Merge pull request #284 from MervinPraison/develop

Update PraisonAI to version 2.0.41 and enhance documentation

.gitignore

@@ -65,4 +65,5 @@ outputs
 agents/praisonaiagents/build
 agents/praisonaiagents/dist
 agents/praisonaiagents/praisonaiagents.egg-info
-.codegpt
+.codegpt
+.praison

Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.38 gunicorn markdown
+RUN pip install flask praisonai==2.0.41 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

agents.yaml

@@ -1,6 +1,7 @@
 framework: praisonai
 process: sequential
 topic: create movie script about cat in mars
+memory: false
 roles:
   researcher:
     backstory: Skilled in research, with a focus on gathering accurate and relevant

agents/memory_simple.py

@@ -1,34 +1,62 @@
 from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
 from praisonaiagents.tools import duckduckgo
 
-research_agent = Agent(
-    role="Research Analyst",
-    goal="Research and document key information about topics",
-    backstory="Expert at analyzing and storing information in memory",
-    llm="gpt-4o-mini",
-    tools=[duckduckgo]
-)
+# Test facts
+fact1 = "The capital city of Jujuha is Hahanu and its population is 102300"
+fact2 = "Three main ingredients in a classic proloder are eggs, sugar, and flour"
+fact3 = "The year the first Josinga was released is 2007"
 
-blog_agent = Agent(
-    role="Blog Writer",
-    goal="Write a blog post about the research",
-    backstory="Expert at writing blog posts",
+fact_agent = Agent(
+    name="Fact Agent",
+    instructions="You are a fact agent, you store and retrieve facts in memory",
     llm="gpt-4o-mini"
 )
 
+research_agent = Agent(
+    name="Research Agent",
+    instructions="You are a research analyst, you research and document key points about topics",
+    llm="gpt-4o-mini"
+)
+
+blog_agent = Agent(
+    name="Blog Agent",
+    instructions="You are a blog writer, you write a blog post about the research",
+    llm="gpt-4o-mini"
+)
+
+fact_task = Task(
+    description="Store the following facts in memory: " + fact1 + ", " + fact2 + ", " + fact3,
+    agent=fact_agent
+)
+
 research_task = Task(
-    description="Research and document key information about topics",
+    description="Research and document 2 key points about AI",
+    agent=research_agent
+)
+
+research_task2 = Task(
+    description="Research and document 2 key points about AI in healthcare",
+    agent=research_agent
+)
+
+research_task3 = Task(
+    description="Research and document 2 key points about AI in education",
+    agent=research_agent
+)
+
+research_task4 = Task(
+    description="Research and document 2 key points about AI in finance",
     agent=research_agent
 )
 
 blog_task = Task(
-    description="Write a blog post about the research",
+    description="Write a blog post about Jujuha",
     agent=blog_agent
 )
 
 agents = PraisonAIAgents(
-    agents=[research_agent, blog_agent],
-    tasks=[research_task, blog_task],
+    agents=[fact_agent, research_agent, blog_agent],
+    tasks=[fact_task, research_task, research_task2, research_task3, research_task4, blog_task],
     memory=True
 )   
 

agents/praisonaiagents/agent/agent.py

@@ -381,7 +381,7 @@ Your Goal: {self.goal}
                     display_generating("", start_time),
                     console=self.console,
                     refresh_per_second=4,
-                    transient=False,  # Changed to False to preserve output
+                    transient=True,  # Changed to False to preserve output
                     vertical_overflow="ellipsis",
                     auto_refresh=True
                 ) as live:
@@ -466,7 +466,15 @@ Your Goal: {self.goal}
                         display_text = next((item["text"] for item in prompt if item["type"] == "text"), "")
                     
                     if display_text and str(display_text).strip():
-                        display_instruction(f"Agent {self.name} is processing prompt: {display_text}", console=self.console)
+                        # Pass agent information to display_instruction
+                        agent_tools = [t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools]
+                        display_instruction(
+                            f"Agent {self.name} is processing prompt: {display_text}", 
+                            console=self.console,
+                            agent_name=self.name,
+                            agent_role=self.role,
+                            agent_tools=agent_tools
+                        )
 
                 response = self._chat_completion(messages, temperature=temperature, tools=tools if tools else None)
                 if not response:
@@ -525,7 +533,7 @@ Your Goal: {self.goal}
                     self.chat_history.append({"role": "user", "content": original_prompt})
                     self.chat_history.append({"role": "assistant", "content": response_text})
                     if self.verbose:
-                        logging.info(f"Agent {self.name} final response: {response_text}")
+                        logging.debug(f"Agent {self.name} final response: {response_text}")
                     display_interaction(original_prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
                     return response_text
 
@@ -630,6 +638,22 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                             item["text"] += "\nReturn ONLY a valid JSON object. No other text or explanation."
                             break
 
+            # Display instruction with agent info if verbose
+            if self.verbose:
+                display_text = prompt
+                if isinstance(prompt, list):
+                    display_text = next((item["text"] for item in prompt if item["type"] == "text"), "")
+                
+                if display_text and str(display_text).strip():
+                    agent_tools = [t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools]
+                    await adisplay_instruction(
+                        f"Agent {self.name} is processing prompt: {display_text}",
+                        console=self.console,
+                        agent_name=self.name,
+                        agent_role=self.role,
+                        agent_tools=agent_tools
+                    )
+
             # Format tools if provided
             formatted_tools = []
             if tools:

agents/praisonaiagents/main.py

@@ -116,10 +116,10 @@ def display_interaction(message, response, markdown=True, generation_time=None,
         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(message), title="Task", 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(message, style="bold green"), title="Task", border_style="cyan"))
         console.print(Panel.fit(Text(response, style="bold blue"), title="Response", border_style="cyan"))
 
 def display_self_reflection(message: str, console=None):
@@ -135,7 +135,7 @@ def display_self_reflection(message: str, console=None):
     
     console.print(Panel.fit(Text(message, style="bold yellow"), title="Self Reflection", border_style="magenta"))
 
-def display_instruction(message: str, console=None):
+def display_instruction(message: str, console=None, agent_name: str = None, agent_role: str = None, agent_tools: List[str] = None):
     if not message or not message.strip():
         return
     if console is None:
@@ -146,7 +146,19 @@ def display_instruction(message: str, console=None):
     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"))
+    # Display agent info if available
+    if agent_name:
+        agent_info = f"[bold #FF9B9B]👤 Agent:[/] [#FFE5E5]{agent_name}[/]"
+        if agent_role:
+            agent_info += f"\n[bold #B4B4B3]Role:[/] [#FFE5E5]{agent_role}[/]"
+        if agent_tools:
+            tools_str = ", ".join(f"[italic #B4D4FF]{tool}[/]" for tool in agent_tools)
+            agent_info += f"\n[bold #86A789]Tools:[/] {tools_str}"
+        console.print(Panel(agent_info, border_style="#D2E3C8", title="[bold]Agent Info[/]", title_align="left", padding=(1, 2)))
+    
+    # Only print if log level is DEBUG
+    if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+        console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
 
 def display_tool_call(message: str, console=None):
     if not message or not message.strip():
@@ -222,10 +234,10 @@ async def adisplay_interaction(message, response, markdown=True, 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(message), title="Task", 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(message, style="bold green"), title="Task", 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):
@@ -241,7 +253,7 @@ async def adisplay_self_reflection(message: str, console=None):
     
     console.print(Panel.fit(Text(message, style="bold yellow"), title="Self Reflection", border_style="magenta"))
 
-async def adisplay_instruction(message: str, console=None):
+async def adisplay_instruction(message: str, console=None, agent_name: str = None, agent_role: str = None, agent_tools: List[str] = None):
     """Async version of display_instruction."""
     if not message or not message.strip():
         return
@@ -252,7 +264,19 @@ async def adisplay_instruction(message: str, console=None):
     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"))
+    # Display agent info if available
+    if agent_name:
+        agent_info = f"[bold #FF9B9B]👤 Agent:[/] [#FFE5E5]{agent_name}[/]"
+        if agent_role:
+            agent_info += f"\n[bold #B4B4B3]Role:[/] [#FFE5E5]{agent_role}[/]"
+        if agent_tools:
+            tools_str = ", ".join(f"[italic #B4D4FF]{tool}[/]" for tool in agent_tools)
+            agent_info += f"\n[bold #86A789]Tools:[/] {tools_str}"
+        console.print(Panel(agent_info, border_style="#D2E3C8", title="[bold]Agent Info[/]", title_align="left", padding=(1, 2)))
+    
+    # Only print if log level is DEBUG
+    if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+        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."""

agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.30"
+version = "0.0.31"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }

agents/uv.lock

@@ -990,7 +990,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.30"
+version = "0.0.31"
 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==2.0.38 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.41 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/langchain.mdx

@@ -0,0 +1,219 @@
+---
+title: "LangChain Agents"
+sidebarTitle: "LangChain Agents"
+description: "Learn how to use LangChain tools and utilities with PraisonAI agents."
+icon: "link-simple"
+---
+
+## Quick Start
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the required packages:
+            ```bash
+            pip install praisonaiagents langchain-community wikipedia
+            ```
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+<CodeGroup>
+  ```python Single Tool
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+  from langchain_community.utilities import WikipediaAPIWrapper
+
+  # Create an agent with Wikipedia tool
+  agent = Agent(
+      name="WikiAgent",
+      role="Research Assistant",
+      goal="Search Wikipedia for accurate information",
+      backstory="I am an AI assistant specialized in Wikipedia research",
+      tools=[WikipediaAPIWrapper],
+      self_reflect=False
+  )
+
+  # Create a research task
+  task = Task(
+      name="wiki_search",
+      description="Research 'Artificial Intelligence' on Wikipedia",
+      expected_output="Comprehensive information from Wikipedia articles",
+      agent=agent
+  )
+
+  # Create and start the workflow
+  agents = PraisonAIAgents(
+      agents=[agent],
+      tasks=[task],
+      verbose=True
+  )
+
+  agents.start()
+  ```
+
+  ```python Multiple Tools
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+  from langchain_community.tools import YouTubeSearchTool
+  from langchain_community.utilities import WikipediaAPIWrapper
+
+  # Create YouTube search agent
+  agent = Agent(
+      name="SearchAgent",
+      role="Research Assistant",
+      goal="Search for information from YouTube",
+      backstory="I am an AI assistant that can search YouTube for relevant videos.",
+      tools=[YouTubeSearchTool],
+      self_reflect=False
+  )
+
+  # Create Wikipedia research agent
+  agent2 = Agent(
+      name="WikiAgent",
+      role="Research Assistant",
+      goal="Search for information from Wikipedia",
+      backstory="I am an AI assistant that can search Wikipedia for accurate information.",
+      tools=[WikipediaAPIWrapper],
+      self_reflect=False
+  )
+
+  # Create YouTube search task
+  task = Task(
+      name="search_task",
+      description="Search for information about 'AI advancements' on YouTube",
+      expected_output="Relevant information from YouTube videos",
+      agent=agent
+  )
+
+  # Create Wikipedia research task
+  task2 = Task(
+      name="wiki_task",
+      description="Search for information about 'AI advancements' on Wikipedia",
+      expected_output="Comprehensive information from Wikipedia articles",
+      agent=agent2
+  )
+
+  # Create and start the workflow
+  agents = PraisonAIAgents(
+      agents=[agent, agent2],
+      tasks=[task, task2],
+      verbose=True
+  )
+
+  agents.start()
+  ```
+</CodeGroup>
+        </Step>
+
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            (Upcoming Feature)
+            Install the PraisonAI package:
+            ```bash
+            pip install "praisonai"
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+roles:
+  researcher:
+    name: SearchAgent
+    role: Research Assistant
+    goal: Search for information from multiple sources
+    backstory: I am an AI assistant that can search YouTube and Wikipedia.
+    tools:
+      - youtube_search
+      - wikipedia
+    tasks:
+      search_task:
+        name: search_task
+        description: Search for information about 'AI advancements' on both YouTube and Wikipedia
+        expected_output: Combined information from YouTube videos and Wikipedia articles
+```
+        </Step>
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+```bash
+praisonai agents.yaml
+```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys)
+  - LangChain compatible tools and utilities
+</Note>
+
+## Understanding LangChain Integration
+
+<Card title="What is LangChain Integration?" icon="question">
+  LangChain integration enables agents to:
+  - Use LangChain's extensive tool ecosystem
+  - Access various data sources and APIs
+  - Leverage pre-built utilities and wrappers
+  - Combine multiple tools in a single agent
+  - Extend agent capabilities with community tools
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Tool Integration" icon="plug">
+    Seamlessly use LangChain tools with PraisonAI agents.
+  </Card>
+  <Card title="Multiple Sources" icon="database">
+    Access various data sources through LangChain utilities.
+  </Card>
+  <Card title="Community Tools" icon="users">
+    Leverage the extensive LangChain community ecosystem.
+  </Card>
+  <Card title="Custom Tools" icon="wrench">
+    Create and integrate custom LangChain tools.
+  </Card>
+</CardGroup>
+
+
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Memory Integration" icon="brain" href="../concepts/memory">
+    Learn how to combine LangChain tools with agent memory
+  </Card>
+  <Card title="Custom Tools" icon="wrench" href="./tools">
+    Create your own custom tools for agents
+  </Card>
+</CardGroup>
+
+<Note>
+  For optimal results, ensure all required dependencies are installed and API keys are properly configured for each tool.
+</Note>

docs/mint.json

@@ -84,6 +84,7 @@
         "features/selfreflection",
         "features/reasoning",
         "features/multimodal",
+        "features/langchain",
         "features/async",
         "features/mini",
         "features/codeagent",

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/2.0.38.tar.gz"
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.41.tar.gz"
     sha256 "1828fb9227d10f991522c3f24f061943a254b667196b40b1a3e4a54a8d30ce32"  # Replace with actual SHA256 checksum
     license "MIT"
   

praisonai/agents_generator.py

@@ -212,9 +212,9 @@ class AgentsGenerator:
         try:
             # Try to import tools.py from current directory
             spec = importlib.util.spec_from_file_location("tools", "tools.py")
-            self.logger.info(f"Spec: {spec}")
+            self.logger.debug(f"Spec: {spec}")
             if spec is None:
-                self.logger.info("tools.py not found in current directory")
+                self.logger.debug("tools.py not found in current directory")
                 return tools_list
 
             module = importlib.util.module_from_spec(spec)
@@ -229,11 +229,13 @@ class AgentsGenerator:
                     globals()[name] = obj
                     # Add to tools list
                     tools_list.append(obj)
-                    self.logger.info(f"Loaded and globalized tool function: {name}")
+                    self.logger.debug(f"Loaded and globalized tool function: {name}")
 
-            self.logger.info(f"Loaded {len(tools_list)} tool functions from tools.py")
-            self.logger.info(f"Tools list: {tools_list}")
+            self.logger.debug(f"Loaded {len(tools_list)} tool functions from tools.py")
+            self.logger.debug(f"Tools list: {tools_list}")
             
+        except FileNotFoundError:
+            self.logger.debug("tools.py not found in current directory")
         except Exception as e:
             self.logger.warning(f"Error loading tools from tools.py: {e}")
             
@@ -539,7 +541,7 @@ class AgentsGenerator:
 
         # Load tools once at the beginning
         tools_list = self.load_tools_from_tools_py()
-        self.logger.info(f"Loaded tools: {tools_list}")
+        self.logger.debug(f"Loaded tools: {tools_list}")
 
         # Create agents from config
         for role, details in config['roles'].items():
@@ -574,7 +576,7 @@ class AgentsGenerator:
                 agent.step_callback = self.agent_callback
 
             agents[role] = agent
-            self.logger.info(f"Created agent {role_filled} with tools: {agent.tools}")
+            self.logger.debug(f"Created agent {role_filled} with tools: {agent.tools}")
 
             # Create tasks for the agent
             for task_name, task_details in details.get('tasks', {}).items():
@@ -596,7 +598,7 @@ class AgentsGenerator:
                     create_directory=task_details.get('create_directory', False)
                 )
 
-                self.logger.info(f"Created task {task_name} with tools: {task.tools}")
+                self.logger.debug(f"Created task {task_name} with tools: {task.tools}")
                 
                 if self.task_callback:
                     task.callback = self.task_callback
@@ -613,6 +615,8 @@ class AgentsGenerator:
                 task.context = context_tasks
 
         # Create and run the PraisonAI agents
+        memory = config.get('memory', False)
+        self.logger.debug(f"Memory: {memory}")
         if config.get('process') == 'hierarchical':
             agents = PraisonAIAgents(
                 agents=list(agents.values()),
@@ -620,12 +624,14 @@ class AgentsGenerator:
                 verbose=True,
                 process="hierarchical",
                 manager_llm=config.get('manager_llm', 'gpt-4o'),
+                memory=memory
             )
         else:
             agents = PraisonAIAgents(
                 agents=list(agents.values()),
                 tasks=tasks,
-                verbose=2
+                verbose=2,
+                memory=memory
             )
 
         self.logger.debug("Final Configuration:")

praisonai/api/call.py

@@ -50,7 +50,7 @@ logger.handlers = []
 # Try to import tools from the root directory
 tools = []
 tools_path = os.path.join(os.getcwd(), 'tools.py')
-logger.info(f"Tools path: {tools_path}")
+logger.debug(f"Tools path: {tools_path}")
 
 def import_tools_from_file(file_path):
     spec = importlib.util.spec_from_file_location("custom_tools", file_path)
@@ -63,9 +63,9 @@ try:
     if os.path.exists(tools_path):
         # tools.py exists in the root directory, import from file
         custom_tools_module = import_tools_from_file(tools_path)
-        logger.info("Successfully imported custom tools from root tools.py")
+        logger.debug("Successfully imported custom tools from root tools.py")
     else:
-        logger.info("No custom tools.py file found in the root directory")
+        logger.debug("No custom tools.py file found in the root directory")
         custom_tools_module = None
 
     if custom_tools_module:

praisonai/cli.py

@@ -73,6 +73,12 @@ except ImportError:
     pass
 
 logging.basicConfig(level=os.environ.get('LOGLEVEL', 'INFO'), format='%(asctime)s - %(levelname)s - %(message)s')
+logging.getLogger('alembic').setLevel(logging.ERROR)
+logging.getLogger('gradio').setLevel(logging.ERROR)
+logging.getLogger('gradio').setLevel(os.environ.get('GRADIO_LOGLEVEL', 'WARNING'))
+logging.getLogger('rust_logger').setLevel(logging.WARNING)
+logging.getLogger('duckduckgo').setLevel(logging.ERROR)
+logging.getLogger('_client').setLevel(logging.ERROR)
 
 def stream_subprocess(command, env=None):
     """

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==2.0.38 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.0.41 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 = "2.0.38"
+version = "2.0.41"
 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>=0.0.30",
+    "praisonaiagents>=0.0.31",
     "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 = "2.0.38"
+version = "2.0.41"
 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 = ">=0.0.30"
+praisonaiagents = ">=0.0.31"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

uv.lock

@@ -3060,7 +3060,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.0.38"
+version = "2.0.41"
 source = { editable = "." }
 dependencies = [
     { name = "instructor" },
@@ -3197,7 +3197,7 @@ 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 = ">=0.0.30" },
+    { name = "praisonaiagents", specifier = ">=0.0.31" },
     { 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" },
@@ -3250,16 +3250,16 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.30"
+version = "0.0.31"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "openai" },
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/21/d2/0974b47b7e38052facc68538c3d616569185588f5bc28f44fa02a960ea19/praisonaiagents-0.0.30.tar.gz", hash = "sha256:28997d411da9a0c0a912f1be5f2cde84d9a40d302efdec09040f447344b9ba53", size = 68012 }
+sdist = { url = "https://files.pythonhosted.org/packages/e6/33/3dbf92fd64ee405b9382e79f688cef390ff9164d72ead030f4246fb9d2d1/praisonaiagents-0.0.31.tar.gz", hash = "sha256:9c372c51c741a0365c1186b087b3a72f4371e6ae2c203fa072b7a89a400e2560", size = 68508 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b5/ce/a3c6f27a14db93ffcf4c079a718b190fd46fb5dc9cf7e6755f356ac921ce/praisonaiagents-0.0.30-py3-none-any.whl", hash = "sha256:d19ab553026bee839a311f554d78fdcecdb670bae6014e3dbde91914817fb7ac", size = 84981 },
+    { url = "https://files.pythonhosted.org/packages/91/7e/6eea88722770bd5f2691d8a0669cc46746961db969ea58d1380ce9e31648/praisonaiagents-0.0.31-py3-none-any.whl", hash = "sha256:41445ca9ab38d26f01df0873837bf8436e7013020474808312eff06894f2ce8e", size = 85455 },
 ]
 
 [[package]]