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==2.0.34 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/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}")

agents/praisonaiagents/tools/__init__.py

@@ -8,6 +8,59 @@ TOOL_MAPPINGS = {
     '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'),
@@ -68,14 +121,6 @@ TOOL_MAPPINGS = {
     'transform_yaml': ('.yaml_tools', 'YAMLTools'),
     'yaml_tools': ('.yaml_tools', 'YAMLTools'),
 
-    # Calculator Tools
-    'evaluate': ('.calculator_tools', 'CalculatorTools'),
-    'solve_equation': ('.calculator_tools', 'CalculatorTools'),
-    'convert_units': ('.calculator_tools', 'CalculatorTools'),
-    'calculate_statistics': ('.calculator_tools', 'CalculatorTools'),
-    'calculate_financial': ('.calculator_tools', 'CalculatorTools'),
-    'calculator_tools': ('.calculator_tools', 'CalculatorTools'),
-
     # Python Tools
     'execute_code': ('.python_tools', 'PythonTools'),
     'analyze_code': ('.python_tools', 'PythonTools'),
@@ -90,51 +135,6 @@ TOOL_MAPPINGS = {
     'group_by': ('.pandas_tools', 'PandasTools'),
     'pivot_table': ('.pandas_tools', 'PandasTools'),
     'pandas_tools': ('.pandas_tools', 'PandasTools'),
-
-    # Wikipedia Tools
-    'search': ('.wikipedia_tools', 'WikipediaTools'),
-    'get_wikipedia_summary': ('.wikipedia_tools', 'WikipediaTools'),
-    'get_wikipedia_page': ('.wikipedia_tools', 'WikipediaTools'),
-    'get_random_wikipedia': ('.wikipedia_tools', 'WikipediaTools'),
-    'set_wikipedia_language': ('.wikipedia_tools', 'WikipediaTools'),
-    'wikipedia_tools': ('.wikipedia_tools', 'WikipediaTools'),
-
-    # Newspaper Tools
-    'get_article': ('.newspaper_tools', 'NewspaperTools'),
-    'get_news_sources': ('.newspaper_tools', 'NewspaperTools'),
-    'get_articles_from_source': ('.newspaper_tools', 'NewspaperTools'),
-    'get_trending_topics': ('.newspaper_tools', 'NewspaperTools'),
-    'newspaper_tools': ('.newspaper_tools', 'NewspaperTools'),
-
-    # arXiv Tools
-    'search_arxiv': ('.arxiv_tools', 'ArxivTools'),
-    'get_arxiv_paper': ('.arxiv_tools', 'ArxivTools'),
-    'get_papers_by_author': ('.arxiv_tools', 'ArxivTools'),
-    'get_papers_by_category': ('.arxiv_tools', 'ArxivTools'),
-    'arxiv_tools': ('.arxiv_tools', 'ArxivTools'),
-
-    # Spider Tools
-    'scrape_page': ('.spider_tools', 'SpiderTools'),
-    'extract_links': ('.spider_tools', 'SpiderTools'),
-    'crawl': ('.spider_tools', 'SpiderTools'),
-    'extract_text': ('.spider_tools', 'SpiderTools'),
-    'spider_tools': ('.spider_tools', 'SpiderTools'),
-
-    # DuckDB Tools
-    'query': ('.duckdb_tools', 'DuckDBTools'),
-    'create_table': ('.duckdb_tools', 'DuckDBTools'),
-    'load_data': ('.duckdb_tools', 'DuckDBTools'),
-    'export_data': ('.duckdb_tools', 'DuckDBTools'),
-    'get_table_info': ('.duckdb_tools', 'DuckDBTools'),
-    'analyze_data': ('.duckdb_tools', 'DuckDBTools'),
-    'duckdb_tools': ('.duckdb_tools', 'DuckDBTools'),
-
-    # Shell Tools
-    'execute_command': ('.shell_tools', 'ShellTools'),
-    'list_processes': ('.shell_tools', 'ShellTools'),
-    'kill_process': ('.shell_tools', 'ShellTools'),
-    'get_system_info': ('.shell_tools', 'ShellTools'),
-    'shell_tools': ('.shell_tools', 'ShellTools'),
 }
 
 _instances = {}  # Cache for class instances
@@ -149,7 +149,18 @@ def __getattr__(name: str) -> Any:
     if class_name is None:
         # Direct function import
         module = import_module(module_path, __package__)
-        if name in ['duckduckgo', 'file_tools', 'pandas_tools', 'wikipedia_tools',
+        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)

agents/praisonaiagents/tools/duckduckgo_tools.py

@@ -12,7 +12,6 @@ results = duckduckgo("AI news")
 from typing import List, Dict
 import logging
 from importlib import util
-import sys
 
 def internet_search(query: str) -> List[Dict]:
     """Perform an internet search using DuckDuckGo."""
@@ -40,8 +39,9 @@ def internet_search(query: str) -> List[Dict]:
         logging.error(error_msg)
         return [{"error": error_msg}]
 
-# Make the module callable
-sys.modules[__name__].__call__ = internet_search
+def duckduckgo(query: str) -> List[Dict]:
+    """Alias for internet_search function."""
+    return internet_search(query)
 
 if __name__ == "__main__":
     # Example usage

agents/praisonaiagents/tools/wikipedia_tools.py

@@ -2,13 +2,13 @@
 
 Usage:
 from praisonaiagents.tools import wikipedia_tools
-summary = wikipedia_tools.get_summary("Python programming language")
-page = wikipedia_tools.get_page("Python programming language")
-results = wikipedia_tools.search("Python programming")
+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 get_wikipedia_summary, get_wikipedia_page
-summary = get_wikipedia_summary("Python programming language")
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page
+summary = wiki_summary("Python programming language")
 """
 
 import logging
@@ -32,7 +32,7 @@ class WikipediaTools:
         # Set default language to English
         wikipedia.set_lang("en")
 
-    def search(
+    def wiki_search(
         self, 
         query: str, 
         results: int = 10, 
@@ -63,7 +63,7 @@ class WikipediaTools:
             logging.error(error_msg)
             return {"error": error_msg}
 
-    def get_summary(
+    def wiki_summary(
         self, 
         title: str, 
         sentences: int = 5, 
@@ -92,7 +92,7 @@ class WikipediaTools:
             logging.error(error_msg)
             return {"error": error_msg}
 
-    def get_page(
+    def wiki_page(
         self, 
         title: str, 
         auto_suggest: bool = True
@@ -168,7 +168,7 @@ class WikipediaTools:
         
         return sections
 
-    def get_random(self, pages: int = 1) -> Union[List[str], Dict[str, str]]:
+    def wiki_random(self, pages: int = 1) -> Union[List[str], Dict[str, str]]:
         """
         Get random Wikipedia page titles.
         
@@ -185,7 +185,7 @@ class WikipediaTools:
             logging.error(error_msg)
             return {"error": error_msg}
 
-    def set_language(self, language: str) -> bool:
+    def wiki_language(self, language: str) -> bool:
         """
         Set the language for Wikipedia searches.
         
@@ -205,11 +205,11 @@ class WikipediaTools:
 
 # Create instance for direct function access
 _wikipedia_tools = WikipediaTools()
-search = _wikipedia_tools.search
-get_summary = _wikipedia_tools.get_summary
-get_page = _wikipedia_tools.get_page
-get_random = _wikipedia_tools.get_random
-set_language = _wikipedia_tools.set_language
+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
@@ -220,7 +220,7 @@ if __name__ == "__main__":
     # 1. Search for a topic
     print("1. Searching Wikipedia")
     print("------------------------------")
-    search_results = search("Python programming language")
+    search_results = wiki_search("Python programming language")
     print("Search results:")
     print(json.dumps(search_results, indent=2))
     print()
@@ -228,7 +228,7 @@ if __name__ == "__main__":
     # 2. Get a page summary
     print("2. Getting Page Summary")
     print("------------------------------")
-    summary = get_summary("Python programming language", sentences=3)
+    summary = wiki_summary("Python programming language", sentences=3)
     print("Summary:")
     print(summary)
     print()
@@ -236,7 +236,7 @@ if __name__ == "__main__":
     # 3. Get detailed page information
     print("3. Getting Full Page Information")
     print("------------------------------")
-    page = get_page("Python programming language")
+    page = wiki_page("Python programming language")
     if isinstance(page, dict) and "error" not in page:
         print("Page sections:")
         for section in page["sections"]:
@@ -252,7 +252,7 @@ if __name__ == "__main__":
     # 4. Get random pages
     print("4. Getting Random Pages")
     print("------------------------------")
-    random_pages = get_random(3)
+    random_pages = wiki_random(3)
     print("Random page titles:")
     print(json.dumps(random_pages, indent=2))
     print()
@@ -260,9 +260,9 @@ if __name__ == "__main__":
     # 5. Try different language
     print("5. Changing Language")
     print("------------------------------")
-    success = set_language("es")
+    success = wiki_language("es")
     if success:
-        summary = get_summary("Python (lenguaje de programación)", sentences=1)
+        summary = wiki_summary("Python (lenguaje de programación)", sentences=1)
         print("Spanish summary:")
         print(summary)
     print()

agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.25"
+version = "0.0.27"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }

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 = "0.0.25"
+version = "0.0.27"
 source = { editable = "." }
 dependencies = [
     { name = "openai" },

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==2.0.34 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

@@ -1,5 +1,5 @@
 ---
-title: "Code Execution Agent"
+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"

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/mint.json

@@ -85,19 +85,11 @@
         "features/async",
         "features/mini",
         "features/codeagent",
+        "features/mathagent",
         "features/structured",
         "features/callbacks"
       ]
     },
-    {
-      "group": "Other Features",
-      "pages": [
-        "train",
-        "framework/crewai",
-        "framework/autogen",
-        "framework/praisonaiagents"
-      ]
-    },
     {
       "group": "Getting Started (No Code)",
       "pages": [
@@ -109,15 +101,6 @@
         "auto"
       ]
     },
-    {
-      "group": "User Interface",
-      "pages": [
-        "ui/ui",
-        "ui/chat",
-        "ui/code",
-        "ui/realtime"
-      ]
-    },
     {
       "group": "Models",
       "pages": [
@@ -170,6 +153,24 @@
         "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"
+      ]
+    },
     {
       "group": "Monitoring",
       "pages": ["monitoring/agentops"]

docs/tools/arxiv_tools.mdx

@@ -1,7 +1,7 @@
 ---
-title: "arXiv Tools"
-description: "Scientific paper search and retrieval tools for AI agents."
-icon: "book"
+title: "arXiv Agent"
+description: "arXiv research paper search and analysis tools for AI agents."
+icon: "book-open"
 ---
 
 <Note>
@@ -11,9 +11,9 @@ icon: "book"
   - `arxiv` package installed
 </Note>
 
-## Quick Start
+## arXiv Tools
 
-Use arXiv Tools to search and retrieve scientific papers with AI agents.
+Use arXiv Tools to search and analyze research papers with AI agents.
 
 <Steps>
   <Step title="Install Dependencies">

docs/tools/calculator_tools.mdx

@@ -1,260 +1,216 @@
 ---
-title: "Calculator Tools"
-description: "Tools for performing mathematical calculations, unit conversions, and financial computations."
+title: "Calculator Agent"
+description: "Mathematical calculation tools for AI agents."
+icon: "calculator"
 ---
 
-## Available Functions
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - `sympy` and `pint` packages installed
+</Note>
 
-```python
-from praisonaiagents.tools import (
-    evaluate, solve_equation, convert_units,
-    calculate_statistics, calculate_financial
-)
-```
+## Calculator Tools
 
-## Function Details
+Use Calculator Tools to perform mathematical calculations with AI agents.
 
-### evaluate(expression: str, variables: Optional[Dict[str, float]] = None, precision: int = 10)
+<Steps>
+  <Step title="Install Dependencies">
+    First, install the required packages:
+    ```bash
+    pip install praisonaiagents sympy pint
+    ```
+  </Step>
 
-Evaluate mathematical expressions:
-- Basic arithmetic
-- Mathematical functions
-- Variable substitution
-- Precision control
-- Error handling
-
-```python
-# Basic arithmetic
-result = evaluate("2 + 2 * 3")
-
-# Complex expressions
-result = evaluate(
-    "sin(pi/2) + sqrt(16)",
-    precision=5
-)
-
-# With variables
-result = evaluate(
-    "x^2 + y",
-    variables={"x": 3, "y": 4}
-)
-
-# Returns: Dict[str, Any]
-# Example output:
-# {
-#     "result": 8.0
-# }
-# or
-# {
-#     "error": "Invalid expression"
-# }
-```
-
-### solve_equation(equation: str, variable: str = 'x')
-
-Solve mathematical equations:
-- Linear equations
-- Quadratic equations
-- Polynomial equations
-- Real solutions
-- Error handling
-
-```python
-# Linear equation
-result = solve_equation("2*x + 3 = 7")
-
-# Quadratic equation
-result = solve_equation(
-    "x^2 - 5*x + 6 = 0"
-)
-
-# Custom variable
-result = solve_equation(
-    "y^2 = 4",
-    variable='y'
-)
-
-# Returns: Dict[str, Any]
-# Example output:
-# {
-#     "solutions": [2.0, 3.0]
-# }
-# or
-# {
-#     "error": "No real solutions found"
-# }
-```
-
-### convert_units(value: float, from_unit: str, to_unit: str)
-
-Convert between units:
-- Length units
-- Mass units
-- Temperature units
-- Time units
-- Error handling
-
-```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"
-)
-
-# Returns: Dict[str, Any]
-# Example output:
-# {
-#     "result": 0.1  # 100 meters = 0.1 kilometers
-# }
-# or
-# {
-#     "error": "Invalid units"
-# }
-```
-
-### calculate_statistics(numbers: List[float])
-
-Calculate statistical metrics:
-- Mean
-- Median
-- Standard deviation
-- Variance
-- Range
-- Min/Max values
-
-```python
-# Basic statistics
-result = calculate_statistics([1, 2, 3, 4, 5])
-
-# Returns: Dict[str, Any]
-# Example output:
-# {
-#     "mean": 3.0,
-#     "median": 3.0,
-#     "std_dev": 1.58,
-#     "variance": 2.5,
-#     "min": 1.0,
-#     "max": 5.0,
-#     "range": 4.0,
-#     "count": 5
-# }
-```
-
-### calculate_financial(principal: float, rate: float, time: float, frequency: str = 'yearly')
-
-Calculate financial metrics:
-- Compound interest
-- Simple interest
-- Interest comparison
-- Multiple frequencies
-
-```python
-# Basic compound interest
-result = calculate_financial(1000, 5, 2)  # $1000, 5%, 2 years
-
-# Monthly compounding
-result = calculate_financial(
-    principal=1000,
-    rate=5,
-    time=2,
-    frequency='monthly'
-)
-
-# Returns: Dict[str, Any]
-# Example output:
-# {
-#     "final_amount": 1102.50,
-#     "compound_interest": 102.50,
-#     "simple_interest": 100.00,
-#     "difference": 2.50
-# }
-```
-
-## Example Agent Configuration
-
-```python
-from praisonaiagents import Agent
-from praisonaiagents.tools import (
-    evaluate, solve_equation, convert_units,
-    calculate_statistics, calculate_financial
-)
-
-agent = Agent(
-    name="MathAssistant",
-    description="An agent that helps with mathematical calculations",
-    tools=[
+  <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>
 
-## Dependencies
+  <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>
 
-The calculator tools require the following packages:
-- sympy: For equation solving and symbolic mathematics
-- pint: For unit conversions
+  <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>
 
-These will be automatically installed when needed.
+  <Step title="Run Agent">
+    Initialize and run the agent:
+    ```python
+    agents = PraisonAIAgents(
+        agents=[calculator_agent],
+        tasks=[calc_task],
+        process="sequential"
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
 
-## Supported Features
+## Understanding Calculator Tools
 
-### Mathematical Functions
-- Basic: +, -, *, /, ^
-- Trigonometric: sin, cos, tan
-- Roots: sqrt
-- Logarithmic: log, exp
-- Constants: pi, e
+<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>
 
-### Unit Categories
-- Length: meters, kilometers, miles, etc.
-- Mass: grams, kilograms, pounds, etc.
-- Temperature: celsius, fahrenheit, kelvin
-- Time: seconds, minutes, hours, days
-- Many more via the pint library
+## Key Components
 
-### Financial Frequencies
-- yearly
-- semi-annual
-- quarterly
-- monthly
-- daily
+<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>
 
-## Common Use Cases
+## Advanced Usage
 
-1. Scientific Calculations:
-```python
-# Complex mathematical expressions
-gravity = evaluate("9.81 * sin(pi/4)^2")
-kinetic = evaluate(
-    "0.5 * m * v^2",
-    variables={"m": 10, "v": 5}
-)
-```
+<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>
 
-2. Unit Conversions:
-```python
-# Multiple conversions
-distances = {
-    "marathon": convert_units(42.195, "kilometers", "miles"),
-    "height": convert_units(180, "centimeters", "feet"),
-    "weight": convert_units(75, "kilograms", "pounds")
-}
-```
+## Error Handling
 
-3. Investment Analysis:
-```python
-# Compare investment strategies
-investments = {
-    "conservative": calculate_financial(10000, 3, 5, "yearly"),
-    "moderate": calculate_financial(10000, 5, 5, "quarterly"),
-    "aggressive": calculate_financial(10000, 8, 5, "monthly")
-}
-```
+<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

@@ -1,6 +1,6 @@
 ---
-title: "CSV Tools"
-description: "Tools for working with CSV files in AI agents."
+title: "CSV Agent"
+description: "CSV file processing tools for AI agents."
 icon: "file-csv"
 ---
 
@@ -8,12 +8,12 @@ icon: "file-csv"
   **Prerequisites**
   - Python 3.10 or higher
   - PraisonAI Agents package installed
-  - Basic understanding of CSV files
+  - `pandas` package installed
 </Note>
 
-## Quick Start
+## CSV Tools
 
-Use CSV Tools to process and manipulate CSV files with AI agents.
+Use CSV Tools to read, write, and manipulate CSV files with AI agents.
 
 <Steps>
   <Step title="Install Dependencies">
@@ -27,7 +27,7 @@ Use CSV Tools to process and manipulate CSV files with AI agents.
     Import the necessary components:
     ```python
     from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import csv, read_csv, write_csv, merge_csv
+    from praisonaiagents.tools import read_csv, write_csv, merge_csv
     ```
   </Step>
 
@@ -39,7 +39,7 @@ Use CSV Tools to process and manipulate CSV files with AI agents.
         role="CSV Processing Specialist",
         goal="Process CSV files efficiently and accurately.",
         backstory="Expert in CSV file manipulation and analysis.",
-        tools=[csv, read_csv, write_csv, merge_csv],
+        tools=[read_csv, write_csv, merge_csv],
         self_reflect=False
     )
     ```
@@ -87,7 +87,7 @@ Use CSV Tools to process and manipulate CSV files with AI agents.
   <Card title="CSV Agent" icon="user-robot">
     Create specialized CSV agents:
     ```python
-    Agent(tools=[csv, read_csv, write_csv, merge_csv])
+    Agent(tools=[read_csv, write_csv, merge_csv])
     ```
   </Card>
   <Card title="CSV Task" icon="list-check">

docs/tools/duckdb_tools.mdx

@@ -1,6 +1,7 @@
 ---
-title: "DuckDB Tools"
-description: "Tools for working with DuckDB databases in AI agents."
+title: "DuckDB Agent"
+description: "DuckDB database tools for AI agents."
+icon: "database"
 ---
 
 <Note>
@@ -11,7 +12,7 @@ description: "Tools for working with DuckDB databases in AI agents."
   - Basic understanding of SQL and databases
 </Note>
 
-## Quick Start
+## DuckDB Tools
 
 Use DuckDB Tools to manage and query databases with AI agents.
 
@@ -27,7 +28,7 @@ Use DuckDB Tools to manage and query databases with AI agents.
     Import the necessary components:
     ```python
     from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import duckdb
+    from praisonaiagents.tools import execute_query, load_csv, export_csv
     ```
   </Step>
 
@@ -39,7 +40,7 @@ Use DuckDB Tools to manage and query databases with AI agents.
         role="Database Management Specialist",
         goal="Manage and query databases efficiently.",
         backstory="Expert in database operations and SQL.",
-        tools=[duckdb],
+        tools=[execute_query, load_csv, export_csv],
         self_reflect=False
     )
     ```
@@ -176,7 +177,7 @@ success = export_csv(
   <Card title="DB Agent" icon="user-robot">
     Create specialized database agents:
     ```python
-    Agent(tools=[duckdb])
+    Agent(tools=[execute_query, load_csv, export_csv])
     ```
   </Card>
   <Card title="DB Task" icon="list-check">
@@ -205,7 +206,7 @@ success = export_csv(
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import duckdb
+from praisonaiagents.tools import execute_query, load_csv, export_csv
 
 # Create database agent
 db_agent = Agent(
@@ -213,7 +214,7 @@ db_agent = Agent(
     role="Database Specialist",
     goal="Query and analyze data efficiently.",
     backstory="Expert in database management and SQL.",
-    tools=[duckdb],
+    tools=[execute_query, load_csv, export_csv],
     self_reflect=False
 )
 
@@ -242,39 +243,18 @@ query_agent = Agent(
     name="QueryProcessor",
     role="SQL Query Specialist",
     goal="Execute SQL queries efficiently.",
-    tools=[duckdb],
+    tools=[execute_query],
     self_reflect=False
 )
 
-# Create analysis agent
-analysis_agent = Agent(
-    name="DataAnalyst",
-    role="Data Analyst",
-    goal="Analyze query results and generate insights.",
-    backstory="Expert in data analysis and reporting.",
+# 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
 )
-
-# Define tasks
-query_task = Task(
-    description="Execute complex SQL queries.",
-    agent=query_agent,
-    name="query_execution"
-)
-
-analysis_task = Task(
-    description="Analyze query results and trends.",
-    agent=analysis_agent,
-    name="result_analysis"
-)
-
-# Run agents
-agents = PraisonAIAgents(
-    agents=[query_agent, analysis_agent],
-    tasks=[query_task, analysis_task],
-    process="sequential"
-)
-agents.start()
 ```
 
 ## Dependencies
@@ -370,7 +350,7 @@ export_csv("SELECT * FROM transformed", "transformed.csv")
         name="DBProcessor",
         role="Database Specialist",
         goal="Process queries accurately and efficiently",
-        tools=[duckdb]
+        tools=[execute_query, load_csv, export_csv]
     )
     ```
   </Accordion>
@@ -394,7 +374,7 @@ export_csv("SELECT * FROM transformed", "transformed.csv")
 querier = Agent(
     name="Querier",
     role="SQL Specialist",
-    tools=[duckdb]
+    tools=[execute_query]
 )
 
 # Analysis agent

docs/tools/duckduckgo_tools.mdx

@@ -1,5 +1,5 @@
 ---
-title: "DuckDuckGo Tools"
+title: "DuckDuckGo Agent"
 description: "Internet search tools using DuckDuckGo for AI agents."
 icon: "magnifying-glass"
 ---
@@ -11,7 +11,7 @@ icon: "magnifying-glass"
   - `duckduckgo-search` package installed
 </Note>
 
-## Quick Start
+## DuckDuckGo Tools
 
 Use DuckDuckGo Tools to perform internet searches with AI agents.
 

docs/tools/excel_tools.mdx

@@ -1,6 +1,7 @@
 ---
-title: "Excel Tools"
-description: "Tools for working with Excel files in AI agents."
+title: "Excel Agent"
+description: "Excel file processing tools for AI agents."
+icon: "file-excel"
 ---
 
 <Note>
@@ -11,7 +12,7 @@ description: "Tools for working with Excel files in AI agents."
   - Basic understanding of Excel files
 </Note>
 
-## Quick Start
+## Excel Tools
 
 Use Excel Tools to process and manipulate Excel files with AI agents.
 
@@ -70,6 +71,46 @@ Use Excel Tools to process and manipulate Excel files with AI agents.
   </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
@@ -177,53 +218,13 @@ success = merge_excel(
 # Returns: bool (True if successful)
 ```
 
-## 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=[excel, 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>
-
 ## Examples
 
 ### Basic Excel Processing Agent
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import excel, read_excel, write_excel, merge_excel
+from praisonaiagents.tools import read_excel, write_excel, merge_excel
 
 # Create Excel agent
 excel_agent = Agent(
@@ -231,7 +232,7 @@ excel_agent = Agent(
     role="Excel Processing Specialist",
     goal="Process Excel files efficiently and accurately.",
     backstory="Expert in spreadsheet manipulation and analysis.",
-    tools=[excel, read_excel, write_excel, merge_excel],
+    tools=[read_excel, write_excel, merge_excel],
     self_reflect=False
 )
 
@@ -260,7 +261,7 @@ processor_agent = Agent(
     name="Processor",
     role="Data Processor",
     goal="Process Excel data systematically.",
-    tools=[excel, read_excel, write_excel, merge_excel],
+    tools=[read_excel, write_excel, merge_excel],
     self_reflect=False
 )
 
@@ -305,7 +306,7 @@ agents.start()
         name="ExcelProcessor",
         role="Excel Processing Specialist",
         goal="Process Excel files accurately and efficiently",
-        tools=[excel, read_excel, write_excel, merge_excel]
+        tools=[read_excel, write_excel, merge_excel]
     )
     ```
   </Accordion>
@@ -314,8 +315,7 @@ agents.start()
     Define specific Excel operations:
     ```python
     Task(
-        description="Process sales data and generate reports",
-        expected_output="Sales analysis report"
+        description="Process sales data and generate reports"
     )
     ```
   </Accordion>
@@ -329,7 +329,7 @@ agents.start()
 processor = Agent(
     name="Processor",
     role="Excel Processor",
-    tools=[excel, read_excel, write_excel, merge_excel]
+    tools=[read_excel, write_excel, merge_excel]
 )
 
 # Analysis agent
@@ -438,3 +438,5 @@ processed = {
 
 # Write back to new file
 write_excel("processed_data.xlsx", processed)
+
+```

docs/tools/file_tools.mdx

@@ -1,6 +1,7 @@
 ---
-title: "File Tools"
-description: "Basic file operation tools for AI agents."
+title: "File Agent"
+description: "File system operation tools for AI agents."
+icon: "folder"
 ---
 
 <Note>
@@ -10,9 +11,9 @@ description: "Basic file operation tools for AI agents."
   - Basic understanding of file operations
 </Note>
 
-## Quick Start
+## File Tools
 
-Use File Tools to manage files and directories with AI agents.
+Use File Tools to perform file system operations with AI agents.
 
 <Steps>
   <Step title="Install Dependencies">
@@ -26,7 +27,7 @@ Use File Tools to manage files and directories with AI agents.
     Import the necessary components:
     ```python
     from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import file_tools
+    from praisonaiagents.tools import read_file, write_file, list_files, get_file_info, copy_file, move_file, delete_file
     ```
   </Step>
 
@@ -38,7 +39,7 @@ Use File Tools to manage files and directories with AI agents.
         role="File System Specialist",
         goal="Manage files and directories efficiently.",
         backstory="Expert in file system operations and organization.",
-        tools=[file_tools],
+        tools=[read_file, write_file, list_files, get_file_info, copy_file, move_file, delete_file],
         self_reflect=False
     )
     ```
@@ -86,7 +87,7 @@ Use File Tools to manage files and directories with AI agents.
   <Card title="File Agent" icon="user-robot">
     Create specialized file agents:
     ```python
-    Agent(tools=[file_tools])
+    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">
@@ -336,22 +337,22 @@ write_file("output.txt", processed)
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import file_tools
+from praisonaiagents.tools import read_file, write_file, list_files, get_file_info
 
 # Create file management agent
 file_agent = Agent(
-    name="FileOrganizer",
-    role="File System Manager",
-    goal="Organize and manage files efficiently.",
-    backstory="Expert in file organization and system management.",
-    tools=[file_tools],
+    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="Sort documents by type and create backup copies.",
-    expected_output="Organized file structure with backups.",
+    description="Organize and process documents.",
+    expected_output="Organized file structure.",
     agent=file_agent,
     name="file_organization"
 )
@@ -365,44 +366,46 @@ agents = PraisonAIAgents(
 agents.start()
 ```
 
-### Advanced File Management with Multiple Agents
+### Advanced File Operations with Multiple Agents
 
 ```python
-# Create file organization agent
-organizer_agent = Agent(
-    name="Organizer",
-    role="File Organizer",
-    goal="Sort and organize files systematically.",
-    tools=[file_tools],
+# 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 backup agent
-backup_agent = Agent(
-    name="Backup",
-    role="Backup Manager",
-    goal="Create and manage file backups.",
-    backstory="Expert in data backup and recovery.",
+# 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
-organize_task = Task(
-    description="Organize files by type and date.",
-    agent=organizer_agent,
-    name="organize_files"
+io_task = Task(
+    description="Read and write files.",
+    expected_output="File operations completed successfully.",
+    agent=io_agent,
+    name="file_io"
 )
 
-backup_task = Task(
-    description="Create backups of organized files.",
-    agent=backup_agent,
-    name="backup_files"
+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=[organizer_agent, backup_agent],
-    tasks=[organize_task, backup_task],
+    agents=[io_agent, management_agent],
+    tasks=[io_task, management_task],
     process="sequential"
 )
 agents.start()
@@ -418,7 +421,7 @@ agents.start()
         name="FileManager",
         role="File System Specialist",
         goal="Manage files efficiently and safely",
-        tools=[file_tools]
+        tools=[read_file, write_file, list_files, get_file_info, copy_file, move_file, delete_file]
     )
     ```
   </Accordion>
@@ -442,7 +445,7 @@ agents.start()
 organizer = Agent(
     name="Organizer",
     role="File Organizer",
-    tools=[file_tools]
+    tools=[read_file, write_file, list_files, get_file_info, copy_file, move_file, delete_file]
 )
 
 # Cleanup agent

docs/tools/json_tools.mdx

@@ -1,6 +1,7 @@
 ---
-title: "JSON Tools"
-description: "Tools for working with JSON files in AI agents."
+title: "JSON Agent"
+description: "JSON data processing tools for AI agents."
+icon: "braces"
 ---
 
 <Note>
@@ -10,7 +11,7 @@ description: "Tools for working with JSON files in AI agents."
   - Basic understanding of JSON format
 </Note>
 
-## Quick Start
+## JSON Tools
 
 Use JSON Tools to process and manipulate JSON files with AI agents.
 
@@ -75,6 +76,46 @@ Use JSON Tools to process and manipulate JSON files with AI agents.
   </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
@@ -371,11 +412,11 @@ write_json(result, "transformed_data.json")
 
 <Card title="What are JSON Tools?" icon="question">
   JSON Tools provide JSON processing capabilities for AI agents:
-  - JSON parsing
-  - File validation
-  - Data extraction
-  - Structure manipulation
-  - Format conversion
+  - File reading and writing
+  - Data validation
+  - Schema validation
+  - Data transformation
+  - Structure analysis
 </Card>
 
 ## Key Components
@@ -384,7 +425,7 @@ write_json(result, "transformed_data.json")
   <Card title="JSON Agent" icon="user-robot">
     Create specialized JSON agents:
     ```python
-    Agent(tools=[json])
+    Agent(tools=[read_json, write_json, merge_json, validate_json, analyze_json, transform_json])
     ```
   </Card>
   <Card title="JSON Task" icon="list-check">
@@ -400,9 +441,9 @@ write_json(result, "transformed_data.json")
     ```
   </Card>
   <Card title="JSON Options" icon="sliders">
-    Customize JSON parameters:
+    Customize JSON operations:
     ```python
-    indent=2, encoding="utf-8"
+    indent=2, sort_keys=True
     ```
   </Card>
 </CardGroup>
@@ -413,24 +454,24 @@ write_json(result, "transformed_data.json")
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import json
+from praisonaiagents.tools import read_json, write_json, validate_json
 
-# Create JSON agent
+# Create JSON processing agent
 json_agent = Agent(
     name="JSONExpert",
-    role="JSON Processing Specialist",
-    goal="Process JSON files efficiently and accurately.",
-    backstory="Expert in JSON file handling and validation.",
-    tools=[json],
+    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="Parse and validate API configurations.",
-    expected_output="Processed and validated JSON data.",
+    description="Process and validate configuration files.",
+    expected_output="Validated JSON configuration.",
     agent=json_agent,
-    name="config_processing"
+    name="config_validation"
 )
 
 # Run agent
@@ -442,44 +483,44 @@ agents = PraisonAIAgents(
 agents.start()
 ```
 
-### Advanced JSON Processing with Multiple Agents
+### Advanced JSON Operations with Multiple Agents
 
 ```python
-# Create JSON processing agent
-processor_agent = Agent(
-    name="Processor",
-    role="JSON Processor",
-    goal="Process JSON files systematically.",
-    tools=[json],
+# 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 validation agent
-validator_agent = Agent(
-    name="Validator",
-    role="Data Validator",
-    goal="Validate JSON structure and content.",
-    backstory="Expert in data validation and verification.",
+# 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
-processing_task = Task(
-    description="Process and transform JSON configurations.",
-    agent=processor_agent,
-    name="json_processing"
-)
-
 validation_task = Task(
-    description="Validate processed JSON data.",
-    agent=validator_agent,
-    name="data_validation"
-)
+    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=[processor_agent, validator_agent],
-    tasks=[processing_task, validation_task],
+    agents=[validation_agent, transform_agent],
+    tasks=[validation_task, transform_task],
     process="sequential"
 )
 agents.start()
@@ -495,7 +536,7 @@ agents.start()
         name="JSONProcessor",
         role="JSON Processing Specialist",
         goal="Process JSON files accurately and safely",
-        tools=[json]
+        tools=[read_json, write_json, merge_json, validate_json, analyze_json, transform_json]
     )
     ```
   </Accordion>
@@ -519,7 +560,7 @@ agents.start()
 processor = Agent(
     name="Processor",
     role="JSON Processor",
-    tools=[json]
+    tools=[read_json, write_json, merge_json, validate_json, analyze_json, transform_json]
 )
 
 # Validation agent

docs/tools/newspaper_tools.mdx

@@ -1,6 +1,6 @@
 ---
-title: "Newspaper Tools"
-description: "News article scraping and analysis tools for AI agents."
+title: "Newspaper Agent"
+description: "News article extraction tools for AI agents."
 icon: "newspaper"
 ---
 
@@ -11,9 +11,9 @@ icon: "newspaper"
   - `newspaper3k` package installed
 </Note>
 
-## Quick Start
+## Newspaper Tools
 
-Use Newspaper Tools to scrape and analyze news articles with AI agents.
+Use Newspaper Tools to extract and analyze news articles with AI agents.
 
 <Steps>
   <Step title="Install Dependencies">
@@ -27,7 +27,7 @@ Use Newspaper Tools to scrape and analyze news articles with AI agents.
     Import the necessary components:
     ```python
     from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import newspaper
+    from praisonaiagents.tools import get_article, get_news_sources, get_articles_from_source, get_trending_topics
     ```
   </Step>
 
@@ -39,7 +39,7 @@ Use Newspaper Tools to scrape and analyze news articles with AI agents.
         role="News Analyst",
         goal="Collect and analyze news articles from various sources.",
         backstory="Expert in news gathering and content analysis.",
-        tools=[newspaper],
+        tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics],
         self_reflect=False
     )
     ```
@@ -87,7 +87,7 @@ Use Newspaper Tools to scrape and analyze news articles with AI agents.
   <Card title="News Agent" icon="user-robot">
     Create specialized news agents:
     ```python
-    Agent(tools=[newspaper])
+    Agent(tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics])
     ```
   </Card>
   <Card title="News Task" icon="list-check">
@@ -247,24 +247,24 @@ topics = get_trending_topics(
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import newspaper
+from praisonaiagents.tools import get_article, get_news_sources, get_articles_from_source, get_trending_topics
 
 # Create news agent
 news_agent = Agent(
-    name="NewsCollector",
+    name="NewsExpert",
     role="News Analyst",
-    goal="Collect and analyze news articles on specific topics.",
-    backstory="Expert in news gathering and content analysis.",
-    tools=[newspaper],
+    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 recent tech news articles about 'artificial intelligence'.",
-    expected_output="Summary of key AI developments with source articles.",
+    description="Analyze tech news trends.",
+    expected_output="Tech news analysis report.",
     agent=news_agent,
-    name="tech_news"
+    name="tech_news_analysis"
 )
 
 # Run agent
@@ -284,7 +284,7 @@ collector_agent = Agent(
     name="Collector",
     role="News Collector",
     goal="Gather comprehensive news coverage.",
-    tools=[newspaper],
+    tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics],
     self_reflect=False
 )
 
@@ -329,7 +329,7 @@ agents.start()
         name="NewsAnalyst",
         role="News Specialist",
         goal="Analyze news content effectively",
-        tools=[newspaper]
+        tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics]
     )
     ```
   </Accordion>
@@ -353,7 +353,7 @@ agents.start()
 monitor = Agent(
     name="Monitor",
     role="News Monitor",
-    tools=[newspaper]
+    tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics]
 )
 
 # Analysis agent

docs/tools/pandas_tools.mdx

@@ -1,6 +1,6 @@
 ---
-title: "Pandas Tools"
-description: "Data manipulation and analysis tools for AI agents using pandas."
+title: "Pandas Agent"
+description: "Pandas data manipulation tools for AI agents."
 icon: "table"
 ---
 
@@ -11,7 +11,7 @@ icon: "table"
   - `pandas` package installed
 </Note>
 
-## Quick Start
+## Pandas Tools
 
 Use Pandas Tools to manipulate and analyze data with AI agents.
 
@@ -27,7 +27,7 @@ Use Pandas Tools to manipulate and analyze data with AI agents.
     Import the necessary components:
     ```python
     from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import pandas_tools
+    from praisonaiagents.tools import read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table
     ```
   </Step>
 
@@ -39,7 +39,7 @@ Use Pandas Tools to manipulate and analyze data with AI agents.
         role="Data Analysis Specialist",
         goal="Analyze and transform data efficiently.",
         backstory="Expert in data manipulation and statistical analysis.",
-        tools=[pandas_tools],
+        tools=[read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table],
         self_reflect=False
     )
     ```
@@ -87,7 +87,7 @@ Use Pandas Tools to manipulate and analyze data with AI agents.
   <Card title="Data Agent" icon="user-robot">
     Create specialized data agents:
     ```python
-    Agent(tools=[pandas_tools])
+    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">
@@ -451,24 +451,24 @@ write_csv(demographics, "demographic_analysis.csv")
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import pandas_tools
+from praisonaiagents.tools import read_csv, filter_data, get_summary, group_by
 
 # Create data analysis agent
 data_agent = Agent(
-    name="DataAnalyst",
-    role="Data Analysis Specialist",
-    goal="Perform comprehensive data analysis.",
-    backstory="Expert in statistical analysis and data visualization.",
-    tools=[pandas_tools],
+    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 customer demographics and purchase patterns.",
-    expected_output="Customer segmentation analysis with insights.",
+    description="Analyze sales performance data.",
+    expected_output="Sales analysis report.",
     agent=data_agent,
-    name="customer_analysis"
+    name="sales_analysis"
 )
 
 # Run agent
@@ -480,44 +480,44 @@ agents = PraisonAIAgents(
 agents.start()
 ```
 
-### Advanced Analysis with Multiple Agents
+### Advanced Data Operations with Multiple Agents
 
 ```python
-# Create data preparation agent
-prep_agent = Agent(
-    name="DataPrep",
-    role="Data Preparation Specialist",
-    goal="Clean and prepare data for analysis.",
-    tools=[pandas_tools],
+# 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="Statistical Analyst",
-    goal="Perform advanced statistical analysis.",
-    backstory="Expert in statistical modeling and interpretation.",
+    role="Data Analysis Specialist",
+    goal="Perform complex data analysis.",
+    tools=[filter_data, get_summary, group_by, pivot_table],
     self_reflect=False
 )
 
 # Define tasks
-prep_task = Task(
-    description="Clean and prepare sales data for analysis.",
-    agent=prep_agent,
-    name="data_prep"
+io_task = Task(
+    description="Import and export data",
+    agent=io_agent,
+    name="data_io"
 )
 
 analysis_task = Task(
-    description="Perform statistical analysis and generate insights.",
+    description="Analyze data",
     agent=analysis_agent,
-    name="statistical_analysis"
-)
+    name="data_analysis"
+)   
 
 # Run agents
 agents = PraisonAIAgents(
-    agents=[prep_agent, analysis_agent],
-    tasks=[prep_task, analysis_task],
+    agents=[io_agent, analysis_agent],
+    tasks=[io_task, analysis_task],
     process="sequential"
 )
 agents.start()
@@ -533,7 +533,7 @@ agents.start()
         name="DataScientist",
         role="Data Analysis Specialist",
         goal="Extract meaningful insights from data",
-        tools=[pandas_tools]
+        tools=[read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table]
     )
     ```
   </Accordion>
@@ -557,7 +557,7 @@ agents.start()
 prep_agent = Agent(
     name="Preparer",
     role="Data Preparation",
-    tools=[pandas_tools]
+    tools=[read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table]
 )
 
 # Analysis agent

docs/tools/python_tools.mdx

@@ -1,6 +1,7 @@
 ---
-title: "Python Tools"
-description: "Tools for Python code execution, analysis, and formatting in AI agents."
+title: "Python Agent"
+description: "Python code execution tools for AI agents."
+icon: "code"
 ---
 
 <Note>
@@ -10,7 +11,7 @@ description: "Tools for Python code execution, analysis, and formatting in AI ag
   - Basic understanding of Python programming
 </Note>
 
-## Quick Start
+## Python Tools
 
 Use Python Tools to execute and manage Python code with AI agents.
 

docs/tools/shell_tools.mdx

@@ -1,6 +1,7 @@
 ---
-title: "Shell Tools"
-description: "Tools for executing shell commands and managing system processes in AI agents."
+title: "Shell Agent"
+description: "Shell command execution tools for AI agents."
+icon: "terminal"
 ---
 
 <Note>
@@ -10,9 +11,9 @@ description: "Tools for executing shell commands and managing system processes i
   - Basic understanding of shell commands
 </Note>
 
-## Quick Start
+## Shell Tools
 
-Use Shell Tools to execute and manage shell commands with AI agents.
+Use Shell Tools to execute shell commands with AI agents.
 
 <Steps>
   <Step title="Install Dependencies">
@@ -26,7 +27,7 @@ Use Shell Tools to execute and manage shell commands with AI agents.
     Import the necessary components:
     ```python
     from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import shell, execute_command, list_processes, kill_process, get_system_info
+    from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info
     ```
   </Step>
 
@@ -38,7 +39,7 @@ Use Shell Tools to execute and manage shell commands with AI agents.
         role="Shell Command Specialist",
         goal="Execute shell commands efficiently and safely.",
         backstory="Expert in command-line operations and automation.",
-        tools=[shell],
+        tools=[execute_command, list_processes, kill_process, get_system_info],
         self_reflect=False
     )
     ```
@@ -80,13 +81,38 @@ Use Shell Tools to execute and manage shell commands with AI agents.
   - Environment management
 </Card>
 
-## Examples
+## Key Components
 
-### Basic Shell Command Agent
+<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 shell, execute_command, list_processes, kill_process, get_system_info
+from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info
 
 # Create shell agent
 shell_agent = Agent(
@@ -94,7 +120,7 @@ shell_agent = Agent(
     role="Shell Command Specialist",
     goal="Execute shell commands efficiently and safely.",
     backstory="Expert in command-line operations and scripting.",
-    tools=[shell],
+    tools=[execute_command, list_processes, kill_process, get_system_info],
     self_reflect=False
 )
 
@@ -123,7 +149,7 @@ command_agent = Agent(
     name="Commander",
     role="Command Executor",
     goal="Execute shell commands systematically.",
-    tools=[shell],
+    tools=[execute_command, list_processes, kill_process, get_system_info],
     self_reflect=False
 )
 
@@ -411,7 +437,7 @@ for cmd in commands:
         name="ShellManager",
         role="Command Line Specialist",
         goal="Execute shell commands safely and efficiently",
-        tools=[shell]
+        tools=[execute_command, list_processes, kill_process, get_system_info]
     )
     ```
   </Accordion>
@@ -435,7 +461,7 @@ for cmd in commands:
 commander = Agent(
     name="Commander",
     role="Shell Commander",
-    tools=[shell]
+    tools=[execute_command]
 )
 
 # Monitor agent

docs/tools/spider_tools.mdx

@@ -1,6 +1,6 @@
 ---
-title: "Spider Tools"
-description: "Web scraping and crawling tools for AI agents."
+title: "Spider Agent"
+description: "Web scraping tools for AI agents."
 icon: "spider"
 ---
 
@@ -8,19 +8,18 @@ icon: "spider"
   **Prerequisites**
   - Python 3.10 or higher
   - PraisonAI Agents package installed
-  - `beautifulsoup4` package installed
-  - `requests` package installed
+  - `scrapy` package installed
 </Note>
 
-## Quick Start
+## Spider Tools
 
-Use Spider Tools to scrape and crawl web content with AI agents.
+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 beautifulsoup4 requests
+    pip install praisonaiagents scrapy
     ```
   </Step>
 
@@ -28,7 +27,7 @@ Use Spider Tools to scrape and crawl web content with AI agents.
     Import the necessary components:
     ```python
     from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import spider
+    from praisonaiagents.tools import scrape_page, extract_links, crawl, extract_text
     ```
   </Step>
 
@@ -40,7 +39,7 @@ Use Spider Tools to scrape and crawl web content with AI agents.
         role="Web Scraping Specialist",
         goal="Extract and analyze web content efficiently.",
         backstory="Expert in web scraping and content extraction.",
-        tools=[spider],
+        tools=[scrape_page, extract_links, crawl, extract_text],
         self_reflect=False
     )
     ```
@@ -75,11 +74,11 @@ Use Spider Tools to scrape and crawl web content with AI agents.
 
 <Card title="What are Spider Tools?" icon="question">
   Spider Tools provide web scraping capabilities for AI agents:
-  - Web page scraping
-  - Content extraction
-  - Link crawling
-  - HTML parsing
-  - Data structuring
+  - Page scraping and downloading
+  - Content extraction and filtering
+  - Link discovery and crawling
+  - HTML parsing and cleaning
+  - Data structuring and formatting
 </Card>
 
 ## Key Components
@@ -88,7 +87,7 @@ Use Spider Tools to scrape and crawl web content with AI agents.
   <Card title="Spider Agent" icon="user-robot">
     Create specialized scraping agents:
     ```python
-    Agent(tools=[spider])
+    Agent(tools=[scrape_page, extract_content, crawl_links, parse_html, structure_data])
     ```
   </Card>
   <Card title="Spider Task" icon="list-check">
@@ -117,30 +116,30 @@ Use Spider Tools to scrape and crawl web content with AI agents.
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import spider
+from praisonaiagents.tools import scrape_page, extract_links, crawl, extract_text
 
-# Create scraping agent
-spider_agent = Agent(
+# Create search agent
+agent = Agent(
     name="WebCrawler",
     role="Web Scraping Specialist",
-    goal="Extract web content efficiently and ethically.",
-    backstory="Expert in web crawling and data extraction.",
-    tools=[spider],
+    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 scraping task
-scraping_task = Task(
-    description="Extract article content from news websites.",
-    expected_output="Structured article data with text and metadata.",
-    agent=spider_agent,
-    name="news_scraping"
+# 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=[spider_agent],
-    tasks=[scraping_task],
+    agents=[agent],
+    tasks=[task],
     process="sequential"
 )
 agents.start()
@@ -154,7 +153,7 @@ scraper_agent = Agent(
     name="Scraper",
     role="Content Scraper",
     goal="Extract web content systematically.",
-    tools=[spider],
+    tools=[scrape_page, crawl_links, parse_html],
     self_reflect=False
 )
 
@@ -164,6 +163,7 @@ analysis_agent = Agent(
     role="Content Analyst",
     goal="Analyze and structure scraped content.",
     backstory="Expert in data analysis and organization.",
+    tools=[extract_content, structure_data],
     self_reflect=False
 )
 
@@ -199,7 +199,7 @@ agents.start()
         name="WebScraper",
         role="Content Extraction Specialist",
         goal="Extract web content ethically and efficiently",
-        tools=[spider]
+        tools=[scrape_page, extract_content, crawl_links, parse_html, structure_data]
     )
     ```
   </Accordion>
@@ -223,13 +223,14 @@ agents.start()
 scraper = Agent(
     name="Scraper",
     role="Web Scraper",
-    tools=[spider]
+    tools=[scrape_page, crawl_links, parse_html]
 )
 
 # Processing agent
 processor = Agent(
     name="Processor",
-    role="Data Processor"
+    role="Data Processor",
+    tools=[extract_content, structure_data]
 )
 
 # Define tasks
@@ -247,4 +248,4 @@ process_task = Task(
 agents = PraisonAIAgents(
     agents=[scraper, processor],
     tasks=[scrape_task, process_task]
-)
+)

docs/tools/wikipedia_tools.mdx

@@ -1,5 +1,5 @@
 ---
-title: "Wikipedia Tools"
+title: "Wikipedia Agent"
 description: "Wikipedia data retrieval tools for AI agents."
 icon: "book"
 ---
@@ -12,7 +12,7 @@ icon: "book"
   - Basic understanding of Wikipedia APIs
 </Note>
 
-## Quick Start
+## Wikipedia Tools
 
 Use Wikipedia Tools to retrieve and analyze Wikipedia content with AI agents.
 
@@ -28,7 +28,7 @@ Use Wikipedia Tools to retrieve and analyze Wikipedia content with AI agents.
     Import the necessary components:
     ```python
     from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import wikipedia
+    from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language
     ```
   </Step>
 
@@ -40,7 +40,7 @@ Use Wikipedia Tools to retrieve and analyze Wikipedia content with AI agents.
         role="Wikipedia Research Specialist",
         goal="Research and analyze Wikipedia content efficiently.",
         backstory="Expert in information retrieval and content analysis.",
-        tools=[wikipedia],
+        tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
         self_reflect=False
     )
     ```
@@ -75,11 +75,11 @@ Use Wikipedia Tools to retrieve and analyze Wikipedia content with AI agents.
 
 <Card title="What are Wikipedia Tools?" icon="question">
   Wikipedia Tools provide research capabilities for AI agents:
-  - Article retrieval
-  - Content search
-  - Information extraction
-  - Summary generation
-  - Reference collection
+  - Article search and retrieval
+  - Content summary generation
+  - Full page information access
+  - Random article discovery
+  - Multi-language support
 </Card>
 
 ## Key Components
@@ -88,7 +88,7 @@ Use Wikipedia Tools to retrieve and analyze Wikipedia content with AI agents.
   <Card title="Wiki Agent" icon="user-robot">
     Create specialized research agents:
     ```python
-    Agent(tools=[wikipedia])
+    Agent(tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language])
     ```
   </Card>
   <Card title="Wiki Task" icon="list-check">
@@ -117,7 +117,7 @@ Use Wikipedia Tools to retrieve and analyze Wikipedia content with AI agents.
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import wikipedia
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language
 
 # Create Wikipedia agent
 wiki_agent = Agent(
@@ -125,7 +125,7 @@ wiki_agent = Agent(
     role="Research Specialist",
     goal="Research topics efficiently and accurately.",
     backstory="Expert in information gathering and analysis.",
-    tools=[wikipedia],
+    tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
     self_reflect=False
 )
 
@@ -144,6 +144,7 @@ agents = PraisonAIAgents(
     process="sequential"
 )
 agents.start()
+
 ```
 
 ### Advanced Research with Multiple Agents
@@ -154,7 +155,7 @@ researcher_agent = Agent(
     name="Researcher",
     role="Content Researcher",
     goal="Research topics systematically.",
-    tools=[wikipedia],
+    tools=[wiki_search, wiki_summary, wiki_page],
     self_reflect=False
 )
 
@@ -164,6 +165,7 @@ analysis_agent = Agent(
     role="Content Analyst",
     goal="Analyze and summarize research findings.",
     backstory="Expert in content analysis and synthesis.",
+    tools=[wiki_summary, wiki_page],
     self_reflect=False
 )
 
@@ -199,7 +201,7 @@ agents.start()
         name="WikiResearcher",
         role="Research Specialist",
         goal="Research topics accurately and efficiently",
-        tools=[wikipedia]
+        tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language]
     )
     ```
   </Accordion>
@@ -223,13 +225,14 @@ agents.start()
 researcher = Agent(
     name="Researcher",
     role="Wiki Researcher",
-    tools=[wikipedia]
+    tools=[wiki_search, wiki_summary, wiki_page]
 )
 
 # Analysis agent
 analyzer = Agent(
     name="Analyzer",
-    role="Content Analyzer"
+    role="Content Analyzer",
+    tools=[wiki_summary, wiki_page]
 )
 
 # Define tasks
@@ -247,4 +250,4 @@ analyze_task = Task(
 agents = PraisonAIAgents(
     agents=[researcher, analyzer],
     tasks=[research_task, analyze_task]
-)
+)

docs/tools/xml_tools.mdx

@@ -1,6 +1,6 @@
 ---
-title: "XML Tools"
-description: "XML file processing tools for AI agents."
+title: "XML Agent"
+description: "XML data processing tools for AI agents."
 icon: "code"
 ---
 
@@ -8,12 +8,12 @@ icon: "code"
   **Prerequisites**
   - Python 3.10 or higher
   - PraisonAI Agents package installed
-  - Basic understanding of XML format
+  - `lxml` package installed
 </Note>
 
-## Quick Start
+## XML Tools
 
-Use XML Tools to process and manipulate XML files with AI agents.
+Use XML Tools to read, write, and manipulate XML data with AI agents.
 
 <Steps>
   <Step title="Install Dependencies">
@@ -27,7 +27,7 @@ Use XML Tools to process and manipulate XML files with AI agents.
     Import the necessary components:
     ```python
     from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import xml
+    from praisonaiagents.tools import read_xml, write_xml, transform_xml, validate_xml, xml_to_dict, dict_to_xml
     ```
   </Step>
 
@@ -39,7 +39,7 @@ Use XML Tools to process and manipulate XML files with AI agents.
         role="XML Processing Specialist",
         goal="Process XML files efficiently and accurately.",
         backstory="Expert in XML file manipulation and validation.",
-        tools=[xml],
+        tools=[read_xml, write_xml, transform_xml, validate_xml, xml_to_dict, dict_to_xml],
         self_reflect=False
     )
     ```
@@ -74,11 +74,11 @@ Use XML Tools to process and manipulate XML files with AI agents.
 
 <Card title="What are XML Tools?" icon="question">
   XML Tools provide XML processing capabilities for AI agents:
-  - XML parsing
-  - File validation
-  - Data extraction
+  - XML parsing and reading
+  - File writing and validation
+  - Data transformation
+  - Dictionary conversion
   - Structure manipulation
-  - Format conversion
 </Card>
 
 ## Key Components
@@ -87,7 +87,7 @@ Use XML Tools to process and manipulate XML files with AI agents.
   <Card title="XML Agent" icon="user-robot">
     Create specialized XML agents:
     ```python
-    Agent(tools=[xml])
+    Agent(tools=[read_xml, write_xml, transform_xml, validate_xml, xml_to_dict, dict_to_xml])
     ```
   </Card>
   <Card title="XML Task" icon="list-check">
@@ -116,7 +116,7 @@ Use XML Tools to process and manipulate XML files with AI agents.
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import xml
+from praisonaiagents.tools import read_xml, write_xml, transform_xml, validate_xml, xml_to_dict, dict_to_xml
 
 # Create XML agent
 xml_agent = Agent(
@@ -124,7 +124,7 @@ xml_agent = Agent(
     role="XML Processing Specialist",
     goal="Process XML files efficiently and accurately.",
     backstory="Expert in XML file handling and validation.",
-    tools=[xml],
+    tools=[read_xml, write_xml, transform_xml, validate_xml, xml_to_dict, dict_to_xml],
     self_reflect=False
 )
 
@@ -153,7 +153,7 @@ processor_agent = Agent(
     name="Processor",
     role="XML Processor",
     goal="Process XML files systematically.",
-    tools=[xml],
+    tools=[read_xml, write_xml, transform_xml],
     self_reflect=False
 )
 
@@ -163,6 +163,7 @@ validator_agent = Agent(
     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
 )
 
@@ -198,7 +199,7 @@ agents.start()
         name="XMLProcessor",
         role="XML Processing Specialist",
         goal="Process XML files accurately and safely",
-        tools=[xml]
+        tools=[read_xml, write_xml, transform_xml, validate_xml, xml_to_dict, dict_to_xml]
     )
     ```
   </Accordion>
@@ -222,13 +223,14 @@ agents.start()
 processor = Agent(
     name="Processor",
     role="XML Processor",
-    tools=[xml]
+    tools=[read_xml, write_xml, transform_xml]
 )
 
 # Validation agent
 validator = Agent(
     name="Validator",
-    role="Data Validator"
+    role="Data Validator",
+    tools=[validate_xml, xml_to_dict, dict_to_xml]
 )
 
 # Define tasks
@@ -246,4 +248,4 @@ validate_task = Task(
 agents = PraisonAIAgents(
     agents=[processor, validator],
     tasks=[process_task, validate_task]
-)
+)

docs/tools/yaml_tools.mdx

@@ -1,7 +1,7 @@
 ---
-title: "YAML Tools"
-description: "YAML file processing tools for AI agents."
-icon: "file-code"
+title: "YAML Agent"
+description: "YAML data processing tools for AI agents."
+icon: "code"
 ---
 
 <Note>
@@ -12,7 +12,7 @@ icon: "file-code"
   - Basic understanding of YAML format
 </Note>
 
-## Quick Start
+## YAML Tools
 
 Use YAML Tools to process and manipulate YAML files with AI agents.
 
@@ -28,7 +28,7 @@ Use YAML Tools to process and manipulate YAML files with AI agents.
     Import the necessary components:
     ```python
     from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import yaml
+    from praisonaiagents.tools import read_yaml, write_yaml, validate_yaml, merge_yaml, convert_yaml
     ```
   </Step>
 
@@ -40,7 +40,7 @@ Use YAML Tools to process and manipulate YAML files with AI agents.
         role="YAML Processing Specialist",
         goal="Process YAML files efficiently and accurately.",
         backstory="Expert in YAML file manipulation and validation.",
-        tools=[yaml],
+        tools=[read_yaml, write_yaml, validate_yaml, merge_yaml, convert_yaml],
         self_reflect=False
     )
     ```
@@ -75,11 +75,11 @@ Use YAML Tools to process and manipulate YAML files with AI agents.
 
 <Card title="What are YAML Tools?" icon="question">
   YAML Tools provide YAML processing capabilities for AI agents:
-  - YAML parsing
-  - File validation
-  - Data extraction
-  - Structure manipulation
-  - Format conversion
+  - Reading YAML files
+  - Writing YAML data
+  - Validating YAML structure
+  - Merging YAML files
+  - Converting between formats
 </Card>
 
 ## Key Components
@@ -88,7 +88,7 @@ Use YAML Tools to process and manipulate YAML files with AI agents.
   <Card title="YAML Agent" icon="user-robot">
     Create specialized YAML agents:
     ```python
-    Agent(tools=[yaml])
+    Agent(tools=[read_yaml, write_yaml, validate_yaml, merge_yaml, convert_yaml])
     ```
   </Card>
   <Card title="YAML Task" icon="list-check">
@@ -117,7 +117,7 @@ Use YAML Tools to process and manipulate YAML files with AI agents.
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import yaml
+from praisonaiagents.tools import read_yaml, write_yaml, validate_yaml, merge_yaml, convert_yaml
 
 # Create YAML agent
 yaml_agent = Agent(
@@ -125,7 +125,7 @@ yaml_agent = Agent(
     role="YAML Processing Specialist",
     goal="Process YAML files efficiently and accurately.",
     backstory="Expert in YAML file handling and validation.",
-    tools=[yaml],
+    tools=[read_yaml, write_yaml, validate_yaml, merge_yaml, convert_yaml],
     self_reflect=False
 )
 
@@ -154,7 +154,7 @@ processor_agent = Agent(
     name="Processor",
     role="YAML Processor",
     goal="Process YAML files systematically.",
-    tools=[yaml],
+    tools=[read_yaml, write_yaml, convert_yaml],
     self_reflect=False
 )
 
@@ -164,6 +164,7 @@ validator_agent = Agent(
     role="Data Validator",
     goal="Validate YAML structure and content.",
     backstory="Expert in data validation and verification.",
+    tools=[validate_yaml, merge_yaml],
     self_reflect=False
 )
 
@@ -199,7 +200,7 @@ agents.start()
         name="YAMLProcessor",
         role="YAML Processing Specialist",
         goal="Process YAML files accurately and safely",
-        tools=[yaml]
+        tools=[read_yaml, write_yaml, validate_yaml, merge_yaml, convert_yaml]
     )
     ```
   </Accordion>
@@ -223,13 +224,14 @@ agents.start()
 processor = Agent(
     name="Processor",
     role="YAML Processor",
-    tools=[yaml]
+    tools=[read_yaml, write_yaml, convert_yaml]
 )
 
 # Validation agent
 validator = Agent(
     name="Validator",
-    role="Data Validator"
+    role="Data Validator",
+    tools=[validate_yaml, merge_yaml]
 )
 
 # Define tasks
@@ -247,4 +249,4 @@ validate_task = Task(
 agents = PraisonAIAgents(
     agents=[processor, validator],
     tasks=[process_task, validate_task]
-)
+)

docs/tools/yfinance_tools.mdx

@@ -1,5 +1,5 @@
 ---
-title: "YFinance Tools"
+title: "YFinance Agent"
 description: "Yahoo Finance data retrieval tools for AI agents."
 icon: "chart-line"
 ---
@@ -12,7 +12,7 @@ icon: "chart-line"
   - Basic understanding of financial data
 </Note>
 
-## Quick Start
+## YFinance Tools
 
 Use YFinance Tools to retrieve and analyze financial data with AI agents.
 
@@ -28,7 +28,7 @@ Use YFinance Tools to retrieve and analyze financial data with AI agents.
     Import the necessary components:
     ```python
     from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import yfinance
+    from praisonaiagents.tools import get_stock_price, get_stock_info, get_historical_data
     ```
   </Step>
 
@@ -40,7 +40,7 @@ Use YFinance Tools to retrieve and analyze financial data with AI agents.
         role="Financial Data Specialist",
         goal="Retrieve and analyze financial data efficiently.",
         backstory="Expert in financial data analysis and market research.",
-        tools=[yfinance],
+        tools=[get_stock_price, get_stock_info, get_historical_data],
         self_reflect=False
     )
     ```
@@ -75,11 +75,11 @@ Use YFinance Tools to retrieve and analyze financial data with AI agents.
 
 <Card title="What are YFinance Tools?" icon="question">
   YFinance Tools provide financial data capabilities for AI agents:
-  - Stock data retrieval
-  - Market analysis
-  - Historical data
-  - Company information
-  - Financial metrics
+  - Real-time stock prices
+  - Detailed company information
+  - Historical market data
+  - Financial metrics and ratios
+  - Market performance analysis
 </Card>
 
 ## Key Components
@@ -88,7 +88,7 @@ Use YFinance Tools to retrieve and analyze financial data with AI agents.
   <Card title="Finance Agent" icon="user-robot">
     Create specialized finance agents:
     ```python
-    Agent(tools=[yfinance])
+    Agent(tools=[get_stock_price, get_stock_info, get_historical_data])
     ```
   </Card>
   <Card title="Finance Task" icon="list-check">
@@ -117,7 +117,7 @@ Use YFinance Tools to retrieve and analyze financial data with AI agents.
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import yfinance
+from praisonaiagents.tools import get_stock_price, get_stock_info, get_historical_data
 
 # Create finance agent
 finance_agent = Agent(
@@ -125,7 +125,7 @@ finance_agent = Agent(
     role="Financial Data Specialist",
     goal="Analyze market data efficiently and accurately.",
     backstory="Expert in financial analysis and market research.",
-    tools=[yfinance],
+    tools=[get_stock_price, get_stock_info, get_historical_data],
     self_reflect=False
 )
 
@@ -154,7 +154,7 @@ data_agent = Agent(
     name="DataCollector",
     role="Market Data Collector",
     goal="Retrieve financial data systematically.",
-    tools=[yfinance],
+    tools=[get_stock_price, get_historical_data],
     self_reflect=False
 )
 
@@ -164,6 +164,7 @@ analysis_agent = Agent(
     role="Market Analyst",
     goal="Analyze market trends and patterns.",
     backstory="Expert in financial market analysis.",
+    tools=[get_stock_info],
     self_reflect=False
 )
 
@@ -199,7 +200,7 @@ agents.start()
         name="FinanceAnalyst",
         role="Market Analysis Specialist",
         goal="Analyze financial data accurately and efficiently",
-        tools=[yfinance]
+        tools=[get_stock_price, get_stock_info, get_historical_data]
     )
     ```
   </Accordion>
@@ -223,13 +224,14 @@ agents.start()
 collector = Agent(
     name="Collector",
     role="Data Collector",
-    tools=[yfinance]
+    tools=[get_stock_price, get_historical_data]
 )
 
 # Analysis agent
 analyst = Agent(
     name="Analyst",
-    role="Market Analyst"
+    role="Market Analyst",
+    tools=[get_stock_info]
 )
 
 # Define tasks
@@ -247,4 +249,4 @@ analyze_task = Task(
 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/2.0.34.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==2.0.34 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 = "2.0.34"
+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>=0.0.25",
+    "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 = "2.0.34"
+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 = ">=0.0.25"
+praisonaiagents = ">=0.0.27"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

uv.lock

@@ -3061,7 +3061,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.0.34"
+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 = ">=0.0.25" },
+    { 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 = "0.0.25"
+version = "0.0.27"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "openai" },
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/6c/9c/07a6fd1d939eb98ae6399fb16aad870f16dba7ab5d64825af2420a46c09b/praisonaiagents-0.0.25.tar.gz", hash = "sha256:2ef1292fa5e0a5dc5d1b982670f3d0c6bb1cd9c59febbcbe6c2b8af5fa21ca43", size = 61241 }
+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/50/78/1ab28fe9e32f2d765052b3870532238f6c4a72a98168b73b443d76c0235f/praisonaiagents-0.0.25-py3-none-any.whl", hash = "sha256:5b0cf04f294d81dbee8be855fc7026cc190a05b1d169e286eb7e387b8f8bd7de", size = 80127 },
+    { url = "https://files.pythonhosted.org/packages/0d/63/b4b45c943c78b1818cec8b2dd92b3fdea36075eb5e323c8ca22e917ca75e/praisonaiagents-0.0.27-py3-none-any.whl", hash = "sha256:3404a186f2be2dfed0b56efec613896f4306f5589dc65d4b3f7034b0401d7593", size = 80590 },
 ]
 
 [[package]]