Merge pull request #222 from MervinPraison/develop

adding praisonaiagents tools to praisonai

Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.7 gunicorn markdown
+RUN pip install flask praisonai==2.0.16 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

README.md

@@ -20,7 +20,129 @@
 
 </div>
 
-Praison AI, leveraging both AutoGen and CrewAI or any other agent framework, represents a low-code, centralised framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing ease of use, customization, and human-agent interaction.
+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.
+
+## Key Features
+
+- 🤖 Automated AI Agents Creation
+- 🔄 Use CrewAI or AutoGen Framework
+- 💯 100+ LLM Support
+- 💻 Chat with ENTIRE Codebase
+- 🖥️ Interactive UIs
+- 📄 YAML-based Configuration
+- 🛠️ Custom Tool Integration
+- 🔍 Internet Search Capability (using Crawl4AI and Tavily)
+- 👁️ Vision Language Model (VLM) Support
+- 🎙️ Real-time Voice Interaction
+
+## Using No Code
+
+### Auto Mode:
+```bash
+pip install praisonai
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+praisonai --auto create a movie script about Robots in Mars
+```
+
+### Initialise Mode:
+```bash
+pip install praisonai
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+praisonai --init create a movie script about Robots in Mars
+praisonai
+```
+
+## Using Coding
+
+Light weight package dedicated for coding:
+```bash
+pip install praisonaiagents
+```
+
+```bash
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+```
+
+Create app.py file and add the code below:
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# 1. Create agents
+researcher = Agent(
+    name="Researcher",
+    role="Senior Research Analyst",
+    goal="Uncover cutting-edge developments in AI and data science",
+    backstory="""You are an expert at a technology research group, 
+    skilled in identifying trends and analyzing complex data.""",
+    verbose=True,
+    llm="gpt-4o",
+    markdown=True
+)
+writer = Agent(
+    name="Writer",
+    role="Tech Content Strategist",
+    goal="Craft compelling content on tech advancements",
+    backstory="""You are a content strategist known for 
+    making complex tech topics interesting and easy to understand.""",
+    llm="gpt-4o",
+    markdown=True
+)
+
+# 2. Define Tasks
+task1 = Task(
+    name="research_task",
+    description="""Analyze 2024's AI advancements. 
+    Find major trends, new technologies, and their effects.""",
+    expected_output="""A detailed report on 2024 AI advancements""",
+    agent=researcher
+)
+
+task2 = Task(
+    name="writing_task",
+    description="""Create a blog post about major AI advancements using the insights you have.
+    Make it interesting, clear, and suited for tech enthusiasts. 
+    It should be at least 4 paragraphs long.""",
+    expected_output="A blog post of at least 4 paragraphs",
+    agent=writer,
+)
+
+agents = PraisonAIAgents(
+    agents=[researcher, writer],
+    tasks=[task1, task2],
+    verbose=False,
+    process="hierarchical",
+    manager_llm="gpt-4o"
+)
+
+result = agents.start()
+```
+
+Run:
+```bash
+python app.py
+```
+
+## Ollama Integration
+```bash
+export OPENAI_BASE_URL=http://localhost:11434/v1
+```
+
+## Groq Integration
+Replace xxxx with Groq API KEY:
+```bash
+export OPENAI_API_KEY=xxxxxxxxxxx
+export OPENAI_BASE_URL=https://api.groq.com/openai/v1
+```
+
+## Logging
+```bash
+export LOGLEVEL=info
+```
+
+Advanced logging:
+```bash
+export LOGLEVEL=debug
+```
 
 <div align="center">
   <picture>
@@ -130,18 +252,7 @@ When installing with `pip install "praisonai[autogen]"`, you get:
 - Multi-agent conversation capabilities
 - Code execution environment
 
-## Key Features
 
-- 🤖 Automated AI Agents Creation
-- 🔄 Use CrewAI or AutoGen Framework
-- 💯 100+ LLM Support
-- 💻 Chat with ENTIRE Codebase
-- 🖥️ Interactive UIs
-- 📄 YAML-based Configuration
-- 🛠️ Custom Tool Integration
-- 🔍 Internet Search Capability (using Crawl4AI and Tavily)
-- 👁️ Vision Language Model (VLM) Support
-- 🎙️ Real-time Voice Interaction
 
 ## TL;DR Multi Agents
 
@@ -353,33 +464,7 @@ if __name__ == "__main__":
 
 ## Commands to Install Dependencies:
 
-1. **Install all dependencies, including dev dependencies:**
-
-   ```sh
-   poetry install
-   ```
-
-2. **Install only documentation dependencies:**
-
-   ```sh
-   poetry install --with docs
-   ```
-
-3. **Install only test dependencies:**
-
-   ```sh
-   poetry install --with test
-   ```
-
-4. **Install only dev dependencies:**
-
-   ```sh
-   poetry install --with dev
-   ```
-
-This configuration ensures that your development dependencies are correctly categorized and installed as needed.
-
-### Using uv (Fast Python Package Installer)
+### Using uv
 ```bash
 # Install uv if you haven't already
 pip install uv

agents.yaml

@@ -1,5 +1,5 @@
 framework: praisonai
-process: hierarchical
+process: sequential
 topic: create movie script about cat in mars
 roles:
   researcher:
@@ -7,18 +7,26 @@ roles:
       information.
     goal: Gather information about Mars and cats
     role: Researcher
+    llm:
+      model: "gpt-4o"
+    reflect_llm:
+      model: "gpt-4o"
+    min_reflect: 2
+    max_reflect: 4
     tasks:
       research_task:
         description: Research about Mars, its environment, and the feasibility of
           a cat being on Mars. Also, research about cat behavior and characteristics.
         expected_output: Document with research findings on Mars and cats.
     tools:
-    - ''
+    - search_tool
   narrative_designer:
     backstory: Skilled in narrative development, with a focus on creating engaging
       stories.
     goal: Create a story concept for a movie about a cat in Mars
     role: Narrative Designer
+    llm:
+      model: "gpt-4o"
     tasks:
       story_concept_development:
         description: Use the research findings to craft a unique story concept for
@@ -31,6 +39,8 @@ roles:
       scripts.
     goal: Write a movie script about a cat in Mars
     role: Scriptwriter
+    llm:
+      model: "gpt-4o"
     tasks:
       scriptwriting_task:
         description: Turn the story concept into a production-ready movie script,

agents/example.py

@@ -1,166 +1,141 @@
-import os
-import logging
-from rich.console import Console
-from rich.panel import Panel
-from rich.text import Text
-from praisonaiagents import Agent, Task, Agents, error_logs
-
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s %(filename)s:%(lineno)d %(levelname)s %(message)s",
-    datefmt="[%X]"
-)
+from praisonaiagents import Agent, Task, PraisonAIAgents, error_logs
+from duckduckgo_search import DDGS
 
 def my_callback(output):
-    logging.info(f"Callback function called after task execution: {output.description}")
-    logging.info(f"Task output: {output}")
+    print(f"Callback Task output: {output}")
 
-def main():
-    # Make sure OPENAI_API_KEY is set
-    if not os.environ.get("OPENAI_API_KEY"):
-        raise ValueError("Please set OPENAI_API_KEY environment variable")
+def internet_search_tool(query) -> list:
+    """
+    Perform a search using DuckDuckGo.
 
-    # Define tools
-    search_tool = {
-        "type": "function",
-        "function": {
-            "name": "search_tool",
-            "description": "Use this to perform search queries",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "query": {"type": "string"},
-                },
-                "required": ["query"],
-            },
-        },
-    }
-    get_weather_tool = {
-        "type": "function",
-        "function": {
-            "name": "get_weather",
-            "description": "Get the weather for a location",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "location": {"type": "string"},
-                },
-                "required": ["location"],
-            },
-        },
-    }
+    Args:
+        query (str): The search query.
 
-    # Create agents
-    researcher = Agent(
-        name="Researcher",
-        role="Senior Research Analyst",
-        goal="Uncover cutting-edge developments in AI and data science",
-        backstory="""You are an expert at a technology research group, 
-        skilled in identifying trends and analyzing complex data.""",
-        verbose=True,
-        allow_delegation=False,
-        tools=[search_tool],
-        llm="gpt-4o",
-        markdown=True
-    )
-    writer = Agent(
-        name="Writer",
-        role="Tech Content Strategist",
-        goal="Craft compelling content on tech advancements",
-        backstory="""You are a content strategist known for 
-        making complex tech topics interesting and easy to understand.""",
-        verbose=True,
-        allow_delegation=True,
-        llm="gpt-4o",
-        tools=[get_weather_tool],
-        markdown=True
-    )
+    Returns:
+        list: A list of search result titles and URLs.
+    """
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=10):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
 
-    # Create tasks
-    task1 = Task(
-        name="research_task",
-        description="""Analyze 2024's AI advancements. 
-        Find major trends, new technologies, and their effects.""",
-        expected_output="""A detailed report on 2024 AI advancements""",
-        agent=researcher,
-        tools=[search_tool]
-    )
+    except Exception as e:
+        print(f"Error during DuckDuckGo search: {e}")
+        return []
 
-    task2 = Task(
-        name="writing_task",
-        description="""Create a blog post about major AI advancements using the insights you have.
-        Make it interesting, clear, and suited for tech enthusiasts. 
-        It should be at least 4 paragraphs long. 
-        Also, call the get_weather tool to get the weather in Paris.""",
-        expected_output="A blog post of at least 4 paragraphs, and weather in Paris",
-        agent=writer,
-        context=[task1],
-        callback=my_callback,
-        tools=[get_weather_tool]
-    )
+# Create agents
+researcher = Agent(
+    name="Researcher",
+    role="Senior Research Analyst",
+    goal="Uncover cutting-edge developments in AI and data science",
+    backstory="""You are an expert at a technology research group, 
+    skilled in identifying trends and analyzing complex data.""",
+    verbose=True,
+    allow_delegation=False,
+    tools=[internet_search_tool],
+    llm="gpt-4o",
+    markdown=True,
+    reflect_llm="gpt-4o",
+    min_reflect=2,
+    max_reflect=4
+)
+writer = Agent(
+    name="Writer",
+    role="Tech Content Strategist",
+    goal="Craft compelling content on tech advancements",
+    backstory="""You are a content strategist known for 
+    making complex tech topics interesting and easy to understand.""",
+    verbose=True,
+    allow_delegation=True,
+    llm="gpt-4o",
+    tools=[],
+    markdown=True
+)
 
-    task3 = Task(
-        name="json_task",
-        description="""Create a json object with a title of "My Task" and content of "My content".""",
-        expected_output="""JSON output with title and content""",
-        agent=researcher,
-    )
+# Create tasks
+task1 = Task(
+    name="research_task",
+    description="""Analyze 2024's AI advancements. 
+    Find major trends, new technologies, and their effects.""",
+    expected_output="""A detailed report on 2024 AI advancements""",
+    agent=researcher,
+    tools=[internet_search_tool]
+)
 
-    task4 = Task(
-        name="save_output_task",
-        description="""Save the AI blog post to a file""",
-        expected_output="""File saved successfully""",
-        agent=writer,
-        context=[task2],
-        output_file='outputs/ai_blog_post.txt',
-        create_directory=True
-    )
+task2 = Task(
+    name="writing_task",
+    description="""Create a blog post about major AI advancements using the insights you have.
+    Make it interesting, clear, and suited for tech enthusiasts. 
+    It should be at least 4 paragraphs long. 
+    Also, call the get_weather tool to get the weather in Paris.""",
+    expected_output="A blog post of at least 4 paragraphs, and weather in Paris",
+    agent=writer,
+    context=[task1],
+    callback=my_callback,
+    tools=[]
+)
 
-    # Create and run agents manager
-    agents = Agents(
-        agents=[researcher, writer],
-        tasks=[task1, task2, task3, task4],
-        verbose=False,
-        process="sequential",  # "sequential" or "hierarchical"
-        manager_llm="gpt-4o"
-    )
+task3 = Task(
+    name="json_task",
+    description="""Create a json object with a title of "My Task" and content of "My content".""",
+    expected_output="""JSON output with title and content""",
+    agent=researcher,
+)
 
-    result = agents.start()
+task4 = Task(
+    name="save_output_task",
+    description="""Save the AI blog post to a file""",
+    expected_output="""File saved successfully""",
+    agent=writer,
+    context=[task2],
+    output_file='outputs/ai_blog_post.txt',
+    create_directory=True
+)
 
-    # Print results and error summary
-    console = Console()
-    
-    # Print task results
-    console.print("\n=== Task Results ===")
-    for task_id, task_status in result['task_status'].items():
-        console.print(f"Task {task_id}: {task_status}")
-        if task_result := result['task_results'].get(task_id):
-            console.print(f"Output: {task_result.raw[:200]}...")  # Show first 200 chars
+# Create and run agents manager
+agents = PraisonAIAgents(
+    agents=[researcher, writer],
+    tasks=[task1, task2, task3, task4],
+    verbose=False,
+    process="sequential",  # "sequential" or "hierarchical"
+    manager_llm="gpt-4o"
+)
 
-    # Print task details
-    console.print("\n=== Task Details ===")
-    for i in range(4):
-        console.print(agents.get_task_details(i))
+result = agents.start()
 
-    # Print agent details
-    console.print("\n=== Agent Details ===")
-    console.print(agents.get_agent_details('Researcher'))
-    console.print(agents.get_agent_details('Writer'))
+# Print results and error summary
+print("\n=== Task Results ===")
+for task_id, task_status in result['task_status'].items():
+    print(f"Task {task_id}: {task_status}")
+    if task_result := result['task_results'].get(task_id):
+        print(f"Output: {task_result.raw[:200]}...")  # Show first 200 chars
 
-    # Print any errors
-    if error_logs:
-        console.print(Panel.fit(Text("Errors Encountered:", style="bold red"), title="Error Summary", border_style="red"))
-        for err in error_logs:
-            console.print(f"- {err}")
-            if "parsing self-reflection json" in err:
-                console.print("  Reason: The self-reflection JSON response was not valid JSON.")
-            elif "Error: Task with ID" in err:
-                console.print("  Reason: Task ID referenced does not exist.")
-            elif "saving task output to file" in err:
-                console.print("  Reason: Possible file permissions or invalid path.")
-            else:
-                console.print("  Reason not identified")
+# Print task details
+print("\n=== Task Details ===")
+for i in range(4):
+    print(agents.get_task_details(i))
 
-if __name__ == "__main__":
-    main() 
+# Print agent details
+print("\n=== Agent Details ===")
+print(agents.get_agent_details('Researcher'))
+print(agents.get_agent_details('Writer'))
+
+# Print any errors
+if error_logs:
+    print("\n=== Error Summary ===")
+    for err in error_logs:
+        print(f"- {err}")
+        if "parsing self-reflection json" in err:
+            print("  Reason: The self-reflection JSON response was not valid JSON.")
+        elif "Error: Task with ID" in err:
+            print("  Reason: Task ID referenced does not exist.")
+        elif "saving task output to file" in err:
+            print("  Reason: Possible file permissions or invalid path.")
+        else:
+            print("  Reason not identified")

agents/praisonaiagents/agent/agent.py

@@ -17,13 +17,123 @@ from ..main import (
 )
 
 class Agent:
+    def _generate_tool_definition(self, function_name):
+        """
+        Generate a tool definition from a function name by inspecting the function.
+        """
+        logging.debug(f"Attempting to generate tool definition for: {function_name}")
+        
+        # First try to get the tool definition if it exists
+        tool_def_name = f"{function_name}_definition"
+        tool_def = globals().get(tool_def_name)
+        logging.debug(f"Looking for {tool_def_name} in globals: {tool_def is not None}")
+        
+        if not tool_def:
+            import __main__
+            tool_def = getattr(__main__, tool_def_name, None)
+            logging.debug(f"Looking for {tool_def_name} in __main__: {tool_def is not None}")
+        
+        if tool_def:
+            logging.debug(f"Found tool definition: {tool_def}")
+            return tool_def
+
+        # Try to find the function in the agent's tools list first
+        func = None
+        for tool in self.tools:
+            if callable(tool) and getattr(tool, '__name__', '') == function_name:
+                func = tool
+                break
+        
+        logging.debug(f"Looking for {function_name} in agent tools: {func is not None}")
+        
+        # If not found in tools, try globals and main
+        if not func:
+            func = globals().get(function_name)
+            logging.debug(f"Looking for {function_name} in globals: {func is not None}")
+            
+            if not func:
+                import __main__
+                func = getattr(__main__, function_name, None)
+                logging.debug(f"Looking for {function_name} in __main__: {func is not None}")
+
+        if not func or not callable(func):
+            logging.debug(f"Function {function_name} not found or not callable")
+            return None
+
+        import inspect
+        sig = inspect.signature(func)
+        logging.debug(f"Function signature: {sig}")
+        
+        parameters = {
+            "type": "object",
+            "properties": {},
+            "required": []
+        }
+        
+        # Parse docstring for parameter descriptions
+        docstring = inspect.getdoc(func)
+        logging.debug(f"Function docstring: {docstring}")
+        
+        param_descriptions = {}
+        if docstring:
+            import re
+            param_section = re.split(r'\s*Args:\s*', docstring)
+            logging.debug(f"Param section split: {param_section}")
+            if len(param_section) > 1:
+                param_lines = param_section[1].split('\n')
+                for line in param_lines:
+                    line = line.strip()
+                    if line and ':' in line:
+                        param_name, param_desc = line.split(':', 1)
+                        param_descriptions[param_name.strip()] = param_desc.strip()
+        
+        logging.debug(f"Parameter descriptions: {param_descriptions}")
+
+        for name, param in sig.parameters.items():
+            param_type = "string"  # Default type
+            if param.annotation != inspect.Parameter.empty:
+                if param.annotation == int:
+                    param_type = "integer"
+                elif param.annotation == float:
+                    param_type = "number"
+                elif param.annotation == bool:
+                    param_type = "boolean"
+                elif param.annotation == list:
+                    param_type = "array"
+                elif param.annotation == dict:
+                    param_type = "object"
+            
+            param_info = {"type": param_type}
+            if name in param_descriptions:
+                param_info["description"] = param_descriptions[name]
+            
+            parameters["properties"][name] = param_info
+            if param.default == inspect.Parameter.empty:
+                parameters["required"].append(name)
+        
+        logging.debug(f"Generated parameters: {parameters}")
+
+        # Extract description from docstring
+        description = docstring.split('\n')[0] if docstring else f"Function {function_name}"
+        
+        tool_def = {
+            "type": "function",
+            "function": {
+                "name": function_name,
+                "description": description,
+                "parameters": parameters
+            }
+        }
+        logging.debug(f"Generated tool definition: {tool_def}")
+        return tool_def
+
     def __init__(
         self,
         name: str,
         role: str,
         goal: str,
         backstory: str,
-        llm: Optional[Union[str, Any]] = "gpt-4o-mini",
+        llm: Optional[Union[str, Any]] = "gpt-4o",
         tools: Optional[List[Any]] = None,
         function_calling_llm: Optional[Any] = None,
         max_iter: int = 20,
@@ -46,14 +156,16 @@ class Agent:
         use_system_prompt: Optional[bool] = True,
         markdown: bool = True,
         self_reflect: bool = True,
-        max_reflection_iter: int = 3
+        max_reflect: int = 3,
+        min_reflect: int = 1,
+        reflect_llm: Optional[str] = None
     ):
         self.name = name
         self.role = role
         self.goal = goal
         self.backstory = backstory
         self.llm = llm
-        self.tools = tools if tools else []
+        self.tools = tools if tools else []  # Store original tools
         self.function_calling_llm = function_calling_llm
         self.max_iter = max_iter
         self.max_rpm = max_rpm
@@ -76,21 +188,45 @@ class Agent:
         self.chat_history = []
         self.markdown = markdown
         self.self_reflect = self_reflect
-        self.max_reflection_iter = max_reflection_iter
-
+        self.max_reflect = max_reflect
+        self.min_reflect = min_reflect
+        self.reflect_llm = reflect_llm
     def execute_tool(self, function_name, arguments):
+        """
+        Execute a tool dynamically based on the function name and arguments.
+        """
         logging.debug(f"{self.name} executing tool {function_name} with arguments: {arguments}")
-        if function_name == "get_weather":
-            location = arguments.get("location", "Unknown Location")
-            return {"temperature": "25C", "condition": "Sunny", "location": location}
-        elif function_name == "search_tool":
-            query = arguments.get("query", "AI trends in 2024")
-            return {"results": [
-                {"title": "AI advancements in 2024", "link": "url1", "summary": "Lots of advancements"},
-                {"title": "New trends in AI", "link": "url2", "summary": "New trends being found"}
-            ]}
-        else:
-            return f"Tool '{function_name}' is not recognized"
+
+        # Try to find the function in the agent's tools list first
+        func = None
+        for tool in self.tools:
+            if callable(tool) and getattr(tool, '__name__', '') == function_name:
+                func = tool
+                break
+        
+        logging.debug(f"Looking for {function_name} in agent tools: {func is not None}")
+        
+        # If not found in tools, try globals and main
+        if not func:
+            func = globals().get(function_name)
+            logging.debug(f"Looking for {function_name} in globals: {func is not None}")
+            
+            if not func:
+                import __main__
+                func = getattr(__main__, function_name, None)
+                logging.debug(f"Looking for {function_name} in __main__: {func is not None}")
+
+        if func and callable(func):
+            try:
+                return func(**arguments)
+            except Exception as e:
+                error_msg = str(e)
+                logging.error(f"Error executing tool {function_name}: {error_msg}")
+                return {"error": error_msg}
+        
+        error_msg = f"Tool '{function_name}' is not callable"
+        logging.error(error_msg)
+        return {"error": error_msg}
 
     def clear_history(self):
         self.chat_history = []
@@ -104,26 +240,25 @@ class Agent:
         logging.debug(f"{self.name} sending messages to LLM: {messages}")
 
         formatted_tools = []
+        if tools is None:
+            tools = self.tools
         if tools:
             for tool in tools:
-                if isinstance(tool, dict):
+                if isinstance(tool, str):
+                    # Generate tool definition for string tool names
+                    tool_def = self._generate_tool_definition(tool)
+                    if tool_def:
+                        formatted_tools.append(tool_def)
+                    else:
+                        logging.warning(f"Could not generate definition for tool: {tool}")
+                elif isinstance(tool, dict):
                     formatted_tools.append(tool)
                 elif hasattr(tool, "to_openai_tool"):
                     formatted_tools.append(tool.to_openai_tool())
-                elif isinstance(tool, str):
-                    formatted_tools.append({
-                        "type": "function",
-                        "function": {
-                            "name": tool,
-                            "description": f"This is a tool called {tool}",
-                            "parameters": {
-                                "type": "object",
-                                "properties": {},
-                            },
-                        }
-                    })
+                elif callable(tool):
+                    formatted_tools.append(self._generate_tool_definition(tool.__name__))
                 else:
-                    display_error(f"Warning: Tool {tool} not recognized")
+                    logging.warning(f"Tool {tool} not recognized")
 
         try:
             initial_response = client.chat.completions.create(
@@ -202,8 +337,8 @@ class Agent:
     def chat(self, prompt, temperature=0.2, tools=None, output_json=None):
         if self.use_system_prompt:
             system_prompt = f"""{self.backstory}\n
-            Your Role: {self.role}\n
-            Your Goal: {self.goal}
+Your Role: {self.role}\n
+Your Goal: {self.goal}
             """
         else:
             system_prompt = None
@@ -223,29 +358,7 @@ class Agent:
                 if self.verbose:
                     display_instruction(f"Agent {self.name} is processing prompt: {prompt}")
 
-                formatted_tools = []
-                if tools:
-                    for tool in tools:
-                        if isinstance(tool, dict):
-                            formatted_tools.append(tool)
-                        elif hasattr(tool, "to_openai_tool"):
-                            formatted_tools.append(tool.to_openai_tool())
-                        elif isinstance(tool, str):
-                            formatted_tools.append({
-                                "type": "function",
-                                "function": {
-                                    "name": tool,
-                                    "description": f"This is a tool called {tool}",
-                                    "parameters": {
-                                        "type": "object",
-                                        "properties": {},
-                                    },
-                                }
-                            })
-                        else:
-                            display_error(f"Warning: Tool {tool} not recognized")
-
-                response = self._chat_completion(messages, temperature=temperature, tools=formatted_tools if formatted_tools else None)
+                response = self._chat_completion(messages, temperature=temperature, tools=tools if tools else None)
                 if not response:
                     return None
                     
@@ -298,17 +411,17 @@ class Agent:
                     return response_text
 
                 reflection_prompt = f"""
-                Reflect on your previous response: '{response_text}'.
-                Identify any flaws, improvements, or actions.
-                Provide a "satisfactory" status ('yes' or 'no').
-                Output MUST be JSON with 'reflection' and 'satisfactory'.
+Reflect on your previous response: '{response_text}'.
+Identify any flaws, improvements, or actions.
+Provide a "satisfactory" status ('yes' or 'no').
+Output MUST be JSON with 'reflection' and 'satisfactory'.
                 """
                 logging.debug(f"{self.name} reflection attempt {reflection_count+1}, sending prompt: {reflection_prompt}")
                 messages.append({"role": "user", "content": reflection_prompt})
 
                 try:
                     reflection_response = client.beta.chat.completions.parse(
-                        model=self.llm,
+                        model=self.reflect_llm if self.reflect_llm else self.llm,
                         messages=messages,
                         temperature=temperature,
                         response_format=ReflectionOutput
@@ -317,35 +430,42 @@ class Agent:
                     reflection_output = reflection_response.choices[0].message.parsed
 
                     if self.verbose:
-                        display_self_reflection(f"Agent {self.name} self reflection: reflection='{reflection_output.reflection}' satisfactory='{reflection_output.satisfactory}'")
+                        display_self_reflection(f"Agent {self.name} self reflection (using {self.reflect_llm if self.reflect_llm else self.llm}): reflection='{reflection_output.reflection}' satisfactory='{reflection_output.satisfactory}'")
 
                     messages.append({"role": "assistant", "content": f"Self Reflection: {reflection_output.reflection} Satisfactory?: {reflection_output.satisfactory}"})
 
-                    if reflection_output.satisfactory == "yes":
+                    # Only consider satisfactory after minimum reflections
+                    if reflection_output.satisfactory == "yes" and reflection_count >= self.min_reflect - 1:
                         if self.verbose:
-                            display_self_reflection("Agent marked the response as satisfactory")
+                            display_self_reflection("Agent marked the response as satisfactory after meeting minimum reflections")
+                        self.chat_history.append({"role": "user", "content": prompt})
                         self.chat_history.append({"role": "assistant", "content": response_text})
                         display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time)
                         return response_text
 
-                    logging.debug(f"{self.name} reflection not satisfactory, requesting regeneration.")
+                    # Check if we've hit max reflections
+                    if reflection_count >= self.max_reflect - 1:
+                        if self.verbose:
+                            display_self_reflection("Maximum reflection count reached, returning current response")
+                        self.chat_history.append({"role": "user", "content": prompt})
+                        self.chat_history.append({"role": "assistant", "content": response_text})
+                        display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time)
+                        return response_text
+
+                    logging.debug(f"{self.name} reflection count {reflection_count + 1}, continuing reflection process")
                     messages.append({"role": "user", "content": "Now regenerate your response using the reflection you made"})
                     response = self._chat_completion(messages, temperature=temperature, tools=None, stream=True)
                     response_text = response.choices[0].message.content.strip()
+                    reflection_count += 1
+                    continue  # Continue the loop for more reflections
+
                 except Exception as e:
                     display_error(f"Error in parsing self-reflection json {e}. Retrying")
                     logging.error("Reflection parsing failed.", exc_info=True)
                     messages.append({"role": "assistant", "content": f"Self Reflection failed."})
+                    reflection_count += 1
+                    continue  # Continue even after error to try again
                 
-                reflection_count += 1
-
-                self.chat_history.append({"role": "user", "content": prompt})
-                self.chat_history.append({"role": "assistant", "content": response_text})
-
-                if self.verbose:
-                    logging.info(f"Agent {self.name} final response: {response_text}")
-                display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time)
-                return response_text
             except Exception as e:
                 display_error(f"Error in chat: {e}")
                 return None 

agents/praisonaiagents/agents/agents.py

@@ -22,7 +22,9 @@ class PraisonAIAgents:
         self.verbose = verbose
         self.max_retries = max_retries
         self.process = process
-        self.manager_llm = manager_llm
+        if not manager_llm:
+            logging.debug("No manager_llm provided. Using OPENAI_MODEL_NAME environment variable or defaulting to 'gpt-4o'")
+        self.manager_llm = manager_llm if manager_llm else os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
         for task in tasks:
             self.add_task(task)
             task.status = "not started"
@@ -62,8 +64,8 @@ class PraisonAIAgents:
         executor_agent = task.agent
 
         task_prompt = f"""
-        You need to do the following task: {task.description}.
-        Expected Output: {task.expected_output}.
+You need to do the following task: {task.description}.
+Expected Output: {task.expected_output}.
         """
         if task.context:
             context_results = ""

agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.4"
+version = "0.0.12"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }

agents/uv.lock

@@ -186,7 +186,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.4"
+version = "0.0.12"
 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==2.0.7 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.16 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/index.md

@@ -10,7 +10,129 @@
 <a href="https://github.com/MervinPraison/PraisonAI"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License" /></a>
 </p>
 
-Praison AI, leveraging both AutoGen and CrewAI or any other agent framework, represents a low-code, centralised framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing ease of use, customization, and human-agent interaction.
+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.
+
+## Key Features
+
+- 🤖 Automated AI Agents Creation
+- 🔄 Use CrewAI or AutoGen Framework
+- 💯 100+ LLM Support
+- 💻 Chat with ENTIRE Codebase
+- 🖥️ Interactive UIs
+- 📄 YAML-based Configuration
+- 🛠️ Custom Tool Integration
+- 🔍 Internet Search Capability (using Crawl4AI and Tavily)
+- 👁️ Vision Language Model (VLM) Support
+- 🎙️ Real-time Voice Interaction
+
+## Using No Code
+
+### Auto Mode:
+```bash
+pip install praisonai
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+praisonai --auto create a movie script about Robots in Mars
+```
+
+### Initialise Mode:
+```bash
+pip install praisonai
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+praisonai --init create a movie script about Robots in Mars
+praisonai
+```
+
+## Using Coding
+
+Light weight package dedicated for coding:
+```bash
+pip install praisonaiagents
+```
+
+```bash
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+```
+
+Create app.py file and add the code below:
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# 1. Create agents
+researcher = Agent(
+    name="Researcher",
+    role="Senior Research Analyst",
+    goal="Uncover cutting-edge developments in AI and data science",
+    backstory="""You are an expert at a technology research group, 
+    skilled in identifying trends and analyzing complex data.""",
+    verbose=True,
+    llm="gpt-4o",
+    markdown=True
+)
+writer = Agent(
+    name="Writer",
+    role="Tech Content Strategist",
+    goal="Craft compelling content on tech advancements",
+    backstory="""You are a content strategist known for 
+    making complex tech topics interesting and easy to understand.""",
+    llm="gpt-4o",
+    markdown=True
+)
+
+# 2. Define Tasks
+task1 = Task(
+    name="research_task",
+    description="""Analyze 2024's AI advancements. 
+    Find major trends, new technologies, and their effects.""",
+    expected_output="""A detailed report on 2024 AI advancements""",
+    agent=researcher
+)
+
+task2 = Task(
+    name="writing_task",
+    description="""Create a blog post about major AI advancements using the insights you have.
+    Make it interesting, clear, and suited for tech enthusiasts. 
+    It should be at least 4 paragraphs long.""",
+    expected_output="A blog post of at least 4 paragraphs",
+    agent=writer,
+)
+
+agents = PraisonAIAgents(
+    agents=[researcher, writer],
+    tasks=[task1, task2],
+    verbose=False,
+    process="hierarchical",
+    manager_llm="gpt-4o"
+)
+
+result = agents.start()
+```
+
+Run:
+```bash
+python app.py
+```
+
+## Ollama Integration
+```bash
+export OPENAI_BASE_URL=http://localhost:11434/v1
+```
+
+## Groq Integration
+Replace xxxx with Groq API KEY:
+```bash
+export OPENAI_API_KEY=xxxxxxxxxxx
+export OPENAI_BASE_URL=https://api.groq.com/openai/v1
+```
+
+## Logging
+```bash
+export LOGLEVEL=info
+```
+
+Advanced logging:
+```bash
+export LOGLEVEL=debug
+```
 
 <div align="center">
   <picture>

poetry.lock

@@ -844,116 +844,103 @@ watchfiles = ">=0.20.0,<0.21.0"
 
 [[package]]
 name = "charset-normalizer"
-version = "3.4.0"
+version = "3.4.1"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.7"
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 ]
 
 [[package]]
@@ -1047,17 +1034,17 @@ uvicorn = {version = ">=0.18.3", extras = ["standard"]}
 
 [[package]]
 name = "clarifai"
-version = "10.7.0"
+version = "10.8.0"
 description = "Clarifai Python SDK"
 optional = true
 python-versions = ">=3.8"
 files = [
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+    {file = "clarifai-10.8.0.tar.gz", hash = "sha256:068fbe63ac0d7d51630db6671db5e52b707f6d72d8e69c1d46092a84ca27a1a9"},
 ]
 
 [package.dependencies]
-clarifai-grpc = ">=10.7.0"
+clarifai-grpc = ">=10.8.0"
 inquirerpy = "0.3.4"
 numpy = ">=1.22.0"
 Pillow = ">=9.5.0"
@@ -1073,18 +1060,18 @@ all = ["pycocotools (==2.0.6)"]
 
 [[package]]
 name = "clarifai-grpc"
-version = "10.7.1"
+version = "10.11.2"
 description = "Clarifai gRPC API Client"
 optional = true
 python-versions = ">=3.8"
 files = [
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+    {file = "clarifai_grpc-10.11.2.tar.gz", hash = "sha256:0a9c614bb2634d82d9abbe19cc60d4ddc3bf68041201f71debe6810575cb2bc3"},
 ]
 
 [package.dependencies]
 googleapis-common-protos = ">=1.53.0"
-grpcio = ">=1.44.0"
+grpcio = ">=1.44.0,<1.65.0"
 protobuf = ">=3.20.3"
 requests = ">=2.25.1"
 
@@ -1496,13 +1483,13 @@ files = [
 
 [[package]]
 name = "duckduckgo-search"
-version = "7.0.2"
+version = "7.1.1"
 description = "Search for words, documents, images, news, maps and text translation using the DuckDuckGo.com search engine."
 optional = true
 python-versions = ">=3.8"
 files = [
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+    {file = "duckduckgo_search-7.1.1-py3-none-any.whl", hash = "sha256:753b2237fd8a9a4cbf06c788aaf0cfc7e00f4623e8f854d0525682ff31770093"},
+    {file = "duckduckgo_search-7.1.1.tar.gz", hash = "sha256:1874e55aa6eac3de2d0e9b556b70f35ed0cd7a4544d7417cf38216275b3de009"},
 ]
 
 [package.dependencies]
@@ -2538,13 +2525,13 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "griffe"
-version = "1.5.1"
+version = "1.5.4"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
 optional = false
 python-versions = ">=3.9"
 files = [
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+    {file = "griffe-1.5.4-py3-none-any.whl", hash = "sha256:ed33af890586a5bebc842fcb919fc694b3dc1bc55b7d9e0228de41ce566b4a1d"},
+    {file = "griffe-1.5.4.tar.gz", hash = "sha256:073e78ad3e10c8378c2f798bd4ef87b92d8411e9916e157fd366a17cc4fd4e52"},
 ]
 
 [package.dependencies]
@@ -2568,70 +2555,61 @@ protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4
 
 [[package]]
 name = "grpcio"
-version = "1.68.1"
+version = "1.64.3"
 description = "HTTP/2-based RPC framework"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "grpcio-1.64.3-cp39-cp39-win32.whl", hash = "sha256:067a1d9539e4822f03982d0736e2e6890b356a255d82452921555165154a3c1e"},
+    {file = "grpcio-1.64.3-cp39-cp39-win_amd64.whl", hash = "sha256:ca272e96578a844a6cb4f3b461c419ae9c30c039cb7ff4e69624d68005ef9c38"},
+    {file = "grpcio-1.64.3.tar.gz", hash = "sha256:f37a0297293918c695e625d7148f99f4e401298d1b6e2bea7a8e9130aa940419"},
 ]
 
 [package.extras]
-protobuf = ["grpcio-tools (>=1.68.1)"]
+protobuf = ["grpcio-tools (>=1.64.3)"]
 
 [[package]]
 name = "grpcio-status"
@@ -4242,13 +4220,13 @@ python-legacy = ["mkdocstrings-python-legacy (>=0.2.1)"]
 
 [[package]]
 name = "mkdocstrings-python"
-version = "1.12.2"
+version = "1.13.0"
 description = "A Python handler for mkdocstrings."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "mkdocstrings_python-1.12.2-py3-none-any.whl", hash = "sha256:7f7d40d6db3cb1f5d19dbcd80e3efe4d0ba32b073272c0c0de9de2e604eda62a"},
-    {file = "mkdocstrings_python-1.12.2.tar.gz", hash = "sha256:7a1760941c0b52a2cd87b960a9e21112ffe52e7df9d0b9583d04d47ed2e186f3"},
+    {file = "mkdocstrings_python-1.13.0-py3-none-any.whl", hash = "sha256:b88bbb207bab4086434743849f8e796788b373bd32e7bfefbf8560ac45d88f97"},
+    {file = "mkdocstrings_python-1.13.0.tar.gz", hash = "sha256:2dbd5757e8375b9720e81db16f52f1856bf59905428fd7ef88005d1370e2f64c"},
 ]
 
 [package.dependencies]
@@ -5498,13 +5476,13 @@ selenium = ">=4.18.1,<5.0.0"
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.4"
+version = "0.0.8"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 optional = false
 python-versions = "*"
 files = [
-    {file = "praisonaiagents-0.0.4-py3-none-any.whl", hash = "sha256:5fec575c87e5eec0cb8970f32f22f990494539f654c3ffade7d0b2dfa3b9d515"},
-    {file = "praisonaiagents-0.0.4.tar.gz", hash = "sha256:4c483f9797d5d48198cc4d528c94bda6e7dd431935c50b2c04e947268141110c"},
+    {file = "praisonaiagents-0.0.8-py3-none-any.whl", hash = "sha256:f290a32da08f329a2b7fbe84039aaea1e49a0dd0060cc8821567f6bf25320657"},
+    {file = "praisonaiagents-0.0.8.tar.gz", hash = "sha256:b442dddb3a45248201006a658d3799699869b045af1bc0b0a073e4c1433db987"},
 ]
 
 [package.dependencies]
@@ -7982,13 +7960,13 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "trio"
-version = "0.27.0"
+version = "0.28.0"
 description = "A friendly Python library for async concurrency and I/O"
 optional = true
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "trio-0.27.0-py3-none-any.whl", hash = "sha256:68eabbcf8f457d925df62da780eff15ff5dc68fd6b367e2dde59f7aaf2a0b884"},
-    {file = "trio-0.27.0.tar.gz", hash = "sha256:1dcc95ab1726b2da054afea8fd761af74bad79bd52381b84eae408e983c76831"},
+    {file = "trio-0.28.0-py3-none-any.whl", hash = "sha256:56d58977acc1635735a96581ec70513cc781b8b6decd299c487d3be2a721cd94"},
+    {file = "trio-0.28.0.tar.gz", hash = "sha256:4e547896fe9e8a5658e54e4c7c5fa1db748cbbbaa7c965e7d40505b928c73c05"},
 ]
 
 [package.dependencies]
@@ -8821,12 +8799,12 @@ type = ["pytest-mypy"]
 agentops = ["agentops"]
 anthropic = ["langchain-anthropic"]
 api = ["flask"]
-autogen = ["crewai", "instructor", "praisonai-tools", "pyautogen"]
-call = ["fastapi", "flaml", "instructor", "openai", "pyngrok", "python-dotenv", "rich", "twilio", "uvicorn", "websockets"]
-chat = ["aiosqlite", "chainlit", "crawl4ai", "greenlet", "instructor", "litellm", "playwright", "pydantic", "rich", "sqlalchemy", "tavily-python"]
+autogen = ["crewai", "praisonai-tools", "pyautogen"]
+call = ["fastapi", "flaml", "openai", "pyngrok", "rich", "twilio", "uvicorn", "websockets"]
+chat = ["aiosqlite", "chainlit", "crawl4ai", "greenlet", "litellm", "playwright", "pydantic", "rich", "sqlalchemy", "tavily-python"]
 code = ["aiosqlite", "chainlit", "crawl4ai", "greenlet", "litellm", "playwright", "pydantic", "sqlalchemy", "tavily-python"]
 cohere = ["langchain-cohere"]
-crewai = ["crewai", "instructor", "praisonai-tools"]
+crewai = ["crewai", "praisonai-tools"]
 google = ["langchain-google-genai"]
 gradio = ["gradio"]
 openai = ["langchain-openai"]
@@ -8837,4 +8815,4 @@ ui = ["chainlit"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "e1864537982b4b9e6b8f063bcb355652f81b3b23ae73945cb3c82f360940bfab"
+content-hash = "e92a18d8479733651141cd833ee1f1a53c863eb65b8a1a0b7360e2f483460f36"

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/2.0.7.tar.gz"
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.16.tar.gz"
     sha256 "1828fb9227d10f991522c3f24f061943a254b667196b40b1a3e4a54a8d30ce32"  # Replace with actual SHA256 checksum
     license "MIT"
   

praisonai/agents_generator.py

@@ -200,6 +200,45 @@ class AgentsGenerator:
                     tools_dict[name] = obj
         return tools_dict
 
+    def load_tools_from_tools_py(self):
+        """
+        Imports and returns all contents from tools.py file.
+        Also adds the tools to the global namespace.
+
+        Returns:
+            list: A list of callable functions with proper formatting
+        """
+        tools_list = []
+        try:
+            # Try to import tools.py from current directory
+            spec = importlib.util.spec_from_file_location("tools", "tools.py")
+            self.logger.info(f"Spec: {spec}")
+            if spec is None:
+                self.logger.info("tools.py not found in current directory")
+                return tools_list
+
+            module = importlib.util.module_from_spec(spec)
+            spec.loader.exec_module(module)
+
+            # Get all module attributes except private ones and classes
+            for name, obj in inspect.getmembers(module):
+                if (not name.startswith('_') and 
+                    callable(obj) and 
+                    not inspect.isclass(obj)):
+                    # Add the function to global namespace
+                    globals()[name] = obj
+                    # Add to tools list
+                    tools_list.append(obj)
+                    self.logger.info(f"Loaded and globalized tool function: {name}")
+
+            self.logger.info(f"Loaded {len(tools_list)} tool functions from tools.py")
+            self.logger.info(f"Tools list: {tools_list}")
+            
+        except Exception as e:
+            self.logger.warning(f"Error loading tools from tools.py: {e}")
+            
+        return tools_list
+
     def generate_crew_and_kickoff(self):
         """
         Generates a crew of agents and initiates tasks based on the provided configuration.
@@ -271,7 +310,7 @@ class AgentsGenerator:
         elif tools_dir_path.is_dir():
             tools_dict.update(self.load_tools_from_module_class(tools_dir_path))
             self.logger.debug("tools folder exists in the root directory")
-        
+
         framework = self.framework or config.get('framework')
 
         if framework == "autogen":
@@ -498,40 +537,26 @@ class AgentsGenerator:
         tasks = []
         tasks_dict = {}
 
+        # Load tools once at the beginning
+        tools_list = self.load_tools_from_tools_py()
+        self.logger.info(f"Loaded tools: {tools_list}")
+
         # Create agents from config
         for role, details in config['roles'].items():
             role_filled = details['role'].format(topic=topic)
             goal_filled = details['goal'].format(topic=topic)
             backstory_filled = details['backstory'].format(topic=topic)
             
-            # Get agent tools
-            agent_tools = [tools_dict[tool] for tool in details.get('tools', []) 
-                          if tool in tools_dict]
-            
-            # Configure LLM
-            llm_model = details.get('llm')
-            if llm_model:
-                llm = llm_model.get("model", os.environ.get("MODEL_NAME", "gpt-4o"))
-            else:
-                llm = os.environ.get("MODEL_NAME", "gpt-4o")
-
-            # Configure function calling LLM
-            function_calling_llm_model = details.get('function_calling_llm')
-            if function_calling_llm_model:
-                function_calling_llm = function_calling_llm_model.get("model", os.environ.get("MODEL_NAME", "openai/gpt-4o"))
-            else:
-                function_calling_llm = os.environ.get("MODEL_NAME", "gpt-4o")
-            
-            # Create PraisonAI agent
+            # Pass all loaded tools to the agent
             agent = PraisonAgent(
                 name=role_filled,
                 role=role_filled,
                 goal=goal_filled,
                 backstory=backstory_filled,
-                tools=agent_tools,
+                tools=tools_list,  # Pass the entire tools list to the agent
                 allow_delegation=details.get('allow_delegation', False),
-                llm=llm,
-                function_calling_llm=function_calling_llm,
+                llm=details.get('llm', {}).get("model", os.environ.get("MODEL_NAME", "gpt-4o")),
+                function_calling_llm=details.get('function_calling_llm', {}).get("model", os.environ.get("MODEL_NAME", "gpt-4o")),
                 max_iter=details.get('max_iter', 15),
                 max_rpm=details.get('max_rpm'),
                 max_execution_time=details.get('max_execution_time'),
@@ -540,25 +565,27 @@ class AgentsGenerator:
                 system_template=details.get('system_template'),
                 prompt_template=details.get('prompt_template'),
                 response_template=details.get('response_template'),
+                reflect_llm=details.get('reflect_llm', {}).get("model", os.environ.get("MODEL_NAME", "gpt-4o")),
+                min_reflect=details.get('min_reflect', 1),
+                max_reflect=details.get('max_reflect', 3),
             )
             
-            # Set agent callback if provided
             if self.agent_callback:
                 agent.step_callback = self.agent_callback
 
             agents[role] = agent
+            self.logger.info(f"Created agent {role_filled} with tools: {agent.tools}")
 
             # Create tasks for the agent
             for task_name, task_details in details.get('tasks', {}).items():
                 description_filled = task_details['description'].format(topic=topic)
                 expected_output_filled = task_details['expected_output'].format(topic=topic)
 
-                # Create task using PraisonAI Task class
                 task = PraisonTask(
                     description=description_filled,
                     expected_output=expected_output_filled,
                     agent=agent,
-                    tools=task_details.get('tools', []),
+                    tools=tools_list,  # Pass the same tools list to the task
                     async_execution=task_details.get('async_execution', False),
                     context=[],
                     config=task_details.get('config', {}),
@@ -568,8 +595,9 @@ class AgentsGenerator:
                     callback=task_details.get('callback'),
                     create_directory=task_details.get('create_directory', False)
                 )
+
+                self.logger.info(f"Created task {task_name} with tools: {task.tools}")
                 
-                # Set task callback if provided
                 if self.task_callback:
                     task.callback = self.task_callback
 
@@ -581,7 +609,7 @@ class AgentsGenerator:
             for task_name, task_details in details.get('tasks', {}).items():
                 task = tasks_dict[task_name]
                 context_tasks = [tasks_dict[ctx] for ctx in task_details.get('context', []) 
-                               if ctx in tasks_dict]
+                            if ctx in tasks_dict]
                 task.context = context_tasks
 
         # Create and run the PraisonAI agents
@@ -599,13 +627,12 @@ class AgentsGenerator:
                 tasks=tasks,
                 verbose=2
             )
-        
+
         self.logger.debug("Final Configuration:")
         self.logger.debug(f"Agents: {agents.agents}")
         self.logger.debug(f"Tasks: {agents.tasks}")
 
         response = agents.start()
-        # result = f"### Task Output ###\n{response}"
         self.logger.debug(f"Result: {response}")
         result = ""
         

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==2.0.7 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.0.16 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

pyproject.toml

@@ -1,18 +1,85 @@
 [project]
 name = "PraisonAI"
-version = "2.0.7"
-description = "PraisonAI application combines AutoGen and CrewAI or similar frameworks into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customization, and efficient human-agent collaboration."
+version = "2.0.16"
+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 = ""
 requires-python = ">=3.10,<3.13"
 authors = [
     { name = "Mervin Praison" }
 ]
+dependencies = [
+    "rich>=13.7",
+    "markdown>=3.5",
+    "pyparsing>=3.0.0",
+    "praisonaiagents>=0.0.12",
+    "python-dotenv>=0.19.0",
+    "instructor>=1.3.3",
+    "PyYAML>=6.0",
+]
+
+[project.optional-dependencies]
+ui = ["chainlit==2.0rc1"]
+gradio = ["gradio>=4.26.0"]
+api = ["flask>=3.0.0"]
+agentops = ["agentops>=0.3.12"]
+google = ["langchain-google-genai>=1.0.4"]
+openai = ["langchain-openai>=0.1.7"]
+anthropic = ["langchain-anthropic>=0.1.13"]
+cohere = ["langchain-cohere>=0.1.4"]
+chat = [
+    "chainlit==2.0rc1",
+    "litellm>=1.41.8",
+    "aiosqlite>=0.20.0",
+    "greenlet>=3.0.3",
+    "tavily-python==0.5.0",
+    "crawl4ai==0.3.4",
+    "sqlalchemy>=2.0.36",
+    "playwright>=1.47.0",
+    "rich",
+    "pydantic<=2.10.1"
+]
+code = [
+    "chainlit==2.0rc1",
+    "litellm>=1.41.8",
+    "aiosqlite>=0.20.0",
+    "greenlet>=3.0.3",
+    "tavily-python==0.5.0",
+    "crawl4ai==0.3.4",
+    "sqlalchemy>=2.0.36",
+    "playwright>=1.47.0",
+    "pydantic<=2.10.1"
+]
+realtime = [
+    "chainlit==2.0rc1",
+    "litellm>=1.41.8",
+    "aiosqlite>=0.20.0",
+    "greenlet>=3.0.3",
+    "tavily-python==0.5.0",
+    "crawl4ai==0.3.4",
+    "websockets>=12.0",
+    "plotly>=5.24.0",
+    "yfinance>=0.2.44",
+    "duckduckgo_search>=6.3.0",
+    "sqlalchemy>=2.0.36"
+]
+call = [
+    "twilio>=7.0.0",
+    "fastapi>=0.95.0",
+    "uvicorn>=0.20.0",
+    "websockets>=12.0",
+    "flaml[automl]>=2.3.1",
+    "pyngrok>=1.4.0",
+    "rich",
+    "openai>=1.54.0",
+]
+crewai = ["crewai>=0.32.0", "praisonai-tools>=0.0.7"]
+autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.7", "crewai"]
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.0.7"
-description = "PraisonAI application combines AutoGen and CrewAI or similar frameworks into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customization, and efficient human-agent collaboration."
+version = "2.0.16"
+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 = ""
 readme = "README.md"
@@ -29,9 +96,9 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.4"
-python-dotenv = ">=1.0.1"
-instructor = ">=0.4.8"
+praisonaiagents = ">=0.0.12"
+python-dotenv = ">=0.19.0"
+instructor = ">=1.3.3"
 PyYAML = ">=6.0"
 pyautogen = {version = ">=0.2.19", optional = true}
 crewai = {version = ">=0.32.0", optional = true}
@@ -144,7 +211,6 @@ chat = [
     "sqlalchemy",
     "playwright",
     "rich",
-    "instructor",
     "pydantic"
 ]
 code = [
@@ -176,16 +242,14 @@ call = [
     "twilio", 
     "fastapi", 
     "uvicorn", 
-    "websockets", 
-    "python-dotenv", 
+    "websockets",
     "flaml", 
     "pyngrok", 
     "rich",
     "openai",
-    "instructor"
 ]
-crewai = ["crewai", "praisonai-tools", "instructor"]
-autogen = ["pyautogen", "praisonai-tools", "instructor", "crewai"]
+crewai = ["crewai", "praisonai-tools"]
+autogen = ["pyautogen", "praisonai-tools", "crewai"]
 
 [tool.poetry-dynamic-versioning]
 enable = true

tools.py

@@ -1,5 +1,52 @@
 # tools.py
 
+from duckduckgo_search import DDGS
+
+def search_tool(query: str) -> list:
+    """
+    Perform a web search using DuckDuckGo and return relevant results.
+
+    Args:
+        query (str): The search query string to look up information about.
+
+    Returns:
+        list: A list of dictionaries containing search results with the following keys:
+            - title (str): The title of the search result
+            - url (str): The URL of the search result
+            - snippet (str): A brief excerpt or description of the search result
+    """
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=10):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", ""),
+            })
+        return results
+
+    except Exception as e:
+        print(f"Error during DuckDuckGo search: {e}")
+        return []
+
+# # Define tools
+# search_tool = {
+#     "type": "function",
+#     "function": {
+#         "name": "search_tool",
+#         "description": "Use this to perform search queries",
+#         "parameters": {
+#             "type": "object",
+#             "properties": {
+#                 "query": {"type": "string"},
+#             },
+#             "required": ["query"],
+#         },
+#     },
+# }
+
+
 # from duckduckgo_search import DDGS
 # from praisonai_tools import BaseTool
 # class InternetSearchTool(BaseTool):

uv.lock

@@ -427,11 +427,11 @@ wheels = [
 
 [[package]]
 name = "certifi"
-version = "2024.8.30"
+version = "2024.12.14"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b0/ee/9b19140fe824b367c04c5e1b369942dd754c4c5462d5674002f75c4dedc1/certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9", size = 168507 }
+sdist = { url = "https://files.pythonhosted.org/packages/0f/bd/1d41ee578ce09523c81a15426705dd20969f5abf006d1afe8aeff0dd776a/certifi-2024.12.14.tar.gz", hash = "sha256:b650d30f370c2b724812bee08008be0c4163b163ddaec3f2546c1caf65f191db", size = 166010 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/12/90/3c9ff0512038035f59d279fddeb79f5f1eccd8859f06d6163c58798b9487/certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8", size = 167321 },
+    { url = "https://files.pythonhosted.org/packages/a5/32/8f6669fc4798494966bf446c8c4a162e0b5d893dff088afddf76414f70e1/certifi-2024.12.14-py3-none-any.whl", hash = "sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56", size = 164927 },
 ]
 
 [[package]]
@@ -516,56 +516,50 @@ wheels = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.4.0"
+version = "3.4.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/f2/4f/e1808dc01273379acc506d18f1504eb2d299bd4131743b9fc54d7be4df1e/charset_normalizer-3.4.0.tar.gz", hash = "sha256:223217c3d4f82c3ac5e29032b3f1c2eb0fb591b72161f86d93f5719079dae93e", size = 106620 }
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