Research Query] --> Search[Web Search]
+    Search --> Analyzer[Content Analyzer]
+    Analyzer --> Synthesizer[Information Synthesizer]
+    Synthesizer --> Out[Research Report]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Synthesizer fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Research Agent can gather, analyze, and synthesize information from various sources.
+
+## Quick Start
+
+<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="Create Script">
+        Create a new file `research_assistant.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        # Create Research Agent
+        research_agent = Agent(
+            name="ResearchAssistant",
+            role="Research Specialist",
+            goal="Conduct comprehensive research and analysis",
+            instructions="You are a Research Agent",
+            tools=[duckduckgo]
+        )
+
+        # Conduct research
+        response = research_agent.start(
+            "Research about AI developments in 2024"
+        )
+        
+        # Save research findings
+        with open('research_report.md', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Research Process
+
+The Research Agent employs a systematic approach to information gathering and analysis:
+
+1. **Web Search**: Uses DuckDuckGo to find relevant sources
+2. **Content Analysis**: Evaluates source credibility and relevance
+3. **Information Synthesis**: Combines findings into coherent insights
+4. **Report Generation**: Creates structured research reports
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Web Research" icon="globe">
+    Comprehensive online information gathering.
+  </Card>
+  <Card title="Content Analysis" icon="microscope">
+    In-depth evaluation of sources.
+  </Card>
+  <Card title="Information Synthesis" icon="layer-group">
+    Integration of multiple sources.
+  </Card>
+  <Card title="Report Generation" icon="file-lines">
+    Structured research documentation.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+agent.start("Research about AI 2024")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex research projects
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving research quality
+- Check out the [Data Analyst Agent](/agents/data-analyst) for data-driven research

docs/agents/shopping.mdx

@@ -0,0 +1,109 @@
+---
+title: "Shopping Agent"
+sidebarTitle: "Shopping"
+description: "Learn how to create AI agents for price comparison and shopping assistance."
+icon: "shopping-cart"
+---
+
+```mermaid
+flowchart LR
+    In[Product Query] --> Search[Store Search]
+    Search --> Analyzer[Price Analyzer]
+    Analyzer --> Comparator[Price Comparator]
+    Comparator --> Out[Price Report]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Comparator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Shopping Agent can search for products, compare prices, and provide shopping recommendations.
+
+## Quick Start
+
+<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="Create Script">
+        Create a new file `shopping_assistant.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        # Create Shopping Agent
+        shopping_agent = Agent(
+            name="ShoppingAssistant",
+            role="Shopping Specialist",
+            goal="Find the best prices and deals across stores",
+            instructions="You are a Shopping Agent",
+            tools=[duckduckgo]
+        )
+
+        # Search for product prices
+        response = shopping_agent.start(
+            "I want to buy iPhone 16 Pro Max, check 5 stores and give me price in table"
+        )
+        
+        # Save price comparison
+        with open('price_comparison.md', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Shopping Assistant
+
+The Shopping Agent combines multiple capabilities for effective price comparison:
+
+1. **Store Search**: Uses DuckDuckGo to find retailers
+2. **Price Analysis**: Gathers pricing information
+3. **Price Comparison**: Compares prices across stores
+4. **Deal Finding**: Identifies special offers and discounts
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Price Comparison" icon="scale-balanced">
+    Compare prices across multiple stores.
+  </Card>
+  <Card title="Deal Detection" icon="tag">
+    Find special offers and discounts.
+  </Card>
+  <Card title="Store Analysis" icon="store">
+    Evaluate retailer reliability.
+  </Card>
+  <Card title="Price Tracking" icon="chart-line">
+    Monitor price changes over time.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Compare product prices
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Shopping Agent", tools=[duckduckgo])
+agent.start("I want to buy iPhone 16 Pro Max, check 5 stores and give me price in table")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex shopping research
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving price accuracy
+- Check out the [Research Agent](/agents/research) for detailed product research

docs/agents/single.mdx

@@ -0,0 +1,102 @@
+---
+title: "Single Agent"
+sidebarTitle: "Single"
+description: "Learn how to create a basic single-purpose AI agent for simple tasks."
+icon: "circle-1"
+---
+
+```mermaid
+flowchart LR
+    In[Input] --> Agent[Single Agent]
+    Agent --> Out[Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how to create and use a simple single-purpose agent.
+
+## Quick Start
+
+<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="Create Script">
+        Create a new file `simple_agent.py`:
+        ```python
+        from praisonaiagents import Agent
+
+        agent = Agent(instructions="You are a Markdown Agent, output in markdown format")
+        agent.start("Write a blog post about AI")
+        ```
+    </Step>
+</Steps>
+
+## Understanding Single Agent
+
+The Single Agent is the simplest form of PraisonAI agent:
+
+1. **Simple Setup**: Minimal configuration required
+2. **Single Purpose**: Focused on one specific task
+3. **Direct Execution**: Straightforward input-output flow
+4. **No Dependencies**: No external tools required
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Simple Setup" icon="square-1">
+    Minimal configuration needed.
+  </Card>
+  <Card title="Focused Task" icon="bullseye">
+    Single-purpose execution.
+  </Card>
+  <Card title="Quick Start" icon="bolt">
+    Rapid implementation.
+  </Card>
+  <Card title="No Tools" icon="toolbox">
+    No external dependencies.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Create a content generation agent
+from praisonaiagents import Agent
+
+# Create content agent
+agent = Agent(
+    instructions="You are a Content Generation Agent, create engaging content"
+)
+
+# Generate content
+response = agent.start("""
+    Write a short story about:
+    - A future world
+    - With advanced AI
+    - And human collaboration
+""")
+
+# Save the story
+with open('ai_story.txt', 'w') as f:
+    f.write(response)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for more complex workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving output quality
+- Check out other specialized agents like the [Markdown Agent](/agents/markdown) for specific use cases

docs/agents/video.mdx

@@ -0,0 +1,149 @@
+---
+title: "Video Agent"
+sidebarTitle: "Video"
+description: "Learn how to create AI agents for video analysis and content understanding."
+icon: "video"
+---
+
+```mermaid
+flowchart LR
+    In[Video Input] --> Analyzer[Content Analyzer]
+    Analyzer --> Detector[Object Detector]
+    Detector --> Transcriber[Text Transcriber]
+    Transcriber --> Out[Analysis Report]
+    
+    style In fill:#8B0000,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Detector fill:#2E8B57,color:#fff
+    style Transcriber fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Video Agent can analyze video content, detect objects, and extract meaningful information.
+
+## Quick Start
+
+<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="Create Script">
+        Create a new file `video_analyzer.py`:
+        ```python
+        from praisonaiagents import Agent, Task, PraisonAIAgents
+
+        # Create Video Analysis Agent
+        video_agent = Agent(
+            name="VideoAnalyst",
+            role="Video Analysis Specialist",
+            goal="Analyze videos to extract meaningful information",
+            backstory="""You are an expert in computer vision and video analysis.
+            You excel at describing content, detecting objects, and understanding context.""",
+            llm="gpt-4o-mini",
+            self_reflect=False
+        )
+
+        # Create video analysis task
+        analysis_task = Task(
+            name="analyze_video",
+            description="""Analyze this video and provide:
+                1. Summary of main events
+                2. Key objects and people
+                3. Text and important information
+                4. Context and setting""",
+            expected_output="Comprehensive video analysis",
+            agent=video_agent,
+            images=["video.mp4"]
+        )
+
+        # Create PraisonAIAgents instance
+        agents = PraisonAIAgents(
+            agents=[video_agent],
+            tasks=[analysis_task],
+            process="sequential",
+            verbose=1
+        )
+
+        # Run analysis
+        agents.start()
+        ```
+    </Step>
+</Steps>
+
+## Understanding Video Analysis
+
+The Video Agent combines multiple capabilities for comprehensive video understanding:
+
+1. **Content Analysis**: Analyzes video scenes and events
+2. **Object Detection**: Identifies objects and people
+3. **Text Extraction**: Captures text shown in videos
+4. **Context Understanding**: Interprets settings and situations
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Scene Analysis" icon="film">
+    Detailed analysis of video scenes.
+  </Card>
+  <Card title="Object Detection" icon="cube">
+    Identification of objects and people.
+  </Card>
+  <Card title="Text Extraction" icon="closed-captioning">
+    Capture of text and captions.
+  </Card>
+  <Card title="Context Analysis" icon="sitemap">
+    Understanding of video context.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Analyze a presentation video
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+video_agent = Agent(
+    name="VideoAnalyst",
+    role="Video Analysis Specialist",
+    goal="Extract information from presentation videos",
+    llm="gpt-4o-mini"
+)
+
+# Create presentation analysis task
+presentation_task = Task(
+    name="analyze_presentation",
+    description="""Analyze this presentation video:
+        1. Extract key points
+        2. Capture slide content
+        3. Note speaker's main arguments
+        4. Summarize Q&A session""",
+    expected_output="Detailed presentation summary",
+    agent=video_agent,
+    images=["presentation.mp4"]
+)
+
+# Run analysis
+agents = PraisonAIAgents(
+    agents=[video_agent],
+    tasks=[presentation_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex video analysis
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving analysis accuracy
+- Check out the [Image Agent](/agents/image) for still image analysis

docs/agents/websearch.mdx

@@ -0,0 +1,124 @@
+---
+title: "Web Search Agent"
+sidebarTitle: "Web Search"
+description: "Learn how to create AI agents for intelligent web searching and information gathering."
+icon: "globe"
+---
+
+```mermaid
+flowchart LR
+    In[Search Query] --> Search[Web Search]
+    Search --> Filter[Content Filter]
+    Filter --> Summarizer[Content Summarizer]
+    Summarizer --> Out[Search Results]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Filter fill:#2E8B57,color:#fff
+    style Summarizer fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Web Search Agent can perform intelligent searches and process web content.
+
+## Quick Start
+
+<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="Create Script">
+        Create a new file `web_search.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        # Create Web Search Agent
+        search_agent = Agent(
+            name="WebSearcher",
+            role="Web Search Specialist",
+            goal="Perform intelligent web searches and gather information",
+            instructions="You are a Web Search Agent",
+            tools=[duckduckgo]
+        )
+
+        # Perform search
+        response = search_agent.start(
+            "Search about AI developments in 2024"
+        )
+        
+        # Save search results
+        with open('search_results.md', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Web Search
+
+The Web Search Agent employs sophisticated search strategies:
+
+1. **Query Processing**: Optimizes search queries
+2. **Content Filtering**: Filters relevant results
+3. **Information Extraction**: Extracts key information
+4. **Result Summarization**: Summarizes findings
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Smart Search" icon="magnifying-glass">
+    Intelligent query processing.
+  </Card>
+  <Card title="Content Filtering" icon="filter">
+    Relevance-based filtering.
+  </Card>
+  <Card title="Information Extraction" icon="scissors">
+    Key information extraction.
+  </Card>
+  <Card title="Result Summary" icon="list">
+    Concise result summaries.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Perform topic research
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(
+    instructions="You are a Web Search Agent",
+    tools=[duckduckgo]
+)
+
+# Research specific topic
+response = agent.start("""
+    Search for information about quantum computing:
+    - Recent breakthroughs
+    - Leading companies
+    - Current applications
+    - Future predictions
+""")
+
+# Save research results
+with open('quantum_computing_research.md', 'w') as f:
+    f.write(response)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex search workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving search relevance
+- Check out the [Research Agent](/agents/research) for comprehensive research capabilities

docs/agents/wikipedia.mdx

@@ -0,0 +1,139 @@
+---
+title: "Wikipedia Agent"
+sidebarTitle: "Wikipedia"
+description: "Learn how to create AI agents for searching and extracting information from Wikipedia."
+icon: "book"
+---
+
+```mermaid
+flowchart LR
+    In[Query] --> Search[Wiki Search]
+    Search --> Page[Page Retrieval]
+    Page --> Summary[Content Summary]
+    Summary --> Out[Knowledge Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Page fill:#2E8B57,color:#fff
+    style Summary fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Wikipedia Agent can search, retrieve, and summarize Wikipedia content.
+
+## Quick Start
+
+<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="Create Script">
+        Create a new file `wikipedia_assistant.py`:
+        ```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="WikipediaAssistant",
+            role="Wikipedia Research Specialist",
+            goal="Extract and summarize Wikipedia content",
+            instructions="You are a Wikipedia Agent",
+            tools=[
+                wiki_search,
+                wiki_summary,
+                wiki_page,
+                wiki_random,
+                wiki_language
+            ],
+            self_reflect=True,
+            min_reflect=3,
+            max_reflect=5
+        )
+
+        # Research a topic
+        response = wiki_agent.start("""
+            What is the history of AI?
+            First search the history of AI
+            Read the page of the history of AI
+            Get the summary of the page
+        """)
+        
+        # Save research results
+        with open('ai_history.md', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Wikipedia Research
+
+The Wikipedia Agent provides comprehensive Wikipedia access through multiple tools:
+
+1. **Wiki Search**: `wiki_search` for finding relevant articles
+2. **Page Access**: `wiki_page` for retrieving full articles
+3. **Summarization**: `wiki_summary` for concise overviews
+4. **Random Articles**: `wiki_random` for discovering content
+5. **Language Support**: `wiki_language` for multilingual access
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Article Search" icon="magnifying-glass">
+    Find relevant Wikipedia articles.
+  </Card>
+  <Card title="Content Retrieval" icon="book-open">
+    Access complete article content.
+  </Card>
+  <Card title="Summarization" icon="compress">
+    Generate concise article summaries.
+  </Card>
+  <Card title="Multilingual" icon="language">
+    Access content in multiple languages.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Research a scientific topic
+from praisonaiagents import Agent
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language
+
+agent = Agent(
+    instructions="You are a Wikipedia Agent", 
+    tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
+    self_reflect=True,
+    min_reflect=3,
+    max_reflect=5,
+)
+agent.start(
+    "What is the history of AI?"
+    "First search the history of AI"
+    "Read the page of the history of AI"
+    "Get the summary of the page"
+)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex Wikipedia research
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving research quality
+- Check out the [Research Agent](/agents/research) for broader research capabilities

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==2.0.35 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.46 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/examples.mdx

@@ -0,0 +1,109 @@
+---
+title: "Examples"
+sidebarTitle: "Examples"
+description: "Explore real-world examples and use cases built with PraisonAI Agents."
+icon: "code"
+---
+
+## Use Cases
+
+<CardGroup cols={2}>
+  <Card 
+    title="Predictive Maintenance" 
+    icon="wrench" 
+    href="/usecases/predictive-maintenance"
+  >
+    Learn how to create AI agents for predictive maintenance and equipment monitoring.
+  </Card>
+  
+  <Card 
+    title="Emergency Response" 
+    icon="truck-medical" 
+    href="/usecases/emergency-response"
+  >
+    Learn how to create AI agents for coordinated emergency response and resource management.
+  </Card>
+
+  <Card 
+    title="Code Review" 
+    icon="code" 
+    href="/usecases/code-review"
+  >
+    Learn how to create AI agents for automated code review and issue resolution.
+  </Card>
+
+  <Card 
+    title="Adaptive Learning" 
+    icon="graduation-cap" 
+    href="/usecases/adaptive-learning"
+  >
+    Learn how to create AI agents for personalized adaptive learning experiences.
+  </Card>
+
+  <Card 
+    title="Supply Chain" 
+    icon="truck" 
+    href="/usecases/supply-chain"
+  >
+    Learn how to create AI agents for supply chain risk management and mitigation.
+  </Card>
+
+  <Card 
+    title="Customer Service" 
+    icon="headset" 
+    href="/usecases/customer-service"
+  >
+    Learn how to create AI agents for automated customer service and support.
+  </Card>
+
+  <Card 
+    title="Fraud Detection" 
+    icon="shield-check" 
+    href="/usecases/fraud-detection"
+  >
+    Learn how to create AI agents for real-time fraud detection and alert management.
+  </Card>
+
+  <Card 
+    title="Healthcare Diagnosis" 
+    icon="stethoscope" 
+    href="/usecases/healthcare-diagnosis"
+  >
+    Learn how to create AI agents for medical diagnosis and treatment recommendations.
+  </Card>
+
+  <Card 
+    title="Smart City" 
+    icon="city" 
+    href="/usecases/smart-city"
+  >
+    Learn how to create AI agents for smart city resource optimization and management.
+  </Card>
+
+  <Card 
+    title="Multilingual Content" 
+    icon="language" 
+    href="/usecases/multilingual-content"
+  >
+    Learn how to create AI agents for multilingual content generation and cultural adaptation.
+  </Card>
+</CardGroup>
+
+## What's Next?
+
+<CardGroup cols={2}>
+  <Card
+    title="Features"
+    icon="stars"
+    href="/features/promptchaining"
+  >
+    Explore advanced features like prompt chaining and parallel execution
+  </Card>
+  <Card
+    title="API Reference"
+    icon="code"
+    href="/api-reference"
+  >
+    View the complete API reference documentation
+  </Card>
+</CardGroup>

docs/features/async.mdx

@@ -1,82 +1,146 @@
 ---
-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>
+```mermaid
+flowchart LR
+    In[In] --> Agent1[AI Agent]
+    Agent1 --> Agent2[AI Agent]
+    Agent1 --> Agent3[AI Agent]
+    Agent1 --> Agent4[AI Agent]
+    Agent2 --> Out[Out]
+    Agent3 --> Out
+    Agent4 --> Out
+    
+    style In fill:#8B0000,color:#fff
+    style Agent1 fill:#2E8B57,color:#fff
+    style Agent2 fill:#2E8B57,color:#fff
+    style Agent3 fill:#2E8B57,color:#fff
+    style Agent4 fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+Async AI Agents allow you to run AI tasks asynchronously, improving performance and efficiency in your applications.
 
 ## 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 +193,9 @@ 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 +423,6 @@ async def main():
 if __name__ == "__main__":
     # Run the main function
     asyncio.run(main())
-
 ```
 
 ### 1. Async Tasks with Callbacks
@@ -402,6 +467,28 @@ 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 +568,11 @@ 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 +1,349 @@
+---
+title: "AutoAgents"
+description: "AutoAgents automatically creates and manages AI agents and tasks based on high-level instructions."
+icon: "robot"
+---
+
+```mermaid
+flowchart LR
+    In[In] --> Plan[Plan Tasks]
+    
+    subgraph Creation[Automatic Agent Creation]
+        Plan --> Agent1[AI Agent 1]
+        Plan --> Agent2[AI Agent 2]
+        Plan --> Agent3[AI Agent 3]
+    end
+    
+    subgraph Tools[Automatic Tool Assignment]
+        Agent1 --> Tool1[Tools 1]
+        Agent2 --> Tool2[Tools 2]
+        Agent3 --> Tool3[Tools 3]
+    end
+    
+    Tools --> Execution
+    
+    subgraph Execution[Execution]
+        Exec1[AI Agent 1] --> Exec2[AI Agent 2]
+        Exec2 --> Exec3[AI Agent 3]
+    end
+    
+    Exec3 --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Plan fill:#2E8B57,color:#fff
+    style Agent1 fill:#2E8B57,color:#fff
+    style Agent2 fill:#2E8B57,color:#fff
+    style Agent3 fill:#2E8B57,color:#fff
+    style Tool1 fill:#2E8B57,color:#fff
+    style Tool2 fill:#2E8B57,color:#fff
+    style Tool3 fill:#2E8B57,color:#fff
+    style Exec1 fill:#2E8B57,color:#fff
+    style Exec2 fill:#2E8B57,color:#fff
+    style Exec3 fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+AutoAgents automatically creates and manages AI agents and tasks based on high-level instructions.
+
+## 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/autonomous-workflow.mdx

@@ -0,0 +1,277 @@
+---
+title: "Agentic Autonomous Workflow"
+sidebarTitle: "Autonomous Workflow"
+description: "Learn how to create AI agents that can autonomously monitor, act, and adapt based on environment feedback."
+icon: "robot"
+---
+
+```mermaid
+flowchart LR
+    Human[Human] <--> LLM[LLM Call]
+    LLM -->|ACTION| Environment[Environment]
+    Environment -->|FEEDBACK| LLM
+    LLM --> Stop[Stop]
+    
+    style Human fill:#8B0000,color:#fff
+    style LLM fill:#2E8B57,color:#fff
+    style Environment fill:#8B0000,color:#fff
+    style Stop fill:#333,color:#fff
+```
+
+An agent-based workflow where LLMs act autonomously within a loop, interacting with their environment and receiving feedback to refine their actions and decisions.
+
+## Quick Start
+
+<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
+        import time
+
+        def get_environment_state():
+            """Simulates getting current environment state"""
+            current_time = int(time.time())
+            states = ["normal", "critical", "optimal"]
+            state = states[current_time % 3]
+            print(f"Environment state: {state}")
+            return state
+
+        def perform_action(state: str):
+            """Simulates performing an action based on state"""
+            actions = {
+                "normal": "maintain",
+                "critical": "fix",
+                "optimal": "enhance"
+            }
+            action = actions.get(state, "observe")
+            print(f"Performing action: {action} for state: {state}")
+            return action
+
+        def get_feedback():
+            """Simulates environment feedback"""
+            current_time = int(time.time())
+            feedback = "positive" if current_time % 2 == 0 else "negative"
+            print(f"Feedback received: {feedback}")
+            return feedback
+
+        # Create specialized agents
+        llm_caller = Agent(
+            name="Environment Monitor",
+            role="State analyzer",
+            goal="Monitor environment and analyze state",
+            instructions="Check environment state and provide analysis",
+            tools=[get_environment_state]
+        )
+
+        action_agent = Agent(
+            name="Action Executor",
+            role="Action performer",
+            goal="Execute appropriate actions based on state",
+            instructions="Determine and perform actions based on environment state",
+            tools=[perform_action]
+        )
+
+        feedback_agent = Agent(
+            name="Feedback Processor",
+            role="Feedback analyzer",
+            goal="Process environment feedback and adapt strategy",
+            instructions="Analyze feedback and provide adaptation recommendations",
+            tools=[get_feedback]
+        )
+
+        # Create tasks for autonomous workflow
+        monitor_task = Task(
+            name="monitor_environment",
+            description="Monitor and analyze environment state",
+            expected_output="Current environment state analysis",
+            agent=llm_caller,
+            is_start=True,
+            task_type="decision",
+            next_tasks=["execute_action"],
+            condition={
+                "normal": ["execute_action"],
+                "critical": ["execute_action"],
+                "optimal": "exit"
+            }
+        )
+
+        action_task = Task(
+            name="execute_action",
+            description="Execute appropriate action based on state",
+            expected_output="Action execution result",
+            agent=action_agent,
+            next_tasks=["process_feedback"]
+        )
+
+        feedback_task = Task(
+            name="process_feedback",
+            description="Process feedback and adapt strategy",
+            expected_output="Strategy adaptation based on feedback",
+            agent=feedback_agent,
+            next_tasks=["monitor_environment"],  # Create feedback loop
+            context=[monitor_task, action_task]  # Access to previous states and actions
+        )
+
+        # Create workflow manager
+        workflow = PraisonAIAgents(
+            agents=[llm_caller, action_agent, feedback_agent],
+            tasks=[monitor_task, action_task, feedback_task],
+            process="workflow",
+            verbose=True
+        )
+
+        def main():
+            print("\nStarting Autonomous Agent Workflow...")
+            print("=" * 50)
+            
+            # Run autonomous workflow
+            results = workflow.start()
+            
+            # Print results
+            print("\nAutonomous Agent Results:")
+            print("=" * 50)
+            for task_id, result in results["task_results"].items():
+                if result:
+                    task_name = result.description
+                    print(f"\nTask: {task_name}")
+                    print(f"Result: {result.raw}")
+                    print("-" * 50)
+
+        if __name__ == "__main__":
+            main()
+        ```
+    </Step>
+
+    <Step title="Start Agents">
+        Type this in your terminal to run your agents:
+        ```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 Python
+</Note>
+
+## Understanding Autonomous Workflow
+
+<Card title="What is Autonomous Workflow?" icon="question">
+  Autonomous Workflow enables:
+  - Continuous environment monitoring
+  - Automated decision-making and action execution
+  - Real-time feedback processing
+  - Self-adapting behavior based on outcomes
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Environment Monitoring" icon="eye">
+    Continuously monitor and analyze environment state.
+  </Card>
+  <Card title="Adaptive Actions" icon="gears">
+    Execute context-aware actions based on state analysis.
+  </Card>
+  <Card title="Feedback Processing" icon="rotate">
+    Process and learn from action outcomes.
+  </Card>
+  <Card title="Self-Optimization" icon="arrow-trend-up">
+    Improve performance through continuous learning.
+  </Card>
+</CardGroup>
+
+## Configuration Options
+
+```python
+# Create a monitor agent
+monitor = Agent(
+    name="Environment Monitor",
+    role="State analyzer",
+    goal="Monitor and analyze state",
+    tools=[get_environment_state],  # Environment monitoring tools
+    verbose=True  # Enable detailed logging
+)
+
+# Create an action agent
+action = Agent(
+    name="Action Executor",
+    role="Action performer",
+    goal="Execute appropriate actions",
+    tools=[perform_action]  # Action execution tools
+)
+
+# Create monitoring task
+monitor_task = Task(
+    name="monitor_environment",
+    description="Monitor environment state",
+    agent=monitor,
+    is_start=True,
+    task_type="decision",
+    condition={
+        "normal": ["execute_action"],
+        "critical": ["execute_action"],
+        "optimal": "exit"
+    }
+)
+
+# Create feedback loop task
+feedback_task = Task(
+    name="process_feedback",
+    description="Process and adapt",
+    agent=feedback_agent,
+    next_tasks=["monitor_environment"],  # Create feedback loop
+    context=[monitor_task, action_task]  # Access history
+)
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Monitoring Issues" icon="triangle-exclamation">
+    If monitoring fails:
+    - Check environment access
+    - Verify state detection
+    - Enable verbose mode for debugging
+  </Card>
+
+  <Card title="Adaptation Flow" icon="diagram-project">
+    If adaptation is incorrect:
+    - Review feedback processing
+    - Check action outcomes
+    - Verify learning loop
+  </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 environment monitoring is reliable and your feedback processing logic is properly configured for effective adaptation.
+</Note>

docs/features/codeagent.mdx

@@ -5,73 +5,134 @@ description: "Learn how to create AI agents that can write and execute Python co
 icon: "code"
 ---
 
-<Note>
-  **Prerequisites**
-  - Python 3.10 or higher
-  - PraisonAI Agents package installed
-  - e2b_code_interpreter package installed
-  - Basic understanding of Python
-</Note>
-
 ## Quick Start
 
-Create AI agents that can write and execute Python code in a sandboxed environment!
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the required packages:
+            ```bash
+            pip install praisonaiagents e2b_code_interpreter
+            ```
+        </Step>
 
-<Steps>
-  <Step title="Install Packages">
-    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="Import Required Components">
-    Import the necessary components:
-    ```python
-    from praisonaiagents import Agent, Task, PraisonAIAgents
-    from e2b_code_interpreter import Sandbox
-    ```
-  </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
 
-  <Step title="Define Code Interpreter">
-    Create a code interpreter function:
-    ```python
-    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))
+            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.append(str(result))
-            logs = {"stdout": list(exec_result.logs.stdout), "stderr": list(exec_result.logs.stderr)}
-            return json.dumps({"results": results, "logs": logs})
-    ```
-  </Step>
+                    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})
 
-  <Step title="Create Code Agents">
-    Set up your code writing and execution agents:
-    ```python
-    code_agent = Agent(
-        name="code_agent",
-        backstory="Expert in writing Python scripts",
-        self_reflect=False
-    )
+            # 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
+            )
 
-    execution_agent = Agent(
-        name="execution_agent",
-        backstory="Expert in executing Python scripts",
-        self_reflect=False,
-        tools=[code_interpreter]
-    )
-    ```
-  </Step>
-</Steps>
+            # 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
 
@@ -83,205 +144,210 @@ Create AI agents that can write and execute Python code in a sandboxed environme
   - Work together in a pipeline (writer → executor)
 </Card>
 
-## Key Components
+## Features
 
 <CardGroup cols={2}>
-  <Card title="Code Writer Agent" icon="pencil">
-    Specialized in writing Python code
-    ```python
-    Agent(
-        name="code_agent",
-        backstory="Expert in writing Python scripts"
-    )
-    ```
+  <Card title="Code Writer" icon="pencil">
+    Writes Python code based on requirements.
   </Card>
-  <Card title="Code Executor Agent" icon="play">
-    Executes code using sandbox
-    ```python
-    Agent(
-        name="execution_agent",
-        tools=[code_interpreter]
-    )
-    ```
+  <Card title="Safe Execution" icon="shield">
+    Executes code in a sandboxed environment.
   </Card>
-  <Card title="Sandbox Environment" icon="box">
-    Safe code execution environment using e2b
+  <Card title="Error Handling" icon="bug">
+    Manages code execution errors and debugging.
   </Card>
-  <Card title="Execution Results" icon="output">
-    Structured output with results and logs
+  <Card title="Results Processing" icon="output">
+    Processes and formats execution results.
   </Card>
 </CardGroup>
 
-## Examples
+## Multi-Agent Code Development
 
-### Simple Hello World Example
+<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
-from praisonaiagents import Agent, Task, PraisonAIAgents
-from e2b_code_interpreter import Sandbox
-
-# Create agents
-code_agent = Agent(
-    name="code_agent",
-    backstory="Expert in writing Python scripts",
-    self_reflect=False
-)
-
-execution_agent = Agent(
-    name="execution_agent",
-    backstory="Expert in executing Python scripts",
-    self_reflect=False,
-    tools=[code_interpreter]
-)
-
-# Define tasks
-code_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_task = Task(
-    description="Execute the Python script",
-    expected_output="The output of the Python script",
-    agent=execution_agent
-)
-
-# Initialize and run agents
-agents = PraisonAIAgents(
-    agents=[code_agent, execution_agent],
-    tasks=[code_task, execution_task]
-)
-result = agents.start()
-```
-
-### Advanced Code Generation
-
-<CodeGroup>
-```python Data Analysis
-# Create a data analysis script
-code_task = Task(
-    description="""
-    Write a Python script that:
-    1. Creates a sample dataset
-    2. Performs basic statistical analysis
-    3. Generates a plot
-    """,
-    agent=code_agent
-)
-```
-
-```python Web Scraping
-# Create a web scraping script
-code_task = Task(
-    description="""
-    Write a Python script that:
-    1. Scrapes a website
-    2. Extracts specific data
-    3. Saves to CSV
-    """,
-    agent=code_agent
-)
-```
-</CodeGroup>
-
-## Best Practices
-
-<AccordionGroup>
-  <Accordion title="Code Safety">
-    Ensure safe code execution:
-    ```python
-    # Always use sandbox environment
-    exec_result = Sandbox().run_code(code)
-    
-    # Handle errors gracefully
-    if exec_result.error:
-        return {"error": str(exec_result.error)}
-    ```
-  </Accordion>
-
-  <Accordion title="Task Description">
-    Write clear code requirements:
-    ```python
-    # Good
-    Task(description="""
-        Write a Python script that:
-        1. Takes user input
-        2. Validates the input
-        3. Processes the data
-        4. Returns formatted output
-    """)
-    
-    # Too vague
-    Task(description="Write some Python code")
-    ```
-  </Accordion>
-</AccordionGroup>
-
-## Common Patterns
-
-### Code Generation and Testing
-```python
-# Generate code
-code_task = Task(
-    description="Write a function to calculate fibonacci numbers",
-    agent=code_agent
-)
-
-# Generate test cases
-test_task = Task(
-    description="Write test cases for the fibonacci function",
-    agent=code_agent
-)
-
-# Execute tests
-execution_task = Task(
-    description="Run the test cases",
-    agent=execution_agent
-)
-```
-
-### Interactive Code Development
-```python
-# Write initial code
-initial_code_task = Task(
-    description="Write initial version of the script",
-    agent=code_agent
-)
-
-# Test and improve
-improvement_task = Task(
-    description="Analyze execution results and improve the code",
-    agent=code_agent
+# 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="Execution Errors" icon="triangle-exclamation">
-    Check sandbox execution results:
-    ```python
-    if exec_result.error:
-        print("[Error]", exec_result.error)
-    ```
+  <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="Code Generation" icon="code">
-    Provide clear requirements and constraints in task description
+
+  <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="Advanced Code Generation" icon="graduation-cap" href="./advanced-code">
-    Learn about complex code generation patterns
+  <Card title="AutoAgents" icon="robot" href="./autoagents">
+    Learn about automatically created and managed AI agents
   </Card>
-  <Card title="Custom Tools" icon="wrench" href="./custom-tools">
-    Create custom code execution tools
+  <Card title="Mini Agents" icon="microchip" href="./mini">
+    Explore lightweight, focused AI agents
   </Card>
 </CardGroup>
 
 <Note>
-  Code execution agents provide a powerful way to generate and run Python code safely. They're perfect for automated code generation, testing, and execution in a controlled environment.
+  For optimal results, ensure code is properly formatted and tested in the sandbox environment before production use.
 </Note>

docs/features/evaluator-optimiser.mdx

@@ -0,0 +1,230 @@
+---
+title: "Agentic Evaluator Optimizer"
+sidebarTitle: "Evaluator Optimizer"
+description: "Learn how to create AI agents that can generate and optimize solutions through iterative feedback."
+icon: "arrows-rotate"
+---
+
+```mermaid
+flowchart LR
+    In[In] --> Generator[LLM Call Generator] 
+    Generator -->|SOLUTION| Evaluator[LLM Call Evaluator] -->|ACCEPTED| Out[Out]
+    Evaluator -->|REJECTED + FEEDBACK| Generator
+    
+    style In fill:#8B0000,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Evaluator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A feedback loop workflow where LLM-generated outputs are evaluated, refined, and optimized iteratively to improve accuracy and relevance.
+
+## Quick Start
+
+<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 generator and evaluator agents
+        generator = Agent(
+            name="Generator",
+            role="Solution generator",
+            goal="Generate initial solutions and incorporate feedback",
+            instructions=(
+                "1. Look at the context from previous tasks.\n"
+                "2. If you see that you have already produced 2 points, then add another 2 new points "
+                "   so that the total becomes 10.\n"
+                "3. Otherwise, just produce the first 2 points.\n"
+                "4. Return only the final list of points, with no extra explanation."
+            )
+        )
+
+        evaluator = Agent(
+            name="Evaluator",
+            role="Solution evaluator",
+            goal="Evaluate solutions and provide improvement feedback",
+            instructions=(
+                "1. Count how many lines in the response start with a number and a period (like '1. ' or '2. ').\n"
+                "2. If there are 10 or more, respond with 'done'.\n"
+                "3. Otherwise, respond with 'more'.\n"
+                "4. Return only the single word: 'done' or 'more'."
+            )
+        )
+
+        # Create tasks for the feedback loop
+        generate_task = Task(
+            name="generate",
+            description="Write 2 points about AI incuding if anything exiting from previous points",
+            expected_output="2 points",
+            agent=generator,
+            is_start=True,
+            task_type="decision",
+            next_tasks=["evaluate"]
+        )
+
+        evaluate_task = Task(
+            name="evaluate",
+            description="Check if there are 10 points about AI",
+            expected_output="more or done",
+            agent=evaluator,
+            next_tasks=["generate"],
+            context=[generate_task],
+            task_type="decision",
+            condition={
+                "more": ["generate"],  # Continue to generate
+                "done": [""]  # Exit when optimization complete
+            }
+        )
+
+        # Create workflow manager
+        workflow = PraisonAIAgents(
+            agents=[generator, evaluator],
+            tasks=[generate_task, evaluate_task],
+            process="workflow",
+            verbose=True
+        )
+
+        # Run optimization workflow
+        results = workflow.start()
+
+        # Print results
+        print("\nEvaluator-Optimizer Results:")
+        for task_id, result in results["task_results"].items():
+            if result:
+                print(f"Task {task_id}: {result.raw}")
+        ```
+    </Step>
+
+    <Step title="Start Agents">
+        Type this in your terminal to run your agents:
+        ```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 Python
+</Note>
+
+## Understanding Evaluator-Optimizer
+
+<Card title="What is Evaluator-Optimizer?" icon="question">
+  Evaluator-Optimizer pattern enables:
+  - Iterative solution generation and refinement
+  - Automated quality evaluation
+  - Feedback-driven optimization
+  - Continuous improvement loops
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Solution Generation" icon="wand-magic-sparkles">
+    Generate solutions based on requirements and feedback.
+  </Card>
+  <Card title="Quality Evaluation" icon="magnifying-glass-chart">
+    Automatically assess solution quality and completeness.
+  </Card>
+  <Card title="Feedback Loop" icon="rotate">
+    Implement iterative improvement through feedback cycles.
+  </Card>
+  <Card title="Process Control" icon="sliders">
+    Monitor and control the optimization process.
+  </Card>
+</CardGroup>
+
+## Configuration Options
+
+```python
+# Create a generator agent
+generator = Agent(
+    name="Generator",
+    role="Solution generator",
+    goal="Generate and improve solutions",
+    instructions="Step-by-step instructions for generation",
+    verbose=True  # Enable detailed logging
+)
+
+# Create an evaluator agent
+evaluator = Agent(
+    name="Evaluator",
+    role="Solution evaluator",
+    goal="Evaluate and provide feedback",
+    instructions="Evaluation criteria and feedback format"
+)
+
+# Create tasks with feedback loop
+generate_task = Task(
+    name="generate",
+    description="Generate solution",
+    agent=generator,
+    is_start=True,
+    task_type="decision",
+    next_tasks=["evaluate"]
+)
+
+evaluate_task = Task(
+    name="evaluate",
+    description="Evaluate solution",
+    agent=evaluator,
+    context=[generate_task],
+    task_type="decision",
+    condition={
+        "more": ["generate"],  # Continue optimization
+        "done": [""]  # Exit when complete
+    }
+)
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Generation Issues" icon="triangle-exclamation">
+    If generation is not improving:
+    - Review generator instructions
+    - Check feedback integration
+    - Enable verbose mode for debugging
+  </Card>
+
+  <Card title="Evaluation Flow" icon="diagram-project">
+    If evaluation cycle is incorrect:
+    - Verify evaluation criteria
+    - Check condition mappings
+    - Review feedback loop connections
+  </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 generator instructions and evaluation criteria are clear and well-defined to achieve the desired optimization outcomes.
+</Note>

docs/features/langchain.mdx

@@ -0,0 +1,219 @@
+---
+title: "LangChain Agents"
+sidebarTitle: "LangChain Agents"
+description: "Learn how to use LangChain tools and utilities with PraisonAI agents."
+icon: "link-simple"
+---
+
+## Quick Start
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the required packages:
+            ```bash
+            pip install praisonaiagents langchain-community wikipedia
+            ```
+        </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 Tool
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+  from langchain_community.utilities import WikipediaAPIWrapper
+
+  # Create an agent with Wikipedia tool
+  agent = Agent(
+      name="WikiAgent",
+      role="Research Assistant",
+      goal="Search Wikipedia for accurate information",
+      backstory="I am an AI assistant specialized in Wikipedia research",
+      tools=[WikipediaAPIWrapper],
+      self_reflect=False
+  )
+
+  # Create a research task
+  task = Task(
+      name="wiki_search",
+      description="Research 'Artificial Intelligence' on Wikipedia",
+      expected_output="Comprehensive information from Wikipedia articles",
+      agent=agent
+  )
+
+  # Create and start the workflow
+  agents = PraisonAIAgents(
+      agents=[agent],
+      tasks=[task],
+      verbose=True
+  )
+
+  agents.start()
+  ```
+
+  ```python Multiple Tools
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+  from langchain_community.tools import YouTubeSearchTool
+  from langchain_community.utilities import WikipediaAPIWrapper
+
+  # Create YouTube search agent
+  agent = Agent(
+      name="SearchAgent",
+      role="Research Assistant",
+      goal="Search for information from YouTube",
+      backstory="I am an AI assistant that can search YouTube for relevant videos.",
+      tools=[YouTubeSearchTool],
+      self_reflect=False
+  )
+
+  # Create Wikipedia research agent
+  agent2 = Agent(
+      name="WikiAgent",
+      role="Research Assistant",
+      goal="Search for information from Wikipedia",
+      backstory="I am an AI assistant that can search Wikipedia for accurate information.",
+      tools=[WikipediaAPIWrapper],
+      self_reflect=False
+  )
+
+  # Create YouTube search task
+  task = Task(
+      name="search_task",
+      description="Search for information about 'AI advancements' on YouTube",
+      expected_output="Relevant information from YouTube videos",
+      agent=agent
+  )
+
+  # Create Wikipedia research task
+  task2 = Task(
+      name="wiki_task",
+      description="Search for information about 'AI advancements' on Wikipedia",
+      expected_output="Comprehensive information from Wikipedia articles",
+      agent=agent2
+  )
+
+  # Create and start the workflow
+  agents = PraisonAIAgents(
+      agents=[agent, agent2],
+      tasks=[task, task2],
+      verbose=True
+  )
+
+  agents.start()
+  ```
+</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">
+            (Upcoming Feature)
+            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
+roles:
+  researcher:
+    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:
+      - youtube_search
+      - wikipedia
+    tasks:
+      search_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
+```
+        </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)
+  - LangChain compatible tools and utilities
+</Note>
+
+## Understanding LangChain Integration
+
+<Card title="What is LangChain Integration?" icon="question">
+  LangChain integration enables agents to:
+  - Use LangChain's extensive tool ecosystem
+  - Access various data sources and APIs
+  - Leverage pre-built utilities and wrappers
+  - Combine multiple tools in a single agent
+  - Extend agent capabilities with community tools
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Tool Integration" icon="plug">
+    Seamlessly use LangChain tools with PraisonAI agents.
+  </Card>
+  <Card title="Multiple Sources" icon="database">
+    Access various data sources through LangChain utilities.
+  </Card>
+  <Card title="Community Tools" icon="users">
+    Leverage the extensive LangChain community ecosystem.
+  </Card>
+  <Card title="Custom Tools" icon="wrench">
+    Create and integrate custom LangChain tools.
+  </Card>
+</CardGroup>
+
+
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Memory Integration" icon="brain" href="../concepts/memory">
+    Learn how to combine LangChain tools with agent memory
+  </Card>
+  <Card title="Custom Tools" icon="wrench" href="./tools">
+    Create your own custom tools for agents
+  </Card>
+</CardGroup>
+
+<Note>
+  For optimal results, ensure all required dependencies are installed and API keys are properly configured for each tool.
+</Note>

docs/features/mathagent.mdx

@@ -5,51 +5,128 @@ description: "Learn how to create AI agents that can perform complex mathematica
 icon: "calculator"
 ---
 
-<Note>
-  **Prerequisites**
-  - Python 3.10 or higher
-  - PraisonAI Agents package installed
-  - Basic understanding of Python
-  - sympy and pint packages (automatically installed when needed)
-</Note>
-
 ## Quick Start
 
-Create AI agents that can perform complex mathematical calculations, unit conversions, and financial computations!
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the PraisonAI Agents package:
+            ```bash
+            pip install praisonaiagents sympy pint
+            ```
+        </Step>
 
-<Steps>
-  <Step title="Install Package">
-    First, install the required 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="Import Required 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 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
+            )
 
-  <Step title="Create Math Agent">
-    Set up your mathematical computation agent:
-    ```python
-    math_agent = Agent(
-        name="MathAssistant",
-        description="Expert in mathematical calculations and analysis",
-        tools=[
-            evaluate, solve_equation, convert_units,
-            calculate_statistics, calculate_financial
-        ]
-    )
-    ```
-  </Step>
-</Steps>
+            # 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
 
@@ -62,180 +139,177 @@ Create AI agents that can perform complex mathematical calculations, unit conver
   - Handle financial calculations
 </Card>
 
-## Key Components
+## Features
 
 <CardGroup cols={2}>
   <Card title="Expression Evaluator" icon="function">
-    Evaluates mathematical expressions
-    ```python
-    evaluate("2 + 2 * 3")
-    evaluate("sin(pi/2) + sqrt(16)")
-    ```
+    Evaluates mathematical expressions and formulas.
   </Card>
   <Card title="Equation Solver" icon="equals">
-    Solves mathematical equations
-    ```python
-    solve_equation("2*x + 3 = 7")
-    solve_equation("x^2 - 5*x + 6 = 0")
-    ```
+    Solves mathematical equations and systems.
   </Card>
   <Card title="Unit Converter" icon="arrows-rotate">
-    Converts between different units
-    ```python
-    convert_units(100, "meters", "kilometers")
-    ```
+    Converts between different units of measurement.
   </Card>
   <Card title="Statistical Calculator" icon="chart-simple">
-    Calculates statistical metrics
-    ```python
-    calculate_statistics([1, 2, 3, 4, 5])
-    ```
+    Calculates statistical metrics and analysis.
   </Card>
 </CardGroup>
 
-## Examples
+## Multi-Agent AI Math Analysis
 
-### Basic Calculations Example
+<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
-from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import evaluate, solve_equation
-
-# Create agent
-math_agent = Agent(
-    name="MathAssistant",
-    description="Expert in mathematical calculations",
-    tools=[evaluate, solve_equation]
-)
-
-# Define tasks
-calculation_task = Task(
-    description="Calculate the value of: (3 + 4) * 2 - sqrt(16)",
-    expected_output="The result of the mathematical expression",
-    agent=math_agent
-)
-
-equation_task = Task(
-    description="Solve the equation: x^2 - 5x + 6 = 0",
-    expected_output="The solutions to the quadratic equation",
-    agent=math_agent
-)
-
-# Initialize and run agents
-agents = PraisonAIAgents(
-    agents=[math_agent],
-    tasks=[calculation_task, equation_task]
-)
-result = agents.start()
-```
-
-### Advanced Mathematical Operations
-
-<CodeGroup>
-```python Statistical Analysis
-# Create a statistical analysis task
-stats_task = Task(
-    description="""
-    Calculate statistical metrics for the dataset:
-    [12, 15, 18, 22, 25, 28, 32, 35, 38, 42]
-    """,
-    agent=math_agent
-)
-```
-
-```python Financial Calculations
-# Create a financial calculation task
-finance_task = Task(
-    description="""
-    Calculate compound interest for:
-    - Principal: $10,000
-    - Rate: 5% annually
-    - Time: 3 years
-    - Compounding: Monthly
-    """,
-    agent=math_agent
-)
-```
-</CodeGroup>
-
-## Best Practices
-
-<AccordionGroup>
-  <Accordion title="Expression Handling">
-    Handle expressions safely:
-    ```python
-    # Good - Using variables dictionary
-    evaluate(
-        "x^2 + y",
-        variables={"x": 3, "y": 4}
-    )
-    
-    # Good - Setting precision
-    evaluate(
-        "pi + e",
-        precision=5
-    )
-    ```
-  </Accordion>
-
-  <Accordion title="Task Description">
-    Write clear mathematical requirements:
-    ```python
-    # Good
-    Task(description="""
-        Calculate the following:
-        1. Area of a circle with radius 5
-        2. Convert the result to square inches
-        3. Round to 2 decimal places
-    """)
-    
-    # Too vague
-    Task(description="Do some math")
-    ```
-  </Accordion>
-</AccordionGroup>
-
-## Common Patterns
-
-### Multi-Step Calculations
-```python
-# Calculate area
-area_task = Task(
-    description="Calculate the area of a circle with radius 5",
-    agent=math_agent
-)
-
-# Convert units
-conversion_task = Task(
-    description="Convert the area from square meters to square feet",
-    agent=math_agent
-)
-
-# Format result
-formatting_task = Task(
-    description="Round the result to 2 decimal places",
-    agent=math_agent
-)
-```
-
-### Statistical Analysis Pipeline
-```python
-# Gather data
-data_task = Task(
-    description="Generate a dataset of 100 random numbers",
-    agent=math_agent
-)
-
-# Calculate statistics
-stats_task = Task(
-    description="Calculate mean, median, mode, and standard deviation",
-    agent=math_agent
-)
-
-# Visualize results
-viz_task = Task(
-    description="Create a summary of the statistical analysis",
-    agent=math_agent
+# 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
 )
 ```
 
@@ -243,37 +317,31 @@ viz_task = Task(
 
 <CardGroup cols={2}>
   <Card title="Expression Errors" icon="triangle-exclamation">
-    Check expression syntax and variable definitions:
-    ```python
-    try:
-        result = evaluate("x + y", variables={"x": 1})
-    except Exception as e:
-        print(f"Error: Missing variable 'y'")
-    ```
+    If expressions are not evaluating:
+    - Check syntax and formatting
+    - Verify variable definitions
+    - Enable verbose mode for debugging
   </Card>
-  <Card title="Unit Conversion" icon="ruler">
-    Ensure compatible unit types and valid unit names
-    ```python
-    # Valid conversion
-    convert_units(100, "celsius", "fahrenheit")
-    
-    # Invalid conversion
-    # convert_units(100, "meters", "celsius")
-    ```
+
+  <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="Advanced Mathematics" icon="graduation-cap" href="./advanced-math">
-    Learn about complex mathematical operations
+  <Card title="AutoAgents" icon="robot" href="./autoagents">
+    Learn about automatically created and managed AI agents
   </Card>
-  <Card title="Custom Math Tools" icon="wrench" href="./custom-tools">
-    Create custom mathematical computation tools
+  <Card title="Mini Agents" icon="microchip" href="./mini">
+    Explore lightweight, focused AI agents
   </Card>
 </CardGroup>
 
 <Note>
-  Math agents provide a powerful way to perform complex mathematical operations, statistical analysis, and financial calculations. They're perfect for data analysis, scientific computing, and financial modeling tasks.
+  For optimal results, ensure mathematical expressions and units are properly formatted for your specific use case.
 </Note>

docs/features/mini.mdx

@@ -4,47 +4,101 @@ 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>
-
-  <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>
+<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 />
 
 <div className="relative w-full aspect-video">
   <iframe
@@ -55,6 +109,7 @@ Create multiple AI agents that can work together in just a few lines of code!
     allowFullScreen
   ></iframe>
 </div>
+<br />
 
 ## Understanding Mini AI Agents
 
@@ -106,34 +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

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/orchestrator-worker.mdx

@@ -0,0 +1,278 @@
+---
+title: "Agentic Orchestrator Worker"
+sidebarTitle: "Orchestrator Worker"
+description: "Learn how to create AI agents that orchestrate and distribute tasks among specialized workers."
+icon: "sitemap"
+---
+
+```mermaid
+flowchart LR
+    In[In] --> Router[LLM Call Router]
+    Router --> LLM1[LLM Call 1]
+    Router --> LLM2[LLM Call 2]
+    Router --> LLM3[LLM Call 3]
+    LLM1 --> Synthesizer[Synthesizer]
+    LLM2 --> Synthesizer
+    LLM3 --> Synthesizer
+    Synthesizer --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Router fill:#2E8B57,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Synthesizer fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow with a central orchestrator directing multiple worker LLMs to perform subtasks, synthesizing their outputs for complex, coordinated operations.
+
+## Quick Start
+
+<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
+        import time
+
+        def get_time_check():
+            current_time = int(time.time())
+            if current_time % 3 == 0:
+                result = 1
+            elif current_time % 3 == 1:
+                result = 2
+            else:
+                result = 3
+            print(f"Time: {current_time}, Result: {result}")
+            return result
+
+        # Create orchestrator and worker agents
+        router = Agent(
+            name="Router",
+            role="Task router",
+            goal="Distribute tasks to based on response from get_time_check",
+            tools=[get_time_check]
+        )
+
+        worker1 = Agent(
+            name="Worker 1",
+            role="Specialized worker",
+            goal="Handle specific subtask type 1",
+        )
+
+        worker2 = Agent(
+            name="Worker 2",
+            role="Specialized worker",
+            goal="Handle specific subtask type 2",
+        )
+
+        worker3 = Agent(
+            name="Worker 3",
+            role="Specialized worker",
+            goal="Handle specific subtask type 3",
+        )
+
+        synthesizer = Agent(
+            name="Synthesizer",
+            role="Result synthesizer",
+            goal="Combine and synthesize worker outputs",
+        )
+
+        # Create orchestrated workflow tasks
+        router_task = Task(
+            name="route_task",
+            description="Analyze input from get_time_check and route to appropriate workers",
+            expected_output="Task routing decision, 1 , 2 or 3",
+            agent=router,
+            is_start=True,
+            task_type="decision",
+            next_tasks=["worker1_task", "worker2_task", "worker3_task"],
+            condition={
+                "1": ["worker1_task"],
+                "2": ["worker2_task"],
+                "3": ["worker3_task"]
+            }
+        )
+
+        worker1_task = Task(
+            name="worker1_task",
+            description="Process type 1 operation",
+            expected_output="Worker 1 result",
+            agent=worker1,
+            next_tasks=["synthesize_task"]
+        )
+
+        worker2_task = Task(
+            name="worker2_task",
+            description="Process type 2 operation",
+            expected_output="Worker 2 result",
+            agent=worker2,
+            next_tasks=["synthesize_task"]
+        )
+
+        worker3_task = Task(
+            name="worker3_task",
+            description="Process type 3 operation",
+            expected_output="Worker 3 result",
+            agent=worker3,
+            next_tasks=["synthesize_task"]
+        )
+
+        synthesize_task = Task(
+            name="synthesize_task",
+            description="Synthesize worker results into final output",
+            expected_output="Final synthesized result",
+            agent=synthesizer,
+            context=[worker1_task, worker2_task, worker3_task]
+        )
+
+        # Create workflow manager
+        workflow = PraisonAIAgents(
+            agents=[router, worker1, worker2, worker3, synthesizer],
+            tasks=[router_task, worker1_task, worker2_task, worker3_task, synthesize_task],
+            process="workflow",
+            verbose=True
+        )
+
+        # Run orchestrated workflow
+        results = workflow.start()
+
+        # Print results
+        print("\nOrchestrator-Workers Results:")
+        for task_id, result in results["task_results"].items():
+            if result:
+                print(f"Task {task_id}: {result.raw}")
+        ```
+    </Step>
+
+    <Step title="Start Agents">
+        Type this in your terminal to run your agents:
+        ```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 Python
+</Note>
+
+## Understanding Orchestrator-Worker Pattern
+
+<Card title="What is Orchestrator-Worker?" icon="question">
+  Orchestrator-Worker pattern enables:
+  - Dynamic task distribution and routing
+  - Specialized worker execution
+  - Result synthesis and aggregation
+  - Coordinated workflow management
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Task Routing" icon="route">
+    Intelligently distribute tasks to specialized workers.
+  </Card>
+  <Card title="Worker Specialization" icon="user-gear">
+    Dedicated agents for specific task types.
+  </Card>
+  <Card title="Result Synthesis" icon="object-group">
+    Combine and process worker outputs effectively.
+  </Card>
+  <Card title="Process Control" icon="sliders">
+    Monitor and manage the orchestrated workflow.
+  </Card>
+</CardGroup>
+
+## Configuration Options
+
+```python
+# Create an orchestrator agent
+router = Agent(
+    name="Router",
+    role="Task router",
+    goal="Distribute tasks based on conditions",
+    tools=[get_time_check],  # Tools for routing decisions
+    verbose=True  # Enable detailed logging
+)
+
+# Create a worker agent
+worker = Agent(
+    name="Worker",
+    role="Specialized worker",
+    goal="Handle specific task type",
+    instructions="Processing instructions"
+)
+
+# Create routing task
+router_task = Task(
+    name="route_task",
+    description="Route tasks to workers",
+    agent=router,
+    is_start=True,
+    task_type="decision",
+    condition={
+        "1": ["worker1_task"],
+        "2": ["worker2_task"]
+    }
+)
+
+# Create synthesis task
+synthesis_task = Task(
+    name="synthesize",
+    description="Combine worker results",
+    agent=synthesizer,
+    context=[worker1_task, worker2_task]  # Reference worker tasks
+)
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Routing Issues" icon="triangle-exclamation">
+    If task routing fails:
+    - Check routing conditions
+    - Verify worker availability
+    - Enable verbose mode for debugging
+  </Card>
+
+  <Card title="Synthesis Flow" icon="diagram-project">
+    If result synthesis is incorrect:
+    - Review worker outputs
+    - Check context connections
+    - Verify synthesis logic
+  </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 routing logic is well-defined and your workers are properly configured for their specialized tasks.
+</Note>

docs/features/parallelisation.mdx

@@ -0,0 +1,252 @@
+---
+title: "Agentic Parallelization"
+sidebarTitle: "Parallelization"
+description: "Learn how to create AI agents that can execute tasks in parallel for improved performance."
+icon: "arrows-split-up-and-left"
+---
+
+```mermaid
+flowchart LR
+    In[In] --> LLM2[LLM Call 2]
+    In --> LLM1[LLM Call 1]
+    In --> LLM3[LLM Call 3]
+    LLM1 --> Aggregator[Aggregator]
+    LLM2 --> Aggregator
+    LLM3 --> Aggregator
+    Aggregator --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Aggregator fill:#fff,color:#000
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow that distributes tasks across multiple LLM calls simultaneously, aggregating results to handle complex or large-scale operations efficiently.
+
+## Quick Start
+
+<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 datetime import datetime
+        import asyncio
+
+        def process_time():
+            """Simulate processing"""
+            current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            print(f"Processing at: {current_time}")
+            return f"Processed at {current_time}"
+
+        # Create parallel processing agents
+        agent1 = Agent(
+            name="Processor 1",
+            role="Time collector",
+            goal="Get the time and return it",
+            tools=[process_time]
+        )
+
+        agent2 = Agent(
+            name="Processor 2",
+            role="Time collector",
+            goal="Get the time and return it",
+            tools=[process_time]
+        )
+
+        agent3 = Agent(
+            name="Processor 3",
+            role="Time collector",
+            goal="Get the time and return it",
+            tools=[process_time]
+        )
+
+        aggregator = Agent(
+            name="Aggregator",
+            role="Result aggregator",
+            goal="Collect all the processed time from all tasks"
+        )
+
+        # Create parallel tasks with memory disabled
+        task1 = Task(
+            name="process_1",
+            description="Use process_time tool to get the time",
+            expected_output="processed time",
+            agent=agent1,
+            is_start=True,
+            async_execution=True
+        )
+
+        task2 = Task(
+            name="process_2",
+            description="Use process_time tool to get the time",
+            expected_output="processed time",
+            agent=agent2,
+            is_start=True,
+            async_execution=True
+        )
+
+        task3 = Task(
+            name="process_3",
+            description="Use process_time tool to get the time",
+            expected_output="processed time",
+            agent=agent3,
+            is_start=True,
+            async_execution=True
+        )
+
+        aggregate_task = Task(
+            name="aggregate",
+            description="Collect all the processed time from all tasks",
+            expected_output="Output all the processed time from all tasks and just the time",
+            agent=aggregator,
+            context=[task1, task2, task3]
+        )
+
+        async def main():
+
+            # Create workflow manager
+            workflow = PraisonAIAgents(
+                agents=[agent1, agent2, agent3, aggregator],
+                tasks=[task1, task2, task3, aggregate_task],
+                process="workflow"
+            )
+
+            # Run parallel workflow
+            results = await workflow.astart()
+
+            # Print results
+            print("\nParallel Processing Results:")
+            for task_id, result in results["task_results"].items():
+                if result:
+                    print(f"Task {task_id}: {result.raw}")
+
+        # Run the async main function
+        if __name__ == "__main__":
+            asyncio.run(main())
+        ```
+    </Step>
+
+    <Step title="Start Agents">
+        Type this in your terminal to run your agents:
+        ```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 Python and async programming
+</Note>
+
+## Understanding Parallelisation
+
+<Card title="What is Parallelisation?" icon="question">
+  Parallelisation enables:
+  - Concurrent execution of multiple tasks
+  - Improved performance through parallel processing
+  - Efficient handling of independent operations
+  - Aggregation of parallel task results
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Parallel Execution" icon="arrows-split-up-and-left">
+    Run multiple tasks simultaneously for improved performance.
+  </Card>
+  <Card title="Async Support" icon="bolt">
+    Built-in support for asynchronous execution.
+  </Card>
+  <Card title="Result Aggregation" icon="layer-group">
+    Combine results from parallel tasks efficiently.
+  </Card>
+  <Card title="Process Control" icon="sliders">
+    Monitor and manage parallel task execution.
+  </Card>
+</CardGroup>
+
+## Configuration Options
+
+```python
+# Create a parallel task
+task = Task(
+    name="parallel_task",
+    description="Task to run in parallel",
+    expected_output="Task result",
+    agent=agent,
+    is_start=True,
+    async_execution=True  # Enable parallel execution
+)
+
+# Create an aggregator task
+aggregator_task = Task(
+    name="aggregate",
+    description="Aggregate results from parallel tasks",
+    expected_output="Combined results",
+    agent=aggregator,
+    context=[task1, task2, task3]  # Reference parallel tasks
+)
+
+# Async workflow execution
+async def run_workflow():
+    workflow = PraisonAIAgents(
+        agents=[agent1, agent2, aggregator],
+        tasks=[task1, task2, aggregator_task],
+        process="workflow",
+        verbose=True
+    )
+    results = await workflow.astart()
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Execution Issues" icon="triangle-exclamation">
+    If parallel execution fails:
+    - Check async configuration
+    - Verify task independence
+    - Monitor resource usage
+  </Card>
+
+  <Card title="Result Aggregation" icon="diagram-project">
+    If aggregation is incorrect:
+    - Review task outputs
+    - Check context connections
+    - Verify aggregator logic
+  </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 parallel tasks are truly independent and your system has sufficient resources to handle concurrent execution.
+</Note>

docs/features/promptchaining.mdx

@@ -0,0 +1,265 @@
+---
+title: "Agentic Prompt Chaining"
+sidebarTitle: "Prompt Chaining"
+description: "Learn how to create AI agents with sequential prompt chaining for complex workflows."
+icon: "link"
+---
+
+```mermaid
+flowchart LR
+    In[In] --> LLM1[LLM Call 1] --> Gate{Gate}
+    Gate -->|Pass| LLM2[LLM Call 2] -->|Output 2| LLM3[LLM Call 3] --> Out[Out]
+    Gate -->|Fail| Exit[Exit]
+    
+    style In fill:#8B0000,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+    style Exit fill:#8B0000,color:#fff
+```
+
+A workflow where the output of one LLM call becomes the input for the next. This sequential design allows for structured reasoning and step-by-step task completion.
+
+## Quick Start
+
+<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
+        import time
+
+        def get_time_check():
+            current_time = int(time.time())
+            result = "even" if current_time % 2 == 0 else "odd"
+            print(f"Time check: {current_time} is {result}")
+            return result
+
+        # Create agents for each step in the chain
+        agent1 = Agent(
+            name="Time Checker",
+            role="Time checker",
+            goal="Check if the time is even or odd",
+            instructions="Check if the time is even or odd",
+            tools=[get_time_check]
+        )
+
+        agent2 = Agent(
+            name="Advanced Analyzer",
+            role="Advanced data analyzer",
+            goal="Perform in-depth analysis of processed data",
+            instructions="Analyze the processed data in detail"
+        )
+
+        agent3 = Agent(
+            name="Final Processor",
+            role="Final data processor",
+            goal="Generate final output based on analysis",
+            instructions="Create final output based on analyzed data"
+        )
+
+        # Create tasks for each step
+        initial_task = Task(
+            name="time_check",
+            description="Getting time check and checking if it is even or odd",
+            expected_output="Getting time check and checking if it is even or odd",
+            agent=agent1,
+            is_start=True,  # Mark as the starting task
+            task_type="decision",  # This task will make a decision
+            next_tasks=["advanced_analysis"],  # Next task if condition passes
+            condition={
+                "even": ["advanced_analysis"],  # If passes, go to advanced analysis
+                "odd": ""  # If fails, exit the chain
+            }
+        )
+
+        analysis_task = Task(
+            name="advanced_analysis",
+            description="Perform advanced analysis on the processed data",
+            expected_output="Analyzed data ready for final processing",
+            agent=agent2,
+            next_tasks=["final_processing"]
+        )
+
+        final_task = Task(
+            name="final_processing",
+            description="Generate final output",
+            expected_output="Final processed result",
+            agent=agent3
+        )
+
+        # Create the workflow manager
+        workflow = PraisonAIAgents(
+            agents=[agent1, agent2, agent3],
+            tasks=[initial_task, analysis_task, final_task],
+            process="workflow",  # Use workflow process type
+            verbose=True
+        )
+
+        # Run the workflow
+        results = workflow.start()
+
+        # Print results
+        print("\nWorkflow Results:")
+        for task_id, result in results["task_results"].items():
+            if result:
+                print(f"Task {task_id}: {result.raw}")
+        ```
+    </Step>
+
+    <Step title="Start Agents">
+        Type this in your terminal to run your agents:
+        ```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 Python
+</Note>
+
+## Understanding Prompt Chaining
+
+<Card title="What is Prompt Chaining?" icon="question">
+  Prompt chaining enables:
+  - Sequential execution of prompts
+  - Data flow between agents
+  - Conditional branching in workflows
+  - Step-by-step processing of complex tasks
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Sequential Processing" icon="arrow-right">
+    Execute tasks in a defined sequence with data passing between steps.
+  </Card>
+  <Card title="Decision Points" icon="code-branch">
+    Implement conditional logic to control workflow progression.
+  </Card>
+  <Card title="Data Flow" icon="arrows-up-down">
+    Pass data seamlessly between agents in the chain.
+  </Card>
+  <Card title="Process Control" icon="sliders">
+    Monitor and control the execution of each step in the chain.
+  </Card>
+</CardGroup>
+
+## Configuration Options to exit the chain
+
+```python
+# Task with chaining configuration
+task = Task(
+    name="time_check",
+    description="Check time and make decision",
+    expected_output="Time check result",
+    agent=agent,
+    is_start=True,
+    task_type="decision",
+    next_tasks=["next_step"],
+    condition={
+        "even": ["next_step"],
+        "odd": ""
+    }
+)
+```
+
+```python
+task = Task(
+    name="time_check",
+    description="Check time and make decision",
+    expected_output="Time check result",
+    agent=agent,
+    is_start=True,
+    task_type="decision",
+    next_tasks=["next_step"],
+    condition={
+        "even": ["next_step"],
+        "odd": "exit"
+    }
+)
+```
+
+```python
+task = Task(
+    name="time_check",
+    description="Check time and make decision",
+    expected_output="Time check result",
+    agent=agent,
+    is_start=True,
+    task_type="decision",
+    next_tasks=["next_step"],
+    condition={
+        "even": ["next_step"],
+        "odd": ["exit"]
+    }
+)
+```
+
+```python
+task = Task(
+    name="time_check",
+    description="Check time and make decision",
+    expected_output="Time check result",
+    agent=agent,
+    is_start=True,
+    task_type="decision",
+    next_tasks=["next_step"],
+    condition={
+        "even": ["next_step"],
+        "odd": [""]
+    }
+)
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Chain Issues" icon="triangle-exclamation">
+    If chain execution fails:
+    - Verify task connections
+    - Check condition logic
+    - Enable verbose mode for debugging
+  </Card>
+
+  <Card title="Data Flow" icon="diagram-project">
+    If data flow is incorrect:
+    - Review task outputs
+    - Check agent configurations
+    - Verify task dependencies
+  </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 chain is properly configured with clear task dependencies and conditions for branching logic.
+</Note>

docs/features/reasoning.mdx

@@ -1,10 +1,139 @@
 ---
-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"
 ---
 
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Think[Think]
+    Think --> Decide[Decide]
+    Decide --> Act[Act]
+    Act --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Think fill:#2E8B57,color:#fff
+    style Decide fill:#2E8B57,color:#fff
+    style Act fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+Learn how to create AI agents with advanced reasoning capabilities for complex problem-solving.
+
+## 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 +144,210 @@ 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/routing.mdx

@@ -0,0 +1,244 @@
+---
+title: "Agentic Routing"
+description: "Learn how to create AI agents that can dynamically route tasks to specialized LLM instances."
+icon: "route"
+---
+
+```mermaid
+flowchart LR
+    In[In] --> Router[LLM Call Router]
+    Router --> LLM1[LLM Call 1]
+    Router --> LLM2[LLM Call 2]
+    Router --> LLM3[LLM Call 3]
+    LLM1 --> Out[Out]
+    LLM2 --> Out
+    LLM3 --> Out
+    
+    style In fill:#8B0000,color:#fff
+    style Router fill:#2E8B57,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A low-latency workflow where inputs are dynamically routed to the most appropriate LLM instance or configuration, optimizing efficiency and specialization.
+
+## Quick Start
+
+<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.agent import Agent
+        from praisonaiagents.task import Task
+        from praisonaiagents.agents import PraisonAIAgents
+        import time
+
+        def get_time_check():
+            current_time = int(time.time())
+            result = "even" if current_time % 2 == 0 else "odd"
+            print(f"Time check: {current_time} is {result}")
+            return result
+
+        # Create specialized agents
+        router = Agent(
+            name="Router",
+            role="Input Router",
+            goal="Evaluate input and determine routing path",
+            instructions="Analyze input and decide whether to proceed or exit",
+            tools=[get_time_check]
+        )
+
+        processor1 = Agent(
+            name="Processor 1",
+            role="Secondary Processor",
+            goal="Process valid inputs that passed initial check",
+            instructions="Process data that passed the routing check"
+        )
+
+        processor2 = Agent(
+            name="Processor 2",
+            role="Final Processor",
+            goal="Perform final processing on validated data",
+            instructions="Generate final output for processed data"
+        )
+
+        # Create tasks with routing logic
+        routing_task = Task(
+            name="initial_routing",
+            description="check the time and return according to what is returned",
+            expected_output="pass or fail based on what is returned",
+            agent=router,
+            is_start=True,
+            task_type="decision",
+            condition={
+                "pass": ["process_valid"],
+                "fail": ["process_invalid"]
+            }
+        )
+
+        processing_task = Task(
+            name="process_valid",
+            description="Process validated input",
+            expected_output="Processed data ready for final step",
+            agent=processor1,
+        )
+
+        final_task = Task(
+            name="process_invalid",
+            description="Generate final output",
+            expected_output="Final processed result",
+            agent=processor2
+        )
+
+        # Create and run workflow
+        workflow = PraisonAIAgents(
+            agents=[router, processor1, processor2],
+            tasks=[routing_task, processing_task, final_task],
+            process="workflow",
+            verbose=True
+        )
+
+        print("\nStarting Routing Workflow...")
+        print("=" * 50)
+
+        results = workflow.start()
+
+        print("\nWorkflow Results:")
+        print("=" * 50)
+        for task_id, result in results["task_results"].items():
+            if result:
+                task_name = result.description
+                print(f"\nTask: {task_name}")
+                print(f"Result: {result.raw}")
+                print("-" * 50)
+        ```
+    </Step>
+
+    <Step title="Start Agents">
+        Type this in your terminal to run your agents:
+        ```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 Python
+</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/KNDVWGN3TpM"
+    title="YouTube video player"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowFullScreen
+  ></iframe>
+</div>
+
+## Understanding Agentic Routing
+
+<Card title="What is Agentic Routing?" icon="question">
+  Agentic routing enables:
+  - Dynamic decision-making in workflows
+  - Conditional task execution paths
+  - Automated process branching
+  - Intelligent workflow management
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Dynamic Routing" icon="route">
+    Route tasks based on real-time decisions and conditions.
+  </Card>
+  <Card title="Conditional Logic" icon="code-branch">
+    Implement complex branching logic in workflows.
+  </Card>
+  <Card title="Task Management" icon="tasks">
+    Handle task dependencies and execution order.
+  </Card>
+  <Card title="Process Control" icon="sliders">
+    Control workflow execution with detailed monitoring.
+  </Card>
+</CardGroup>
+
+## Configuration Options
+
+```python
+# Create a router agent
+router = Agent(
+    name="Router",
+    role="Input Router",
+    goal="Evaluate input and determine routing path",
+    instructions="Analyze input and decide whether to proceed or exit",
+    tools=[get_time_check],  # Custom tools for routing decisions
+    verbose=True  # Enable detailed logging
+)
+
+# Task with routing configuration
+routing_task = Task(
+    name="initial_routing",
+    description="Route based on conditions",
+    expected_output="Routing decision",
+    agent=router,
+    is_start=True,
+    task_type="decision",
+    condition={
+        "pass": ["next_task"],
+        "fail": ["alternate_task"]
+    }
+)
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Routing Issues" icon="triangle-exclamation">
+    If routing doesn't work as expected:
+    - Verify condition mappings
+    - Check task dependencies
+    - Enable verbose mode for debugging
+  </Card>
+
+  <Card title="Workflow Flow" icon="diagram-project">
+    If workflow is unclear:
+    - Review task connections
+    - Verify agent configurations
+    - Check routing conditions
+  </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 routing conditions are well-defined and your task dependencies are properly configured.
+</Note>

docs/features/selfreflection.mdx

@@ -1,10 +1,140 @@
 ---
 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"
 ---
 
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Reflect[Self Reflection]
+    Reflect --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Reflect fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+Self-reflection enables agents to evaluate and improve their own responses before delivering them.
+
+## 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 +145,209 @@ 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

@@ -4,62 +4,133 @@ description: "Learn how to create AI agents that return structured, type-safe ou
 icon: "cube"
 ---
 
-<Note>
-  **Prerequisites**
-  - Python 3.10 or higher
-  - PraisonAI Agents package installed
-  - Basic understanding of Python and Pydantic
-</Note>
-
 ## Quick Start
 
-Create AI agents that return structured, validated outputs 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
+            ```
+        </Step>
 
-<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="Import Required Components">
-    Import the necessary components:
-    ```python
-    from praisonaiagents import Agent, Task, PraisonAIAgents, Tools
-    from pydantic import BaseModel
-    ```
-  </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
 
-  <Step title="Define Your Data Model">
-    Create a Pydantic model to define your output structure:
-    ```python
-    class AnalysisReport(BaseModel):
-        title: str
-        findings: str
-        summary: str
-    ```
-  </Step>
+            # Define your data structure
+            class AnalysisReport(BaseModel):
+                title: str
+                findings: str
+                summary: str
 
-  <Step title="Create Structured Tasks">
-    Create tasks with structured outputs:
-    ```python
-    research_task = Task(
-        description="Research recent AI developments",
-        agent=researcher,
-        expected_output="Research findings"
-    )
+            # 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
+            )
 
-    analysis_task = Task(
-        description="Analyze research findings",
-        agent=analyst,
-        output_pydantic=AnalysisReport,  # For Pydantic output
-        # or output_json=AnalysisReport  # For JSON output
-        expected_output="Structured analysis report"
-    )
-    ```
-  </Step>
-</Steps>
+            # 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
 
@@ -71,221 +142,196 @@ Create AI agents that return structured, validated outputs in just a few lines o
   - Ensure consistent output format across agent responses
 </Card>
 
-## Key Components
+## Features
 
 <CardGroup cols={2}>
   <Card title="Pydantic Models" icon="cube">
-    Define your data structure
-    ```python
-    class ResearchReport(BaseModel):
-        title: str
-        content: str
-    ```
-  </Card>
-  <Card title="Task Configuration" icon="gear">
-    Configure output format
-    ```python
-    Task(output_pydantic=Model)
-    # or
-    Task(output_json=Model)
-    ```
-  </Card>
-  <Card title="Output Validation" icon="check">
-    Automatic validation of responses against your model
-  </Card>
-  <Card title="Format Options" icon="code">
-    Choose between Pydantic objects or JSON output
-  </Card>
-</CardGroup>
-
-## Output Types
-
-<CardGroup cols={2}>
-  <Card title="Pydantic Output" icon="cube">
-    ```python
-    task = Task(
-        output_pydantic=AnalysisReport
-    )
-    # Returns Pydantic object
-    ```
-    Get type-safe Pydantic model instances
+    Define exact data structures with validation.
   </Card>
   <Card title="JSON Output" icon="brackets-curly">
-    ```python
-    task = Task(
-        output_json=AnalysisReport
-    )
-    # Returns JSON dict
-    ```
-    Get JSON dictionary matching model schema
+    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>
 
-## Examples
+## Multi-Agent Structured Analysis
 
-### Research Analysis with Structured Output
+<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
-from praisonaiagents import Agent, Task, PraisonAIAgents, Tools
-from pydantic import BaseModel
-
-# Define output structure
-class AnalysisReport(BaseModel):
-    title: str
-    findings: str
-    summary: str
-
-# Create agents
-researcher = Agent(
-    name="AIResearcher",
-    role="Technology Research Analyst",
-    tools=[Tools.internet_search]
+# 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
 )
 
-analyst = Agent(
-    name="DataAnalyst",
-    role="Data Insights Specialist"
-)
-
-# 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",
-    agent=analyst,
-    output_pydantic=AnalysisReport,
-    expected_output="Structured analysis report"
-)
-
-# Initialize and run agents
-agents = PraisonAIAgents(
-    agents=[researcher, analyst],
-    tasks=[research_task, analysis_task]
-)
-result = agents.start()
-```
-
-### Different Output Formats
-
-<CodeGroup>
-```python Pydantic
-# Get Pydantic model instance
-analysis_task = Task(
-    description="Analyze findings",
-    agent=analyst,
-    output_pydantic=AnalysisReport
-)
-
-# Result will be AnalysisReport instance
-result.pydantic.title
-result.pydantic.findings
-result.pydantic.summary
-```
-
-```python JSON
-# Get JSON dictionary
-analysis_task = Task(
-    description="Analyze findings",
-    agent=analyst,
-    output_json=AnalysisReport
-)
-
-# Result will be dictionary
-result.json_dict['title']
-result.json_dict['findings']
-result.json_dict['summary']
-```
-</CodeGroup>
-
-## Best Practices
-
-<AccordionGroup>
-  <Accordion title="Model Design">
-    Design clear, focused Pydantic models:
-    ```python
-    # Good
-    class ResearchReport(BaseModel):
-        title: str
-        findings: str
-        summary: str
-    
-    # Too complex
-    class Report(BaseModel):
-        title: str
-        subtitle: Optional[str]
-        findings: List[Dict[str, Any]]
-    ```
-  </Accordion>
-
-  <Accordion title="Output Format Selection">
-    Choose the right output format:
-    ```python
-    # Use Pydantic when you need:
-    # - Type validation
-    # - Model methods
-    task = Task(output_pydantic=Model)
-    
-    # Use JSON when you need:
-    # - Simple dictionary access
-    # - Serialization
-    task = Task(output_json=Model)
-    ```
-  </Accordion>
-</AccordionGroup>
-
-## Common Patterns
-
-### Research and Structured Analysis
-```python
-class ResearchReport(BaseModel):
-    topic: str
-    findings: str
-    sources: list[str]
-
-class Analysis(BaseModel):
-    key_points: list[str]
-    implications: str
-    recommendations: str
-
-research_task = Task(
-    description="Research quantum computing",
-    agent=researcher,
-    output_pydantic=ResearchReport
-)
-
-analysis_task = Task(
-    description="Analyze research implications",
-    agent=analyst,
-    output_pydantic=Analysis
-)
-```
-
-### Data Collection and Summary
-```python
-class DataPoint(BaseModel):
-    timestamp: str
-    value: float
-    source: str
-
-class Summary(BaseModel):
-    average: float
-    trends: str
-    insights: str
-
-collection_task = Task(
-    description="Collect data points",
-    output_pydantic=DataPoint
-)
-
-summary_task = Task(
-    description="Summarize data",
-    output_pydantic=Summary
+# 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
 )
 ```
 
@@ -293,30 +339,31 @@ summary_task = Task(
 
 <CardGroup cols={2}>
   <Card title="Validation Errors" icon="triangle-exclamation">
-    Check if your model matches the expected output structure
-    ```python
-    # Print model schema
-    print(Model.schema_json())
-    ```
+    If model validation fails:
+    - Check data types match model
+    - Verify required fields
+    - Enable verbose mode for debugging
   </Card>
+
   <Card title="Output Format" icon="code">
-    Verify you're accessing the right output field:
-    - `result.pydantic` for Pydantic
-    - `result.json_dict` for JSON
+    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="Advanced Models" icon="graduation-cap" href="./advanced-models">
-    Learn about advanced Pydantic model configurations
+  <Card title="AutoAgents" icon="robot" href="./autoagents">
+    Learn about automatically created and managed AI agents
   </Card>
-  <Card title="Custom Validation" icon="check-double" href="./validation">
-    Add custom validation to your models
+  <Card title="Mini Agents" icon="microchip" href="./mini">
+    Explore lightweight, focused AI agents
   </Card>
 </CardGroup>
 
 <Note>
-  Structured outputs make your AI agents more reliable and their responses more predictable. They're perfect for production systems where data consistency is crucial.
+  For optimal results, ensure your Pydantic models accurately represent your desired output structure.
 </Note>