Merge pull request #275 from MervinPraison/develop

Develop

Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.31 gunicorn markdown
+RUN pip install flask praisonai==2.0.36 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

agents/.cursorrules

@@ -0,0 +1,6 @@
+Make it minimal change as possible
+
+Firstly try not to make any modification to the existing code as possible
+
+Only if its highly required, without that if it can't be done, then modify the existing. 
+So by default try to add new code section and not modify the existing. 

agents/code_agents_example.py

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

agents/example2.py

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

agents/example_custom_tools.py

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

agents/langchain_example.py

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

agents/langchain_logs.py

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

agents/praisonaiagents/agent/agent.py

@@ -18,6 +18,7 @@ from ..main import (
     client,
     error_logs
 )
+import inspect
 
 if TYPE_CHECKING:
     from ..task.task import Task
@@ -67,9 +68,24 @@ class Agent:
             return None
 
         import inspect
+        # Langchain tools
+        if inspect.isclass(func) and hasattr(func, 'run'):
+            original_func = func
+            func = func.run
+            function_name = original_func.__name__
+
         sig = inspect.signature(func)
         logging.debug(f"Function signature: {sig}")
         
+        # Skip self, *args, **kwargs, so they don't get passed in arguments
+        parameters_list = []
+        for name, param in sig.parameters.items():
+            if name == "self":
+                continue
+            if param.kind in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD):
+                continue
+            parameters_list.append((name, param))
+
         parameters = {
             "type": "object",
             "properties": {},
@@ -95,7 +111,7 @@ class Agent:
         
         logging.debug(f"Parameter descriptions: {param_descriptions}")
 
-        for name, param in sig.parameters.items():
+        for name, param in parameters_list:
             param_type = "string"  # Default type
             if param.annotation != inspect.Parameter.empty:
                 if param.annotation == int:
@@ -249,25 +265,31 @@ Your Goal: {self.goal}
         # Try to find the function in the agent's tools list first
         func = None
         for tool in self.tools:
-            if callable(tool) and getattr(tool, '__name__', '') == function_name:
+            if (callable(tool) and getattr(tool, '__name__', '') == function_name) or \
+               (inspect.isclass(tool) and tool.__name__ == function_name):
                 func = tool
                 break
         
-        logging.debug(f"Looking for {function_name} in agent tools: {func is not None}")
-        
-        # If not found in tools, try globals and main
-        if not func:
+        if func is None:
+            # If not found in tools, try globals and main
             func = globals().get(function_name)
-            logging.debug(f"Looking for {function_name} in globals: {func is not None}")
-            
             if not func:
                 import __main__
                 func = getattr(__main__, function_name, None)
-                logging.debug(f"Looking for {function_name} in __main__: {func is not None}")
 
-        if func and callable(func):
+        if func:
             try:
-                return func(**arguments)
+                # If it's a class with run method, instantiate and call run
+                if inspect.isclass(func) and hasattr(func, 'run'):
+                    instance = func()
+                    # Extract only the parameters that run() expects
+                    run_params = {k: v for k, v in arguments.items() 
+                                if k in inspect.signature(instance.run).parameters 
+                                and k != 'self'}
+                    return instance.run(**run_params)
+                # Otherwise treat as regular function
+                elif callable(func):
+                    return func(**arguments)
             except Exception as e:
                 error_msg = str(e)
                 logging.error(f"Error executing tool {function_name}: {error_msg}")
@@ -394,7 +416,7 @@ Your Goal: {self.goal}
             display_error(f"Error in chat completion: {e}")
             return None
 
-    def chat(self, prompt, temperature=0.2, tools=None, output_json=None):
+    def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
         if self.use_system_prompt:
             system_prompt = f"""{self.backstory}\n
 Your Role: {self.role}\n
@@ -402,6 +424,8 @@ Your Goal: {self.goal}
             """
             if output_json:
                 system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_json.schema_json()}"
+            elif output_pydantic:
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_pydantic.schema_json()}"
         else:
             system_prompt = None
 
@@ -410,9 +434,9 @@ Your Goal: {self.goal}
             messages.append({"role": "system", "content": system_prompt})
         messages.extend(self.chat_history)
 
-        # Modify prompt if output_json is specified
+        # Modify prompt if output_json or output_pydantic is specified
         original_prompt = prompt
-        if output_json:
+        if output_json or output_pydantic:
             if isinstance(prompt, str):
                 prompt += "\nReturn ONLY a valid JSON object. No other text or explanation."
             elif isinstance(prompt, list):
@@ -487,23 +511,15 @@ Your Goal: {self.goal}
                         return None
                     response_text = response.choices[0].message.content.strip()
 
-                # Handle output_json if specified
-                if output_json:
-                    try:
-                        # Clean the response text to get only JSON
-                        cleaned_json = self.clean_json_output(response_text)
-                        # Parse into Pydantic model
-                        parsed_model = output_json.model_validate_json(cleaned_json)
-                        # Add to chat history and return
-                        self.chat_history.append({"role": "user", "content": original_prompt})
-                        self.chat_history.append({"role": "assistant", "content": response_text})
-                        if self.verbose:
-                            display_interaction(original_prompt, response_text, markdown=self.markdown, 
-                                             generation_time=time.time() - start_time, console=self.console)
-                        return parsed_model
-                    except Exception as e:
-                        display_error(f"Failed to parse response as {output_json.__name__}: {e}")
-                        return None
+                # Handle output_json or output_pydantic if specified
+                if output_json or output_pydantic:
+                    # Add to chat history and return raw response
+                    self.chat_history.append({"role": "user", "content": original_prompt})
+                    self.chat_history.append({"role": "assistant", "content": response_text})
+                    if self.verbose:
+                        display_interaction(original_prompt, response_text, markdown=self.markdown, 
+                                         generation_time=time.time() - start_time, console=self.console)
+                    return response_text
 
                 if not self.self_reflect:
                     self.chat_history.append({"role": "user", "content": original_prompt})
@@ -585,19 +601,21 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             cleaned = cleaned[:-3].strip()
         return cleaned 
 
-    async def achat(self, prompt, temperature=0.2, tools=None, output_json=None):
+    async def achat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
         """Async version of chat method"""
         try:
             # Build system prompt
             system_prompt = self.system_prompt
             if output_json:
                 system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_json.schema_json()}"
+            elif output_pydantic:
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_pydantic.schema_json()}"
 
             # Build messages
             if isinstance(prompt, str):
                 messages = [
                     {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": prompt + ("\nReturn ONLY a valid JSON object. No other text or explanation." if output_json else "")}
+                    {"role": "user", "content": prompt + ("\nReturn ONLY a valid JSON object. No other text or explanation." if (output_json or output_pydantic) else "")}
                 ]
             else:
                 # For multimodal prompts
@@ -605,7 +623,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                     {"role": "system", "content": system_prompt},
                     {"role": "user", "content": prompt}
                 ]
-                if output_json:
+                if output_json or output_pydantic:
                     # Add JSON instruction to text content
                     for item in messages[-1]["content"]:
                         if item["type"] == "text":
@@ -639,22 +657,15 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                     tools=formatted_tools
                 )
                 return await self._achat_completion(response, tools)
-            elif output_json:
+            elif output_json or output_pydantic:
                 response = await async_client.chat.completions.create(
                     model=self.llm,
                     messages=messages,
                     temperature=temperature,
                     response_format={"type": "json_object"}
                 )
-                result = response.choices[0].message.content
-                # Clean and parse the JSON response
-                cleaned_json = self.clean_json_output(result)
-                try:
-                    parsed = json.loads(cleaned_json)
-                    return output_json(**parsed)
-                except Exception as e:
-                    display_error(f"Error parsing JSON response: {e}")
-                    return None
+                # Return the raw response
+                return response.choices[0].message.content
             else:
                 response = await async_client.chat.completions.create(
                     model=self.llm,

agents/praisonaiagents/agents/agents.py

@@ -195,10 +195,17 @@ Here are the results of previous tasks that might be useful:\n
 
             agent_output = await executor_agent.achat(
                 _get_multimodal_message(task_prompt, task.images),
-                tools=task.tools
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
             )
         else:
-            agent_output = await executor_agent.achat(task_prompt, tools=task.tools)
+            agent_output = await executor_agent.achat(
+                task_prompt,
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
+            )
 
         if agent_output:
             task_output = TaskOutput(
@@ -405,10 +412,17 @@ Here are the results of previous tasks that might be useful:\n
 
             agent_output = executor_agent.chat(
                 _get_multimodal_message(task_prompt, task.images),
-                tools=task.tools
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
             )
         else:
-            agent_output = executor_agent.chat(task_prompt, tools=task.tools)
+            agent_output = executor_agent.chat(
+                task_prompt,
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
+            )
 
         if agent_output:
             task_output = TaskOutput(

agents/praisonaiagents/tools/README.md

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

agents/praisonaiagents/tools/__init__.py

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

agents/praisonaiagents/tools/arxiv_tools.py

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

agents/praisonaiagents/tools/calculator_tools.py

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

agents/praisonaiagents/tools/csv_tools.py

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

agents/praisonaiagents/tools/duckdb_tools.py

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

agents/praisonaiagents/tools/duckduckgo_tools.py

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

agents/praisonaiagents/tools/excel_tools.py

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

agents/praisonaiagents/tools/file_tools.py

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

agents/praisonaiagents/tools/json_tools.py

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

agents/praisonaiagents/tools/newspaper_tools.py

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

agents/praisonaiagents/tools/pandas_tools.py

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

agents/praisonaiagents/tools/python_tools.py

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

agents/praisonaiagents/tools/shell_tools.py

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

agents/praisonaiagents/tools/spider_tools.py

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

agents/praisonaiagents/tools/test.py

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

agents/praisonaiagents/tools/tools.py

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

agents/praisonaiagents/tools/wikipedia_tools.py

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

agents/praisonaiagents/tools/xml_tools.py

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

agents/praisonaiagents/tools/yaml_tools.py

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

agents/praisonaiagents/tools/yfinance_tools.py

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

agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.22"
+version = "0.0.27"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }

agents/structured_agents_example.py

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

agents/tools_example.py

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

agents/uv.lock

@@ -186,7 +186,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.22"
+version = "0.0.27"
 source = { editable = "." }
 dependencies = [
     { name = "openai" },
@@ -304,7 +304,7 @@ name = "tqdm"
 version = "4.67.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "platform_system == 'Windows'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a8/4b/29b4ef32e036bb34e4ab51796dd745cdba7ed47ad142a9f4a1eb8e0c744d/tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2", size = 169737 }
 wheels = [

docs/api/praisonai/deploy.html

@@ -110,7 +110,7 @@ Loads environment variables from .env file or system and sets them.</p>
             file.write(&#34;FROM python:3.11-slim\n&#34;)
             file.write(&#34;WORKDIR /app\n&#34;)
             file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==2.0.31 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.36 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/features/codeagent.mdx

@@ -0,0 +1,287 @@
+---
+title: "Code Execution AI Agent"
+sidebarTitle: "Code Agent"
+description: "Learn how to create AI agents that can write and execute Python code safely using e2b code interpreter."
+icon: "code"
+---
+
+<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!
+
+<Steps>
+  <Step title="Install Packages">
+    First, install the required packages:
+    ```bash
+    pip install praisonaiagents e2b_code_interpreter
+    ```
+  </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="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))
+                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>
+
+  <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
+    )
+
+    execution_agent = Agent(
+        name="execution_agent",
+        backstory="Expert in executing Python scripts",
+        self_reflect=False,
+        tools=[code_interpreter]
+    )
+    ```
+  </Step>
+</Steps>
+
+## Understanding Code Agents
+
+<Card title="What are Code Agents?" icon="question">
+  Code agents are specialized AI agents that can:
+  - Write Python code based on requirements
+  - Execute code safely in a sandboxed environment
+  - Handle code execution results and errors
+  - Work together in a pipeline (writer → executor)
+</Card>
+
+## Key Components
+
+<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>
+  <Card title="Code Executor Agent" icon="play">
+    Executes code using sandbox
+    ```python
+    Agent(
+        name="execution_agent",
+        tools=[code_interpreter]
+    )
+    ```
+  </Card>
+  <Card title="Sandbox Environment" icon="box">
+    Safe code execution environment using e2b
+  </Card>
+  <Card title="Execution Results" icon="output">
+    Structured output with results and logs
+  </Card>
+</CardGroup>
+
+## Examples
+
+### Simple Hello World Example
+
+```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
+)
+```
+
+## 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>
+  <Card title="Code Generation" icon="code">
+    Provide clear requirements and constraints in task description
+  </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>
+  <Card title="Custom Tools" icon="wrench" href="./custom-tools">
+    Create custom code execution tools
+  </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.
+</Note>

docs/features/mathagent.mdx

@@ -0,0 +1,279 @@
+---
+title: "Math AI Agent"
+sidebarTitle: "Math Agent"
+description: "Learn how to create AI agents that can perform complex mathematical calculations, unit conversions, and financial computations."
+icon: "calculator"
+---
+
+<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!
+
+<Steps>
+  <Step title="Install Package">
+    First, install the required package:
+    ```bash
+    pip install praisonaiagents
+    ```
+  </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 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>
+
+## Understanding Math Agents
+
+<Card title="What are Math Agents?" icon="question">
+  Math agents are specialized AI agents that can:
+  - Evaluate mathematical expressions
+  - Solve equations and systems of equations
+  - Perform unit conversions
+  - Calculate statistical metrics
+  - Handle financial calculations
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="Expression Evaluator" icon="function">
+    Evaluates mathematical expressions
+    ```python
+    evaluate("2 + 2 * 3")
+    evaluate("sin(pi/2) + sqrt(16)")
+    ```
+  </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")
+    ```
+  </Card>
+  <Card title="Unit Converter" icon="arrows-rotate">
+    Converts between different units
+    ```python
+    convert_units(100, "meters", "kilometers")
+    ```
+  </Card>
+  <Card title="Statistical Calculator" icon="chart-simple">
+    Calculates statistical metrics
+    ```python
+    calculate_statistics([1, 2, 3, 4, 5])
+    ```
+  </Card>
+</CardGroup>
+
+## Examples
+
+### Basic Calculations Example
+
+```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
+)
+```
+
+## Troubleshooting
+
+<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'")
+    ```
+  </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>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Advanced Mathematics" icon="graduation-cap" href="./advanced-math">
+    Learn about complex mathematical operations
+  </Card>
+  <Card title="Custom Math Tools" icon="wrench" href="./custom-tools">
+    Create custom mathematical computation tools
+  </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.
+</Note>

docs/features/mini.mdx

@@ -46,6 +46,16 @@ Create multiple AI agents that can work together in just a few lines of code!
   </Step>
 </Steps>
 
+<div className="relative w-full aspect-video">
+  <iframe
+    className="absolute top-0 left-0 w-full h-full"
+    src="https://www.youtube.com/embed/OkvYp5aAGSg"
+    title="YouTube video player"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowFullScreen
+  ></iframe>
+</div>
+
 ## Understanding Mini AI Agents
 
 <Card title="What are Mini AI Agents?" icon="question">
@@ -245,4 +255,4 @@ agents = Agents(agents=[collector, summarizer, writer])
 
 <Note>
   Mini AI Agents are designed to be simple yet powerful. They're perfect for quick prototypes and straightforward automation tasks.
-</Note>
+</Note>

docs/features/structured.mdx

@@ -0,0 +1,322 @@
+---
+title: "Structured AI Agents"
+description: "Learn how to create AI agents that return structured, type-safe outputs using Pydantic models and JSON."
+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!
+
+<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, Task, PraisonAIAgents, Tools
+    from pydantic import BaseModel
+    ```
+  </Step>
+
+  <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>
+
+  <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"
+    )
+
+    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>
+
+## Understanding Structured Outputs
+
+<Card title="What are Structured Outputs?" icon="question">
+  Structured outputs allow you to:
+  - Define exact shape of data using Pydantic models
+  - Get type-safe, validated responses
+  - Choose between Pydantic objects or JSON
+  - Ensure consistent output format across agent responses
+</Card>
+
+## Key Components
+
+<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
+  </Card>
+  <Card title="JSON Output" icon="brackets-curly">
+    ```python
+    task = Task(
+        output_json=AnalysisReport
+    )
+    # Returns JSON dict
+    ```
+    Get JSON dictionary matching model schema
+  </Card>
+</CardGroup>
+
+## Examples
+
+### Research Analysis with Structured Output
+
+```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]
+)
+
+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
+)
+```
+
+## Troubleshooting
+
+<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())
+    ```
+  </Card>
+  <Card title="Output Format" icon="code">
+    Verify you're accessing the right output field:
+    - `result.pydantic` for Pydantic
+    - `result.json_dict` for JSON
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Advanced Models" icon="graduation-cap" href="./advanced-models">
+    Learn about advanced Pydantic model configurations
+  </Card>
+  <Card title="Custom Validation" icon="check-double" href="./validation">
+    Add custom validation to your models
+  </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.
+</Note>

docs/mint.json

@@ -76,6 +76,20 @@
         "concepts/tools"
       ]
     },
+    {
+      "group": "Features",
+      "pages": [
+        "features/selfreflection",
+        "features/reasoning",
+        "framework/multimodalagents",
+        "features/async",
+        "features/mini",
+        "features/codeagent",
+        "features/mathagent",
+        "features/structured",
+        "features/callbacks"
+      ]
+    },
     {
       "group": "Getting Started (No Code)",
       "pages": [
@@ -87,30 +101,6 @@
         "auto"
       ]
     },
-    {
-      "group": "User Interface",
-      "pages": [
-        "ui/ui",
-        "ui/chat",
-        "ui/code",
-        "ui/realtime"
-      ]
-    },
-    {
-      "group": "Features",
-      "pages": [
-        "features/selfreflection",
-        "features/reasoning",
-        "framework/multimodalagents",
-        "features/async",
-        "features/mini",
-        "features/callbacks",
-        "train",
-        "framework/praisonaiagents",
-        "framework/crewai",
-        "framework/autogen"
-      ]
-    },
     {
       "group": "Models",
       "pages": [
@@ -127,8 +117,25 @@
       ]
     },
     {
-      "group": "Tools (No Code)",
+      "group": "Tools",
       "pages": [
+        "tools/duckduckgo_tools",
+        "tools/arxiv_tools",
+        "tools/calculator_tools",
+        "tools/csv_tools",
+        "tools/duckdb_tools",
+        "tools/excel_tools",
+        "tools/file_tools",
+        "tools/json_tools",
+        "tools/newspaper_tools",
+        "tools/pandas_tools",
+        "tools/python_tools",
+        "tools/shell_tools",
+        "tools/spider_tools",
+        "tools/wikipedia_tools",
+        "tools/xml_tools",
+        "tools/yaml_tools",
+        "tools/yfinance_tools",
         "tools",
         "tools/custom",
         "tools/gpt",
@@ -141,7 +148,27 @@
         "tools/reddit",
         "tools/you.com",
         "tools/crawl4ai",
-        "tools/mem0"
+        "tools/mem0",
+        "tools/composio",
+        "tools/googlecalendar"
+      ]
+    },
+    {
+      "group": "Other Features",
+      "pages": [
+        "train",
+        "framework/crewai",
+        "framework/autogen",
+        "framework/praisonaiagents"
+      ]
+    },
+    {
+      "group": "User Interface",
+      "pages": [
+        "ui/ui",
+        "ui/chat",
+        "ui/code",
+        "ui/realtime"
       ]
     },
     {

docs/tools/arxiv_tools.mdx

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

docs/tools/calculator_tools.mdx

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

docs/tools/csv_tools.mdx

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

docs/tools/duckdb_tools.mdx

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

docs/tools/duckduckgo_tools.mdx

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

docs/tools/excel_tools.mdx

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

docs/tools/file_tools.mdx

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

docs/tools/json_tools.mdx

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

docs/tools/newspaper_tools.mdx

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

docs/tools/pandas_tools.mdx

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

docs/tools/python_tools.mdx

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

docs/tools/shell_tools.mdx

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

docs/tools/spider_tools.mdx

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

docs/tools/wikipedia_tools.mdx

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

docs/tools/xml_tools.mdx

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

docs/tools/yaml_tools.mdx

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

docs/tools/yfinance_tools.mdx

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

praisonai.rb

@@ -3,7 +3,7 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.31.tar.gz"
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.36.tar.gz"
     sha256 "1828fb9227d10f991522c3f24f061943a254b667196b40b1a3e4a54a8d30ce32"  # Replace with actual SHA256 checksum
     license "MIT"
   

praisonai/deploy.py

@@ -56,7 +56,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.0.31 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.0.36 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.0.31"
+version = "2.0.36"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human–agent collaboration."
 readme = "README.md"
 license = ""
@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.0.22",
+    "praisonaiagents>=0.0.27",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
@@ -84,7 +84,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.7", "crewai"]
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.0.31"
+version = "2.0.36"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human–agent collaboration."
 authors = ["Mervin Praison"]
 license = ""
@@ -102,7 +102,7 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.22"
+praisonaiagents = ">=0.0.27"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

uv.lock

@@ -672,7 +672,7 @@ name = "click"
 version = "8.1.8"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "platform_system == 'Windows'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593 }
 wheels = [
@@ -3061,7 +3061,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.0.31"
+version = "2.0.36"
 source = { editable = "." }
 dependencies = [
     { name = "instructor" },
@@ -3198,7 +3198,7 @@ requires-dist = [
     { name = "plotly", marker = "extra == 'realtime'", specifier = ">=5.24.0" },
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.7" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.7" },
-    { name = "praisonaiagents", specifier = ">=0.0.22" },
+    { name = "praisonaiagents", specifier = ">=0.0.27" },
     { name = "pyautogen", marker = "extra == 'autogen'", specifier = ">=0.2.19" },
     { name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
     { name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -3251,16 +3251,16 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.22"
+version = "0.0.27"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "openai" },
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/c8/8d/a11ac1cfb7be9e03b7b498ddc2f7881e25fbf3a7dd08f8907aaecb5bac86/praisonaiagents-0.0.22.tar.gz", hash = "sha256:e40b079b9760fe08150e6641edf532cc9078ab330d42f38899678042cdbb94ca", size = 25454 }
+sdist = { url = "https://files.pythonhosted.org/packages/05/c9/b368dc9c23e9d0df0b2d427055b71e842beebc15e3d005520b52baf05481/praisonaiagents-0.0.27.tar.gz", hash = "sha256:a195a8ea902ef437cc2ba8a6cac4ff76d7c4a208ef387ff69e752343e53ce104", size = 61683 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/3a/8d/e75e2be19ff20f382b58b73e451c7bc54ab7179a71510b1ebb33183c6361/praisonaiagents-0.0.22-py3-none-any.whl", hash = "sha256:727ee9ed1b51cee757c03bfffdf55df1909091572e892d5aeeb3c4f54d7fb6c9", size = 31817 },
+    { url = "https://files.pythonhosted.org/packages/0d/63/b4b45c943c78b1818cec8b2dd92b3fdea36075eb5e323c8ca22e917ca75e/praisonaiagents-0.0.27-py3-none-any.whl", hash = "sha256:3404a186f2be2dfed0b56efec613896f4306f5589dc65d4b3f7034b0401d7593", size = 80590 },
 ]
 
 [[package]]
@@ -4641,7 +4641,7 @@ name = "tqdm"
 version = "4.67.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "platform_system == 'Windows'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a8/4b/29b4ef32e036bb34e4ab51796dd745cdba7ed47ad142a9f4a1eb8e0c744d/tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2", size = 169737 }
 wheels = [
@@ -5021,7 +5021,7 @@ version = "2.1.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
-    { name = "nvidia-nccl-cu12", marker = "platform_machine != 'aarch64' and platform_system == 'Linux'" },
+    { name = "nvidia-nccl-cu12", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" },
     { name = "scipy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/48/b0/131ffc4a15fd3acee9be3a7baa6b2fa6faa479799c51b880de9fc3ddf550/xgboost-2.1.3.tar.gz", hash = "sha256:7699ec4226156887d3afc665c63ab87469db9d46e361c702ba9fccd22535730c", size = 1090326 }