Merge pull request #268 from MervinPraison/develop

adding structured agents and code agents

Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.29 gunicorn markdown
+RUN pip install flask praisonai==2.0.32 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

agents/code_agents_example.py

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

agents/praisonaiagents/agent/agent.py

@@ -140,7 +140,7 @@ class Agent:
         goal: Optional[str] = None,
         backstory: Optional[str] = None,
         instructions: Optional[str] = None,
-        llm: Optional[Union[str, Any]] = "gpt-4o",
+        llm: Optional[Union[str, Any]] = None,
         tools: Optional[List[Any]] = None,
         function_calling_llm: Optional[Any] = None,
         max_iter: int = 20,
@@ -189,7 +189,8 @@ class Agent:
             self.self_reflect = True if self_reflect is None else self_reflect
         
         self.instructions = instructions
-        self.llm = llm
+        # 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
@@ -214,7 +215,8 @@ class Agent:
         self.markdown = markdown
         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
@@ -392,7 +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
@@ -400,6 +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
 
@@ -408,9 +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):
@@ -485,23 +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})
@@ -583,19 +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
@@ -603,7 +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":
@@ -637,22 +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,

agents/praisonaiagents/agents/agents.py

@@ -54,9 +54,11 @@ 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:
@@ -193,10 +195,17 @@ Here are the results of previous tasks that might be useful:\n
 
             agent_output = await executor_agent.achat(
                 _get_multimodal_message(task_prompt, task.images),
-                tools=task.tools
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
             )
         else:
-            agent_output = await executor_agent.achat(task_prompt, tools=task.tools)
+            agent_output = await executor_agent.achat(
+                task_prompt,
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
+            )
 
         if agent_output:
             task_output = TaskOutput(
@@ -403,10 +412,17 @@ Here are the results of previous tasks that might be useful:\n
 
             agent_output = executor_agent.chat(
                 _get_multimodal_message(task_prompt, task.images),
-                tools=task.tools
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
             )
         else:
-            agent_output = executor_agent.chat(task_prompt, tools=task.tools)
+            agent_output = executor_agent.chat(
+                task_prompt,
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
+            )
 
         if agent_output:
             task_output = TaskOutput(

agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.21"
+version = "0.0.23"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }

agents/structured_agents_example.py

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

agents/uv.lock

@@ -186,7 +186,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.21"
+version = "0.0.23"
 source = { editable = "." }
 dependencies = [
     { name = "openai" },

docs/api/praisonai/deploy.html

@@ -110,7 +110,7 @@ Loads environment variables from .env file or system and sets them.</p>
             file.write(&#34;FROM python:3.11-slim\n&#34;)
             file.write(&#34;WORKDIR /app\n&#34;)
             file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==2.0.29 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;)
             

docs/features/codeagent.mdx

@@ -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>

docs/features/mini.mdx

@@ -1,248 +0,0 @@
----
-title: "Mini AI Agents"
-description: "Learn how to create simple yet powerful AI agents in just a few lines of code."
-icon: "robot"
----
-
-<Note>
-  **Prerequisites**
-  - Python 3.10 or higher
-  - PraisonAI Agents package installed
-  - Basic understanding of Python
-</Note>
-
-## Quick Start
-
-Create multiple AI agents that can work together 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, Agents
-    ```
-  </Step>
-
-  <Step title="Create Your Agents">
-    Create specialized agents with specific roles:
-    ```python 
-    summarise_agent = Agent(instructions="Summarise what is Photosynthesis")
-    ```
-  </Step>
-
-  <Step title="Start Your Agents">
-    Initialize and run your agents:
-    ```python
-    agents = Agents(agents=[summarise_agent])
-    agents.start()
-    ```
-  </Step>
-</Steps>
-
-## Understanding Mini AI Agents
-
-<Card title="What are Mini AI Agents?" icon="question">
-  Mini AI Agents are simplified yet powerful AI agents that can:
-  - Work together to accomplish tasks
-  - Use tools like internet search
-  - Process and summarize information
-  - Execute tasks sequentially
-</Card>
-
-## Key Components
-
-<CardGroup cols={2}>
-  <Card title="Agent" icon="user-robot">
-    Individual AI agents with specific roles and capabilities
-    ```python
-    Agent(instructions="Your agent's role description")
-    ```
-  </Card>
-  <Card title="Tools" icon="wrench">
-    Built-in tools that agents can use
-    ```python
-    tools=[Tools.internet_search]
-    ```
-  </Card>
-  <Card title="Agents Manager" icon="users">
-    Coordinates multiple agents
-    ```python
-    Agents(agents=[agent1, agent2])
-    ```
-  </Card>
-  <Card title="Sequential Flow" icon="arrow-right">
-    Agents work in sequence, passing results to each other
-  </Card>
-</CardGroup>
-
-## Available Tools
-
-<CardGroup cols={2}>
-  <Card title="Internet Search" icon="search">
-    ```python
-    Tools.internet_search
-    ```
-    Search the internet using DuckDuckGo
-    <Note>
-      Requires: `pip install duckduckgo_search`
-    </Note>
-  </Card>
-</CardGroup>
-
-## Examples
-
-### Basic Research and Summary
-
-```terminal
-pip install praisonaiagents duckduckgo_search
-```
-
-```python
-from praisonaiagents import Agent, Agents, Tools
-
-# Create a research agent
-research_agent = Agent(
-    instructions="You are a research agent to search internet about AI 2024",
-    tools=[Tools.internet_search]
-)
-
-# Create a summarize agent
-summarise_agent = Agent(
-    instructions="You are a summarize agent to summarise research agent's findings"
-)
-
-# Initialize and run agents
-agents = Agents(agents=[research_agent, summarise_agent])
-agents.start()
-```
-
-### Custom Instructions
-
-<CodeGroup>
-```python Basic
-# Simple instructions
-research_agent = Agent(
-    instructions="Research about climate change"
-)
-```
-
-```python Advanced
-# Detailed instructions
-research_agent = Agent(
-    instructions="""
-    You are a research agent focused on gathering information about:
-    1. Latest AI developments in 2024
-    2. Major breakthroughs in machine learning
-    3. New AI applications in industry
-    
-    Provide detailed and accurate information from reliable sources.
-    """
-)
-```
-</CodeGroup>
-
-## Best Practices
-
-<AccordionGroup>
-  <Accordion title="Agent Instructions">
-    Write clear and specific instructions:
-    ```python
-    # Good
-    Agent(instructions="Research and analyze the latest AI developments in 2024")
-    
-    # Too vague
-    Agent(instructions="Research AI")
-    ```
-  </Accordion>
-
-  <Accordion title="Tool Usage">
-    Provide tools only to agents that need them:
-    ```python
-    # Research agent needs search
-    research_agent = Agent(
-        instructions="Research AI",
-        tools=[Tools.internet_search]
-    )
-    
-    # Summary agent doesn't need search
-    summary_agent = Agent(
-        instructions="Summarize findings"
-    )
-    ```
-  </Accordion>
-</AccordionGroup>
-
-## Common Patterns
-
-### Research and Analysis
-```python
-# Research agent
-researcher = Agent(
-    instructions="Research latest developments in quantum computing",
-    tools=[Tools.internet_search]
-)
-
-# Analysis agent
-analyst = Agent(
-    instructions="Analyze and explain the research findings in simple terms"
-)
-
-agents = Agents(agents=[researcher, analyst])
-```
-
-### Information Processing
-```python
-# Data collector
-collector = Agent(
-    instructions="Collect information about renewable energy",
-    tools=[Tools.internet_search]
-)
-
-# Summarizer
-summarizer = Agent(
-    instructions="Create a concise summary of the collected information"
-)
-
-# Report writer
-writer = Agent(
-    instructions="Write a detailed report based on the summary"
-)
-
-agents = Agents(agents=[collector, summarizer, writer])
-```
-
-## Troubleshooting
-
-<CardGroup cols={2}>
-  <Card title="Tool Not Available" icon="triangle-exclamation">
-    If using `Tools.internet_search`, install required package:
-    ```bash
-    pip install duckduckgo_search
-    ```
-  </Card>
-  <Card title="Agent Communication" icon="comments">
-    Ensure agent instructions are clear and complementary
-  </Card>
-</CardGroup>
-
-## Next Steps
-
-<CardGroup cols={2}>
-  <Card title="Advanced Agents" icon="graduation-cap" href="./advanced">
-    Learn about advanced agent configurations
-  </Card>
-  <Card title="Custom Tools" icon="screwdriver-wrench" href="./tools">
-    Create your own agent tools
-  </Card>
-</CardGroup>
-
-<Note>
-  Mini AI Agents are designed to be simple yet powerful. They're perfect for quick prototypes and straightforward automation tasks.
-</Note>

docs/features/structured.mdx

@@ -0,0 +1,322 @@
+---
+title: "Structured AI Agents"
+description: "Learn how to create AI agents that return structured, type-safe outputs using Pydantic models and JSON."
+icon: "cube"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - Basic understanding of Python and Pydantic
+</Note>
+
+## Quick Start
+
+Create AI agents that return structured, validated outputs in just a few lines of code!
+
+<Steps>
+  <Step title="Install Package">
+    First, install the PraisonAI Agents package:
+    ```bash
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Import Required Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents, Tools
+    from pydantic import BaseModel
+    ```
+  </Step>
+
+  <Step title="Define Your Data Model">
+    Create a Pydantic model to define your output structure:
+    ```python
+    class AnalysisReport(BaseModel):
+        title: str
+        findings: str
+        summary: str
+    ```
+  </Step>
+
+  <Step title="Create Structured Tasks">
+    Create tasks with structured outputs:
+    ```python
+    research_task = Task(
+        description="Research recent AI developments",
+        agent=researcher,
+        expected_output="Research findings"
+    )
+
+    analysis_task = Task(
+        description="Analyze research findings",
+        agent=analyst,
+        output_pydantic=AnalysisReport,  # For Pydantic output
+        # or output_json=AnalysisReport  # For JSON output
+        expected_output="Structured analysis report"
+    )
+    ```
+  </Step>
+</Steps>
+
+## Understanding Structured Outputs
+
+<Card title="What are Structured Outputs?" icon="question">
+  Structured outputs allow you to:
+  - Define exact shape of data using Pydantic models
+  - Get type-safe, validated responses
+  - Choose between Pydantic objects or JSON
+  - Ensure consistent output format across agent responses
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="Pydantic Models" icon="cube">
+    Define your data structure
+    ```python
+    class ResearchReport(BaseModel):
+        title: str
+        content: str
+    ```
+  </Card>
+  <Card title="Task Configuration" icon="gear">
+    Configure output format
+    ```python
+    Task(output_pydantic=Model)
+    # or
+    Task(output_json=Model)
+    ```
+  </Card>
+  <Card title="Output Validation" icon="check">
+    Automatic validation of responses against your model
+  </Card>
+  <Card title="Format Options" icon="code">
+    Choose between Pydantic objects or JSON output
+  </Card>
+</CardGroup>
+
+## Output Types
+
+<CardGroup cols={2}>
+  <Card title="Pydantic Output" icon="cube">
+    ```python
+    task = Task(
+        output_pydantic=AnalysisReport
+    )
+    # Returns Pydantic object
+    ```
+    Get type-safe Pydantic model instances
+  </Card>
+  <Card title="JSON Output" icon="brackets-curly">
+    ```python
+    task = Task(
+        output_json=AnalysisReport
+    )
+    # Returns JSON dict
+    ```
+    Get JSON dictionary matching model schema
+  </Card>
+</CardGroup>
+
+## Examples
+
+### Research Analysis with Structured Output
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents, Tools
+from pydantic import BaseModel
+
+# Define output structure
+class AnalysisReport(BaseModel):
+    title: str
+    findings: str
+    summary: str
+
+# Create agents
+researcher = Agent(
+    name="AIResearcher",
+    role="Technology Research Analyst",
+    tools=[Tools.internet_search]
+)
+
+analyst = Agent(
+    name="DataAnalyst",
+    role="Data Insights Specialist"
+)
+
+# Define structured tasks
+research_task = Task(
+    name="gather_research",
+    description="Research recent AI developments in 2024",
+    agent=researcher,
+    expected_output="Research findings"
+)
+
+analysis_task = Task(
+    name="analyze_findings",
+    description="Analyze research findings and create structured report",
+    agent=analyst,
+    output_pydantic=AnalysisReport,
+    expected_output="Structured analysis report"
+)
+
+# Initialize and run agents
+agents = PraisonAIAgents(
+    agents=[researcher, analyst],
+    tasks=[research_task, analysis_task]
+)
+result = agents.start()
+```
+
+### Different Output Formats
+
+<CodeGroup>
+```python Pydantic
+# Get Pydantic model instance
+analysis_task = Task(
+    description="Analyze findings",
+    agent=analyst,
+    output_pydantic=AnalysisReport
+)
+
+# Result will be AnalysisReport instance
+result.pydantic.title
+result.pydantic.findings
+result.pydantic.summary
+```
+
+```python JSON
+# Get JSON dictionary
+analysis_task = Task(
+    description="Analyze findings",
+    agent=analyst,
+    output_json=AnalysisReport
+)
+
+# Result will be dictionary
+result.json_dict['title']
+result.json_dict['findings']
+result.json_dict['summary']
+```
+</CodeGroup>
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Model Design">
+    Design clear, focused Pydantic models:
+    ```python
+    # Good
+    class ResearchReport(BaseModel):
+        title: str
+        findings: str
+        summary: str
+    
+    # Too complex
+    class Report(BaseModel):
+        title: str
+        subtitle: Optional[str]
+        findings: List[Dict[str, Any]]
+    ```
+  </Accordion>
+
+  <Accordion title="Output Format Selection">
+    Choose the right output format:
+    ```python
+    # Use Pydantic when you need:
+    # - Type validation
+    # - Model methods
+    task = Task(output_pydantic=Model)
+    
+    # Use JSON when you need:
+    # - Simple dictionary access
+    # - Serialization
+    task = Task(output_json=Model)
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### Research and Structured Analysis
+```python
+class ResearchReport(BaseModel):
+    topic: str
+    findings: str
+    sources: list[str]
+
+class Analysis(BaseModel):
+    key_points: list[str]
+    implications: str
+    recommendations: str
+
+research_task = Task(
+    description="Research quantum computing",
+    agent=researcher,
+    output_pydantic=ResearchReport
+)
+
+analysis_task = Task(
+    description="Analyze research implications",
+    agent=analyst,
+    output_pydantic=Analysis
+)
+```
+
+### Data Collection and Summary
+```python
+class DataPoint(BaseModel):
+    timestamp: str
+    value: float
+    source: str
+
+class Summary(BaseModel):
+    average: float
+    trends: str
+    insights: str
+
+collection_task = Task(
+    description="Collect data points",
+    output_pydantic=DataPoint
+)
+
+summary_task = Task(
+    description="Summarize data",
+    output_pydantic=Summary
+)
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Validation Errors" icon="triangle-exclamation">
+    Check if your model matches the expected output structure
+    ```python
+    # Print model schema
+    print(Model.schema_json())
+    ```
+  </Card>
+  <Card title="Output Format" icon="code">
+    Verify you're accessing the right output field:
+    - `result.pydantic` for Pydantic
+    - `result.json_dict` for JSON
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Advanced Models" icon="graduation-cap" href="./advanced-models">
+    Learn about advanced Pydantic model configurations
+  </Card>
+  <Card title="Custom Validation" icon="check-double" href="./validation">
+    Add custom validation to your models
+  </Card>
+</CardGroup>
+
+<Note>
+  Structured outputs make your AI agents more reliable and their responses more predictable. They're perfect for production systems where data consistency is crucial.
+</Note>

docs/mint.json

@@ -104,6 +104,8 @@
         "framework/multimodalagents",
         "features/async",
         "features/mini",
+        "features/codeagent",
+        "features/structured",
         "features/callbacks",
         "train",
         "framework/praisonaiagents",

poetry.lock

@@ -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"
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 python-versions = ">=3.8"
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 ]
 
 [package.extras]
-protobuf = ["grpcio-tools (>=1.68.1)"]
+protobuf = ["grpcio-tools (>=1.69.0)"]
 
 [[package]]
 name = "grpcio-status"
@@ -2805,13 +2806,13 @@ files = [
 
 [[package]]
 name = "huggingface-hub"
-version = "0.27.0"
+version = "0.27.1"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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-    {file = "huggingface_hub-0.27.0.tar.gz", hash = "sha256:902cce1a1be5739f5589e560198a65a8edcfd3b830b1666f36e4b961f0454fac"},
+    {file = "huggingface_hub-0.27.1-py3-none-any.whl", hash = "sha256:1c5155ca7d60b60c2e2fc38cbb3ffb7f7c3adf48f824015b219af9061771daec"},
+    {file = "huggingface_hub-0.27.1.tar.gz", hash = "sha256:c004463ca870283909d715d20f066ebd6968c2207dae9393fdffb3c1d4d8f98b"},
 ]
 
 [package.dependencies]
@@ -2853,13 +2854,13 @@ pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_ve
 
 [[package]]
 name = "identify"
-version = "2.6.4"
+version = "2.6.5"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "identify-2.6.4-py2.py3-none-any.whl", hash = "sha256:993b0f01b97e0568c179bb9196391ff391bfb88a99099dbf5ce392b68f42d0af"},
-    {file = "identify-2.6.4.tar.gz", hash = "sha256:285a7d27e397652e8cafe537a6cc97dd470a970f48fb2e9d979aa38eae5513ac"},
+    {file = "identify-2.6.5-py2.py3-none-any.whl", hash = "sha256:14181a47091eb75b337af4c23078c9d09225cd4c48929f521f3bf16b09d02566"},
+    {file = "identify-2.6.5.tar.gz", hash = "sha256:c10b33f250e5bba374fae86fb57f3adcebf1161bce7cdf92031915fd480c13bc"},
 ]
 
 [package.extras]
@@ -2900,13 +2901,13 @@ test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "p
 
 [[package]]
 name = "importlib-resources"
-version = "6.4.5"
+version = "6.5.2"
 description = "Read resources from Python packages"
 optional = true
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "importlib_resources-6.4.5-py3-none-any.whl", hash = "sha256:ac29d5f956f01d5e4bb63102a5a19957f1b9175e45649977264a1416783bb717"},
-    {file = "importlib_resources-6.4.5.tar.gz", hash = "sha256:980862a1d16c9e147a59603677fa2aa5fd82b87f223b6cb870695bcfce830065"},
+    {file = "importlib_resources-6.5.2-py3-none-any.whl", hash = "sha256:789cfdc3ed28c78b67a06acb8126751ced69a3d5f79c095a98298cd8a760ccec"},
+    {file = "importlib_resources-6.5.2.tar.gz", hash = "sha256:185f87adef5bcc288449d98fb4fba07cea78bc036455dd44c5fc4a2fe78fed2c"},
 ]
 
 [package.extras]
@@ -3975,13 +3976,13 @@ files = [
 
 [[package]]
 name = "marshmallow"
-version = "3.23.2"
+version = "3.24.0"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "marshmallow-3.23.2-py3-none-any.whl", hash = "sha256:bcaf2d6fd74fb1459f8450e85d994997ad3e70036452cbfa4ab685acb19479b3"},
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+    {file = "marshmallow-3.24.0-py3-none-any.whl", hash = "sha256:459922b7a1fd3d29d5082ddcadfcea0efd98985030e71d3ef0dd8f44f406e41d"},
+    {file = "marshmallow-3.24.0.tar.gz", hash = "sha256:378572f727e52123d00de1bdd9b7ea7bed18bbfedc7f9bfbcddaf78925a8d602"},
 ]
 
 [package.dependencies]
@@ -3989,7 +3990,7 @@ packaging = ">=17.0"
 
 [package.extras]
 dev = ["marshmallow[tests]", "pre-commit (>=3.5,<5.0)", "tox"]
-docs = ["alabaster (==1.0.0)", "autodocsumm (==0.2.14)", "sphinx (==8.1.3)", "sphinx-issues (==5.0.0)", "sphinx-version-warning (==1.1.2)"]
+docs = ["alabaster (==1.0.0)", "autodocsumm (==0.2.14)", "sphinx (==8.1.3)", "sphinx-issues (==5.0.0)"]
 tests = ["pytest", "simplejson"]
 
 [[package]]
@@ -4548,18 +4549,18 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
 
 [[package]]
 name = "nbconvert"
-version = "7.16.4"
+version = "7.16.5"
 description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 beautifulsoup4 = "*"
-bleach = "!=5.0.0"
+bleach = {version = "!=5.0.0", extras = ["css"]}
 defusedxml = "*"
 jinja2 = ">=3.0"
 jupyter-core = ">=4.7"
@@ -4571,7 +4572,6 @@ nbformat = ">=5.7"
 packaging = "*"
 pandocfilters = ">=1.4.1"
 pygments = ">=2.4.1"
-tinycss2 = "*"
 traitlets = ">=5.1"
 
 [package.extras]
@@ -4727,13 +4727,13 @@ sympy = "*"
 
 [[package]]
 name = "openai"
-version = "1.58.1"
+version = "1.59.3"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "openai-1.59.3.tar.gz", hash = "sha256:7f7fff9d8729968588edf1524e73266e8593bb6cab09298340efb755755bb66f"},
 ]
 
 [package.dependencies]
@@ -5438,13 +5438,13 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "posthog"
-version = "3.7.4"
+version = "3.7.5"
 description = "Integrate PostHog into any python application."
 optional = true
 python-versions = "*"
 files = [
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-    {file = "posthog-3.7.4.tar.gz", hash = "sha256:19384bd09d330f9787a7e2446aba14c8057ece56144970ea2791072d4e40cd36"},
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+    {file = "posthog-3.7.5.tar.gz", hash = "sha256:8ba40ab623da35db72715fc87fe7dccb7fc272ced92581fe31db2d4dbe7ad761"},
 ]
 
 [package.dependencies]
@@ -5488,13 +5488,13 @@ selenium = ">=4.18.1,<5.0.0"
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.14"
+version = "0.0.23"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -5522,20 +5522,20 @@ virtualenv = ">=20.10.0"
 
 [[package]]
 name = "primp"
-version = "0.9.2"
+version = "0.9.3"
 description = "HTTP client that can impersonate web browsers, mimicking their headers and `TLS/JA3/JA4/HTTP2` fingerprints"
 optional = true
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -6161,13 +6161,13 @@ requests = ">=2.14.0"
 
 [[package]]
 name = "pygments"
-version = "2.18.0"
+version = "2.19.1"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -7168,29 +7168,29 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "ruff"
-version = "0.8.4"
+version = "0.8.6"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
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 [[package]]
 name = "selenium"
@@ -7358,23 +7365,23 @@ files = [
 
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-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.7.0)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.8.0)"]
 core = ["importlib_metadata (>=6)", "jaraco.collections", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging", "packaging (>=24.2)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
 cover = ["pytest-cov"]
 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"

praisonai.rb

@@ -3,7 +3,7 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.29.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"
   

praisonai/deploy.py

@@ -56,7 +56,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.0.29 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')
             

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.0.29"
+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>=0.0.21",
+    "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 = "2.0.29"
+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 = ">=0.0.21"
+praisonaiagents = ">=0.0.23"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

uv.lock

@@ -3061,7 +3061,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.0.29"
+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 = ">=0.0.21" },
+    { 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 = "0.0.21"
+version = "0.0.23"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "openai" },
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ff/2a/9da2df9f6fc96e5c3192005f4bad3422fa7675fab1f07bfad9fcff93cd91/praisonaiagents-0.0.21.tar.gz", hash = "sha256:f6072220114ecb937b50ac675ace9f8b335667231637eed796e16ae1fc0656b1", size = 25460 }
+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/bc/61/3904cd43f66320d8b7ce407fc18c143842d3779716f51dc10b0faa9757e9/praisonaiagents-0.0.21-py3-none-any.whl", hash = "sha256:16f18d4e553471a740a03921e49d961c53ca881b287f422d65e77ae2c4c88275", size = 31742 },
+    { url = "https://files.pythonhosted.org/packages/56/75/c3a1cf2adaf0811ab538029054fa993e82caffba2853546f41766b7e0aab/praisonaiagents-0.0.23-py3-none-any.whl", hash = "sha256:d6aaa406fc993ccd0a763aa426b7139c955e1cd99e6b204b06817ebc1a5855e3", size = 31734 },
 ]
 
 [[package]]