Merge pull request #268 from MervinPraison/develop

adding structured agents and code agents
2026-11-09 03:39:37 -10:00 · 2025-01-06 18:38:51 +00:00 · 2025-01-06 18:26:56 +00:00 · 2025-01-06 10:06:33 +00:00 · 2025-01-06 10:06:11 +00:00 · 2025-01-06 09:52:09 +00:00
32 changed files with 2946 additions and 789 deletions
@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.24 gunicorn markdown
+RUN pip install flask praisonai==2.0.32 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]
@@ -7,12 +7,8 @@ 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
+    min_reflect: 1
+    max_reflect: 2
    tasks:
      research_task:
        description: Research about Mars, its environment, and the feasibility of
@@ -25,8 +21,6 @@ roles:
      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
@@ -39,8 +33,6 @@ 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,
@@ -0,0 +1,50 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import json
+from e2b_code_interpreter import Sandbox
+
+def code_interpreter(code: str):
+    """
+    A function to demonstrate running Python code dynamically using e2b_code_interpreter.
+    """
+    print(f"\n{'='*50}\n> Running following AI-generated code:\n{code}\n{'='*50}")
+    exec_result = Sandbox().run_code(code)
+    if exec_result.error:
+        print("[Code Interpreter error]", exec_result.error)
+        return {"error": str(exec_result.error)}
+    else:
+        results = []
+        for result in exec_result.results:
+            if hasattr(result, '__iter__'):
+                results.extend(list(result))
+            else:
+                results.append(str(result))
+        logs = {"stdout": list(exec_result.logs.stdout), "stderr": list(exec_result.logs.stderr)}
+        return json.dumps({"results": results, "logs": logs})
+
+code_agent = Agent(
+    name="code_agent",
+    llm="gpt-4o-mini",
+    backstory="Expert in writing Python scripts",
+    self_reflect=False
+)
+execution_agent = Agent(
+    name="execution_agent",
+    llm="gpt-4o-mini",
+    backstory="Expert in executing Python scripts",
+    self_reflect=False,
+    tools=[code_interpreter]
+)
+
+code_agent_task = Task(
+    description="Write a simple Python script to print 'Hello, World!'",
+    expected_output="A Python script that prints 'Hello, World!'",
+    agent=code_agent
+)
+execution_agent_task = Task(
+    description="Execute the Python script",
+    expected_output="The output of the Python script",
+    agent=execution_agent
+)
+
+agents = PraisonAIAgents(agents=[code_agent, execution_agent], tasks=[code_agent_task, execution_agent_task])
+agents.start()
@@ -1,4 +1,12 @@
-from praisonaiagents import Agent, Task, PraisonAIAgents, error_logs, register_display_callback
+from praisonaiagents import (
+    Agent, 
+    Task, 
+    PraisonAIAgents, 
+    error_logs, 
+    register_display_callback,
+    sync_display_callbacks,
+    async_display_callbacks
+)
 from duckduckgo_search import DDGS
 from rich.console import Console
 import json
@@ -0,0 +1,6 @@
+from praisonaiagents import Agent, Agents, Tools
+
+research_agent = Agent(instructions="You are a research agent to search internet about AI 2024", tools=[Tools.internet_search])
+summarise_agent = Agent(instructions="You are a summarize agent to summarise in points")
+agents = Agents(agents=[research_agent, summarise_agent])
+agents.start()
@@ -5,6 +5,7 @@ Praison AI Agents - A package for hierarchical AI agent task execution
 from .agent.agent import Agent
 from .agents.agents import PraisonAIAgents
 from .task.task import Task
+from .tools.tools import Tools
 from .main import (
    TaskOutput,
    ReflectionOutput,
@@ -17,12 +18,18 @@ from .main import (
    clean_triple_backticks,
    error_logs,
    register_display_callback,
-    display_callbacks,
+    sync_display_callbacks,
+    async_display_callbacks,
 )

+# Add Agents as an alias for PraisonAIAgents
+Agents = PraisonAIAgents
+
 __all__ = [
    'Agent',
    'PraisonAIAgents',
+    'Agents',
+    'Tools',
    'Task',
    'TaskOutput',
    'ReflectionOutput',
@@ -35,5 +42,6 @@ __all__ = [
    'clean_triple_backticks',
    'error_logs',
    'register_display_callback',
-    'display_callbacks',
+    'sync_display_callbacks',
+    'async_display_callbacks',
 ] 
@@ -3,7 +3,7 @@ import time
 import json
 import logging
 import asyncio
-from typing import List, Optional, Any, Dict, Union, Literal
+from typing import List, Optional, Any, Dict, Union, Literal, TYPE_CHECKING
 from rich.console import Console
 from rich.live import Live
 from openai import AsyncOpenAI
@@ -19,6 +19,9 @@ from ..main import (
    error_logs
 )

+if TYPE_CHECKING:
+    from ..task.task import Task
+
 class Agent:
    def _generate_tool_definition(self, function_name):
        """
@@ -132,11 +135,12 @@ class Agent:

    def __init__(
        self,
-        name: str,
-        role: str,
-        goal: str,
-        backstory: str,
-        llm: Optional[Union[str, Any]] = "gpt-4o",
+        name: Optional[str] = None,
+        role: Optional[str] = None,
+        goal: Optional[str] = None,
+        backstory: Optional[str] = None,
+        instructions: Optional[str] = None,
+        llm: Optional[Union[str, Any]] = None,
        tools: Optional[List[Any]] = None,
        function_calling_llm: Optional[Any] = None,
        max_iter: int = 20,
@@ -158,16 +162,35 @@ class Agent:
        knowledge_sources: Optional[List[Any]] = None,
        use_system_prompt: Optional[bool] = True,
        markdown: bool = True,
-        self_reflect: bool = True,
+        self_reflect: Optional[bool] = None,
        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
+        # Handle backward compatibility for required fields
+        if all(x is None for x in [name, role, goal, backstory, instructions]):
+            raise ValueError("At least one of name, role, goal, backstory, or instructions must be provided")
+
+        # If instructions are provided, use them to set role, goal, and backstory
+        if instructions:
+            self.name = name or "Agent"
+            self.role = role or "Assistant"
+            self.goal = goal or instructions
+            self.backstory = backstory or instructions
+            # Set self_reflect to False by default for instruction-based agents
+            self.self_reflect = False if self_reflect is None else self_reflect
+        else:
+            # Use provided values or defaults
+            self.name = name or "Agent"
+            self.role = role or "Assistant"
+            self.goal = goal or "Help the user with their tasks"
+            self.backstory = backstory or "I am an AI assistant"
+            # Default to True for traditional agents if not specified
+            self.self_reflect = True if self_reflect is None else self_reflect
+        
+        self.instructions = instructions
+        # Check for model name in environment variable if not provided
+        self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
        self.tools = tools if tools else []  # Store original tools
        self.function_calling_llm = function_calling_llm
        self.max_iter = max_iter
@@ -190,10 +213,10 @@ class Agent:
        self.use_system_prompt = use_system_prompt
        self.chat_history = []
        self.markdown = markdown
-        self.self_reflect = self_reflect
        self.max_reflect = max_reflect
        self.min_reflect = min_reflect
-        self.reflect_llm = reflect_llm
+        # Use the same model selection logic for reflect_llm
+        self.reflect_llm = reflect_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
        self.console = Console()  # Create a single console instance for the agent
        
        # Initialize system prompt
@@ -202,6 +225,21 @@ Your Role: {self.role}\n
 Your Goal: {self.goal}
        """

+    def generate_task(self) -> 'Task':
+        """Generate a Task object from the agent's instructions"""
+        from ..task.task import Task
+        
+        description = self.instructions if self.instructions else f"Execute task as {self.role} with goal: {self.goal}"
+        expected_output = "Complete the assigned task successfully"
+        
+        return Task(
+            name=self.name,
+            description=description,
+            expected_output=expected_output,
+            agent=self,
+            tools=self.tools
+        )
+
    def execute_tool(self, function_name, arguments):
        """
        Execute a tool dynamically based on the function name and arguments.
@@ -356,7 +394,7 @@ Your Goal: {self.goal}
            display_error(f"Error in chat completion: {e}")
            return None

-    def chat(self, prompt, temperature=0.2, tools=None, output_json=None):
+    def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
        if self.use_system_prompt:
            system_prompt = f"""{self.backstory}\n
 Your Role: {self.role}\n
@@ -364,6 +402,8 @@ Your Goal: {self.goal}
            """
            if output_json:
                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_json.schema_json()}"
+            elif output_pydantic:
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_pydantic.schema_json()}"
        else:
            system_prompt = None

@@ -372,9 +412,9 @@ Your Goal: {self.goal}
            messages.append({"role": "system", "content": system_prompt})
        messages.extend(self.chat_history)

-        # Modify prompt if output_json is specified
+        # Modify prompt if output_json or output_pydantic is specified
        original_prompt = prompt
-        if output_json:
+        if output_json or output_pydantic:
            if isinstance(prompt, str):
                prompt += "\nReturn ONLY a valid JSON object. No other text or explanation."
            elif isinstance(prompt, list):
@@ -449,23 +489,15 @@ Your Goal: {self.goal}
                        return None
                    response_text = response.choices[0].message.content.strip()

-                # Handle output_json if specified
-                if output_json:
-                    try:
-                        # Clean the response text to get only JSON
-                        cleaned_json = self.clean_json_output(response_text)
-                        # Parse into Pydantic model
-                        parsed_model = output_json.model_validate_json(cleaned_json)
-                        # Add to chat history and return
-                        self.chat_history.append({"role": "user", "content": original_prompt})
-                        self.chat_history.append({"role": "assistant", "content": response_text})
-                        if self.verbose:
-                            display_interaction(original_prompt, response_text, markdown=self.markdown, 
-                                             generation_time=time.time() - start_time, console=self.console)
-                        return parsed_model
-                    except Exception as e:
-                        display_error(f"Failed to parse response as {output_json.__name__}: {e}")
-                        return None
+                # Handle output_json or output_pydantic if specified
+                if output_json or output_pydantic:
+                    # Add to chat history and return raw response
+                    self.chat_history.append({"role": "user", "content": original_prompt})
+                    self.chat_history.append({"role": "assistant", "content": response_text})
+                    if self.verbose:
+                        display_interaction(original_prompt, response_text, markdown=self.markdown, 
+                                         generation_time=time.time() - start_time, console=self.console)
+                    return response_text

                if not self.self_reflect:
                    self.chat_history.append({"role": "user", "content": original_prompt})
@@ -547,19 +579,21 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
            cleaned = cleaned[:-3].strip()
        return cleaned 

-    async def achat(self, prompt, temperature=0.2, tools=None, output_json=None):
+    async def achat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
        """Async version of chat method"""
        try:
            # Build system prompt
            system_prompt = self.system_prompt
            if output_json:
                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_json.schema_json()}"
+            elif output_pydantic:
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_pydantic.schema_json()}"

            # Build messages
            if isinstance(prompt, str):
                messages = [
                    {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": prompt + ("\nReturn ONLY a valid JSON object. No other text or explanation." if output_json else "")}
+                    {"role": "user", "content": prompt + ("\nReturn ONLY a valid JSON object. No other text or explanation." if (output_json or output_pydantic) else "")}
                ]
            else:
                # For multimodal prompts
@@ -567,7 +601,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                    {"role": "system", "content": system_prompt},
                    {"role": "user", "content": prompt}
                ]
-                if output_json:
+                if output_json or output_pydantic:
                    # Add JSON instruction to text content
                    for item in messages[-1]["content"]:
                        if item["type"] == "text":
@@ -601,22 +635,15 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                    tools=formatted_tools
                )
                return await self._achat_completion(response, tools)
-            elif output_json:
+            elif output_json or output_pydantic:
                response = await async_client.chat.completions.create(
                    model=self.llm,
                    messages=messages,
                    temperature=temperature,
                    response_format={"type": "json_object"}
                )
-                result = response.choices[0].message.content
-                # Clean and parse the JSON response
-                cleaned_json = self.clean_json_output(result)
-                try:
-                    parsed = json.loads(cleaned_json)
-                    return output_json(**parsed)
-                except Exception as e:
-                    display_error(f"Error parsing JSON response: {e}")
-                    return None
+                # Return the raw response
+                return response.choices[0].message.content
            else:
                response = await async_client.chat.completions.create(
                    model=self.llm,
@@ -41,7 +41,10 @@ def process_video(video_path: str, seconds_per_frame=2):
    return base64_frames

 class PraisonAIAgents:
-    def __init__(self, agents, tasks, verbose=0, completion_checker=None, max_retries=5, process="sequential", manager_llm=None):
+    def __init__(self, agents, tasks=None, verbose=0, completion_checker=None, max_retries=5, process="sequential", manager_llm=None):
+        if not agents:
+            raise ValueError("At least one agent must be provided")
+            
        self.agents = agents
        self.tasks = {}
        if max_retries < 3:
@@ -51,12 +54,41 @@ class PraisonAIAgents:
        self.verbose = verbose
        self.max_retries = max_retries
        self.process = process
-        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')
+        
+        # Check for manager_llm in environment variable if not provided
+        self.manager_llm = manager_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        if self.verbose:
+            logging.info(f"Using model {self.manager_llm} for manager")
+        
+        # If no tasks provided, generate them from agents
+        if tasks is None:
+            tasks = []
+            for agent in self.agents:
+                task = agent.generate_task()
+                tasks.append(task)
+            logging.info(f"Auto-generated {len(tasks)} tasks from agents")
+        else:
+            # Validate tasks for backward compatibility
+            if not tasks:
+                raise ValueError("If tasks are provided, at least one task must be present")
+            logging.info(f"Using {len(tasks)} provided tasks")
+        
+        # Add tasks and set their status
        for task in tasks:
            self.add_task(task)
            task.status = "not started"
+            
+        # If tasks were auto-generated from agents or process is sequential, set up sequential flow
+        if len(tasks) > 1 and (process == "sequential" or all(task.next_tasks == [] for task in tasks)):
+            for i in range(len(tasks) - 1):
+                # Set up next task relationship
+                tasks[i].next_tasks = [tasks[i + 1].name]
+                # Set up context for the next task to include the current task
+                if tasks[i + 1].context is None:
+                    tasks[i + 1].context = []
+                tasks[i + 1].context.append(tasks[i])
+            logging.info("Set up sequential flow with automatic context passing")
+        
        self._state = {}  # Add state storage at PraisonAIAgents level

    def add_task(self, task):
@@ -119,9 +151,9 @@ Expected Output: {task.expected_output}.
                else:
                    context_results += f"Previous task {context_task.name if context_task.name else context_task.description} had no result.\n"
            task_prompt += f"""
-            Here are the results of previous tasks that might be useful:\n
-            {context_results}
-            """
+Here are the results of previous tasks that might be useful:\n
+{context_results}
+"""
        task_prompt += "Please provide only the final result of your work. Do not add any conversation or extra explanation."

        if self.verbose >= 2:
@@ -163,10 +195,17 @@ Expected Output: {task.expected_output}.

            agent_output = await executor_agent.achat(
                _get_multimodal_message(task_prompt, task.images),
-                tools=task.tools
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
            )
        else:
-            agent_output = await executor_agent.achat(task_prompt, tools=task.tools)
+            agent_output = await executor_agent.achat(
+                task_prompt,
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
+            )

        if agent_output:
            task_output = TaskOutput(
@@ -320,7 +359,7 @@ Expected Output: {task.expected_output}.
        task_prompt = f"""
 You need to do the following task: {task.description}.
 Expected Output: {task.expected_output}.
-        """
+"""
        if task.context:
            context_results = ""
            for context_task in task.context:
@@ -329,9 +368,9 @@ Expected Output: {task.expected_output}.
                else:
                    context_results += f"Previous task {context_task.name if context_task.name else context_task.description} had no result.\n"
            task_prompt += f"""
-            Here are the results of previous tasks that might be useful:\n
-            {context_results}
-            """
+Here are the results of previous tasks that might be useful:\n
+{context_results}
+"""
        task_prompt += "Please provide only the final result of your work. Do not add any conversation or extra explanation."

        if self.verbose >= 2:
@@ -373,10 +412,17 @@ Expected Output: {task.expected_output}.

            agent_output = executor_agent.chat(
                _get_multimodal_message(task_prompt, task.images),
-                tools=task.tools
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
            )
        else:
-            agent_output = executor_agent.chat(task_prompt, tools=task.tools)
+            agent_output = executor_agent.chat(
+                task_prompt,
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
+            )

        if agent_output:
            task_output = TaskOutput(
@@ -12,6 +12,7 @@ from rich.text import Text
 from rich.markdown import Markdown
 from rich.logging import RichHandler
 from rich.live import Live
+import asyncio

 LOGLEVEL = os.environ.get('LOGLEVEL', 'INFO').upper()

@@ -25,12 +26,49 @@ logging.basicConfig(
 # Global list to store error logs
 error_logs = []

-# Global callback registry
-display_callbacks = {}
+# Separate registries for sync and async callbacks
+sync_display_callbacks = {}
+async_display_callbacks = {}

-def register_display_callback(display_type: str, callback_fn):
-    """Register a callback function for a specific display type."""
-    display_callbacks[display_type] = callback_fn
+# At the top of the file, add display_callbacks to __all__
+__all__ = [
+    'error_logs',
+    'register_display_callback',
+    'sync_display_callbacks',
+    'async_display_callbacks',
+    # ... other exports
+]
+
+def register_display_callback(display_type: str, callback_fn, is_async: bool = False):
+    """Register a synchronous or asynchronous callback function for a specific display type.
+    
+    Args:
+        display_type (str): Type of display event ('interaction', 'self_reflection', etc.)
+        callback_fn: The callback function to register
+        is_async (bool): Whether the callback is asynchronous
+    """
+    if is_async:
+        async_display_callbacks[display_type] = callback_fn
+    else:
+        sync_display_callbacks[display_type] = callback_fn
+
+async def execute_callback(display_type: str, **kwargs):
+    """Execute both sync and async callbacks for a given display type.
+    
+    Args:
+        display_type (str): Type of display event
+        **kwargs: Arguments to pass to the callback functions
+    """
+    # Execute synchronous callback if registered
+    if display_type in sync_display_callbacks:
+        callback = sync_display_callbacks[display_type]
+        loop = asyncio.get_event_loop()
+        await loop.run_in_executor(None, lambda: callback(**kwargs))
+    
+    # Execute asynchronous callback if registered
+    if display_type in async_display_callbacks:
+        callback = async_display_callbacks[display_type]
+        await callback(**kwargs)

 def _clean_display_content(content: str, max_length: int = 20000) -> str:
    """Helper function to clean and truncate content for display."""
@@ -53,11 +91,10 @@ def _clean_display_content(content: str, max_length: int = 20000) -> str:
    return content.strip()

 def display_interaction(message, response, markdown=True, generation_time=None, console=None):
-    """Display the interaction between user and assistant."""
+    """Synchronous version of display_interaction."""
    if console is None:
        console = Console()
    
-    # Handle multimodal content (list)
    if isinstance(message, list):
        text_content = next((item["text"] for item in message if item["type"] == "text"), "")
        message = text_content
@@ -65,16 +102,16 @@ def display_interaction(message, response, markdown=True, generation_time=None,
    message = _clean_display_content(str(message))
    response = _clean_display_content(str(response))

-    # Execute callback if registered
-    if 'interaction' in display_callbacks:
-        display_callbacks['interaction'](
+    # Execute synchronous callback if registered
+    if 'interaction' in sync_display_callbacks:
+        sync_display_callbacks['interaction'](
            message=message,
            response=response,
            markdown=markdown,
            generation_time=generation_time
        )

-    # Existing display logic...
+    # Rest of the display logic...
    if generation_time:
        console.print(Text(f"Response generated in {generation_time:.1f}s", style="dim"))

@@ -93,8 +130,8 @@ def display_self_reflection(message: str, console=None):
    message = _clean_display_content(str(message))
    
    # Execute callback if registered
-    if 'self_reflection' in display_callbacks:
-        display_callbacks['self_reflection'](message=message)
+    if 'self_reflection' in sync_display_callbacks:
+        sync_display_callbacks['self_reflection'](message=message)
    
    console.print(Panel.fit(Text(message, style="bold yellow"), title="Self Reflection", border_style="magenta"))

@@ -106,8 +143,8 @@ def display_instruction(message: str, console=None):
    message = _clean_display_content(str(message))
    
    # Execute callback if registered
-    if 'instruction' in display_callbacks:
-        display_callbacks['instruction'](message=message)
+    if 'instruction' in sync_display_callbacks:
+        sync_display_callbacks['instruction'](message=message)
    
    console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))

@@ -119,8 +156,8 @@ def display_tool_call(message: str, console=None):
    message = _clean_display_content(str(message))
    
    # Execute callback if registered
-    if 'tool_call' in display_callbacks:
-        display_callbacks['tool_call'](message=message)
+    if 'tool_call' in sync_display_callbacks:
+        sync_display_callbacks['tool_call'](message=message)
    
    console.print(Panel.fit(Text(message, style="bold cyan"), title="Tool Call", border_style="green"))

@@ -132,8 +169,8 @@ def display_error(message: str, console=None):
    message = _clean_display_content(str(message))
    
    # Execute callback if registered
-    if 'error' in display_callbacks:
-        display_callbacks['error'](message=message)
+    if 'error' in sync_display_callbacks:
+        sync_display_callbacks['error'](message=message)
    
    console.print(Panel.fit(Text(message, style="bold red"), title="Error", border_style="red"))
    error_logs.append(message)
@@ -150,8 +187,114 @@ def display_generating(content: str = "", start_time: Optional[float] = None):
    content = _clean_display_content(str(content))
    
    # Execute callback if registered
-    if 'generating' in display_callbacks:
-        display_callbacks['generating'](
+    if 'generating' in sync_display_callbacks:
+        sync_display_callbacks['generating'](
+            content=content,
+            elapsed_time=elapsed_str.strip() if elapsed_str else None
+        )
+    
+    return Panel(Markdown(content), title=f"Generating...{elapsed_str}", border_style="green")
+
+# Async versions with 'a' prefix
+async def adisplay_interaction(message, response, markdown=True, generation_time=None, console=None):
+    """Async version of display_interaction."""
+    if console is None:
+        console = Console()
+    
+    if isinstance(message, list):
+        text_content = next((item["text"] for item in message if item["type"] == "text"), "")
+        message = text_content
+
+    message = _clean_display_content(str(message))
+    response = _clean_display_content(str(response))
+
+    # Execute callbacks
+    await execute_callback(
+        'interaction',
+        message=message,
+        response=response,
+        markdown=markdown,
+        generation_time=generation_time
+    )
+
+    # Rest of the display logic...
+    if generation_time:
+        console.print(Text(f"Response generated in {generation_time:.1f}s", style="dim"))
+
+    if markdown:
+        console.print(Panel.fit(Markdown(message), title="Message", border_style="cyan"))
+        console.print(Panel.fit(Markdown(response), title="Response", border_style="cyan"))
+    else:
+        console.print(Panel.fit(Text(message, style="bold green"), title="Message", border_style="cyan"))
+        console.print(Panel.fit(Text(response, style="bold blue"), title="Response", border_style="cyan"))
+
+async def adisplay_self_reflection(message: str, console=None):
+    """Async version of display_self_reflection."""
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    if 'self_reflection' in async_display_callbacks:
+        await async_display_callbacks['self_reflection'](message=message)
+    
+    console.print(Panel.fit(Text(message, style="bold yellow"), title="Self Reflection", border_style="magenta"))
+
+async def adisplay_instruction(message: str, console=None):
+    """Async version of display_instruction."""
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    if 'instruction' in async_display_callbacks:
+        await async_display_callbacks['instruction'](message=message)
+    
+    console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
+
+async def adisplay_tool_call(message: str, console=None):
+    """Async version of display_tool_call."""
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    if 'tool_call' in async_display_callbacks:
+        await async_display_callbacks['tool_call'](message=message)
+    
+    console.print(Panel.fit(Text(message, style="bold cyan"), title="Tool Call", border_style="green"))
+
+async def adisplay_error(message: str, console=None):
+    """Async version of display_error."""
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    if 'error' in async_display_callbacks:
+        await async_display_callbacks['error'](message=message)
+    
+    console.print(Panel.fit(Text(message, style="bold red"), title="Error", border_style="red"))
+    error_logs.append(message)
+
+async def adisplay_generating(content: str = "", start_time: Optional[float] = None):
+    """Async version of display_generating."""
+    if not content or not str(content).strip():
+        return Panel("", title="", border_style="green")
+    
+    elapsed_str = ""
+    if start_time is not None:
+        elapsed = time.time() - start_time
+        elapsed_str = f" {elapsed:.1f}s"
+    
+    content = _clean_display_content(str(content))
+    
+    if 'generating' in async_display_callbacks:
+        await async_display_callbacks['generating'](
            content=content,
            elapsed_time=elapsed_str.strip() if elapsed_str else None
        )
@@ -0,0 +1,4 @@
+"""Tools package for PraisonAI Agents"""
+from .tools import Tools
+
+__all__ = ['Tools'] 
@@ -0,0 +1,40 @@
+"""Tools module for PraisonAI Agents"""
+from typing import List, Dict
+import logging
+import importlib
+
+class Tools:
+    @staticmethod
+    def internet_search(query: str) -> List[Dict]:
+        """
+        Perform a search using DuckDuckGo.
+
+        Args:
+            query (str): The search query.
+
+        Returns:
+            list: A list of search result titles, URLs, and snippets.
+        """
+        # Check if duckduckgo_search is installed
+        if importlib.util.find_spec("duckduckgo_search") is None:
+            error_msg = "DuckDuckGo search is not available. Please install duckduckgo_search package using: pip install duckduckgo_search"
+            logging.error(error_msg)
+            return [{"error": error_msg}]
+
+        try:
+            # Import only when needed
+            from duckduckgo_search import DDGS
+            results = []
+            ddgs = DDGS()
+            for result in ddgs.text(keywords=query, max_results=5):
+                results.append({
+                    "title": result.get("title", ""),
+                    "url": result.get("href", ""),
+                    "snippet": result.get("body", "")
+                })
+            return results
+
+        except Exception as e:
+            error_msg = f"Error during DuckDuckGo search: {e}"
+            logging.error(error_msg)
+            return [{"error": error_msg}] 
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "praisonaiagents"
-version = "0.0.18"
+version = "0.0.23"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
    { name="Mervin Praison" }
@@ -0,0 +1,56 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents, Tools
+from pydantic import BaseModel
+
+class AnalysisReport(BaseModel):
+    title: str
+    findings: str
+    summary: str
+
+# Create a researcher agent
+researcher = Agent(
+    name="AIResearcher",
+    role="Technology Research Analyst",
+    goal="Analyze and structure information about AI developments",
+    backstory="Expert analyst specializing in AI technology trends",
+    verbose=True,
+    llm="gpt-4o-mini",
+    tools=[Tools.internet_search],
+    self_reflect=False
+)
+
+# Create an analyst agent
+analyst = Agent(
+    name="DataAnalyst",
+    role="Data Insights Specialist",
+    goal="Structure and analyze research findings",
+    backstory="Senior data analyst with expertise in pattern recognition",
+    verbose=True,
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# Define structured tasks
+research_task = Task(
+    name="gather_research",
+    description="Research recent AI developments in 2024",
+    agent=researcher,
+    expected_output="Research findings"
+)
+
+analysis_task = Task(
+    name="analyze_findings",
+    description="Analyze research findings and create structured report. No additional text or explanation.",
+    agent=analyst,
+    output_json=AnalysisReport,
+    expected_output="JSON object"
+)
+
+# Initialize and run agents
+agents = PraisonAIAgents(
+    agents=[researcher, analyst],
+    tasks=[research_task, analysis_task],
+    process="sequential",
+    verbose=True
+)
+result = agents.start()
+print(result)
@@ -186,7 +186,7 @@ wheels = [

 [[package]]
 name = "praisonaiagents"
-version = "0.0.18"
+version = "0.0.23"
 source = { editable = "." }
 dependencies = [
    { name = "openai" },
@@ -1 +0,0 @@
-# Welcome to Praison AI! 🚀🤖
@@ -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==2.0.24 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.32 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;)
            
@@ -1,12 +1,13 @@
 ---
-title: "Callbacks"
+title: "AI Agents with Callbacks"
+sidebarTitle: "Callbacks"
 description: "Learn how to implement callbacks to monitor and log AI agent interactions, errors, and task completions."
 icon: "bell"
 ---

 <Note>
  **Prerequisites**
-  - Python 3.8 or higher
+  - Python 3.10 or higher
  - PraisonAI Agents package installed
  - Basic understanding of Python functions
 </Note>
@@ -40,8 +41,17 @@ icon: "bell"
        print(f"Received message: {message}")
        print(f"Got response: {response}")
    
-    # Register the callback
-    register_display_callback('interaction', simple_callback)
+    # Register as synchronous callback
+    register_display_callback('interaction', simple_callback, is_async=False)
+
+    # For async callbacks
+    async def async_simple_callback(message=None, response=None, **kwargs):
+        await asyncio.sleep(0)  # Non-blocking pause
+        print(f"Received message: {message}")
+        print(f"Got response: {response}")
+    
+    # Register as async callback
+    register_display_callback('interaction', async_simple_callback, is_async=True)
    ```
  </Step>

@@ -75,6 +85,10 @@ icon: "bell"
  </Step>
 </Steps>

+<Note>
+  PraisonAI Agents supports both synchronous and asynchronous callbacks. When registering a callback, specify `is_async=True` for async callbacks. Both sync and async callbacks for the same display type can be executed simultaneously.
+</Note>
+
 ## Understanding Callbacks

 <Card title="What are Callbacks?" icon="question">
@@ -124,8 +138,16 @@ logging.basicConfig(level=logging.INFO)
 def log_callback(message=None, **kwargs):
    logging.info(f"Agent message: {message}")

-# Register callback
-register_display_callback('interaction', log_callback)
+# Register synchronous callback
+register_display_callback('interaction', log_callback, is_async=False)
+
+# Register asynchronous callback
+async def async_log_callback(message=None, **kwargs):
+    await asyncio.sleep(0)
+    logging.info(f"Agent message: {message}")
+
+# Register as async callback
+register_display_callback('interaction', async_log_callback, is_async=True)
 ```

 ```python Advanced
@@ -475,8 +497,260 @@ def main():
 if __name__ == "__main__":
    main()

+## Async Callbacks
+
+Async callbacks allow you to handle events asynchronously, which is particularly useful for long-running operations or when dealing with multiple agents simultaneously.
+
+### Basic Async Callback Implementation
+
+```python
+import asyncio
+from praisonaiagents import register_display_callback
+
+async def async_interaction_callback(message=None, response=None, **kwargs):
+    """Async callback for handling interactions"""
+    await asyncio.sleep(0)  # Non-blocking pause
+    print(f"Async processing - Message: {message}")
+    print(f"Response: {response}")
+
+# Register the async callback
+register_display_callback('interaction', async_interaction_callback)
 ```

+### Complete Async Example
+
+```python
+import asyncio
+from praisonaiagents import Agent, Task, PraisonAIAgents, register_display_callback
+import logging
+from datetime import datetime
+
+# Setup async logging
+async def setup_async_logging():
+    logging.basicConfig(
+        filename='async_ai_interactions.log',
+        level=logging.INFO,
+        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+    )
+
+# Async callbacks
+async def async_interaction_callback(message=None, response=None, markdown=None, generation_time=None):
+    await asyncio.sleep(0)
+    logging.info(f"""
+    === ASYNC INTERACTION ===
+    Time: {datetime.now()}
+    Generation Time: {generation_time}s
+    Message: {message}
+    Response: {response}
+    """)
+
+async def async_error_callback(message=None):
+    await asyncio.sleep(0)
+    logging.error(f"""
+    === ASYNC ERROR ===
+    Time: {datetime.now()}
+    Message: {message}
+    """)
+
+# Register async callbacks
+register_display_callback('interaction', async_interaction_callback, is_async=True)
+register_display_callback('error', async_error_callback, is_async=True)
+
+# Create async task callback
+async def async_task_callback(output):
+    await asyncio.sleep(0)
+    logging.info(f"""
+    === ASYNC TASK COMPLETED ===
+    Time: {datetime.now()}
+    Description: {output.description}
+    Agent: {output.agent}
+    Output: {output.raw[:200]}...
+    """)
+
+async def main():
+    await setup_async_logging()
+    
+    # Create agent with async capabilities
+    agent = Agent(
+        name="AsyncAgent",
+        role="Assistant",
+        goal="Demonstrate async callbacks",
+        backstory="I am an async-capable assistant",
+        verbose=True
+    )
+
+    # Create task with async callback
+    task = Task(
+        name="async_task",
+        description="Demonstrate async callbacks",
+        agent=agent,
+        expected_output="Async demonstration complete",
+        callback=async_task_callback
+    )
+
+    # Create and run agents with async support
+    agents = PraisonAIAgents(
+        agents=[agent],
+        tasks=[task],
+        verbose=True
+    )
+
+    await agents.start_async()
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+### Available Async Functions
+
+PraisonAI Agents provides several async versions of display functions, prefixed with 'a'. Here's the complete list:
+
+<CardGroup cols={2}>
+  <Card title="adisplay_interaction" icon="comments">
+    ```python
+    async def adisplay_interaction(
+        message, 
+        response, 
+        markdown=True, 
+        generation_time=None, 
+        console=None
+    )
+    ```
+    Async version of display_interaction for showing conversations.
+  </Card>
+  
+  <Card title="adisplay_self_reflection" icon="brain">
+    ```python
+    async def adisplay_self_reflection(
+        message: str, 
+        console=None
+    )
+    ```
+    Async version for displaying agent's self-reflection moments.
+  </Card>
+
+  <Card title="adisplay_instruction" icon="list-check">
+    ```python
+    async def adisplay_instruction(
+        message: str, 
+        console=None
+    )
+    ```
+    Async version for showing instructions.
+  </Card>
+
+  <Card title="adisplay_tool_call" icon="wrench">
+    ```python
+    async def adisplay_tool_call(
+        message: str, 
+        console=None
+    )
+    ```
+    Async version for displaying tool calls.
+  </Card>
+
+  <Card title="adisplay_error" icon="triangle-exclamation">
+    ```python
+    async def adisplay_error(
+        message: str, 
+        console=None
+    )
+    ```
+    Async version for error messages.
+  </Card>
+
+  <Card title="adisplay_generating" icon="spinner">
+    ```python
+    async def adisplay_generating(
+        content: str = "", 
+        start_time: Optional[float] = None
+    )
+    ```
+    Async version for showing generation progress.
+  </Card>
+</CardGroup>
+
+#### Example Usage
+
+```python
+import asyncio
+from praisonaiagents import (
+    adisplay_interaction,
+    adisplay_error,
+    adisplay_tool_call
+)
+
+async def main():
+    # Display an interaction
+    await adisplay_interaction(
+        message="What's the weather?",
+        response="Let me check that for you.",
+        generation_time=0.5
+    )
+
+    # Display a tool call
+    await adisplay_tool_call(
+        "Calling weather API for location data..."
+    )
+
+    # Handle an error
+    try:
+        raise Exception("API connection failed")
+    except Exception as e:
+        await adisplay_error(str(e))
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+### Best Practices for Async Callbacks
+
+<AccordionGroup>
+  <Accordion title="Error Handling">
+    Implement proper error handling in async callbacks:
+    ```python
+    async def safe_async_callback(message=None, **kwargs):
+        try:
+            await asyncio.sleep(0)
+            # Process the callback
+            await process_message(message)
+        except Exception as e:
+            logging.error(f"Async callback error: {e}")
+    ```
+  </Accordion>
+
+  <Accordion title="Performance">
+    Optimize async callbacks for better performance:
+    ```python
+    async def optimized_async_callback(message=None, **kwargs):
+        # Use asyncio.gather for concurrent operations
+        results = await asyncio.gather(
+            process_message(message),
+            update_status(),
+            log_interaction(message)
+        )
+        return results
+    ```
+  </Accordion>
+</AccordionGroup>
+
+### Key Considerations
+
+<CardGroup cols={2}>
+  <Card title="Concurrency" icon="arrows-split-up-and-left">
+    - Use asyncio.gather for parallel operations
+    - Avoid blocking calls in async callbacks
+    - Handle concurrent access to shared resources
+  </Card>
+  <Card title="Resource Management" icon="server">
+    - Implement proper cleanup in async contexts
+    - Use async context managers when appropriate
+    - Monitor memory usage in long-running callbacks
+  </Card>
+</CardGroup>
+
+<Note>
+  When using async callbacks, ensure all dependent operations are also async-compatible to prevent blocking the event loop.
+</Note>

 ## Troubleshooting

@@ -0,0 +1,287 @@
+---
+title: "Code Execution Agent"
+sidebarTitle: "Code Agent"
+description: "Learn how to create AI agents that can write and execute Python code safely using e2b code interpreter."
+icon: "code"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - e2b_code_interpreter package installed
+  - Basic understanding of Python
+</Note>
+
+## Quick Start
+
+Create AI agents that can write and execute Python code in a sandboxed environment!
+
+<Steps>
+  <Step title="Install Packages">
+    First, install the required packages:
+    ```bash
+    pip install praisonaiagents e2b_code_interpreter
+    ```
+  </Step>
+
+  <Step title="Import Required Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from e2b_code_interpreter import Sandbox
+    ```
+  </Step>
+
+  <Step title="Define Code Interpreter">
+    Create a code interpreter function:
+    ```python
+    def code_interpreter(code: str):
+        print(f"\n{'='*50}\n> Running following AI-generated code:\n{code}\n{'='*50}")
+        exec_result = Sandbox().run_code(code)
+        if exec_result.error:
+            print("[Code Interpreter error]", exec_result.error)
+            return {"error": str(exec_result.error)}
+        else:
+            results = []
+            for result in exec_result.results:
+                if hasattr(result, '__iter__'):
+                    results.extend(list(result))
+                else:
+                    results.append(str(result))
+            logs = {"stdout": list(exec_result.logs.stdout), "stderr": list(exec_result.logs.stderr)}
+            return json.dumps({"results": results, "logs": logs})
+    ```
+  </Step>
+
+  <Step title="Create Code Agents">
+    Set up your code writing and execution agents:
+    ```python
+    code_agent = Agent(
+        name="code_agent",
+        backstory="Expert in writing Python scripts",
+        self_reflect=False
+    )
+
+    execution_agent = Agent(
+        name="execution_agent",
+        backstory="Expert in executing Python scripts",
+        self_reflect=False,
+        tools=[code_interpreter]
+    )
+    ```
+  </Step>
+</Steps>
+
+## Understanding Code Agents
+
+<Card title="What are Code Agents?" icon="question">
+  Code agents are specialized AI agents that can:
+  - Write Python code based on requirements
+  - Execute code safely in a sandboxed environment
+  - Handle code execution results and errors
+  - Work together in a pipeline (writer → executor)
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="Code Writer Agent" icon="pencil">
+    Specialized in writing Python code
+    ```python
+    Agent(
+        name="code_agent",
+        backstory="Expert in writing Python scripts"
+    )
+    ```
+  </Card>
+  <Card title="Code Executor Agent" icon="play">
+    Executes code using sandbox
+    ```python
+    Agent(
+        name="execution_agent",
+        tools=[code_interpreter]
+    )
+    ```
+  </Card>
+  <Card title="Sandbox Environment" icon="box">
+    Safe code execution environment using e2b
+  </Card>
+  <Card title="Execution Results" icon="output">
+    Structured output with results and logs
+  </Card>
+</CardGroup>
+
+## Examples
+
+### Simple Hello World Example
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from e2b_code_interpreter import Sandbox
+
+# Create agents
+code_agent = Agent(
+    name="code_agent",
+    backstory="Expert in writing Python scripts",
+    self_reflect=False
+)
+
+execution_agent = Agent(
+    name="execution_agent",
+    backstory="Expert in executing Python scripts",
+    self_reflect=False,
+    tools=[code_interpreter]
+)
+
+# Define tasks
+code_task = Task(
+    description="Write a simple Python script to print 'Hello, World!'",
+    expected_output="A Python script that prints 'Hello, World!'",
+    agent=code_agent
+)
+
+execution_task = Task(
+    description="Execute the Python script",
+    expected_output="The output of the Python script",
+    agent=execution_agent
+)
+
+# Initialize and run agents
+agents = PraisonAIAgents(
+    agents=[code_agent, execution_agent],
+    tasks=[code_task, execution_task]
+)
+result = agents.start()
+```
+
+### Advanced Code Generation
+
+<CodeGroup>
+```python Data Analysis
+# Create a data analysis script
+code_task = Task(
+    description="""
+    Write a Python script that:
+    1. Creates a sample dataset
+    2. Performs basic statistical analysis
+    3. Generates a plot
+    """,
+    agent=code_agent
+)
+```
+
+```python Web Scraping
+# Create a web scraping script
+code_task = Task(
+    description="""
+    Write a Python script that:
+    1. Scrapes a website
+    2. Extracts specific data
+    3. Saves to CSV
+    """,
+    agent=code_agent
+)
+```
+</CodeGroup>
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Code Safety">
+    Ensure safe code execution:
+    ```python
+    # Always use sandbox environment
+    exec_result = Sandbox().run_code(code)
+    
+    # Handle errors gracefully
+    if exec_result.error:
+        return {"error": str(exec_result.error)}
+    ```
+  </Accordion>
+
+  <Accordion title="Task Description">
+    Write clear code requirements:
+    ```python
+    # Good
+    Task(description="""
+        Write a Python script that:
+        1. Takes user input
+        2. Validates the input
+        3. Processes the data
+        4. Returns formatted output
+    """)
+    
+    # Too vague
+    Task(description="Write some Python code")
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### Code Generation and Testing
+```python
+# Generate code
+code_task = Task(
+    description="Write a function to calculate fibonacci numbers",
+    agent=code_agent
+)
+
+# Generate test cases
+test_task = Task(
+    description="Write test cases for the fibonacci function",
+    agent=code_agent
+)
+
+# Execute tests
+execution_task = Task(
+    description="Run the test cases",
+    agent=execution_agent
+)
+```
+
+### Interactive Code Development
+```python
+# Write initial code
+initial_code_task = Task(
+    description="Write initial version of the script",
+    agent=code_agent
+)
+
+# Test and improve
+improvement_task = Task(
+    description="Analyze execution results and improve the code",
+    agent=code_agent
+)
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Execution Errors" icon="triangle-exclamation">
+    Check sandbox execution results:
+    ```python
+    if exec_result.error:
+        print("[Error]", exec_result.error)
+    ```
+  </Card>
+  <Card title="Code Generation" icon="code">
+    Provide clear requirements and constraints in task description
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Advanced Code Generation" icon="graduation-cap" href="./advanced-code">
+    Learn about complex code generation patterns
+  </Card>
+  <Card title="Custom Tools" icon="wrench" href="./custom-tools">
+    Create custom code execution tools
+  </Card>
+</CardGroup>
+
+<Note>
+  Code execution agents provide a powerful way to generate and run Python code safely. They're perfect for automated code generation, testing, and execution in a controlled environment.
+</Note>
@@ -0,0 +1,322 @@
+---
+title: "Structured AI Agents"
+description: "Learn how to create AI agents that return structured, type-safe outputs using Pydantic models and JSON."
+icon: "cube"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - Basic understanding of Python and Pydantic
+</Note>
+
+## Quick Start
+
+Create AI agents that return structured, validated outputs in just a few lines of code!
+
+<Steps>
+  <Step title="Install Package">
+    First, install the PraisonAI Agents package:
+    ```bash
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Import Required Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents, Tools
+    from pydantic import BaseModel
+    ```
+  </Step>
+
+  <Step title="Define Your Data Model">
+    Create a Pydantic model to define your output structure:
+    ```python
+    class AnalysisReport(BaseModel):
+        title: str
+        findings: str
+        summary: str
+    ```
+  </Step>
+
+  <Step title="Create Structured Tasks">
+    Create tasks with structured outputs:
+    ```python
+    research_task = Task(
+        description="Research recent AI developments",
+        agent=researcher,
+        expected_output="Research findings"
+    )
+
+    analysis_task = Task(
+        description="Analyze research findings",
+        agent=analyst,
+        output_pydantic=AnalysisReport,  # For Pydantic output
+        # or output_json=AnalysisReport  # For JSON output
+        expected_output="Structured analysis report"
+    )
+    ```
+  </Step>
+</Steps>
+
+## Understanding Structured Outputs
+
+<Card title="What are Structured Outputs?" icon="question">
+  Structured outputs allow you to:
+  - Define exact shape of data using Pydantic models
+  - Get type-safe, validated responses
+  - Choose between Pydantic objects or JSON
+  - Ensure consistent output format across agent responses
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="Pydantic Models" icon="cube">
+    Define your data structure
+    ```python
+    class ResearchReport(BaseModel):
+        title: str
+        content: str
+    ```
+  </Card>
+  <Card title="Task Configuration" icon="gear">
+    Configure output format
+    ```python
+    Task(output_pydantic=Model)
+    # or
+    Task(output_json=Model)
+    ```
+  </Card>
+  <Card title="Output Validation" icon="check">
+    Automatic validation of responses against your model
+  </Card>
+  <Card title="Format Options" icon="code">
+    Choose between Pydantic objects or JSON output
+  </Card>
+</CardGroup>
+
+## Output Types
+
+<CardGroup cols={2}>
+  <Card title="Pydantic Output" icon="cube">
+    ```python
+    task = Task(
+        output_pydantic=AnalysisReport
+    )
+    # Returns Pydantic object
+    ```
+    Get type-safe Pydantic model instances
+  </Card>
+  <Card title="JSON Output" icon="brackets-curly">
+    ```python
+    task = Task(
+        output_json=AnalysisReport
+    )
+    # Returns JSON dict
+    ```
+    Get JSON dictionary matching model schema
+  </Card>
+</CardGroup>
+
+## Examples
+
+### Research Analysis with Structured Output
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents, Tools
+from pydantic import BaseModel
+
+# Define output structure
+class AnalysisReport(BaseModel):
+    title: str
+    findings: str
+    summary: str
+
+# Create agents
+researcher = Agent(
+    name="AIResearcher",
+    role="Technology Research Analyst",
+    tools=[Tools.internet_search]
+)
+
+analyst = Agent(
+    name="DataAnalyst",
+    role="Data Insights Specialist"
+)
+
+# Define structured tasks
+research_task = Task(
+    name="gather_research",
+    description="Research recent AI developments in 2024",
+    agent=researcher,
+    expected_output="Research findings"
+)
+
+analysis_task = Task(
+    name="analyze_findings",
+    description="Analyze research findings and create structured report",
+    agent=analyst,
+    output_pydantic=AnalysisReport,
+    expected_output="Structured analysis report"
+)
+
+# Initialize and run agents
+agents = PraisonAIAgents(
+    agents=[researcher, analyst],
+    tasks=[research_task, analysis_task]
+)
+result = agents.start()
+```
+
+### Different Output Formats
+
+<CodeGroup>
+```python Pydantic
+# Get Pydantic model instance
+analysis_task = Task(
+    description="Analyze findings",
+    agent=analyst,
+    output_pydantic=AnalysisReport
+)
+
+# Result will be AnalysisReport instance
+result.pydantic.title
+result.pydantic.findings
+result.pydantic.summary
+```
+
+```python JSON
+# Get JSON dictionary
+analysis_task = Task(
+    description="Analyze findings",
+    agent=analyst,
+    output_json=AnalysisReport
+)
+
+# Result will be dictionary
+result.json_dict['title']
+result.json_dict['findings']
+result.json_dict['summary']
+```
+</CodeGroup>
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Model Design">
+    Design clear, focused Pydantic models:
+    ```python
+    # Good
+    class ResearchReport(BaseModel):
+        title: str
+        findings: str
+        summary: str
+    
+    # Too complex
+    class Report(BaseModel):
+        title: str
+        subtitle: Optional[str]
+        findings: List[Dict[str, Any]]
+    ```
+  </Accordion>
+
+  <Accordion title="Output Format Selection">
+    Choose the right output format:
+    ```python
+    # Use Pydantic when you need:
+    # - Type validation
+    # - Model methods
+    task = Task(output_pydantic=Model)
+    
+    # Use JSON when you need:
+    # - Simple dictionary access
+    # - Serialization
+    task = Task(output_json=Model)
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### Research and Structured Analysis
+```python
+class ResearchReport(BaseModel):
+    topic: str
+    findings: str
+    sources: list[str]
+
+class Analysis(BaseModel):
+    key_points: list[str]
+    implications: str
+    recommendations: str
+
+research_task = Task(
+    description="Research quantum computing",
+    agent=researcher,
+    output_pydantic=ResearchReport
+)
+
+analysis_task = Task(
+    description="Analyze research implications",
+    agent=analyst,
+    output_pydantic=Analysis
+)
+```
+
+### Data Collection and Summary
+```python
+class DataPoint(BaseModel):
+    timestamp: str
+    value: float
+    source: str
+
+class Summary(BaseModel):
+    average: float
+    trends: str
+    insights: str
+
+collection_task = Task(
+    description="Collect data points",
+    output_pydantic=DataPoint
+)
+
+summary_task = Task(
+    description="Summarize data",
+    output_pydantic=Summary
+)
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Validation Errors" icon="triangle-exclamation">
+    Check if your model matches the expected output structure
+    ```python
+    # Print model schema
+    print(Model.schema_json())
+    ```
+  </Card>
+  <Card title="Output Format" icon="code">
+    Verify you're accessing the right output field:
+    - `result.pydantic` for Pydantic
+    - `result.json_dict` for JSON
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Advanced Models" icon="graduation-cap" href="./advanced-models">
+    Learn about advanced Pydantic model configurations
+  </Card>
+  <Card title="Custom Validation" icon="check-double" href="./validation">
+    Add custom validation to your models
+  </Card>
+</CardGroup>
+
+<Note>
+  Structured outputs make your AI agents more reliable and their responses more predictable. They're perfect for production systems where data consistency is crucial.
+</Note>
@@ -103,6 +103,9 @@
        "features/reasoning",
        "framework/multimodalagents",
        "features/async",
+        "features/mini",
+        "features/codeagent",
+        "features/structured",
        "features/callbacks",
        "train",
        "framework/praisonaiagents",
@@ -244,13 +244,13 @@ vertex = ["google-auth (>=2,<3)"]

 [[package]]
 name = "anyio"
-version = "4.7.0"
+version = "4.8.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"},
-    {file = "anyio-4.7.0.tar.gz", hash = "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48"},
+    {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"},
+    {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"},
 ]

 [package.dependencies]
@@ -261,7 +261,7 @@ typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}

 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
 trio = ["trio (>=0.26.1)"]

 [[package]]
@@ -480,6 +480,7 @@ files = [
 ]

 [package.dependencies]
+tinycss2 = {version = ">=1.1.0,<1.5", optional = true, markers = "extra == \"css\""}
 webencodings = "*"

 [package.extras]
@@ -1091,13 +1092,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}

 [[package]]
 name = "cohere"
-version = "5.13.4"
+version = "5.13.5"
 description = ""
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "cohere-5.13.4-py3-none-any.whl", hash = "sha256:f3d81cef93ded8ea57dde6d4a547598846c9e836c18834fe0c6d2bb3da1d8e8f"},
-    {file = "cohere-5.13.4.tar.gz", hash = "sha256:ade5e817368a73dbdaa2bf5a812ad0952d59bdf3e583928e5207774b964d7d6b"},
+    {file = "cohere-5.13.5-py3-none-any.whl", hash = "sha256:cce9754003239f7a1c862ed6ebb54aad7996da2c1225688e8af9b91b0845cab5"},
+    {file = "cohere-5.13.5.tar.gz", hash = "sha256:036ffe2da54a2928412d5895c79f0bf11c91c51f6f562cb2b8fb85ed48c68ed7"},
 ]

 [package.dependencies]
@@ -1483,19 +1484,19 @@ files = [

 [[package]]
 name = "duckduckgo-search"
-version = "7.1.1"
+version = "7.2.0"
 description = "Search for words, documents, images, news, maps and text translation using the DuckDuckGo.com search engine."
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "duckduckgo_search-7.1.1-py3-none-any.whl", hash = "sha256:753b2237fd8a9a4cbf06c788aaf0cfc7e00f4623e8f854d0525682ff31770093"},
-    {file = "duckduckgo_search-7.1.1.tar.gz", hash = "sha256:1874e55aa6eac3de2d0e9b556b70f35ed0cd7a4544d7417cf38216275b3de009"},
+    {file = "duckduckgo_search-7.2.0-py3-none-any.whl", hash = "sha256:511abfa5e3e98592ae6a155d819b981adaac9e66c1ee2bb2eb0d7086e29316e2"},
+    {file = "duckduckgo_search-7.2.0.tar.gz", hash = "sha256:2163d4b78388ab70731094fdc86f4474d9fb9027c975e5aa2f0638e596a7e7b8"},
 ]

 [package.dependencies]
 click = ">=8.1.7"
 lxml = ">=5.3.0"
-primp = ">=0.9.2"
+primp = ">=0.9.3"

 [package.extras]
 dev = ["mypy (>=1.13.0)", "pytest (>=8.3.4)", "pytest-dependency (>=0.6.0)", "ruff (>=0.8.3)"]
@@ -2086,13 +2087,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]

 [[package]]
 name = "google-api-python-client"
-version = "2.156.0"
+version = "2.157.0"
 description = "Google API Client Library for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google_api_python_client-2.156.0-py2.py3-none-any.whl", hash = "sha256:6352185c505e1f311f11b0b96c1b636dcb0fec82cd04b80ac5a671ac4dcab339"},
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+    {file = "google_api_python_client-2.157.0-py2.py3-none-any.whl", hash = "sha256:0b0231db106324c659bf8b85f390391c00da57a60ebc4271e33def7aac198c75"},
+    {file = "google_api_python_client-2.157.0.tar.gz", hash = "sha256:2ee342d0967ad1cedec43ccd7699671d94bff151e1f06833ea81303f9a6d86fd"},
 ]

 [package.dependencies]
@@ -2539,13 +2540,13 @@ colorama = ">=0.4"

 [[package]]
 name = "grpc-google-iam-v1"
-version = "0.13.1"
+version = "0.14.0"
 description = "IAM API client library"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "grpc-google-iam-v1-0.13.1.tar.gz", hash = "sha256:3ff4b2fd9d990965e410965253c0da6f66205d5a8291c4c31c6ebecca18a9001"},
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+    {file = "grpc_google_iam_v1-0.14.0-py2.py3-none-any.whl", hash = "sha256:fb4a084b30099ba3ab07d61d620a0d4429570b13ff53bd37bac75235f98b7da4"},
+    {file = "grpc_google_iam_v1-0.14.0.tar.gz", hash = "sha256:c66e07aa642e39bb37950f9e7f491f70dad150ac9801263b42b2814307c2df99"},
 ]

 [package.dependencies]
@@ -2555,70 +2556,70 @@ 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 = "1.69.0"
 description = "HTTP/2-based RPC framework"
 optional = false
 python-versions = ">=3.8"
 files = [
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-protobuf = ["grpcio-tools (>=1.68.1)"]
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 [[package]]
 name = "grpcio-status"
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 [[package]]
 name = "identify"
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 python-versions = ">=3.9"
 files = [
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 [[package]]
 name = "importlib-resources"
-version = "6.4.5"
+version = "6.5.2"
 description = "Read resources from Python packages"
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 [[package]]
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 jupyter-core = ">=4.7"
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 traitlets = ">=5.1"

 [package.extras]
@@ -4727,13 +4727,13 @@ sympy = "*"

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 [package.dependencies]
@@ -5438,13 +5438,13 @@ testing = ["pytest", "pytest-benchmark"]

 [[package]]
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 [[package]]
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 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
 enabler = ["pytest-enabler (>=2.2)"]
-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
-type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (>=1.12,<1.14)", "pytest-mypy"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.14.*)", "pytest-mypy"]

 [[package]]
 name = "shapely"
@@ -8271,13 +8278,13 @@ test = ["aiohttp (>=3.10.5)", "flake8 (>=5.0,<6.0)", "mypy (>=0.800)", "psutil",

 [[package]]
 name = "virtualenv"
-version = "20.28.0"
+version = "20.28.1"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "virtualenv-20.28.0-py3-none-any.whl", hash = "sha256:23eae1b4516ecd610481eda647f3a7c09aea295055337331bb4e6892ecce47b0"},
-    {file = "virtualenv-20.28.0.tar.gz", hash = "sha256:2c9c3262bb8e7b87ea801d715fae4495e6032450c71d2309be9550e7364049aa"},
+    {file = "virtualenv-20.28.1-py3-none-any.whl", hash = "sha256:412773c85d4dab0409b83ec36f7a6499e72eaf08c80e81e9576bca61831c71cb"},
+    {file = "virtualenv-20.28.1.tar.gz", hash = "sha256:5d34ab240fdb5d21549b76f9e8ff3af28252f5499fb6d6f031adac4e5a8c5329"},
 ]

 [package.dependencies]
@@ -8822,9 +8829,9 @@ gradio = ["gradio"]
 openai = ["langchain-openai"]
 realtime = ["aiosqlite", "chainlit", "crawl4ai", "duckduckgo_search", "greenlet", "litellm", "plotly", "sqlalchemy", "tavily-python", "websockets", "yfinance"]
 train = []
-ui = ["chainlit"]
+ui = ["aiosqlite", "chainlit", "greenlet", "pydantic", "sqlalchemy"]

 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "eb9727e6cb210753564b5c109a9e353f65d5f4c79889e0a3c4e8ce5080ee23e4"
+content-hash = "596dee1ab307caacfd8041e13a29b60d0b793274e7ea45890b827a92e8c2dd40"
@@ -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/2.0.24.tar.gz"
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.32.tar.gz"
    sha256 "1828fb9227d10f991522c3f24f061943a254b667196b40b1a3e4a54a8d30ce32"  # Replace with actual SHA256 checksum
    license "MIT"
  
@@ -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==2.0.24 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.0.32 gunicorn markdown\n")
            file.write("EXPOSE 8080\n")
            file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
            
@@ -0,0 +1,57 @@
+import logging
+from typing import Dict, Any, Callable, Optional, Union
+import asyncio
+
+logger = logging.getLogger(__name__)
+
+class CallbackManager:
+    """Manages callbacks for the PraisonAI UI"""
+    
+    def __init__(self):
+        self._callbacks: Dict[str, Dict[str, Union[Callable, bool]]] = {}
+    
+    def register(self, name: str, callback: Callable, is_async: bool = False) -> None:
+        """Register a callback function"""
+        self._callbacks[name] = {
+            'func': callback,
+            'is_async': is_async
+        }
+    
+    async def call(self, name: str, **kwargs) -> None:
+        """Call a registered callback"""
+        if name not in self._callbacks:
+            logger.warning(f"No callback registered for {name}")
+            return
+            
+        callback_info = self._callbacks[name]
+        func = callback_info['func']
+        is_async = callback_info['is_async']
+        
+        try:
+            if is_async:
+                await func(**kwargs)
+            else:
+                if asyncio.iscoroutinefunction(func):
+                    await func(**kwargs)
+                else:
+                    await asyncio.get_event_loop().run_in_executor(None, lambda: func(**kwargs))
+        except Exception as e:
+            logger.error(f"Error in callback {name}: {str(e)}")
+
+# Global callback manager instance
+callback_manager = CallbackManager()
+
+def register_callback(name: str, callback: Callable, is_async: bool = False) -> None:
+    """Register a callback with the global callback manager"""
+    callback_manager.register(name, callback, is_async)
+
+async def trigger_callback(name: str, **kwargs) -> None:
+    """Trigger a callback from the global callback manager"""
+    await callback_manager.call(name, **kwargs)
+
+# Decorator for registering callbacks
+def callback(name: str, is_async: bool = False):
+    def decorator(func):
+        register_callback(name, func, is_async)
+        return func
+    return decorator 
@@ -0,0 +1,474 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import os
+import importlib
+import inspect
+import yaml
+import logging
+from .callbacks import trigger_callback
+import asyncio
+import chainlit as cl
+from queue import Queue
+
+logger = logging.getLogger(__name__)
+agent_file = "agents.yaml"
+
+with open(agent_file, 'r') as f:
+    config = yaml.safe_load(f)
+
+topic = "get from the message content from the chainlit user message"
+
+# Create a message queue
+message_queue = Queue()
+
+async def process_message_queue():
+    """Process messages in the queue and send them to Chainlit"""
+    while True:
+        try:
+            if not message_queue.empty():
+                msg_data = message_queue.get()
+                await cl.Message(**msg_data).send()
+            await asyncio.sleep(0.1)  # Small delay to prevent busy waiting
+        except Exception as e:
+            logger.error(f"Error processing message queue: {e}")
+
+def load_tools_from_tools_py():
+    """
+    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")
+        logger.info(f"Spec: {spec}")
+        if spec is None:
+            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
+                # Format the tool as an OpenAI function
+                tool = {
+                    "type": "function",
+                    "function": {
+                        "name": name,
+                        "description": obj.__doc__ or f"Function to {name.replace('_', ' ')}",
+                        "parameters": {
+                            "type": "object",
+                            "properties": {
+                                "query": {
+                                    "type": "string",
+                                    "description": "The search query to look up information about"
+                                }
+                            },
+                            "required": ["query"]
+                        }
+                    }
+                }
+                # Add formatted tool to tools list
+                tools_list.append(tool)
+                logger.info(f"Loaded and globalized tool function: {name}")
+
+        logger.info(f"Loaded {len(tools_list)} tool functions from tools.py")
+        logger.info(f"Tools list: {tools_list}")
+        
+    except Exception as e:
+        logger.warning(f"Error loading tools from tools.py: {e}")
+        
+    return tools_list
+
+async def step_callback(step_details):
+    """Callback for agent steps"""
+    logger.info(f"[CALLBACK DEBUG] Step callback triggered with details: {step_details}")
+    try:
+        # Queue message for agent response
+        if step_details.get("response"):
+            message_queue.put({
+                "content": f"Agent Response: {step_details.get('response')}",
+                "author": step_details.get("agent_name", "Agent")
+            })
+            logger.info("[CALLBACK DEBUG] Queued agent response message")
+        
+        # Queue message for tool usage
+        if step_details.get("tool_name"):
+            message_queue.put({
+                "content": f"🛠️ Using tool: {step_details.get('tool_name')}",
+                "author": "System"
+            })
+            logger.info("[CALLBACK DEBUG] Queued tool usage message")
+            
+    except Exception as e:
+        logger.error(f"[CALLBACK DEBUG] Error in step callback: {str(e)}", exc_info=True)
+
+async def task_callback(task_output):
+    """Callback for task completion"""
+    logger.info(f"[CALLBACK DEBUG] Task callback triggered with output: {task_output}")
+    try:
+        # Create message content
+        if hasattr(task_output, 'raw'):
+            content = task_output.raw
+        elif hasattr(task_output, 'content'):
+            content = task_output.content
+        else:
+            content = str(task_output)
+            
+        # Queue the message
+        message_queue.put({
+            "content": f"Task Output: {content}",
+            "author": "Task"
+        })
+        logger.info("[CALLBACK DEBUG] Queued task completion message")
+        
+    except Exception as e:
+        logger.error(f"[CALLBACK DEBUG] Error in task callback: {str(e)}", exc_info=True)
+
+async def step_callback_wrapper(step_details):
+    logger.info(f"[CALLBACK DEBUG] Step callback wrapper triggered with details: {step_details}")
+    try:
+        # Check if we have a Chainlit context
+        if not cl.context.context_var.get():
+            logger.warning("[CALLBACK DEBUG] No Chainlit context available in wrapper")
+            return
+        logger.info("[CALLBACK DEBUG] Chainlit context found in wrapper")
+
+        # Create a message for the agent's response
+        if step_details.get("response"):
+            logger.info(f"[CALLBACK DEBUG] Sending agent response from wrapper: {step_details.get('response')}")
+            try:
+                await cl.Message(
+                    content=f"{role_name}: {step_details.get('response')}",
+                    author=role_name,
+                ).send()
+                logger.info("[CALLBACK DEBUG] Successfully sent agent response message from wrapper")
+            except Exception as e:
+                logger.error(f"[CALLBACK DEBUG] Error sending agent response message from wrapper: {str(e)}")
+
+        # Create a message for any tool usage
+        if step_details.get("tool_name"):
+            logger.info(f"[CALLBACK DEBUG] Sending tool usage from wrapper: {step_details.get('tool_name')}")
+            try:
+                await cl.Message(
+                    content=f"🛠️ {role_name} is using tool: {step_details.get('tool_name')}",
+                    author="System",
+                ).send()
+                logger.info("[CALLBACK DEBUG] Successfully sent tool usage message from wrapper")
+            except Exception as e:
+                logger.error(f"[CALLBACK DEBUG] Error sending tool usage message from wrapper: {str(e)}")
+
+        # Create a message for any thoughts or reasoning
+        if step_details.get("thought"):
+            logger.info(f"[CALLBACK DEBUG] Sending thought from wrapper: {step_details.get('thought')}")
+            try:
+                await cl.Message(
+                    content=f"💭 {role_name}'s thought: {step_details.get('thought')}",
+                    author=role_name,
+                ).send()
+                logger.info("[CALLBACK DEBUG] Successfully sent thought message from wrapper")
+            except Exception as e:
+                logger.error(f"[CALLBACK DEBUG] Error sending thought message from wrapper: {str(e)}")
+
+    except Exception as e:
+        logger.error(f"[CALLBACK DEBUG] Error in step callback wrapper: {str(e)}", exc_info=True)
+        try:
+            await cl.Message(
+                content=f"Error in step callback: {str(e)}",
+                author="System",
+            ).send()
+        except Exception as send_error:
+            logger.error(f"[CALLBACK DEBUG] Error sending error message: {str(send_error)}")
+
+async def task_callback_wrapper(task_output):
+    logger.info(f"[CALLBACK DEBUG] Task callback wrapper triggered with output type: {type(task_output)}")
+    try:
+        # Check if we have a Chainlit context
+        if not cl.context.context_var.get():
+            logger.warning("[CALLBACK DEBUG] No Chainlit context available in task wrapper")
+            return
+        logger.info("[CALLBACK DEBUG] Chainlit context found in task wrapper")
+
+        # Create a message for task completion
+        if hasattr(task_output, 'raw'):
+            content = task_output.raw
+            logger.info("[CALLBACK DEBUG] Using raw output")
+        elif hasattr(task_output, 'content'):
+            content = task_output.content
+            logger.info("[CALLBACK DEBUG] Using content output")
+        else:
+            content = str(task_output)
+            logger.info("[CALLBACK DEBUG] Using string representation of output")
+            
+        logger.info(f"[CALLBACK DEBUG] Sending task completion message from wrapper: {content[:100]}...")
+        try:
+            await cl.Message(
+                content=f"✅ {role_name} completed task:\n{content}",
+                author=role_name,
+            ).send()
+            logger.info("[CALLBACK DEBUG] Successfully sent task completion message from wrapper")
+        except Exception as e:
+            logger.error(f"[CALLBACK DEBUG] Error sending task completion message from wrapper: {str(e)}")
+
+        # If there are any additional task details
+        if hasattr(task_output, 'details'):
+            logger.info("[CALLBACK DEBUG] Task has additional details")
+            try:
+                await cl.Message(
+                    content=f"📝 Additional details:\n{task_output.details}",
+                    author=role_name,
+                ).send()
+                logger.info("[CALLBACK DEBUG] Successfully sent additional details message")
+            except Exception as e:
+                logger.error(f"[CALLBACK DEBUG] Error sending additional details message: {str(e)}")
+
+    except Exception as e:
+        logger.error(f"[CALLBACK DEBUG] Error in task callback wrapper: {str(e)}", exc_info=True)
+        try:
+            await cl.Message(
+                content=f"Error in task callback: {str(e)}",
+                author="System",
+            ).send()
+        except Exception as send_error:
+            logger.error(f"[CALLBACK DEBUG] Error sending error message: {str(send_error)}")
+
+def sync_task_callback_wrapper(task_output):
+    logger.info("[CALLBACK DEBUG] Sync task callback wrapper triggered")
+    try:
+        # Create a new event loop for this thread if there isn't one
+        try:
+            loop = asyncio.get_event_loop()
+            logger.info("[CALLBACK DEBUG] Got existing event loop")
+        except RuntimeError:
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            logger.info("[CALLBACK DEBUG] Created new event loop")
+        
+        if loop.is_running():
+            # If loop is running, schedule the callback
+            logger.info("[CALLBACK DEBUG] Loop is running, scheduling callback")
+            asyncio.run_coroutine_threadsafe(
+                task_callback_wrapper(task_output),
+                loop
+            )
+        else:
+            # If loop is not running, run it directly
+            logger.info("[CALLBACK DEBUG] Loop is not running, running callback directly")
+            loop.run_until_complete(task_callback_wrapper(task_output))
+            
+    except Exception as e:
+        logger.error(f"[CALLBACK DEBUG] Error in sync task callback: {str(e)}", exc_info=True)
+
+def sync_step_callback_wrapper(step_details):
+    logger.info("[CALLBACK DEBUG] Sync step callback wrapper triggered")
+    try:
+        # Create a new event loop for this thread if there isn't one
+        try:
+            loop = asyncio.get_event_loop()
+            logger.info("[CALLBACK DEBUG] Got existing event loop")
+        except RuntimeError:
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            logger.info("[CALLBACK DEBUG] Created new event loop")
+        
+        if loop.is_running():
+            # If loop is running, schedule the callback
+            logger.info("[CALLBACK DEBUG] Loop is running, scheduling callback")
+            asyncio.run_coroutine_threadsafe(
+                step_callback_wrapper(step_details),
+                loop
+            )
+        else:
+            # If loop is not running, run it directly
+            logger.info("[CALLBACK DEBUG] Loop is not running, running callback directly")
+            loop.run_until_complete(step_callback_wrapper(step_details))
+            
+    except Exception as e:
+        logger.error(f"[CALLBACK DEBUG] Error in sync step callback: {str(e)}", exc_info=True)
+
+async def ui_run_praisonai(config, topic, tools_dict):
+    """Run PraisonAI with the given configuration and topic."""
+    logger = logging.getLogger(__name__)
+    logger.setLevel(logging.DEBUG)
+    agents = {}
+    tasks = []
+    tasks_dict = {}
+
+    try:
+        # Start message queue processor
+        queue_processor = asyncio.create_task(process_message_queue())
+        
+        # Create agents for each role
+        for role, details in config['roles'].items():
+            # Format the role name and other details
+            role_name = details.get('name', role).format(topic=topic)
+            role_filled = details.get('role', role).format(topic=topic)
+            goal_filled = details['goal'].format(topic=topic)
+            backstory_filled = details['backstory'].format(topic=topic)
+
+            # Test message to verify Chainlit is working
+            await cl.Message(
+                content=f"[DEBUG] Creating agent: {role_name}",
+                author="System"
+            ).send()
+
+            # Create a sync wrapper for the step callback
+            def step_callback_sync(step_details):
+                try:
+                    # Create a new event loop for this thread
+                    loop = asyncio.new_event_loop()
+                    asyncio.set_event_loop(loop)
+                    
+                    # Add agent name to step details
+                    step_details["agent_name"] = role_name
+                    
+                    # Run the callback
+                    loop.run_until_complete(step_callback(step_details))
+                    loop.close()
+                except Exception as e:
+                    logger.error(f"[CALLBACK DEBUG] Error in step callback: {str(e)}", exc_info=True)
+
+            agent = Agent(
+                name=role_name,
+                role=role_filled,
+                goal=goal_filled,
+                backstory=backstory_filled,
+                llm=details.get('llm', 'gpt-4o'),
+                verbose=True,
+                allow_delegation=details.get('allow_delegation', False),
+                max_iter=details.get('max_iter', 15),
+                max_rpm=details.get('max_rpm'),
+                max_execution_time=details.get('max_execution_time'),
+                cache=details.get('cache', True),
+                step_callback=step_callback_sync
+            )
+            agents[role] = agent
+
+        # Create tasks for each role
+        for role, details in config['roles'].items():
+            agent = agents[role]
+            tools_list = []
+            
+            # Get tools for this role
+            for tool_name in details.get('tools', []):
+                if tool_name in tools_dict:
+                    tool_func = tools_dict[tool_name]
+                    tools_list.append(tool_func)
+
+            # 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)
+
+                # Test message to verify task creation
+                await cl.Message(
+                    content=f"[DEBUG] Created task: {task_name} for agent {role_name}",
+                    author="System"
+                ).send()
+
+                # Create a sync wrapper for the task callback
+                def task_callback_sync(task_output):
+                    try:
+                        # Create a new event loop for this thread
+                        loop = asyncio.new_event_loop()
+                        asyncio.set_event_loop(loop)
+                        
+                        # Run the callback
+                        loop.run_until_complete(task_callback(task_output))
+                        loop.close()
+                    except Exception as e:
+                        logger.error(f"[CALLBACK DEBUG] Error in task callback: {str(e)}", exc_info=True)
+
+                task = Task(
+                    description=description_filled,
+                    expected_output=expected_output_filled,
+                    agent=agent,
+                    tools=tools_list,
+                    async_execution=True,
+                    context=[],
+                    config=task_details.get('config', {}),
+                    output_json=task_details.get('output_json'),
+                    output_pydantic=task_details.get('output_pydantic'),
+                    output_file=task_details.get('output_file', ""),
+                    callback=task_callback_sync,
+                    create_directory=task_details.get('create_directory', False)
+                )
+
+                tasks.append(task)
+                tasks_dict[task_name] = task
+
+        # Set up task contexts
+        for role, details in config['roles'].items():
+            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]
+                task.context = context_tasks
+
+        # Send the start message
+        await cl.Message(
+            content="Starting PraisonAI agents execution...",
+            author="System"
+        ).send()
+
+        # Create and run the PraisonAI agents
+        if config.get('process') == 'hierarchical':
+            crew = PraisonAIAgents(
+                agents=list(agents.values()),
+                tasks=tasks,
+                verbose=True,
+                process="hierarchical",
+                manager_llm=config.get('manager_llm', 'gpt-4o')
+            )
+        else:
+            crew = PraisonAIAgents(
+                agents=list(agents.values()),
+                tasks=tasks,
+                verbose=2
+            )
+
+        # Store the crew in the user session
+        cl.user_session.set("crew", crew)
+
+        # Run the agents in a separate thread
+        loop = asyncio.get_event_loop()
+        response = await loop.run_in_executor(None, crew.start)
+        
+        logger.debug(f"[CALLBACK DEBUG] Result: {response}")
+        
+        # Convert response to string if it's not already
+        if hasattr(response, 'raw'):
+            result = response.raw
+        elif hasattr(response, 'content'):
+            result = response.content
+        else:
+            result = str(response)
+        
+        # Send the completion message
+        await cl.Message(
+            content="PraisonAI agents execution completed.",
+            author="System"
+        ).send()
+        
+        # After getting the response, wait a bit for remaining messages
+        await asyncio.sleep(1)  # Give time for final messages to be processed
+        queue_processor.cancel()  # Stop the queue processor
+        
+        return result
+        
+    except Exception as e:
+        error_msg = f"Error in ui_run_praisonai: {str(e)}"
+        logger.error(error_msg, exc_info=True)
+        await cl.Message(
+            content=error_msg,
+            author="System"
+        ).send()
+        raise
@@ -0,0 +1,81 @@
+import os
+import logging
+from dotenv import load_dotenv
+import chainlit as cl
+from chainlit.types import ThreadDict
+import yaml
+import sys
+import os
+from datetime import datetime
+
+# Add the parent directory to sys.path to allow imports
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
+
+from praisonai.ui.colab import ui_run_praisonai, load_tools_from_tools_py
+from praisonai.ui.callbacks import callback, trigger_callback
+
+# Load environment variables
+load_dotenv()
+
+# Set up logging
+logger = logging.getLogger(__name__)
+log_level = os.getenv("LOGLEVEL", "INFO").upper()
+logger.setLevel(log_level)
+
+# Load agent configuration
+agent_file = "agents.yaml"
+with open(agent_file, 'r') as f:
+    config = yaml.safe_load(f)
+
+# Load tools
+tools_dict = load_tools_from_tools_py()
+
+@cl.on_message
+async def main(message: cl.Message):
+    """Main message handler for Chainlit"""
+    try:
+        logger.info(f"Processing message: {message.content}")
+        await cl.Message(
+            content=f"🔄 Processing your request about: {message.content}...",
+            author="System"
+        ).send()
+
+        await cl.Message(
+            content="Using Running PraisonAI Agents...",
+            author="System"
+        ).send()
+
+        # Run PraisonAI with the message content as the topic
+        result = await ui_run_praisonai(config, message.content, tools_dict)
+        
+        # Send the final result
+        await cl.Message(
+            content=result,
+            author="System"
+        ).send()
+
+    except Exception as e:
+        error_msg = f"Error running PraisonAI agents: {str(e)}"
+        logger.error(error_msg, exc_info=True)
+        await cl.Message(
+            content=error_msg,
+            author="System"
+        ).send()
+
+@cl.on_chat_start
+async def start():
+    """Handler for chat start"""
+    await cl.Message(
+        content="👋 Welcome! I'm your AI assistant. What would you like to work on?",
+        author="System"
+    ).send()
+
+# Authentication setup (optional)
+if os.getenv("CHAINLIT_AUTH_SECRET"):
+    @cl.password_auth_callback
+    def auth_callback(username: str, password: str) -> cl.User:
+        # Replace with your authentication logic
+        if username == os.getenv("CHAINLIT_USERNAME", "admin") and \
+           password == os.getenv("CHAINLIT_PASSWORD", "admin"):
+            return cl.User(identifier=username, metadata={"role": "user"})
+        return None 
@@ -1 +1 @@
-# Welcome to Praison AI! 🚀🤖
+# Welcome to PraisonAI! 🚀
@@ -0,0 +1,133 @@
+# Understanding Tool Integration in AI Agents - A Beginner's Guide
+
+## Overview
+This guide explains how to properly integrate tools (functions) that an AI agent can use, making them both understandable to the OpenAI API and executable by your code.
+
+## Key Components
+
+### 1. Tool Definition Structure
+```python
+# Example tool definition in tools.py
+def search_tool(query: str) -> list:
+    """
+    Perform a web search using DuckDuckGo.
+
+    Args:
+        query (str): The search query string.
+
+    Returns:
+        list: Search results with title, url, and snippet.
+    """
+    # Function implementation...
+```
+
+### 2. Tool Dictionary Format
+```python
+tools_dict = {
+    'search_tool': {
+        'type': 'function',
+        'function': {
+            'name': 'search_tool',
+            'description': '...', 
+            'parameters': {
+                'type': 'object',
+                'properties': {
+                    'query': {'type': 'string'}
+                }
+            }
+        },
+        'callable': search_tool  # The actual Python function
+    }
+}
+```
+
+## The Two-Part System
+
+### Part 1: OpenAI API Communication
+```python
+# task_tools: What OpenAI understands
+task_tools = []
+tool_def = tools_dict[tool_name].copy()
+callable_func = tool_def.pop("callable")  # Remove the Python function
+task_tools.append(tool_def)  # Add clean JSON-serializable definition
+```
+
+### Part 2: Function Execution
+```python
+# role_tools: What your code executes
+role_tools = []
+role_tools.append(callable_func)  # Store the actual function
+agent.tools = role_tools  # Give agent access to executable functions
+```
+
+## Putting It All Together
+
+```python
+# Initialize empty lists
+role_tools = []  # For executable functions
+task_tools = []  # For OpenAI API definitions
+
+# Process each tool
+for tool_name in tools_list:
+    if tool_name in tools_dict:
+        # 1. Get the tool definition
+        tool_def = tools_dict[tool_name].copy()
+        
+        # 2. Separate the callable function
+        callable_func = tool_def.pop("callable")
+        
+        # 3. Store the function for execution
+        role_tools.append(callable_func)
+        
+        # 4. Store the API definition
+        task_tools.append(tool_def)
+        
+        # 5. Give agent access to functions
+        agent.tools = role_tools
+
+# Create task with API definitions
+task = Task(
+    description="...",
+    tools=task_tools,  # OpenAI API will use these
+    agent=agent,       # Agent has access to callable functions
+    # ... other parameters ...
+)
+```
+
+## Why This Works
+
+1. **API Communication**
+   - OpenAI API receives clean JSON tool definitions
+   - No Python functions that would cause serialization errors
+
+2. **Function Execution**
+   - Agent has access to actual Python functions
+   - Can execute tools when OpenAI decides to use them
+
+3. **Separation of Concerns**
+   - `task_tools`: Describes what tools can do (for OpenAI)
+   - `role_tools`: Actually does the work (for Python)
+
+## Common Errors and Solutions
+
+1. **"Invalid type for 'tools[0]'"**
+   - Cause: Sending null or invalid tool definition to OpenAI
+   - Solution: Use proper tool definition format in `task_tools`
+
+2. **"Object of type function is not JSON serializable"**
+   - Cause: Trying to send Python function to OpenAI API
+   - Solution: Remove callable function from API definition
+
+3. **"Tool is not callable"**
+   - Cause: Agent doesn't have access to executable functions
+   - Solution: Set `agent.tools = role_tools`
+
+## Best Practices
+
+1. Always initialize both `task_tools` and `role_tools` lists
+2. Make clean copies of tool definitions to avoid modifying originals
+3. Keep tool definitions JSON-serializable for API communication
+4. Ensure agents have access to callable functions
+5. Document tool parameters and return values clearly
+
+This structure maintains clean separation between API communication and actual function execution, making your AI agent system both reliable and maintainable.
@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.0.24"
+version = "2.0.32"
 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>=0.0.18",
+    "praisonaiagents>=0.0.23",
    "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 = "2.0.24"
+version = "2.0.32"
 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 = ">=0.0.18"
+praisonaiagents = ">=0.0.23"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"
@@ -3061,7 +3061,7 @@ wheels = [

 [[package]]
 name = "praisonai"
-version = "2.0.24"
+version = "2.0.32"
 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 = ">=0.0.18" },
+    { name = "praisonaiagents", specifier = ">=0.0.23" },
    { 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 = "0.0.18"
+version = "0.0.23"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "openai" },
    { name = "pydantic" },
    { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/09/e4/9bf056c4a9ecb5cf47766b9442b3b608881f530c4c28e233cbc6adb1f2d1/praisonaiagents-0.0.18.tar.gz", hash = "sha256:56744f82f806999fca328f9f9f8ae8b5c40cf312cf4fb6c0c34c30661f206d23", size = 23337 }
+sdist = { url = "https://files.pythonhosted.org/packages/a8/87/08ff2ac7c365251fa07973fded3e68c2edc1173d8514e6fd90fc8ee7b131/praisonaiagents-0.0.23.tar.gz", hash = "sha256:1814d537b2625a5c7d4b61d07f5d5bd043f0ad8afc31fe9d4eb99a5ca54101bf", size = 25375 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/78/b8/abfccfc0e82832721987b9c58d703f163a5573c5615ae5b5f3609f8877a0/praisonaiagents-0.0.18-py3-none-any.whl", hash = "sha256:fa9374f065ade18535ce05e27d2bb1ca326810ed6bbec5421597526f31add792", size = 29334 },
+    { url = "https://files.pythonhosted.org/packages/56/75/c3a1cf2adaf0811ab538029054fa993e82caffba2853546f41766b7e0aab/praisonaiagents-0.0.23-py3-none-any.whl", hash = "sha256:d6aaa406fc993ccd0a763aa426b7139c955e1cd99e6b204b06817ebc1a5855e3", size = 31734 },
 ]

 [[package]]
Author	SHA1	Message	Date
Mervin Praison	c460c9da60	Merge pull request #268 from MervinPraison/develop adding structured agents and code agents	2025-01-06 18:38:51 +00:00
MervinPraison	656887c700	adding structured agents and code agents	2025-01-06 18:26:56 +00:00
Mervin Praison	e0089cac41	Merge pull request #267 from MervinPraison/develop fixing bug setting openai_model_name env variable	2025-01-06 10:06:33 +00:00
MervinPraison	bf5e069c7b	fixing bug setting openai_model_name env variable	2025-01-06 10:06:11 +00:00
Mervin Praison	94cf264b4c	Merge pull request #266 from MervinPraison/develop setting openai_model_name env variable to set	2025-01-06 09:52:09 +00:00
MervinPraison	ab44fd4b27	setting openai_model_name env variable to set	2025-01-06 09:51:50 +00:00
Mervin Praison	7bad6cf7e9	Merge pull request #265 from MervinPraison/develop adding mini agents	2025-01-06 09:31:54 +00:00
MervinPraison	4ffbe48c58	adding mini agents	2025-01-06 09:31:39 +00:00
Mervin Praison	476eeb1184	Merge pull request #264 from MervinPraison/develop bug fix ui load	2025-01-06 01:07:03 +00:00
MervinPraison	550769017a	bug fix ui load	2025-01-06 01:06:24 +00:00
Mervin Praison	cccd75ca75	Merge pull request #263 from MervinPraison/develop Develop	2025-01-06 00:18:12 +00:00
MervinPraison	395f8fb5ef	praisonai agents ui working with tools	2025-01-06 00:17:54 +00:00
MervinPraison	e4893ad948	working with other callbacks	2025-01-05 23:08:20 +00:00
Mervin Praison	e717b625d2	Merge pull request #262 from MervinPraison/develop Develop	2025-01-05 07:39:54 +00:00
MervinPraison	84638ad759	praisonai ui working	2025-01-05 07:39:36 +00:00
MervinPraison	626bc963f7	ui working together	2025-01-05 07:11:06 +00:00
Mervin Praison	efa7110b38	Merge pull request #260 from MervinPraison/develop adding async callbacks	2025-01-05 00:20:03 +00:00
MervinPraison	539c10796c	adding ui with instant messages working	2025-01-05 00:19:26 +00:00
MervinPraison	815becb6f2	async callback fixes for main display	2025-01-04 22:48:21 +00:00
MervinPraison	87790695db	adding async callbacks	2025-01-04 17:19:19 +00:00