Merge pull request #273 from MervinPraison/develop

fixing internet search tool

Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.33 gunicorn markdown
+RUN pip install flask praisonai==2.0.35 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

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/tools.py

@@ -1,5 +1,5 @@
 """Tools module for PraisonAI Agents"""
-from .internet_search import internet_search
+from .duckduckgo_tools import internet_search
 
 class Tools:
     """Tools class for backward compatibility"""

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 = "0.0.24"
+version = "0.0.26"
 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 = "0.0.24"
+version = "0.0.26"
 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==2.0.33 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.35 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/features/mini.mdx

@@ -46,6 +46,16 @@ Create multiple AI agents that can work together in just a few lines of code!
   </Step>
 </Steps>
 
+<div className="relative w-full aspect-video">
+  <iframe
+    className="absolute top-0 left-0 w-full h-full"
+    src="https://www.youtube.com/embed/OkvYp5aAGSg"
+    title="YouTube video player"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowFullScreen
+  ></iframe>
+</div>
+
 ## Understanding Mini AI Agents
 
 <Card title="What are Mini AI Agents?" icon="question">

docs/mint.json

@@ -76,17 +76,6 @@
         "concepts/tools"
       ]
     },
-    {
-      "group": "Getting Started (No Code)",
-      "pages": [
-        "introduction",
-        "tldr",
-        "installation",
-        "initialise",
-        "run",
-        "auto"
-      ]
-    },
     {
       "group": "Features",
       "pages": [
@@ -96,21 +85,20 @@
         "features/async",
         "features/mini",
         "features/codeagent",
+        "features/mathagent",
         "features/structured",
-        "features/callbacks",
-        "train",
-        "framework/praisonaiagents",
-        "framework/crewai",
-        "framework/autogen"
+        "features/callbacks"
       ]
     },
     {
-      "group": "User Interface",
+      "group": "Getting Started (No Code)",
       "pages": [
-        "ui/ui",
-        "ui/chat",
-        "ui/code",
-        "ui/realtime"
+        "introduction",
+        "tldr",
+        "installation",
+        "initialise",
+        "run",
+        "auto"
       ]
     },
     {
@@ -129,7 +117,7 @@
       ]
     },
     {
-      "group": "Tools (No Code)",
+      "group": "Tools",
       "pages": [
         "tools/duckduckgo_tools",
         "tools/arxiv_tools",
@@ -165,6 +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

@@ -27,7 +27,7 @@ Use arXiv Tools to search and retrieve scientific papers with AI agents.
     Import the necessary components:
     ```python
     from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import arxiv
+    from praisonaiagents.tools import search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category
     ```
   </Step>
 
@@ -39,7 +39,7 @@ Use arXiv Tools to search and retrieve scientific papers with AI agents.
         role="Scientific Literature Specialist",
         goal="Find and analyze relevant scientific papers.",
         backstory="Expert in academic research and literature review.",
-        tools=[arxiv],
+        tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category],
         self_reflect=False
     )
     ```
@@ -81,34 +81,214 @@ Use arXiv Tools to search and retrieve scientific papers with AI agents.
   - PDF download options
 </Card>
 
-## Key Components
+## Available Functions
 
-<CardGroup cols={2}>
-  <Card title="Research Agent" icon="user-robot">
-    Create specialized research agents:
-    ```python
-    Agent(tools=[arxiv])
-    ```
-  </Card>
-  <Card title="Research Task" icon="list-check">
-    Define research tasks:
-    ```python
-    Task(description="research_query")
-    ```
-  </Card>
-  <Card title="Process Types" icon="arrows-split-up-and-left">
-    Sequential or parallel processing:
-    ```python
-    process="sequential"
-    ```
-  </Card>
-  <Card title="Search Options" icon="sliders">
-    Customize search parameters:
-    ```python
-    max_results=10, sort_by="relevance"
-    ```
-  </Card>
-</CardGroup>
+```python
+from praisonaiagents.tools import search_arxiv
+from praisonaiagents.tools import get_arxiv_paper
+from praisonaiagents.tools import get_papers_by_author
+from praisonaiagents.tools import get_papers_by_category
+```
+
+## Function Details
+
+### search_arxiv(query: str, max_results: int = 10, sort_by: str = "relevance", sort_order: str = "descending", include_fields: Optional[List[str]] = None)
+
+Search arXiv for papers:
+- Flexible query support
+- Customizable results
+- Multiple sorting options
+- Field selection
+- Error handling
+
+```python
+# Basic search
+papers = search_arxiv("quantum computing")
+
+# Advanced search
+papers = search_arxiv(
+    query="quantum computing",
+    max_results=5,
+    sort_by="submittedDate",
+    sort_order="descending",
+    include_fields=["title", "authors", "summary"]
+)
+
+# Returns: List[Dict[str, Any]]
+# Example output:
+# [
+#     {
+#         "arxiv_id": "2401.00123",
+#         "title": "Quantum Computing: A Survey",
+#         "authors": ["John Doe", "Jane Smith"],
+#         "summary": "This paper reviews...",
+#         "published": "2024-01-01T00:00:00",
+#         "updated": "2024-01-02T00:00:00"
+#     },
+#     ...
+# ]
+```
+
+### get_arxiv_paper(paper_id: str, include_fields: Optional[List[str]] = None)
+
+Get specific paper details:
+- Direct ID lookup
+- Full paper metadata
+- Customizable fields
+- PDF/Abstract links
+
+```python
+# Get paper by ID
+paper = get_arxiv_paper("2401.00123")
+
+# Get specific fields
+paper = get_arxiv_paper(
+    paper_id="2401.00123",
+    include_fields=["title", "authors", "pdf_url"]
+)
+
+# Returns: Dict[str, Any]
+# Example output:
+# {
+#     "arxiv_id": "2401.00123",
+#     "title": "Quantum Computing: A Survey",
+#     "authors": ["John Doe", "Jane Smith"],
+#     "pdf_url": "https://arxiv.org/pdf/2401.00123",
+#     "abstract_url": "https://arxiv.org/abs/2401.00123",
+#     "published": "2024-01-01T00:00:00",
+#     "updated": "2024-01-02T00:00:00"
+# }
+```
+
+### get_papers_by_author(author: str, max_results: int = 10, sort_by: str = "submittedDate", sort_order: str = "descending", include_fields: Optional[List[str]] = None)
+
+Search papers by author:
+- Author-specific search
+- Publication timeline
+- Sort options
+- Field selection
+
+```python
+# Get author's papers
+papers = get_papers_by_author("Yoshua Bengio")
+
+# Recent papers first
+papers = get_papers_by_author(
+    author="Yoshua Bengio",
+    max_results=5,
+    sort_by="submittedDate",
+    sort_order="descending"
+)
+
+# Returns: List[Dict[str, Any]]
+# Example output:
+# [
+#     {
+#         "arxiv_id": "2401.00124",
+#         "title": "Deep Learning Advances",
+#         "authors": ["Yoshua Bengio", "Other Author"],
+#         "published": "2024-01-01T00:00:00"
+#     },
+#     ...
+# ]
+```
+
+### get_papers_by_category(category: str, max_results: int = 10, sort_by: str = "submittedDate", sort_order: str = "descending", include_fields: Optional[List[str]] = None)
+
+Search papers by category:
+- Category-specific search
+- Latest publications
+- Sort options
+- Field selection
+
+```python
+# Get papers in category
+papers = get_papers_by_category("cs.AI")
+
+# Recent machine learning papers
+papers = get_papers_by_category(
+    category="cs.LG",
+    max_results=5,
+    sort_by="submittedDate",
+    sort_order="descending"
+)
+
+# Returns: List[Dict[str, Any]]
+# Example output:
+# [
+#     {
+#         "arxiv_id": "2401.00125",
+#         "title": "New ML Algorithm",
+#         "categories": ["cs.LG", "cs.AI"],
+#         "published": "2024-01-01T00:00:00"
+#     },
+#     ...
+# ]
+```
+
+## Dependencies
+
+The arXiv tools require the following package:
+- arxiv: For accessing the arXiv API
+
+This will be automatically installed when needed.
+
+## Available Fields
+
+When using `include_fields`, you can select from:
+- title: Paper title
+- authors: List of authors
+- summary: Abstract text
+- comment: Author comments
+- journal_ref: Journal reference
+- doi: Digital Object Identifier
+- primary_category: Main arXiv category
+- categories: All arXiv categories
+- links: PDF and abstract URLs
+
+## Common Use Cases
+
+1. Literature Review:
+```python
+# Search multiple related topics
+topics = ["machine learning", "neural networks", "deep learning"]
+all_papers = []
+for topic in topics:
+    papers = search_arxiv(
+        topic,
+        max_results=5,
+        sort_by="relevance"
+    )
+    all_papers.extend(papers)
+```
+
+2. Author Research:
+```python
+# Get recent papers by multiple authors
+authors = ["Yoshua Bengio", "Geoffrey Hinton", "Yann LeCun"]
+author_papers = {}
+for author in authors:
+    papers = get_papers_by_author(
+        author,
+        max_results=3,
+        sort_by="submittedDate"
+    )
+    author_papers[author] = papers
+```
+
+3. Category Monitoring:
+```python
+# Monitor new papers in AI categories
+categories = ["cs.AI", "cs.LG", "cs.CL"]
+new_papers = {}
+for category in categories:
+    papers = get_papers_by_category(
+        category,
+        max_results=5,
+        sort_by="submittedDate"
+    )
+    new_papers[category] = papers
+```
 
 ## Examples
 
@@ -116,7 +296,7 @@ Use arXiv Tools to search and retrieve scientific papers with AI agents.
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import arxiv
+from praisonaiagents.tools import search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category
 
 # Create research agent
 research_agent = Agent(
@@ -124,7 +304,7 @@ research_agent = Agent(
     role="Scientific Literature Specialist",
     goal="Find relevant scientific papers on specified topics.",
     backstory="Expert in academic research and literature analysis.",
-    tools=[arxiv],
+    tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category],
     self_reflect=False
 )
 
@@ -153,7 +333,7 @@ research_agent = Agent(
     name="Researcher",
     role="Literature Specialist",
     goal="Gather comprehensive scientific literature.",
-    tools=[arxiv],
+    tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category],
     self_reflect=False
 )
 
@@ -198,7 +378,7 @@ agents.start()
         name="Researcher",
         role="Literature Specialist",
         goal="Find relevant scientific papers",
-        tools=[arxiv]
+        tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category]
     )
     ```
   </Accordion>
@@ -222,7 +402,7 @@ agents.start()
 searcher = Agent(
     name="Searcher",
     role="Literature Specialist",
-    tools=[arxiv]
+    tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category]
 )
 
 # Review agent

docs/tools/calculator_tools.mdx

@@ -1,250 +1,294 @@
 ---
 title: "Calculator Tools"
-description: "Mathematical computation tools for AI agents."
-icon: "calculator"
+description: "Tools for performing mathematical calculations, unit conversions, and financial computations."
 ---
 
-<Note>
-  **Prerequisites**
-  - Python 3.10 or higher
-  - PraisonAI Agents package installed
-  - Basic understanding of mathematical operations
-</Note>
+## Available Functions
 
-## Quick Start
+```python
+from praisonaiagents.tools import (
+    evaluate, solve_equation, convert_units,
+    calculate_statistics, calculate_financial
+)
+```
 
-Use Calculator Tools to perform mathematical computations with AI agents.
-
-<Steps>
-  <Step title="Install Dependencies">
-    First, install the required package:
-    ```bash
-    pip install praisonaiagents
-    ```
-  </Step>
-
-  <Step title="Import Components">
-    Import the necessary components:
-    ```python
-    from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import calculator
-    ```
-  </Step>
-
-  <Step title="Create Agent">
-    Create a calculator agent:
-    ```python
-    calc_agent = Agent(
-        name="Calculator",
-        role="Mathematical Specialist",
-        goal="Perform mathematical computations accurately.",
-        backstory="Expert in mathematical calculations and analysis.",
-        tools=[calculator],
-        self_reflect=False
-    )
-    ```
-  </Step>
-
-  <Step title="Define Task">
-    Define the calculation task:
-    ```python
-    calc_task = Task(
-        description="Calculate financial metrics for quarterly report.",
-        expected_output="Computed financial metrics with analysis.",
-        agent=calc_agent,
-        name="financial_calculations"
-    )
-    ```
-  </Step>
-
-  <Step title="Run Agent">
-    Initialize and run the agent:
-    ```python
-    agents = PraisonAIAgents(
-        agents=[calc_agent],
-        tasks=[calc_task],
-        process="sequential"
-    )
-    agents.start()
-    ```
-  </Step>
-</Steps>
-
-## Understanding Calculator Tools
-
-<Card title="What are Calculator Tools?" icon="question">
-  Calculator Tools provide mathematical capabilities for AI agents:
-  - Basic arithmetic
-  - Advanced mathematics
-  - Statistical calculations
-  - Financial computations
-  - Unit conversions
-</Card>
-
-## Key Components
-
-<CardGroup cols={2}>
-  <Card title="Calculator Agent" icon="user-robot">
-    Create specialized calculator agents:
-    ```python
-    Agent(tools=[calculator])
-    ```
-  </Card>
-  <Card title="Calculator Task" icon="list-check">
-    Define calculation tasks:
-    ```python
-    Task(description="calculation_query")
-    ```
-  </Card>
-  <Card title="Process Types" icon="arrows-split-up-and-left">
-    Sequential or parallel processing:
-    ```python
-    process="sequential"
-    ```
-  </Card>
-  <Card title="Calculation Options" icon="sliders">
-    Customize calculation parameters:
-    ```python
-    precision=2, format="decimal"
-    ```
-  </Card>
-</CardGroup>
-
-## Examples
-
-### Basic Calculator Agent
+## Example Code
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import calculator
-
-# Create calculator agent
-calc_agent = Agent(
-    name="MathExpert",
-    role="Mathematical Specialist",
-    goal="Perform calculations accurately and efficiently.",
-    backstory="Expert in mathematical computations and analysis.",
-    tools=[calculator],
-    self_reflect=False
+from praisonaiagents.tools import (
+    evaluate, solve_equation, convert_units,
+    calculate_statistics, calculate_financial
 )
 
-# Define calculation task
-calc_task = Task(
-    description="Calculate investment returns and compound interest.",
-    expected_output="Detailed investment calculations with analysis.",
-    agent=calc_agent,
-    name="investment_analysis"
+agent = Agent(
+    name="MathAssistant",
+    tools=[
+        evaluate, solve_equation, convert_units,
+        calculate_statistics, calculate_financial
+    ]
 )
 
+task = Task(
+    description="Calculate the area of a circle with radius 5",
+    expected_output="The area of the circle is 78.54 square units",
+    agent=agent,
+    name="calculate_circle_area"
+)
+
+
 # Run agent
 agents = PraisonAIAgents(
-    agents=[calc_agent],
-    tasks=[calc_task],
+    agents=[agent],
+    tasks=[task],
     process="sequential"
 )
 agents.start()
 ```
 
-### Advanced Calculations with Multiple Agents
+## Function Details
+
+### evaluate(expression: str, variables: Optional[Dict[str, float]] = None, precision: int = 10)
+
+Evaluate mathematical expressions:
+- Basic arithmetic
+- Mathematical functions
+- Variable substitution
+- Precision control
+- Error handling
 
 ```python
-# Create calculation agent
-calculator_agent = Agent(
-    name="Calculator",
-    role="Math Calculator",
-    goal="Perform complex calculations systematically.",
-    tools=[calculator],
-    self_reflect=False
+# Basic arithmetic
+result = evaluate("2 + 2 * 3")
+
+# Complex expressions
+result = evaluate(
+    "sin(pi/2) + sqrt(16)",
+    precision=5
 )
 
-# Create analysis agent
-analysis_agent = Agent(
-    name="Analyzer",
-    role="Data Analyst",
-    goal="Analyze and interpret calculation results.",
-    backstory="Expert in data analysis and interpretation.",
-    self_reflect=False
+# With variables
+result = evaluate(
+    "x^2 + y",
+    variables={"x": 3, "y": 4}
 )
 
-# Define tasks
-calculation_task = Task(
-    description="Calculate statistical metrics for dataset.",
-    agent=calculator_agent,
-    name="statistical_analysis"
-)
-
-analysis_task = Task(
-    description="Analyze and interpret the statistical results.",
-    agent=analysis_agent,
-    name="result_analysis"
-)
-
-# Run agents
-agents = PraisonAIAgents(
-    agents=[calculator_agent, analysis_agent],
-    tasks=[calculation_task, analysis_task],
-    process="sequential"
-)
-agents.start()
+# Returns: Dict[str, Any]
+# Example output:
+# {
+#     "result": 8.0
+# }
+# or
+# {
+#     "error": "Invalid expression"
+# }
 ```
 
-## Best Practices
+### solve_equation(equation: str, variable: str = 'x')
 
-<AccordionGroup>
-  <Accordion title="Agent Configuration">
-    Configure agents with clear calculation focus:
-    ```python
-    Agent(
-        name="Calculator",
-        role="Mathematical Specialist",
-        goal="Perform calculations accurately and efficiently",
-        tools=[calculator]
-    )
-    ```
-  </Accordion>
+Solve mathematical equations:
+- Linear equations
+- Quadratic equations
+- Polynomial equations
+- Real solutions
+- Error handling
 
-  <Accordion title="Task Definition">
-    Define specific calculation objectives:
-    ```python
-    Task(
-        description="Calculate financial metrics and growth rates",
-        expected_output="Detailed financial analysis"
-    )
-    ```
-  </Accordion>
-</AccordionGroup>
-
-## Common Patterns
-
-### Calculation Pipeline
 ```python
-# Calculator agent
-calculator = Agent(
-    name="Calculator",
-    role="Math Calculator",
-    tools=[calculator]
+# Linear equation
+result = solve_equation("2*x + 3 = 7")
+
+# Quadratic equation
+result = solve_equation(
+    "x^2 - 5*x + 6 = 0"
 )
 
-# Analysis agent
-analyzer = Agent(
-    name="Analyzer",
-    role="Result Analyzer"
+# Custom variable
+result = solve_equation(
+    "y^2 = 4",
+    variable='y'
 )
 
-# Define tasks
-calc_task = Task(
-    description="Perform calculations",
-    agent=calculator
+# 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"
 )
 
-analysis_task = Task(
-    description="Analyze results",
-    agent=analyzer
+# 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'
 )
 
-# Run workflow
-agents = PraisonAIAgents(
-    agents=[calculator, analyzer],
-    tasks=[calc_task, analysis_task]
+# 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=[
+        evaluate, solve_equation, convert_units,
+        calculate_statistics, calculate_financial
+    ]
 )
 ```
+
+## Dependencies
+
+The calculator tools require the following packages:
+- sympy: For equation solving and symbolic mathematics
+- pint: For unit conversions
+
+These will be automatically installed when needed.
+
+## Supported Features
+
+### Mathematical Functions
+- Basic: +, -, *, /, ^
+- Trigonometric: sin, cos, tan
+- Roots: sqrt
+- Logarithmic: log, exp
+- Constants: pi, e
+
+### 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
+
+### Financial Frequencies
+- yearly
+- semi-annual
+- quarterly
+- monthly
+- daily
+
+## Common Use Cases
+
+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}
+)
+```
+
+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")
+}
+```
+
+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")
+}
+```

docs/tools/csv_tools.mdx

@@ -1,6 +1,6 @@
 ---
 title: "CSV Tools"
-description: "CSV file processing tools for AI agents."
+description: "Tools for working with CSV files in AI agents."
 icon: "file-csv"
 ---
 
@@ -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
+    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],
+        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])
+    Agent(tools=[read_csv, write_csv, merge_csv])
     ```
   </Card>
   <Card title="CSV Task" icon="list-check">
@@ -110,13 +110,148 @@ Use CSV Tools to process and manipulate CSV files with AI agents.
   </Card>
 </CardGroup>
 
+## Available Functions
+
+```python
+from praisonaiagents.tools import read_csv
+from praisonaiagents.tools import write_csv
+from praisonaiagents.tools import merge_csv
+```
+
+## Function Details
+
+### read_csv(filepath: str, encoding: str = 'utf-8', delimiter: str = ',', header: Union[int, List[int], None] = 0, usecols: Optional[List[str]] = None, dtype: Optional[Dict[str, str]] = None, parse_dates: Optional[List[str]] = None, na_values: Optional[List[str]] = None, nrows: Optional[int] = None)
+
+Reads a CSV file with advanced options:
+- Supports multiple encodings (utf-8, ascii, etc.)
+- Configurable column delimiter
+- Flexible header row handling
+- Column selection and data type specification
+- Date parsing
+- Custom NA/NaN value handling
+- Row limit option
+
+```python
+# Basic usage
+data = read_csv("data.csv")
+
+# Advanced usage with options
+data = read_csv(
+    "data.csv",
+    encoding='utf-8',
+    delimiter=',',
+    header=0,
+    usecols=['name', 'age', 'city'],
+    dtype={'age': 'int32'},
+    parse_dates=['birth_date'],
+    na_values=['N/A', 'missing'],
+    nrows=1000
+)
+# Returns: List[Dict[str, Any]]
+# Example: [{"name": "Alice", "age": 25, "city": "New York"}, ...]
+```
+
+### write_csv(filepath: str, data: List[Dict[str, Any]], encoding: str = 'utf-8', delimiter: str = ',', index: bool = False, header: bool = True, float_format: Optional[str] = None, date_format: Optional[str] = None, mode: str = 'w')
+
+Writes data to a CSV file with formatting options:
+- Supports different encodings
+- Configurable delimiter
+- Optional row indices
+- Optional headers
+- Custom float and date formatting
+- Append mode support
+
+```python
+# Basic usage
+data = [
+    {"name": "Alice", "age": 25, "city": "New York"},
+    {"name": "Bob", "age": 30, "city": "San Francisco"}
+]
+success = write_csv("output.csv", data)
+
+# Advanced usage with formatting
+success = write_csv(
+    "output.csv",
+    data,
+    encoding='utf-8',
+    delimiter=',',
+    index=False,
+    header=True,
+    float_format='%.2f',
+    date_format='%Y-%m-%d',
+    mode='w'
+)
+# Returns: bool (True if successful)
+```
+
+### merge_csv(files: List[str], output_file: str, how: str = 'inner', on: Optional[Union[str, List[str]]] = None, suffixes: Optional[tuple] = None)
+
+Merges multiple CSV files with flexible join options:
+- Supports different join types (inner, outer, left, right)
+- Merge on single or multiple columns
+- Custom suffixes for overlapping columns
+- Automatic handling of column name conflicts
+
+```python
+# Merge two files on a common column
+success = merge_csv(
+    files=["employees.csv", "salaries.csv"],
+    output_file="merged.csv",
+    how='inner',
+    on='employee_id'
+)
+
+# Advanced merge with multiple columns and custom suffixes
+success = merge_csv(
+    files=["data1.csv", "data2.csv", "data3.csv"],
+    output_file="merged.csv",
+    how='outer',
+    on=['id', 'department'],
+    suffixes=('_2022', '_2023')
+)
+# Returns: bool (True if successful)
+```
+
+## Dependencies
+
+The CSV tools require the following Python packages:
+- pandas: For advanced CSV operations and data manipulation
+
+These will be automatically installed when needed.
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import read_csv, write_csv, merge_csv
+
+agent = Agent(
+    name="CSVProcessor",
+    description="An agent that processes CSV files",
+    tools=[read_csv, write_csv, merge_csv]
+)
+```
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- File not found errors
+- Permission errors
+- Encoding errors
+- Data format errors
+- Missing dependency errors
+
+Errors are logged and returned in a consistent format:
+- Success cases return the expected data type
+- Error cases return a dict with an "error" key containing the error message
+
 ## Examples
 
 ### Basic CSV Processing Agent
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import csv
+from praisonaiagents.tools import read_csv, write_csv, merge_csv
 
 # Create CSV agent
 csv_agent = Agent(
@@ -124,7 +259,7 @@ csv_agent = Agent(
     role="CSV Processing Specialist",
     goal="Process CSV files efficiently and accurately.",
     backstory="Expert in data processing and analysis.",
-    tools=[csv],
+    tools=[read_csv, write_csv, merge_csv],
     self_reflect=False
 )
 
@@ -153,7 +288,7 @@ processor_agent = Agent(
     name="Processor",
     role="Data Processor",
     goal="Process CSV data systematically.",
-    tools=[csv],
+    tools=[read_csv, write_csv, merge_csv],
     self_reflect=False
 )
 
@@ -198,7 +333,7 @@ agents.start()
         name="CSVProcessor",
         role="CSV Processing Specialist",
         goal="Process CSV files accurately and efficiently",
-        tools=[csv]
+        tools=[read_csv, write_csv, merge_csv]
     )
     ```
   </Accordion>
@@ -222,7 +357,7 @@ agents.start()
 processor = Agent(
     name="Processor",
     role="CSV Processor",
-    tools=[csv]
+    tools=[read_csv, write_csv, merge_csv]
 )
 
 # Analysis agent

docs/tools/duckdb_tools.mdx

@@ -1,7 +1,6 @@
 ---
 title: "DuckDB Tools"
-description: "DuckDB database tools for AI agents."
-icon: "database"
+description: "Tools for working with DuckDB databases in AI agents."
 ---
 
 <Note>
@@ -28,7 +27,7 @@ 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>
 
@@ -40,7 +39,7 @@ 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
     )
     ```
@@ -71,6 +70,95 @@ Use DuckDB Tools to manage and query databases with AI agents.
   </Step>
 </Steps>
 
+## Available Functions
+
+```python
+from praisonaiagents.tools import execute_query
+from praisonaiagents.tools import load_csv
+from praisonaiagents.tools import export_csv
+```
+
+## Function Details
+
+### execute_query(query: str, params: Optional[Union[tuple, dict]] = None, return_df: bool = True)
+
+Executes SQL queries with advanced features:
+- Supports parameterized queries
+- Returns results as DataFrame records or raw tuples
+- Full SQL query support (SELECT, INSERT, UPDATE, DELETE, etc.)
+- Automatic connection management
+
+```python
+# Basic SELECT query
+results = execute_query("SELECT * FROM employees")
+
+# Parameterized query
+results = execute_query(
+    "SELECT * FROM employees WHERE department = ? AND salary > ?",
+    params=('Engineering', 75000)
+)
+
+# Named parameters
+results = execute_query(
+    "SELECT * FROM employees WHERE department = :dept",
+    params={'dept': 'Engineering'}
+)
+# Returns: List[Dict[str, Any]]
+# Example: [{"id": 1, "name": "Alice", "department": "Engineering"}, ...]
+```
+
+### load_csv(table_name: str, filepath: str, schema: Optional[Dict[str, str]] = None, if_exists: str = 'replace')
+
+Loads CSV files into DuckDB tables:
+- Optional schema definition
+- Flexible table existence handling
+- Automatic type inference
+- Support for large files
+
+```python
+# Basic usage - auto schema inference
+success = load_csv("employees", "employees.csv")
+
+# With custom schema
+schema = {
+    "id": "INTEGER PRIMARY KEY",
+    "name": "VARCHAR",
+    "salary": "DECIMAL(10,2)",
+    "hire_date": "DATE"
+}
+success = load_csv(
+    "employees",
+    "employees.csv",
+    schema=schema,
+    if_exists='replace'
+)
+# Returns: bool (True if successful)
+```
+
+### export_csv(query: str, filepath: str, params: Optional[Union[tuple, dict]] = None)
+
+Exports query results to CSV files:
+- Supports parameterized queries
+- Automatic header generation
+- Configurable output formatting
+- Large result set handling
+
+```python
+# Export simple query results
+success = export_csv(
+    "SELECT * FROM employees",
+    "exported_employees.csv"
+)
+
+# Export filtered data with parameters
+success = export_csv(
+    "SELECT * FROM employees WHERE department = ? AND year = ?",
+    "eng_2023.csv",
+    params=('Engineering', 2023)
+)
+# Returns: bool (True if successful)
+```
+
 ## Understanding DuckDB Tools
 
 <Card title="What are DuckDB Tools?" icon="question">
@@ -88,7 +176,7 @@ Use DuckDB Tools to manage and query databases with AI agents.
   <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">
@@ -117,7 +205,7 @@ Use DuckDB Tools to manage and query databases with AI agents.
 
 ```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(
@@ -125,7 +213,7 @@ 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
 )
 
@@ -154,39 +242,101 @@ 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"
-)
+## Dependencies
 
-analysis_task = Task(
-    description="Analyze query results and trends.",
-    agent=analysis_agent,
-    name="result_analysis"
-)
+The DuckDB tools require the following Python packages:
+- duckdb: For database operations
+- pandas: For data manipulation and CSV handling
 
-# Run agents
-agents = PraisonAIAgents(
-    agents=[query_agent, analysis_agent],
-    tasks=[query_task, analysis_task],
-    process="sequential"
+These will be automatically installed when needed.
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import execute_query, load_csv, export_csv
+
+agent = Agent(
+    name="DBProcessor",
+    description="An agent that works with DuckDB databases",
+    tools=[execute_query, load_csv, export_csv]
 )
-agents.start()
+```
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- Database connection errors
+- Query syntax errors
+- File I/O errors
+- Schema validation errors
+- Missing dependency errors
+
+Errors are logged and returned in a consistent format:
+- Success cases return the expected data type
+- Error cases return a dict with an "error" key containing the error message
+
+## Common Use Cases
+
+1. Data Analysis:
+```python
+# Load data
+load_csv("sales", "sales_data.csv")
+
+# Analyze with SQL
+results = execute_query("""
+    SELECT 
+        department,
+        COUNT(*) as count,
+        AVG(salary) as avg_salary
+    FROM employees
+    GROUP BY department
+    HAVING count > 5
+    ORDER BY avg_salary DESC
+""")
+```
+
+2. Data Export:
+```python
+# Export filtered and aggregated data
+export_csv("""
+    SELECT 
+        date_trunc('month', date) as month,
+        SUM(amount) as total_sales
+    FROM sales
+    GROUP BY 1
+    ORDER BY 1
+""", "monthly_sales.csv")
+```
+
+3. Data Transformation:
+```python
+# Load and transform data
+load_csv("raw_data", "input.csv")
+execute_query("""
+    CREATE TABLE transformed AS
+    SELECT 
+        id,
+        UPPER(name) as name,
+        ROUND(amount, 2) as amount,
+        DATE_TRUNC('day', timestamp) as date
+    FROM raw_data
+""")
+export_csv("SELECT * FROM transformed", "transformed.csv")
 ```
 
 ## Best Practices
@@ -199,7 +349,7 @@ agents.start()
         name="DBProcessor",
         role="Database Specialist",
         goal="Process queries accurately and efficiently",
-        tools=[duckdb]
+        tools=[execute_query, load_csv, export_csv]
     )
     ```
   </Accordion>
@@ -223,7 +373,7 @@ agents.start()
 querier = Agent(
     name="Querier",
     role="SQL Specialist",
-    tools=[duckdb]
+    tools=[execute_query]
 )
 
 # Analysis agent

docs/tools/excel_tools.mdx

@@ -1,7 +1,6 @@
 ---
 title: "Excel Tools"
-description: "Excel file processing tools for AI agents."
-icon: "file-excel"
+description: "Tools for working with Excel files in AI agents."
 ---
 
 <Note>
@@ -28,7 +27,7 @@ Use Excel Tools to process and manipulate Excel files with AI agents.
     Import the necessary components:
     ```python
     from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import excel
+    from praisonaiagents.tools import read_excel, write_excel, merge_excel
     ```
   </Step>
 
@@ -40,7 +39,7 @@ Use Excel Tools to process and manipulate Excel files with AI agents.
         role="Excel Processing Specialist",
         goal="Process Excel files efficiently and accurately.",
         backstory="Expert in Excel file manipulation and analysis.",
-        tools=[excel],
+        tools=[read_excel, write_excel, merge_excel],
         self_reflect=False
     )
     ```
@@ -88,7 +87,7 @@ Use Excel Tools to process and manipulate Excel files with AI agents.
   <Card title="Excel Agent" icon="user-robot">
     Create specialized Excel agents:
     ```python
-    Agent(tools=[excel])
+    Agent(tools=[read_excel, write_excel, merge_excel])
     ```
   </Card>
   <Card title="Excel Task" icon="list-check">
@@ -111,13 +110,120 @@ Use Excel Tools to process and manipulate Excel files with AI agents.
   </Card>
 </CardGroup>
 
+## Available Functions
+
+```python
+from praisonaiagents.tools import read_excel
+from praisonaiagents.tools import write_excel
+from praisonaiagents.tools import merge_excel
+```
+
+## Function Details
+
+### read_excel(filepath: str, sheet_name: Optional[Union[str, int, List[Union[str, int]]]] = 0, header: Optional[int] = 0, usecols: Optional[List[str]] = None, skiprows: Optional[Union[int, List[int]]] = None, na_values: Optional[List[str]] = None, dtype: Optional[Dict[str, str]] = None)
+
+Reads Excel files with advanced options:
+- Support for multiple sheets
+- Flexible header handling
+- Column selection
+- Row skipping
+- Custom NA values
+- Data type specification
+
+```python
+# Basic usage - read first sheet
+data = read_excel("data.xlsx")
+
+# Read specific sheets
+data = read_excel(
+    "data.xlsx",
+    sheet_name=['Sheet1', 'Sheet2'],
+    header=0,
+    usecols=['A', 'B', 'C'],
+    skiprows=[0, 1],
+    na_values=['NA', 'missing'],
+    dtype={'age': 'int32', 'salary': 'float64'}
+)
+# Returns: Dict[str, List[Dict[str, Any]]] for multiple sheets
+# Example: {
+#   'Sheet1': [{"name": "Alice", "age": 25}, ...],
+#   'Sheet2': [{"dept": "Engineering", "count": 10}, ...]
+# }
+```
+
+### write_excel(filepath: str, data: Union[Dict[str, List[Dict[str, Any]]], List[Dict[str, Any]]], sheet_name: Optional[str] = None, index: bool = False, header: bool = True, mode: str = 'w')
+
+Writes data to Excel files with formatting:
+- Multiple sheet support
+- Append mode for existing files
+- Optional row indices
+- Optional headers
+- Flexible data structure handling
+
+```python
+# Write single sheet
+data = [
+    {"name": "Alice", "age": 25, "city": "New York"},
+    {"name": "Bob", "age": 30, "city": "San Francisco"}
+]
+success = write_excel("output.xlsx", data, sheet_name="Employees")
+
+# Write multiple sheets
+data = {
+    "Employees": [
+        {"name": "Alice", "age": 25},
+        {"name": "Bob", "age": 30}
+    ],
+    "Departments": [
+        {"name": "Engineering", "count": 50},
+        {"name": "Sales", "count": 30}
+    ]
+}
+success = write_excel(
+    "company.xlsx",
+    data,
+    index=False,
+    header=True,
+    mode='w'
+)
+# Returns: bool (True if successful)
+```
+
+### merge_excel(files: List[str], output_file: str, how: str = 'inner', on: Optional[Union[str, List[str]]] = None, suffixes: Optional[Tuple[str, str]] = None)
+
+Merges multiple Excel files:
+- Flexible join operations
+- Multiple key columns support
+- Custom column suffix handling
+- Preserves data types
+
+```python
+# Merge two files on a common column
+success = merge_excel(
+    files=["employees.xlsx", "salaries.xlsx"],
+    output_file="merged.xlsx",
+    how='inner',
+    on='employee_id'
+)
+
+# Advanced merge with multiple keys and custom suffixes
+success = merge_excel(
+    files=["data2022.xlsx", "data2023.xlsx"],
+    output_file="combined.xlsx",
+    how='outer',
+    on=['id', 'department'],
+    suffixes=('_2022', '_2023')
+)
+# Returns: bool (True if successful)
+```
+
 ## Examples
 
 ### Basic Excel Processing Agent
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import excel
+from praisonaiagents.tools import read_excel, write_excel, merge_excel
 
 # Create Excel agent
 excel_agent = Agent(
@@ -125,7 +231,7 @@ excel_agent = Agent(
     role="Excel Processing Specialist",
     goal="Process Excel files efficiently and accurately.",
     backstory="Expert in spreadsheet manipulation and analysis.",
-    tools=[excel],
+    tools=[read_excel, write_excel, merge_excel],
     self_reflect=False
 )
 
@@ -154,7 +260,7 @@ processor_agent = Agent(
     name="Processor",
     role="Data Processor",
     goal="Process Excel data systematically.",
-    tools=[excel],
+    tools=[read_excel, write_excel, merge_excel],
     self_reflect=False
 )
 
@@ -199,7 +305,7 @@ agents.start()
         name="ExcelProcessor",
         role="Excel Processing Specialist",
         goal="Process Excel files accurately and efficiently",
-        tools=[excel]
+        tools=[read_excel, write_excel, merge_excel]
     )
     ```
   </Accordion>
@@ -223,7 +329,7 @@ agents.start()
 processor = Agent(
     name="Processor",
     role="Excel Processor",
-    tools=[excel]
+    tools=[read_excel, write_excel, merge_excel]
 )
 
 # Analysis agent
@@ -248,3 +354,87 @@ agents = PraisonAIAgents(
     agents=[processor, analyzer],
     tasks=[process_task, analyze_task]
 )
+
+```
+
+## Dependencies
+
+The Excel tools require the following Python packages:
+- pandas: For data manipulation
+- openpyxl: For Excel file operations
+
+These will be automatically installed when needed.
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import read_excel, write_excel, merge_excel
+
+agent = Agent(
+    name="ExcelProcessor",
+    description="An agent that processes Excel files",
+    tools=[read_excel, write_excel, merge_excel]
+)
+```
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- File not found errors
+- Permission errors
+- Invalid sheet names
+- Data format errors
+- Missing dependency errors
+
+Errors are logged and returned in a consistent format:
+- Success cases return the expected data type
+- Error cases return a dict with an "error" key containing the error message
+
+## Common Use Cases
+
+1. Data Analysis:
+```python
+# Read and analyze employee data
+data = read_excel(
+    "employees.xlsx",
+    sheet_name="Salaries",
+    usecols=["department", "salary"]
+)
+
+# Write summary to new sheet
+summary = [
+    {"department": dept, "avg_salary": sum(d["salary"] for d in dept_data) / len(dept_data)}
+    for dept, dept_data in groupby(data, key=lambda x: x["department"])
+]
+write_excel("summary.xlsx", summary, sheet_name="Salary Summary")
+```
+
+2. Data Consolidation:
+```python
+# Merge monthly reports
+monthly_files = ["jan.xlsx", "feb.xlsx", "mar.xlsx"]
+merge_excel(
+    files=monthly_files,
+    output_file="q1_report.xlsx",
+    how='outer',
+    on='transaction_id'
+)
+```
+
+3. Multi-sheet Processing:
+```python
+# Read multiple sheets
+data = read_excel(
+    "company_data.xlsx",
+    sheet_name=None  # Read all sheets
+)
+
+# Process each sheet
+processed = {
+    sheet: [process_record(record) for record in records]
+    for sheet, records in data.items()
+}
+
+# Write back to new file
+write_excel("processed_data.xlsx", processed)

docs/tools/file_tools.mdx

@@ -1,7 +1,6 @@
 ---
 title: "File Tools"
-description: "File system operations and management tools for AI agents."
-icon: "folder"
+description: "Basic file operation tools for AI agents."
 ---
 
 <Note>
@@ -27,7 +26,7 @@ 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>
 
@@ -39,7 +38,7 @@ 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
     )
     ```
@@ -87,7 +86,7 @@ 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">
@@ -110,28 +109,249 @@ Use File Tools to manage files and directories with AI agents.
   </Card>
 </CardGroup>
 
+## Available Functions
+
+```python
+from praisonaiagents.tools import read_file
+from praisonaiagents.tools import write_file
+from praisonaiagents.tools import list_files
+from praisonaiagents.tools import get_file_info
+from praisonaiagents.tools import copy_file
+from praisonaiagents.tools import move_file
+from praisonaiagents.tools import delete_file
+```
+
+## Function Details
+
+### read_file(filepath: str, encoding: str = 'utf-8')
+
+Reads content from a file:
+- Supports different encodings
+- Error handling for missing files
+- Automatic encoding detection
+
+```python
+# Basic usage
+content = read_file("example.txt")
+
+# With specific encoding
+content = read_file("data.txt", encoding="latin-1")
+
+# Returns: str (file content)
+# Error case: Returns error message as string
+```
+
+### write_file(filepath: str, content: str, encoding: str = 'utf-8')
+
+Writes content to a file:
+- Creates directories if they don't exist
+- Supports different encodings
+- Overwrites existing files
+
+```python
+# Basic usage
+success = write_file("output.txt", "Hello World")
+
+# With specific encoding and nested path
+success = write_file(
+    "path/to/data/output.txt",
+    "Content with special chars: áéíóú",
+    encoding="utf-8"
+)
+# Returns: bool (True if successful)
+```
+
+### list_files(directory: str, pattern: Optional[str] = None)
+
+Lists files in a directory:
+- Optional pattern matching
+- Detailed file information
+- Recursive directory support
+
+```python
+# List all files
+files = list_files("/path/to/directory")
+
+# With pattern matching
+files = list_files("/path/to/directory", pattern="*.txt")
+
+# Returns: List[Dict[str, Union[str, int]]]
+# Example: [
+#   {
+#     'name': 'example.txt',
+#     'path': '/path/to/directory/example.txt',
+#     'size': 1024,
+#     'modified': 1609459200.0,
+#     'created': 1609459100.0
+#   },
+#   ...
+# ]
+```
+
+### get_file_info(filepath: str)
+
+Gets detailed information about a file:
+- File metadata
+- Size and timestamps
+- Type information
+- Path details
+
+```python
+# Get file information
+info = get_file_info("example.txt")
+
+# Returns: Dict[str, Union[str, int]]
+# Example: {
+#   'name': 'example.txt',
+#   'path': '/absolute/path/to/example.txt',
+#   'size': 1024,
+#   'modified': 1609459200.0,
+#   'created': 1609459100.0,
+#   'is_file': True,
+#   'is_dir': False,
+#   'extension': '.txt',
+#   'parent': '/absolute/path/to'
+# }
+```
+
+### copy_file(src: str, dst: str)
+
+Copies a file with metadata:
+- Preserves timestamps
+- Creates destination directories
+- Handles existing files
+
+```python
+# Basic copy
+success = copy_file("source.txt", "destination.txt")
+
+# Copy to new directory
+success = copy_file(
+    "data.txt",
+    "backup/2023/data_backup.txt"
+)
+# Returns: bool (True if successful)
+```
+
+### move_file(src: str, dst: str)
+
+Moves or renames a file:
+- Creates destination directories
+- Handles existing files
+- Cross-device moves
+
+```python
+# Move file
+success = move_file("old/path/file.txt", "new/path/file.txt")
+
+# Rename file
+success = move_file(
+    "document.txt",
+    "document_v2.txt"
+)
+# Returns: bool (True if successful)
+```
+
+### delete_file(filepath: str)
+
+Deletes a file:
+- Safe deletion
+- Error handling
+- Non-recursive (files only)
+
+```python
+# Delete file
+success = delete_file("unwanted.txt")
+# Returns: bool (True if successful)
+```
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import (
+    read_file, write_file, list_files,
+    get_file_info, copy_file, move_file, delete_file
+)
+
+agent = Agent(
+    name="FileManager",
+    description="An agent that manages files",
+    tools=[
+        read_file, write_file, list_files,
+        get_file_info, copy_file, move_file, delete_file
+    ]
+)
+```
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- File not found errors
+- Permission errors
+- Path errors
+- I/O errors
+- Encoding errors
+
+Errors are handled consistently:
+- File operations return bool for success/failure
+- Information functions return error details in result
+- All errors are logged for debugging
+
+## Common Use Cases
+
+1. File Organization:
+```python
+# List and organize files by extension
+files = list_files("downloads", pattern="*.*")
+for file in files:
+    ext = file['extension']
+    if ext:
+        dst = f"organized/{ext[1:]}/{file['name']}"
+        move_file(file['path'], dst)
+```
+
+2. File Backup:
+```python
+# Create timestamped backups
+import time
+files = list_files("important_data")
+backup_time = time.strftime("%Y%m%d_%H%M%S")
+for file in files:
+    dst = f"backup/{backup_time}/{file['name']}"
+    copy_file(file['path'], dst)
+```
+
+3. File Processing:
+```python
+# Read, process, and write files
+content = read_file("input.txt")
+processed = process_content(content)  # Your processing function
+write_file("output.txt", processed)
+```
+
 ## Examples
 
 ### Basic File Management Agent
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import 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"
 )
@@ -145,44 +365,46 @@ 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()
@@ -198,7 +420,7 @@ 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>
@@ -222,7 +444,7 @@ 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

@@ -1,7 +1,6 @@
 ---
 title: "JSON Tools"
-description: "JSON file processing tools for AI agents."
-icon: "brackets-curly"
+description: "Tools for working with JSON files in AI agents."
 ---
 
 <Note>
@@ -27,7 +26,10 @@ Use JSON Tools to process and manipulate JSON files with AI agents.
     Import the necessary components:
     ```python
     from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import json
+    from praisonaiagents.tools import (
+        read_json, write_json, merge_json,
+        validate_json, analyze_json, transform_json
+    )
     ```
   </Step>
 
@@ -39,7 +41,10 @@ Use JSON Tools to process and manipulate JSON files with AI agents.
         role="JSON Processing Specialist",
         goal="Process JSON files efficiently and accurately.",
         backstory="Expert in JSON file manipulation and validation.",
-        tools=[json],
+        tools=[
+            read_json, write_json, merge_json,
+            validate_json, analyze_json, transform_json
+        ],
         self_reflect=False
     )
     ```
@@ -74,11 +79,11 @@ Use JSON Tools to process and manipulate JSON files with AI agents.
 
 <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
@@ -87,7 +92,7 @@ Use JSON Tools to process and manipulate JSON files with AI agents.
   <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">
@@ -103,9 +108,341 @@ Use JSON Tools to process and manipulate JSON files with AI agents.
     ```
   </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>
+
+## Available Functions
+
+```python
+from praisonaiagents.tools import read_json
+from praisonaiagents.tools import write_json
+from praisonaiagents.tools import merge_json
+from praisonaiagents.tools import validate_json
+from praisonaiagents.tools import analyze_json
+from praisonaiagents.tools import transform_json
+```
+
+## Function Details
+
+### read_json(filepath: str, encoding: str = 'utf-8', validate_schema: Optional[Dict[str, Any]] = None)
+
+Reads JSON files with schema validation:
+- Optional schema validation
+- Custom encoding support
+- Error handling
+
+```python
+# Basic usage
+data = read_json("config.json")
+
+# With schema validation
+schema = {
+    "type": "object",
+    "properties": {
+        "name": {"type": "string"},
+        "age": {"type": "integer"}
+    }
+}
+data = read_json(
+    "user.json",
+    encoding="utf-8",
+    validate_schema=schema
+)
+
+# Returns: Dict[str, Any] (JSON data)
+# Error case: Returns dict with 'error' key
+```
+
+### write_json(data: Union[Dict[str, Any], List[Any]], filepath: str, encoding: str = 'utf-8', indent: int = 2, sort_keys: bool = False, ensure_ascii: bool = False)
+
+Writes data to JSON files:
+- Pretty printing with indentation
+- Optional key sorting
+- Unicode support
+- Directory creation
+
+```python
+# Basic usage
+data = {"name": "Alice", "age": 30}
+success = write_json(data, "output.json")
+
+# With formatting options
+data = {
+    "users": [
+        {"name": "Alice", "age": 30},
+        {"name": "Bob", "age": 25}
+    ]
+}
+success = write_json(
+    data,
+    "users.json",
+    indent=4,
+    sort_keys=True,
+    ensure_ascii=False
+)
+# Returns: bool (True if successful)
+```
+
+### merge_json(files: List[str], output_file: str, merge_arrays: bool = True, overwrite_duplicates: bool = True)
+
+Merges multiple JSON files:
+- Deep merging of objects
+- Array handling options
+- Duplicate key handling
+- Nested structure support
+
+```python
+# Merge configuration files
+success = merge_json(
+    files=["config1.json", "config2.json"],
+    output_file="merged_config.json"
+)
+
+# Advanced merge with options
+success = merge_json(
+    files=["base.json", "override.json"],
+    output_file="final.json",
+    merge_arrays=True,
+    overwrite_duplicates=False
+)
+# Returns: bool (True if successful)
+```
+
+### validate_json(data: Union[Dict[str, Any], str], schema: Dict[str, Any])
+
+Validates JSON against a schema:
+- JSON Schema support
+- Detailed error messages
+- File or data validation
+
+```python
+# Validate data
+schema = {
+    "type": "object",
+    "required": ["name", "email"],
+    "properties": {
+        "name": {"type": "string"},
+        "email": {"type": "string", "format": "email"},
+        "age": {"type": "integer", "minimum": 0}
+    }
+}
+
+# Validate data directly
+data = {"name": "Alice", "email": "[email protected]", "age": 25}
+is_valid, error = validate_json(data, schema)
+
+# Validate file
+is_valid, error = validate_json("user.json", schema)
+# Returns: Tuple[bool, Optional[str]]
+```
+
+### analyze_json(data: Union[Dict[str, Any], str], max_depth: int = 10)
+
+Analyzes JSON structure:
+- Type information
+- Size metrics
+- Nested structure analysis
+- Sample data
+
+```python
+# Analyze file
+analysis = analyze_json("data.json", max_depth=5)
+
+# Analyze data structure
+data = {
+    "users": [
+        {"name": "Alice", "scores": [95, 87, 92]},
+        {"name": "Bob", "scores": [88, 85, 90]}
+    ]
+}
+analysis = analyze_json(data)
+
+# Returns: Dict[str, Any]
+# Example output:
+# {
+#   'analysis_time': '2023-01-01T12:00:00',
+#   'structure': {
+#     'type': 'dict',
+#     'size': 1,
+#     'keys': ['users'],
+#     'children': {
+#       'users': {
+#         'type': 'list',
+#         'length': 2,
+#         'element_types': ['dict'],
+#         'sample_elements': [...]
+#       }
+#     }
+#   }
+# }
+```
+
+### transform_json(data: Union[Dict[str, Any], str], transformations: List[Dict[str, Any]])
+
+Transforms JSON data:
+- Multiple operations
+- Path-based modifications
+- Nested transformations
+- Operation types: set, delete, rename, move
+
+```python
+# Transform data
+transformations = [
+    {
+        "operation": "set",
+        "path": "user.settings.theme",
+        "value": "dark"
+    },
+    {
+        "operation": "move",
+        "path": "old.config",
+        "value": "new.config"
+    },
+    {
+        "operation": "delete",
+        "path": "temporary.data"
+    }
+]
+
+# Transform file
+result = transform_json("config.json", transformations)
+
+# Transform data directly
+data = {"user": {"name": "Alice"}}
+result = transform_json(data, transformations)
+# Returns: Dict[str, Any] (transformed data)
+```
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import (
+    read_json, write_json, merge_json,
+    validate_json, analyze_json, transform_json
+)
+
+agent = Agent(
+    name="JSONProcessor",
+    description="An agent that processes JSON data",
+    tools=[
+        read_json, write_json, merge_json,
+        validate_json, analyze_json, transform_json
+    ]
+)
+```
+
+## Dependencies
+
+The JSON tools require the following Python packages:
+- jsonschema: For JSON schema validation
+
+These will be automatically installed when needed.
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- File I/O errors
+- JSON parsing errors
+- Schema validation errors
+- Transformation errors
+
+Errors are handled consistently:
+- File operations return bool for success/failure
+- Data operations return error details in result
+- All errors are logged for debugging
+
+## Common Use Cases
+
+1. Configuration Management:
+```python
+# Merge multiple config files
+success = merge_json(
+    ["base_config.json", "env_config.json", "user_config.json"],
+    "final_config.json",
+    merge_arrays=True,
+    overwrite_duplicates=True
+)
+
+# Validate configuration
+schema = {
+    "type": "object",
+    "required": ["database", "api"],
+    "properties": {
+        "database": {
+            "type": "object",
+            "required": ["host", "port"]
+        },
+        "api": {
+            "type": "object",
+            "required": ["key"]
+        }
+    }
+}
+is_valid, error = validate_json("final_config.json", schema)
+```
+
+2. Data Analysis:
+```python
+# Analyze data structure
+analysis = analyze_json("large_dataset.json", max_depth=3)
+print(f"Dataset size: {analysis['structure']['size']} entries")
+print(f"Available fields: {analysis['structure']['keys']}")
+```
+
+3. Data Transformation:
+```python
+# Transform data format
+transformations = [
+    {"operation": "rename", "path": "firstName", "value": "first_name"},
+    {"operation": "rename", "path": "lastName", "value": "last_name"},
+    {"operation": "move", "path": "address.zip", "value": "address.postal_code"},
+    {"operation": "delete", "path": "temporary_data"}
+]
+result = transform_json("user_data.json", transformations)
+write_json(result, "transformed_data.json")
+```
+
+## Understanding JSON Tools
+
+<Card title="What are JSON Tools?" icon="question">
+  JSON Tools provide JSON processing capabilities for AI agents:
+  - File reading and writing
+  - Data validation
+  - Schema validation
+  - Data transformation
+  - Structure analysis
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="JSON Agent" icon="user-robot">
+    Create specialized JSON agents:
+    ```python
+    Agent(tools=[read_json, write_json, merge_json, validate_json, analyze_json, transform_json])
+    ```
+  </Card>
+  <Card title="JSON Task" icon="list-check">
+    Define JSON tasks:
+    ```python
+    Task(description="json_operation")
+    ```
+  </Card>
+  <Card title="Process Types" icon="arrows-split-up-and-left">
+    Sequential or parallel processing:
+    ```python
+    process="sequential"
+    ```
+  </Card>
+  <Card title="JSON Options" icon="sliders">
+    Customize JSON operations:
+    ```python
+    indent=2, sort_keys=True
     ```
   </Card>
 </CardGroup>
@@ -116,24 +453,24 @@ Use JSON Tools to process and manipulate JSON files with AI agents.
 
 ```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
@@ -145,44 +482,44 @@ 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()
@@ -198,7 +535,7 @@ 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>
@@ -222,7 +559,7 @@ 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
@@ -247,4 +584,3 @@ agents = PraisonAIAgents(
     agents=[processor, validator],
     tasks=[process_task, validate_task]
 )
-```

docs/tools/newspaper_tools.mdx

@@ -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">
@@ -110,30 +110,161 @@ Use Newspaper Tools to scrape and analyze news articles with AI agents.
   </Card>
 </CardGroup>
 
+## Available Functions
+
+```python
+from praisonaiagents.tools import get_article
+from praisonaiagents.tools import get_news_sources
+from praisonaiagents.tools import get_articles_from_source
+from praisonaiagents.tools import get_trending_topics
+```
+
+## Function Details
+
+### get_article(url: str, language: str = 'en')
+
+Extracts and parses news articles:
+- Full text extraction
+- Author detection
+- Date parsing
+- Image extraction
+- NLP processing
+
+```python
+# Basic usage
+article = get_article("https://example.com/article")
+
+# With language specification
+article = get_article(
+    "https://lemonde.fr/article",
+    language="fr"
+)
+
+# Returns: Dict[str, Any]
+# Example output:
+# {
+#   "url": "https://example.com/article",
+#   "title": "Breaking News Story",
+#   "text": "Full article text...",
+#   "authors": ["John Doe", "Jane Smith"],
+#   "publish_date": "2025-01-07T05:31:38",
+#   "top_image": "https://example.com/image.jpg",
+#   "images": ["https://example.com/image1.jpg", ...],
+#   "movies": ["https://example.com/video1.mp4", ...],
+#   "source_domain": "example.com",
+#   "keywords": ["technology", "ai", ...],
+#   "summary": "Article summary..."
+# }
+```
+
+### get_news_sources(category: Optional[str] = None, language: str = 'en', country: Optional[str] = None)
+
+Gets news sources by category:
+- Category filtering
+- Language support
+- Country filtering
+- Domain information
+
+```python
+# Get all sources
+sources = get_news_sources()
+
+# Get technology news sources
+tech_sources = get_news_sources(
+    category="technology",
+    language="en",
+    country="us"
+)
+
+# Returns: List[Dict[str, str]]
+# Example output:
+# [
+#   {
+#     "url": "https://techcrunch.com",
+#     "domain": "techcrunch.com",
+#     "name": "TechCrunch",
+#     "category": "technology"
+#   },
+#   ...
+# ]
+```
+
+### get_articles_from_source(source_url: str, limit: int = 10, language: str = 'en')
+
+Gets recent articles from a source:
+- Configurable limit
+- Language support
+- Full article parsing
+- Error handling
+
+```python
+# Get latest articles
+articles = get_articles_from_source(
+    "https://techcrunch.com",
+    limit=5
+)
+
+# Get articles in specific language
+articles = get_articles_from_source(
+    "https://lemonde.fr",
+    limit=10,
+    language="fr"
+)
+
+# Returns: List[Dict[str, Any]]
+# Each article has the same structure as get_article()
+```
+
+### get_trending_topics(sources: Optional[List[str]] = None, limit: int = 10, language: str = 'en')
+
+Analyzes trending topics:
+- Cross-source analysis
+- Keyword extraction
+- Customizable sources
+- Topic ranking
+
+```python
+# Get trending topics from default sources
+topics = get_trending_topics(limit=5)
+
+# Get topics from specific sources
+topics = get_trending_topics(
+    sources=[
+        "https://techcrunch.com",
+        "https://wired.com"
+    ],
+    limit=10,
+    language="en"
+)
+
+# Returns: List[str]
+# Example: ["artificial intelligence", "climate change", ...]
+```
+
 ## Examples
 
 ### Basic News Agent
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import 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
@@ -153,7 +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
 )
 
@@ -198,7 +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>
@@ -222,7 +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
@@ -247,3 +378,87 @@ agents = PraisonAIAgents(
     agents=[monitor, analyst],
     tasks=[monitor_task, analysis_task]
 )
+
+```
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import (
+    get_article, get_news_sources,
+    get_articles_from_source, get_trending_topics
+)
+
+agent = Agent(
+    name="NewsAnalyzer",
+    description="An agent that analyzes news articles",
+    tools=[
+        get_article, get_news_sources,
+        get_articles_from_source, get_trending_topics
+    ]
+)
+```
+
+## Dependencies
+
+The newspaper tools require the following Python packages:
+- newspaper3k: For article extraction and parsing
+- nltk: For NLP processing (automatically installed with newspaper3k)
+
+These will be automatically installed when needed.
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- Network errors
+- Parsing errors
+- Language errors
+- Source availability errors
+
+Errors are handled consistently:
+- Success cases return the expected data type
+- Error cases return a dict with an "error" key
+- All errors are logged for debugging
+
+## Common Use Cases
+
+1. News Monitoring:
+```python
+# Monitor tech news
+sources = get_news_sources(category="technology")
+for source in sources:
+    articles = get_articles_from_source(
+        source["url"],
+        limit=5
+    )
+    for article in articles:
+        if "ai" in article.get("keywords", []):
+            print(f"AI article found: {article['title']}")
+```
+
+2. Trend Analysis:
+```python
+# Analyze trending topics
+topics = get_trending_topics(limit=10)
+print("Current hot topics:")
+for i, topic in enumerate(topics, 1):
+    print(f"{i}. {topic}")
+```
+
+3. Content Aggregation:
+```python
+# Aggregate news from multiple sources
+def aggregate_news(categories):
+    all_articles = []
+    for category in categories:
+        sources = get_news_sources(category=category)
+        for source in sources[:3]:  # Top 3 sources per category
+            articles = get_articles_from_source(
+                source["url"],
+                limit=3
+            )
+            all_articles.extend(articles)
+    return all_articles
+
+news = aggregate_news(["technology", "business", "science"])

docs/tools/pandas_tools.mdx

@@ -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">
@@ -110,30 +110,365 @@ Use Pandas Tools to manipulate and analyze data with AI agents.
   </Card>
 </CardGroup>
 
+## Available Functions
+
+```python
+from praisonaiagents.tools import read_csv
+from praisonaiagents.tools import read_excel
+from praisonaiagents.tools import write_csv
+from praisonaiagents.tools import write_excel
+from praisonaiagents.tools import filter_data
+from praisonaiagents.tools import get_summary
+from praisonaiagents.tools import group_by
+from praisonaiagents.tools import pivot_table
+```
+
+## Function Details
+
+### read_csv(filepath: str, **kwargs)
+
+Reads CSV files into pandas DataFrames:
+- Flexible data import
+- Error handling
+- Additional pandas options
+
+```python
+# Basic usage
+df = read_csv("data.csv")
+
+# With additional options
+df = read_csv(
+    "data.csv",
+    encoding="utf-8",
+    sep=",",
+    header=0,
+    usecols=["name", "age", "salary"]
+)
+
+# Returns: pd.DataFrame
+# Error case: Returns dict with 'error' key
+```
+
+### read_excel(filepath: str, **kwargs)
+
+Reads Excel files into pandas DataFrames:
+- Multiple sheet support
+- Error handling
+- Additional pandas options
+
+```python
+# Basic usage
+df = read_excel("data.xlsx")
+
+# With sheet specification
+df = read_excel(
+    "data.xlsx",
+    sheet_name="Sheet1",
+    header=0,
+    usecols="A:D"
+)
+
+# Returns: pd.DataFrame
+# Error case: Returns dict with 'error' key
+```
+
+### write_csv(df: pd.DataFrame, filepath: str, **kwargs)
+
+Writes DataFrames to CSV files:
+- Directory creation
+- Error handling
+- Formatting options
+
+```python
+# Basic usage
+success = write_csv(df, "output.csv")
+
+# With formatting options
+success = write_csv(
+    df,
+    "output.csv",
+    index=False,
+    encoding="utf-8",
+    sep=",",
+    date_format="%Y-%m-%d"
+)
+# Returns: bool (True if successful)
+```
+
+### write_excel(df: pd.DataFrame, filepath: str, **kwargs)
+
+Writes DataFrames to Excel files:
+- Directory creation
+- Error handling
+- Excel-specific options
+
+```python
+# Basic usage
+success = write_excel(df, "output.xlsx")
+
+# With formatting options
+success = write_excel(
+    df,
+    "output.xlsx",
+    sheet_name="Data",
+    index=False,
+    freeze_panes=(1,0)
+)
+# Returns: bool (True if successful)
+```
+
+### filter_data(df: pd.DataFrame, query: str)
+
+Filters DataFrames using query strings:
+- SQL-like syntax
+- Complex conditions
+- Efficient filtering
+
+```python
+# Basic filtering
+filtered_df = filter_data(df, "age > 25")
+
+# Complex conditions
+filtered_df = filter_data(
+    df,
+    "age > 25 and salary >= 50000 and city == 'New York'"
+)
+
+# Returns: pd.DataFrame
+# Error case: Returns dict with 'error' key
+```
+
+### get_summary(df: pd.DataFrame)
+
+Generates comprehensive data summaries:
+- Basic statistics
+- Data types
+- Memory usage
+- Null counts
+
+```python
+# Get data summary
+summary = get_summary(df)
+
+# Returns: Dict[str, Any]
+# Example output:
+# {
+#   "shape": [1000, 5],
+#   "columns": ["name", "age", "salary", "city"],
+#   "dtypes": {
+#     "name": "object",
+#     "age": "int64",
+#     "salary": "float64",
+#     "city": "object"
+#   },
+#   "null_counts": {
+#     "name": 0,
+#     "age": 2,
+#     "salary": 1,
+#     "city": 0
+#   },
+#   "numeric_summary": {
+#     "age": {
+#       "count": 998,
+#       "mean": 35.5,
+#       "std": 12.3,
+#       "min": 18,
+#       "25%": 28,
+#       "50%": 35,
+#       "75%": 45,
+#       "max": 65
+#     },
+#     ...
+#   },
+#   "memory_usage": 40960
+# }
+```
+
+### group_by(df: pd.DataFrame, columns: Union[str, List[str]], agg_dict: Dict[str, Union[str, List[str]]])
+
+Groups and aggregates data:
+- Multiple grouping columns
+- Multiple aggregations
+- Custom functions
+
+```python
+# Basic grouping
+grouped_df = group_by(
+    df,
+    columns="city",
+    agg_dict={"salary": "mean"}
+)
+
+# Complex grouping
+grouped_df = group_by(
+    df,
+    columns=["city", "department"],
+    agg_dict={
+        "salary": ["mean", "sum", "count"],
+        "age": ["min", "max", "mean"]
+    }
+)
+
+# Returns: pd.DataFrame
+# Error case: Returns dict with 'error' key
+```
+
+### pivot_table(df: pd.DataFrame, index: Union[str, List[str]], columns: Optional[Union[str, List[str]]] = None, values: Optional[Union[str, List[str]]] = None, aggfunc: str = "mean")
+
+Creates pivot tables:
+- Multiple index levels
+- Column pivoting
+- Custom aggregations
+- Flexible reshaping
+
+```python
+# Basic pivot
+pivot_df = pivot_table(
+    df,
+    index="city",
+    columns="department",
+    values="salary"
+)
+
+# Complex pivot
+pivot_df = pivot_table(
+    df,
+    index=["city", "department"],
+    columns="year",
+    values=["salary", "headcount"],
+    aggfunc={
+        "salary": "mean",
+        "headcount": "sum"
+    }
+)
+
+# Returns: pd.DataFrame
+# Error case: Returns dict with 'error' key
+```
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import (
+    read_csv, read_excel, write_csv, write_excel,
+    filter_data, get_summary, group_by, pivot_table
+)
+
+agent = Agent(
+    name="DataAnalyzer",
+    description="An agent that analyzes data using pandas",
+    tools=[
+        read_csv, read_excel, write_csv, write_excel,
+        filter_data, get_summary, group_by, pivot_table
+    ]
+)
+```
+
+## Dependencies
+
+The pandas tools require the following Python packages:
+- pandas: For data manipulation and analysis
+- numpy: For numerical operations (automatically installed with pandas)
+- openpyxl: For Excel file support (optional)
+
+These will be automatically installed when needed.
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- File I/O errors
+- Data format errors
+- Query syntax errors
+- Memory errors
+
+Errors are handled consistently:
+- File operations return bool for success/failure
+- Data operations return error details in result
+- All errors are logged for debugging
+
+## Common Use Cases
+
+1. Data Analysis:
+```python
+# Load and analyze sales data
+df = read_csv("sales_data.csv")
+summary = get_summary(df)
+print(f"Total records: {summary['shape'][0]}")
+print(f"Missing values: {summary['null_counts']}")
+
+# Filter high-value transactions
+high_value = filter_data(df, "amount > 10000")
+write_excel(high_value, "high_value_sales.xlsx")
+```
+
+2. Sales Reporting:
+```python
+# Create sales summary by region and product
+sales_summary = group_by(
+    df,
+    columns=["region", "product"],
+    agg_dict={
+        "amount": ["sum", "mean", "count"],
+        "profit": ["sum", "mean"]
+    }
+)
+
+# Create pivot table for quarterly analysis
+quarterly_sales = pivot_table(
+    df,
+    index="product",
+    columns="quarter",
+    values="amount",
+    aggfunc="sum"
+)
+```
+
+3. Data Transformation:
+```python
+# Load and clean customer data
+customers = read_excel("customers.xlsx")
+filtered = filter_data(
+    customers,
+    "age >= 18 and status == 'active'"
+)
+
+# Group by demographics
+demographics = group_by(
+    filtered,
+    columns=["age_group", "region"],
+    agg_dict={
+        "spending": ["mean", "sum"],
+        "visits": "count"
+    }
+)
+write_csv(demographics, "demographic_analysis.csv")
+```
+
 ## Examples
 
 ### Basic Data Analysis Agent
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import 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
@@ -145,44 +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()
@@ -198,7 +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>
@@ -222,7 +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
@@ -247,4 +582,3 @@ agents = PraisonAIAgents(
     agents=[prep_agent, analyst],
     tasks=[prep_task, analysis_task]
 )
-```

docs/tools/python_tools.mdx

@@ -1,7 +1,6 @@
 ---
 title: "Python Tools"
-description: "Python code execution tools for AI agents."
-icon: "python"
+description: "Tools for Python code execution, analysis, and formatting in AI agents."
 ---
 
 <Note>
@@ -27,7 +26,10 @@ Use Python Tools to execute and manage Python code with AI agents.
     Import the necessary components:
     ```python
     from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import python
+    from praisonaiagents.tools import (
+        execute_code, analyze_code, format_code,
+        lint_code, disassemble_code
+    )
     ```
   </Step>
 
@@ -39,7 +41,10 @@ Use Python Tools to execute and manage Python code with AI agents.
         role="Python Code Specialist",
         goal="Execute Python code efficiently and safely.",
         backstory="Expert in Python programming and code execution.",
-        tools=[python],
+        tools=[
+            execute_code, analyze_code, format_code,
+            lint_code, disassemble_code
+        ],
         self_reflect=False
     )
     ```
@@ -70,6 +75,293 @@ Use Python Tools to execute and manage Python code with AI agents.
   </Step>
 </Steps>
 
+## Available Functions
+
+```python
+from praisonaiagents.tools import execute_code
+from praisonaiagents.tools import analyze_code
+from praisonaiagents.tools import format_code
+from praisonaiagents.tools import lint_code
+from praisonaiagents.tools import disassemble_code
+```
+
+## Function Details
+
+### execute_code(code: str, globals_dict: Optional[Dict[str, Any]] = None, locals_dict: Optional[Dict[str, Any]] = None, timeout: int = 30, max_output_size: int = 10000)
+
+Safely executes Python code:
+- Isolated execution environment
+- Output capture
+- Error handling
+- Timeout protection
+- Output size limits
+
+```python
+# Basic execution
+result = execute_code("print('Hello, World!')")
+
+# With custom environment
+result = execute_code(
+    """
+    x = 10
+    y = 20
+    print(f'Sum: {x + y}')
+    """,
+    globals_dict={'__builtins__': __builtins__},
+    timeout=5
+)
+
+# Returns: Dict[str, Any]
+# Example output:
+# {
+#     'result': None,
+#     'stdout': 'Hello, World!\n',
+#     'stderr': '',
+#     'success': True
+# }
+```
+
+### analyze_code(code: str)
+
+Analyzes Python code structure:
+- Import statements
+- Function definitions
+- Class definitions
+- Variable usage
+- Code complexity
+
+```python
+# Analyze code structure
+analysis = analyze_code("""
+def greet(name):
+    return f"Hello, {name}!"
+
+class Person:
+    def __init__(self, name):
+        self.name = name
+""")
+
+# Returns: Dict[str, Any]
+# Example output:
+# {
+#     'imports': [],
+#     'functions': [
+#         {
+#             'name': 'greet',
+#             'args': ['name'],
+#             'decorators': []
+#         }
+#     ],
+#     'classes': [
+#         {
+#             'name': 'Person',
+#             'bases': [],
+#             'decorators': []
+#         }
+#     ],
+#     'variables': ['name'],
+#     'complexity': {
+#         'lines': 6,
+#         'functions': 2,
+#         'classes': 1,
+#         'branches': 0
+#     }
+# }
+```
+
+### format_code(code: str, style: str = 'black', line_length: int = 88)
+
+Formats Python code:
+- Multiple style options
+- Line length control
+- PEP 8 compliance
+- Consistent formatting
+
+```python
+# Format with black
+formatted = format_code("""
+def messy_function(x,y,   z):
+    if x>0:
+     return y+z
+    else:
+     return y-z
+""")
+
+# Format with PEP 8
+formatted = format_code(
+    """
+    def messy_function(x,y,   z):
+        if x>0:
+         return y+z
+        else:
+         return y-z
+    """,
+    style='pep8',
+    line_length=79
+)
+
+# Returns: str
+# Example output:
+# def messy_function(x, y, z):
+#     if x > 0:
+#         return y + z
+#     else:
+#         return y - z
+```
+
+### lint_code(code: str)
+
+Lints Python code for issues:
+- Code quality checks
+- Style violations
+- Potential bugs
+- Best practices
+
+```python
+# Lint code for issues
+results = lint_code("""
+def bad_function():
+    unused_var = 42
+    return 'result'
+""")
+
+# Returns: Dict[str, List[Dict[str, Any]]]
+# Example output:
+# {
+#     'errors': [],
+#     'warnings': [
+#         {
+#             'type': 'warning',
+#             'module': 'bad_function',
+#             'obj': 'unused_var',
+#             'line': 2,
+#             'column': 4,
+#             'path': '<string>',
+#             'symbol': 'unused-variable',
+#             'message': 'Unused variable "unused_var"',
+#             'message-id': 'W0612'
+#         }
+#     ],
+#     'conventions': []
+# }
+```
+
+### disassemble_code(code: str)
+
+Disassembles Python code to bytecode:
+- Bytecode inspection
+- Performance analysis
+- Code optimization
+- Debugging support
+
+```python
+# Disassemble code to bytecode
+bytecode = disassemble_code("""
+def add(a, b):
+    return a + b
+""")
+
+# Returns: str
+# Example output:
+#  1           0 LOAD_CONST               0 (<code object add at ...>)
+#              2 LOAD_CONST               1 ('add')
+#              4 MAKE_FUNCTION            0
+#              6 STORE_NAME               0 (add)
+#              8 LOAD_CONST               2 (None)
+#             10 RETURN_VALUE
+```
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import (
+    execute_code, analyze_code, format_code,
+    lint_code, disassemble_code
+)
+
+agent = Agent(
+    name="PythonDeveloper",
+    description="An agent that helps with Python development",
+    tools=[
+        execute_code, analyze_code, format_code,
+        lint_code, disassemble_code
+    ]
+)
+```
+
+## Dependencies
+
+The Python tools require the following packages:
+- black: For code formatting (black style)
+- autopep8: For code formatting (PEP 8 style)
+- pylint: For code linting
+
+These will be automatically installed when needed.
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- Code execution errors
+- Syntax errors
+- Import errors
+- Timeout errors
+- Memory errors
+
+Errors are handled consistently:
+- Success cases return expected data type
+- Error cases return None or error details
+- All errors are logged for debugging
+
+## Common Use Cases
+
+1. Code Testing:
+```python
+# Test code execution
+test_code = """
+def factorial(n):
+    return 1 if n <= 1 else n * factorial(n - 1)
+
+result = factorial(5)
+print(f"Factorial: {result}")
+"""
+result = execute_code(test_code)
+print(f"Output: {result['stdout']}")
+```
+
+2. Code Quality:
+```python
+# Check code quality
+code = """
+def process_data(data):
+    processed = []
+    for item in data:
+        if item > 0:
+            processed.append(item * 2)
+    return processed
+"""
+analysis = analyze_code(code)
+lint_results = lint_code(code)
+formatted = format_code(code)
+```
+
+3. Code Analysis:
+```python
+# Analyze code structure
+code = """
+class DataProcessor:
+    def __init__(self, data):
+        self.data = data
+    
+    def process(self):
+        return [x * 2 for x in self.data]
+"""
+structure = analyze_code(code)
+bytecode = disassemble_code(code)
+print(f"Classes: {structure['classes']}")
+print(f"Functions: {structure['functions']}")
+```
+
 ## Understanding Python Tools
 
 <Card title="What are Python Tools?" icon="question">
@@ -81,42 +373,16 @@ Use Python Tools to execute and manage Python code with AI agents.
   - Environment control
 </Card>
 
-## Key Components
-
-<CardGroup cols={2}>
-  <Card title="Python Agent" icon="user-robot">
-    Create specialized Python agents:
-    ```python
-    Agent(tools=[python])
-    ```
-  </Card>
-  <Card title="Python Task" icon="list-check">
-    Define Python tasks:
-    ```python
-    Task(description="python_operation")
-    ```
-  </Card>
-  <Card title="Process Types" icon="arrows-split-up-and-left">
-    Sequential or parallel processing:
-    ```python
-    process="sequential"
-    ```
-  </Card>
-  <Card title="Python Options" icon="sliders">
-    Customize execution parameters:
-    ```python
-    timeout=30, capture_output=True
-    ```
-  </Card>
-</CardGroup>
-
 ## Examples
 
 ### Basic Python Execution Agent
 
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import python
+from praisonaiagents.tools import (
+    execute_code, analyze_code, format_code,
+    lint_code, disassemble_code
+)
 
 # Create Python agent
 python_agent = Agent(
@@ -124,7 +390,10 @@ python_agent = Agent(
     role="Code Execution Specialist",
     goal="Execute Python code efficiently and safely.",
     backstory="Expert in Python programming and execution.",
-    tools=[python],
+    tools=[
+        execute_code, analyze_code, format_code,
+        lint_code, disassemble_code
+    ],
     self_reflect=False
 )
 
@@ -153,7 +422,10 @@ executor_agent = Agent(
     name="CodeExecutor",
     role="Code Execution Specialist",
     goal="Execute Python code efficiently.",
-    tools=[python],
+    tools=[
+        execute_code, analyze_code, format_code,
+        lint_code, disassemble_code
+    ],
     self_reflect=False
 )
 
@@ -198,7 +470,10 @@ agents.start()
         name="PythonExecutor",
         role="Code Execution Specialist",
         goal="Execute code safely and efficiently",
-        tools=[python]
+        tools=[
+            execute_code, analyze_code, format_code,
+            lint_code, disassemble_code
+        ]
     )
     ```
   </Accordion>
@@ -222,7 +497,10 @@ agents.start()
 executor = Agent(
     name="Executor",
     role="Code Executor",
-    tools=[python]
+    tools=[
+        execute_code, analyze_code, format_code,
+        lint_code, disassemble_code
+    ]
 )
 
 # Monitor agent

docs/tools/shell_tools.mdx

@@ -1,7 +1,6 @@
 ---
 title: "Shell Tools"
-description: "Command-line interface tools for AI agents."
-icon: "terminal"
+description: "Tools for executing shell commands and managing system processes in AI agents."
 ---
 
 <Note>
@@ -27,7 +26,7 @@ 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
+    from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info
     ```
   </Step>
 
@@ -39,7 +38,7 @@ 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
     )
     ```
@@ -87,13 +86,13 @@ Use Shell Tools to execute and manage shell commands with AI agents.
   <Card title="Shell Agent" icon="user-robot">
     Create specialized shell agents:
     ```python
-    Agent(tools=[shell])
+    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_command")
+    Task(description="shell_operation")
     ```
   </Card>
   <Card title="Process Types" icon="arrows-split-up-and-left">
@@ -103,20 +102,16 @@ Use Shell Tools to execute and manage shell commands with AI agents.
     ```
   </Card>
   <Card title="Shell Options" icon="sliders">
-    Customize shell parameters:
+    Customize shell operations:
     ```python
     timeout=30, shell=True
     ```
   </Card>
 </CardGroup>
 
-## Examples
-
-### Basic Shell Command Agent
-
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import shell
+from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info
 
 # Create shell agent
 shell_agent = Agent(
@@ -124,7 +119,7 @@ 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
 )
 
@@ -153,7 +148,7 @@ 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
 )
 
@@ -188,6 +183,249 @@ agents = PraisonAIAgents(
 agents.start()
 ```
 
+## Available Functions
+
+```python
+from praisonaiagents.tools import execute_command
+from praisonaiagents.tools import list_processes
+from praisonaiagents.tools import kill_process
+from praisonaiagents.tools import get_system_info
+```
+
+## Function Details
+
+### execute_command(command: str, cwd: Optional[str] = None, timeout: int = 30, shell: bool = False, env: Optional[Dict[str, str]] = None, max_output_size: int = 10000)
+
+Safely executes shell commands:
+- Timeout protection
+- Output capture
+- Error handling
+- Environment control
+- Working directory control
+
+```python
+# Basic command execution
+result = execute_command("ls -la")
+
+# Advanced execution
+result = execute_command(
+    "python script.py",
+    cwd="/path/to/scripts",
+    timeout=60,
+    env={"PYTHONPATH": "/custom/path"},
+    shell=True
+)
+
+# Returns: Dict[str, Union[str, int, bool]]
+# Example output:
+# {
+#     'stdout': 'command output...',
+#     'stderr': 'error output if any',
+#     'exit_code': 0,
+#     'success': True,
+#     'execution_time': 0.123
+# }
+```
+
+### list_processes()
+
+Lists running system processes:
+- Process details
+- Resource usage
+- User information
+- Performance metrics
+
+```python
+# Get list of running processes
+processes = list_processes()
+
+# Sort by CPU usage
+cpu_intensive = sorted(
+    processes,
+    key=lambda x: x['cpu_percent'],
+    reverse=True
+)[:5]
+
+# Returns: List[Dict[str, Union[int, str, float]]]
+# Example output:
+# [
+#     {
+#         'pid': 1234,
+#         'name': 'python',
+#         'username': 'user',
+#         'memory_percent': 2.5,
+#         'cpu_percent': 15.3
+#     },
+#     ...
+# ]
+```
+
+### kill_process(pid: int, force: bool = False)
+
+Terminates system processes:
+- Graceful termination
+- Force kill option
+- Error handling
+- Access control
+
+```python
+# Graceful termination
+result = kill_process(1234)
+
+# Force kill
+result = kill_process(
+    pid=1234,
+    force=True  # Use SIGKILL
+)
+
+# Returns: Dict[str, Union[bool, str]]
+# Example output:
+# {
+#     'success': True,
+#     'message': 'Process 1234 killed successfully'
+# }
+# or
+# {
+#     'success': False,
+#     'message': 'Access denied to kill process 1234'
+# }
+```
+
+### get_system_info()
+
+Retrieves system information:
+- CPU statistics
+- Memory usage
+- Disk space
+- Platform details
+- Boot time
+
+```python
+# Get system information
+info = get_system_info()
+
+# Access specific metrics
+print(f"CPU Usage: {info['cpu']['percent']}%")
+print(f"Memory Free: {info['memory']['free']} bytes")
+
+# Returns: Dict[str, Union[float, int, str, Dict]]
+# Example output:
+# {
+#     'cpu': {
+#         'percent': 45.2,
+#         'cores': 8,
+#         'physical_cores': 4
+#     },
+#     'memory': {
+#         'total': 16000000000,
+#         'available': 8000000000,
+#         'percent': 50.0,
+#         'used': 8000000000,
+#         'free': 4000000000
+#     },
+#     'disk': {
+#         'total': 500000000000,
+#         'used': 250000000000,
+#         'free': 250000000000,
+#         'percent': 50.0
+#     },
+#     'boot_time': 1641544800,
+#     'platform': 'Darwin'
+# }
+```
+
+## Example Agent Configuration
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.tools import (
+    execute_command, list_processes,
+    kill_process, get_system_info
+)
+
+agent = Agent(
+    name="SystemManager",
+    description="An agent that manages system processes and executes commands",
+    tools=[
+        execute_command, list_processes,
+        kill_process, get_system_info
+    ]
+)
+```
+
+## Dependencies
+
+The shell tools require the following package:
+- psutil: For system and process information
+
+This will be automatically installed when needed.
+
+## Error Handling
+
+All functions include comprehensive error handling:
+- Command execution errors
+- Process access errors
+- Permission errors
+- Timeout errors
+- Resource errors
+
+Errors are handled consistently:
+- Success cases return expected data type
+- Error cases return error details in result
+- All errors are logged for debugging
+
+## Common Use Cases
+
+1. System Monitoring:
+```python
+# Monitor system resources
+info = get_system_info()
+if info['cpu']['percent'] > 90:
+    print("High CPU usage detected!")
+if info['memory']['percent'] > 80:
+    print("Low memory warning!")
+
+# List resource-intensive processes
+processes = sorted(
+    list_processes(),
+    key=lambda x: x['memory_percent'],
+    reverse=True
+)[:5]
+print("Top memory users:", processes)
+```
+
+2. Process Management:
+```python
+# Find and kill zombie processes
+for process in list_processes():
+    if process['name'] == 'zombie_process':
+        result = kill_process(
+            process['pid'],
+            force=True
+        )
+        print(f"Kill result: {result['message']}")
+```
+
+3. Command Execution:
+```python
+# Run a series of maintenance commands
+commands = [
+    "apt-get update",
+    "apt-get upgrade -y",
+    "apt-get autoremove -y"
+]
+for cmd in commands:
+    result = execute_command(
+        cmd,
+        timeout=300,
+        shell=True
+    )
+    if result['success']:
+        print(f"Command succeeded: {cmd}")
+    else:
+        print(f"Command failed: {result['stderr']}")
+```
+
 ## Best Practices
 
 <AccordionGroup>
@@ -198,7 +436,7 @@ agents.start()
         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>
@@ -222,7 +460,7 @@ agents.start()
 commander = Agent(
     name="Commander",
     role="Shell Commander",
-    tools=[shell]
+    tools=[execute_command]
 )
 
 # Monitor agent

docs/tools/spider_tools.mdx

@@ -28,7 +28,7 @@ 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 +40,7 @@ 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 +75,11 @@ 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 +88,7 @@ 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 +117,30 @@ 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 +154,7 @@ 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 +164,7 @@ 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 +200,7 @@ 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 +224,14 @@ 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 +249,4 @@ process_task = Task(
 agents = PraisonAIAgents(
     agents=[scraper, processor],
     tasks=[scrape_task, process_task]
-)
+)

docs/tools/wikipedia_tools.mdx

@@ -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

@@ -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

@@ -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

@@ -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/2.0.33.tar.gz"
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.35.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==2.0.33 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.0.35 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 = "2.0.33"
+version = "2.0.35"
 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>=0.0.24",
+    "praisonaiagents>=0.0.26",
     "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 = "2.0.33"
+version = "2.0.35"
 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 = ">=0.0.24"
+praisonaiagents = ">=0.0.26"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

uv.lock

@@ -3061,7 +3061,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.0.33"
+version = "2.0.34"
 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 = ">=0.0.23" },
+    { name = "praisonaiagents", specifier = ">=0.0.25" },
     { 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 = "0.0.23"
+version = "0.0.25"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "openai" },
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/a8/87/08ff2ac7c365251fa07973fded3e68c2edc1173d8514e6fd90fc8ee7b131/praisonaiagents-0.0.23.tar.gz", hash = "sha256:1814d537b2625a5c7d4b61d07f5d5bd043f0ad8afc31fe9d4eb99a5ca54101bf", size = 25375 }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/9c/07a6fd1d939eb98ae6399fb16aad870f16dba7ab5d64825af2420a46c09b/praisonaiagents-0.0.25.tar.gz", hash = "sha256:2ef1292fa5e0a5dc5d1b982670f3d0c6bb1cd9c59febbcbe6c2b8af5fa21ca43", size = 61241 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/56/75/c3a1cf2adaf0811ab538029054fa993e82caffba2853546f41766b7e0aab/praisonaiagents-0.0.23-py3-none-any.whl", hash = "sha256:d6aaa406fc993ccd0a763aa426b7139c955e1cd99e6b204b06817ebc1a5855e3", size = 31734 },
+    { url = "https://files.pythonhosted.org/packages/50/78/1ab28fe9e32f2d765052b3870532238f6c4a72a98168b73b443d76c0235f/praisonaiagents-0.0.25-py3-none-any.whl", hash = "sha256:5b0cf04f294d81dbee8be855fc7026cc190a05b1d169e286eb7e387b8f8bd7de", size = 80127 },
 ]
 
 [[package]]