Release v3.5.3

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=3.5.2" \
+    "praisonai>=3.5.3" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=3.5.2" \
+    "praisonai>=3.5.3" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=3.5.2" \
+    "praisonai>=3.5.3" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/agent_centric_api.py

@@ -134,7 +134,6 @@ def example_real_chat():
         instructions="You are a helpful math tutor. Be concise.",
         reflection=False,
         web=False,
-        verbose=False,
     )
     
     response = agent.start("What is the square root of 144?")

examples/background/basic_background.py

@@ -22,19 +22,18 @@ async def main():
     print("Agent-Centric Background Tasks Demo")
     print("=" * 60)
     
-    # Create a background runner with config
+    # Create a background runner with config (used standalone, not passed to Agent)
     runner = BackgroundRunner(config=BackgroundConfig(max_concurrent_tasks=3))
     
-    # Create an Agent with background task support
+    # Create an Agent
     agent = Agent(
         name="AsyncAssistant",
         instructions="You are a helpful research assistant.",
-        background=runner
     )
     
     print("\n--- Agent with Background Support Created ---")
     print(f"Agent: {agent.name}")
-    print(f"Background runner: {agent.background is not None}")
+    print(f"Background runner: {runner is not None}")
     
     # Define some example tasks
     async def research_task(topic: str) -> str:
@@ -49,17 +48,17 @@ async def main():
         await asyncio.sleep(1.0)
         return f"Analysis complete: {data}"
     
-    # Submit tasks via agent's background runner
+    # Submit tasks via background runner
     print("\n--- Submitting Background Tasks ---")
     
-    task1 = await agent.background.submit(
+    task1 = await runner.submit(
         research_task, 
         args=("AI trends 2025",), 
         name="research_ai"
     )
     print(f"✅ Submitted research task: {task1.id[:8]}")
     
-    task2 = await agent.background.submit(
+    task2 = await runner.submit(
         analysis_task, 
         args=("market data",), 
         name="analyze_market"
@@ -68,7 +67,7 @@ async def main():
     
     # List all tasks
     print("\n--- Task Status ---")
-    for task in agent.background.tasks:
+    for task in runner.tasks:
         print(f"  [{task.id[:8]}] {task.status.value}")
     
     # Wait for tasks to complete
@@ -83,7 +82,7 @@ async def main():
         print(f"  {status} {task.result}")
     
     # Cleanup
-    agent.background.clear_completed()
+    runner.clear_completed()
     print("\n✅ Cleared completed tasks")
     
     print("\n" + "=" * 60)

examples/checkpoints/basic_checkpoints.py

@@ -33,29 +33,28 @@ async def main():
         test_file.write_text("# Initial content\nprint('Hello, World!')\n")
         print(f"\n✅ Created initial file: {test_file.name}")
         
-        # Create checkpoint service
-        checkpoints = CheckpointService(workspace_dir=workspace)
+        # Create checkpoint service (used standalone, not passed to Agent)
+        checkpoint_service = CheckpointService(workspace_dir=workspace)
         
-        # Create an Agent with checkpoint support
+        # Create an Agent
         agent = Agent(
             name="RefactorBot",
             instructions="You are a code refactoring assistant.",
-            checkpoints=checkpoints
         )
         
         print("\n--- Agent with Checkpoint Support Created ---")
         print(f"Agent: {agent.name}")
-        print(f"Checkpoints enabled: {agent.checkpoints is not None}")
+        print(f"Checkpoint service initialized: {checkpoint_service is not None}")
         
         try:
             # Initialize the shadow git repository
             print("\n--- Initializing Checkpoint Service ---")
-            await agent.checkpoints.initialize()
+            await checkpoint_service.initialize()
             print("✅ Shadow git repository initialized")
             
             # Save first checkpoint
             print("\n--- Saving Checkpoint 1: Initial State ---")
-            result1 = await agent.checkpoints.save("Initial state")
+            result1 = await checkpoint_service.save("Initial state")
             print(f"✅ Checkpoint saved: {result1.checkpoint.id[:8]}")
             print(f"   Message: {result1.checkpoint.message}")
             
@@ -71,18 +70,18 @@ async def main():
             
             # Save second checkpoint
             print("\n--- Saving Checkpoint 2: After Refactoring ---")
-            result2 = await agent.checkpoints.save("Added features")
+            result2 = await checkpoint_service.save("Added features")
             print(f"✅ Checkpoint saved: {result2.checkpoint.id[:8]}")
             
             # List all checkpoints
             print("\n--- Listing All Checkpoints ---")
-            checkpoints_list = await agent.checkpoints.list_checkpoints()
+            checkpoints_list = await checkpoint_service.list_checkpoints()
             for i, cp in enumerate(checkpoints_list, 1):
                 print(f"  {i}. [{cp.id[:8]}] {cp.message}")
             
             # Show diff
             print("\n--- Current Diff ---")
-            diff = await agent.checkpoints.diff()
+            diff = await checkpoint_service.diff()
             if diff.files:
                 for f in diff.files:
                     print(f"  {f.status}: {f.path}")
@@ -95,7 +94,7 @@ async def main():
             
             # Restore to checkpoint 2
             print(f"\n--- Restoring to Checkpoint: {result2.checkpoint.id[:8]} ---")
-            await agent.checkpoints.restore(result2.checkpoint.id)
+            await checkpoint_service.restore(result2.checkpoint.id)
             print("✅ Restored successfully!")
             
             # Verify restoration

examples/context/00_agent_fast_context_basic.py

@@ -28,7 +28,6 @@ def main():
         name="CodeAssistant",
         instructions="You are a helpful code assistant.",
         context=True,  # Enable context management with defaults
-        verbose=False
     )
     
     print("\n✓ Agent created with context management enabled")

examples/routing/routellm_example.py

@@ -0,0 +1,26 @@
+"""
+RouteLLM Integration Example
+
+Route requests between strong and weak models to optimize cost.
+
+Prerequisites:
+1. pip install routellm
+2. export OPENAI_API_KEY=your-api-key
+3. Start RouteLLM server:
+   python -m routellm.openai_server \
+     --routers mf \
+     --strong-model gpt-4o \
+     --weak-model gpt-4o-mini \
+     --port 6060
+"""
+
+from praisonaiagents import Agent
+
+# Basic usage with RouteLLM routing
+agent = Agent(
+    llm="router-mf-0.5",
+    base_url="http://localhost:6060/v1"
+)
+
+response = agent.chat("What is 2+2?")
+print(response)

examples/routing/routellm_multi_agent.py

@@ -0,0 +1,49 @@
+"""
+RouteLLM Multi-Agent Example
+
+Prerequisites:
+1. pip install routellm
+2. export OPENAI_API_KEY=your-api-key
+3. Start RouteLLM server:
+   python -m routellm.openai_server \
+     --routers mf \
+     --strong-model gpt-4o \
+     --weak-model gpt-4o-mini \
+     --port 6060
+"""
+
+from praisonaiagents import Agent, PraisonAIAgents, Task
+
+ROUTELLM_URL = "http://localhost:6060/v1"
+
+researcher = Agent(
+    name="Researcher",
+    role="Research analyst",
+    goal="Find and analyze information",
+    llm="router-mf-0.5",
+    base_url=ROUTELLM_URL
+)
+
+writer = Agent(
+    name="Writer",
+    role="Content writer",
+    goal="Write clear and engaging content",
+    llm="router-mf-0.5",
+    base_url=ROUTELLM_URL
+)
+
+task1 = Task(
+    description="Research the latest AI trends in 2024",
+    agent=researcher,
+    expected_output="Research summary with key findings"
+)
+
+task2 = Task(
+    description="Write a brief article based on the research",
+    agent=writer,
+    expected_output="Short article about AI trends"
+)
+
+agents = PraisonAIAgents(agents=[researcher, writer], tasks=[task1, task2])
+result = agents.start()
+print(result)

examples/routing/routellm_workflow.py

@@ -0,0 +1,40 @@
+"""
+RouteLLM Workflow Example
+
+Prerequisites:
+1. pip install routellm
+2. export OPENAI_API_KEY=your-api-key
+3. Start RouteLLM server:
+   python -m routellm.openai_server \
+     --routers mf \
+     --strong-model gpt-4o \
+     --weak-model gpt-4o-mini \
+     --port 6060
+"""
+
+from praisonaiagents import Agent, Workflow
+
+ROUTELLM_URL = "http://localhost:6060/v1"
+
+workflow = Workflow(
+    name="Analysis Pipeline",
+    steps=[
+        Agent(
+            name="Analyzer",
+            role="Data analyst",
+            goal="Analyze data and extract insights",
+            llm="router-mf-0.5",
+            base_url=ROUTELLM_URL
+        ),
+        Agent(
+            name="Reporter",
+            role="Report writer",
+            goal="Create clear reports from analysis",
+            llm="router-mf-0.5",
+            base_url=ROUTELLM_URL
+        )
+    ]
+)
+
+result = workflow.run("Analyze the impact of AI on software development")
+print(result["output"])

src/praisonai-agents/basic-agents-fixed.py

@@ -1,21 +1,15 @@
 #!/usr/bin/env python3
 
-from praisonaiagents import Agent, force_shutdown_telemetry
+from praisonaiagents import Agent
 
 print("Creating agent...")
 agent = Agent(
     instructions="You are a helpful assistant",
-    llm="gpt-5-nano"
+    llm="gpt-4o-mini"
 )
 
 print("Starting agent...")
 result = agent.start("Why is the sky blue?")
 
 print("Agent completed successfully!")
-print(f"Result: {result}")
-
-# Optional: Clean up telemetry system when script is completely done
-print("Cleaning up telemetry...")
-force_shutdown_telemetry()
-
-print("Script finished normally.") 
+print(f"Result: {result}") 

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.10.1"
+version = "0.10.2"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"

src/praisonai-agents/streaming-gemini.py

@@ -2,9 +2,10 @@ from praisonaiagents import Agent
 
 agent = Agent(
     instructions="You are a helpful assistant",
-    llm="gemini/gemini-2.5-flash",
-    stream=True
+    llm="gemini/gemini-2.5-flash"
 )
 
-for chunk in agent.start("Write a report on about the history of the world"):
-    print(chunk, end="", flush=True)
+# Use chat method with stream=True for streaming responses
+for chunk in agent.chat("Write a short paragraph about the history of computing", stream=True):
+    print(chunk, end="", flush=True)
+print()  # Final newline

src/praisonai-agents/streaming-openai.py

@@ -2,9 +2,10 @@ from praisonaiagents import Agent
 
 agent = Agent(
     instructions="You are a helpful assistant",
-    llm="gpt-5-nano",
-    stream=True
+    llm="gpt-4o-mini"
 )
 
-for chunk in agent.start("Write a report on about the history of the world"):
-    print(chunk, end="", flush=True)
+# Use chat method with stream=True for streaming responses
+for chunk in agent.chat("Write a short paragraph about the history of computing", stream=True):
+    print(chunk, end="", flush=True)
+print()  # Final newline

src/praisonai-agents/uv.lock

@@ -2422,7 +2422,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.10.1"
+version = "0.10.2"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },

src/praisonai/agents.yaml

@@ -1,49 +1,27 @@
-# Movie Script Workflow - Cat in Mars
-# Uses the modern agents + steps format
-
-name: Create Movie Script About Cat in Mars
-description: A sequential workflow to research, design narrative, and write a movie script
 framework: praisonai
-
-workflow:
-  verbose: true
-
-agents:
-  researcher:
-    name: Researcher
-    role: Research Analyst
-    goal: Gather information about Mars and cats
-    backstory: Skilled in research, with a focus on gathering accurate and relevant information.
-    instructions: |
-      Research about Mars, its environment, and the feasibility of a cat being on Mars.
-      Also, research about cat behavior and characteristics.
+topic: --help
+roles:
+  file_reader:
+    backstory: A diligent agent skilled in parsing and extracting data from various
+      file types.
+    goal: Extract relevant information from the input file
+    role: File Reader
+    tasks:
+      read_task:
+        description: Read the contents of a specified file to retrieve necessary information.
+        expected_output: Extracted content from the file ready for processing.
     tools:
-      - search_tool
-
-  narrative_designer:
-    name: Narrative Designer
-    role: Story Concept Developer
-    goal: Create a story concept for a movie about a cat in Mars
-    backstory: Skilled in narrative development, with a focus on creating engaging stories.
-    instructions: |
-      Use the research findings to craft a unique story concept for a movie about a cat in Mars.
-      Create narrative arcs, character bios, and settings.
-
-  scriptwriter:
-    name: Scriptwriter
-    role: Script Writer
-    goal: Write a movie script about a cat in Mars
-    backstory: Expert in dialogue and script structure, translating concepts into scripts.
-    instructions: |
-      Turn the story concept into a production-ready movie script,
-      including dialogue and scene details.
-
-steps:
-  - agent: researcher
-    action: "Research about Mars environment and cat behavior for the movie concept"
-
-  - agent: narrative_designer
-    action: "Create story concept based on research: {{previous_output}}"
-
-  - agent: scriptwriter
-    action: "Write the final movie script based on the story concept: {{previous_output}}"
+    - read_file
+  command_executor:
+    backstory: An experienced agent with expertise in command execution and automation.
+    goal: Execute commands based on the information gathered
+    role: Command Executor
+    tasks:
+      execute_task:
+        description: Take the extracted information and execute relevant commands,
+          logging outputs to a file.
+        expected_output: Execution results written to the output file.
+    tools:
+    - execute_command
+    - write_file
+dependencies: []

src/praisonai/praisonai.rb

@@ -3,8 +3,8 @@ 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/v3.5.2.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.5.2.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.5.3.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.5.3.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/agents_generator.py

@@ -327,6 +327,24 @@ class AgentsGenerator:
             # Route to YAMLWorkflowParser for advanced workflow patterns
             return self._run_yaml_workflow(config)
 
+        # Canonical format conversion: 'agents' -> 'roles', 'instructions' -> 'backstory'
+        # This ensures backward compatibility while supporting the new canonical format
+        if 'agents' in config and 'roles' not in config:
+            config['roles'] = {}
+            for agent_name, agent_config in config['agents'].items():
+                role_config = dict(agent_config) if agent_config else {}
+                # Convert 'instructions' to 'backstory' if present
+                if 'instructions' in role_config and 'backstory' not in role_config:
+                    role_config['backstory'] = role_config.pop('instructions')
+                # Ensure required fields have defaults
+                if 'role' not in role_config:
+                    role_config['role'] = agent_name.replace('_', ' ').title()
+                if 'goal' not in role_config:
+                    role_config['goal'] = role_config.get('backstory', 'Complete the assigned task')
+                if 'backstory' not in role_config:
+                    role_config['backstory'] = f'You are a {role_config["role"]}'
+                config['roles'][agent_name] = role_config
+
         # Get workflow input: 'input' is canonical, 'topic' is alias for backward compatibility
         topic = config.get('input', config.get('topic', ''))
         tools_dict = {}
@@ -805,26 +823,21 @@ class AgentsGenerator:
             backstory_filled = details['backstory'].format(topic=topic)
             
             # Pass all loaded tools to the agent
+            # Get LLM from config or environment
+            llm_config = details.get('llm', {})
+            llm_model = llm_config.get("model") if isinstance(llm_config, dict) else llm_config
+            llm_model = llm_model or os.environ.get("MODEL_NAME") or "gpt-4o-mini"
+            
             agent = PraisonAgent(
                 name=role_filled,
                 role=role_filled,
                 goal=goal_filled,
                 backstory=backstory_filled,
+                instructions=details.get('instructions'),
                 tools=tools_list,  # Pass the entire tools list to the agent
                 allow_delegation=details.get('allow_delegation', False),
-                llm=details.get('llm', {}).get("model") or os.environ.get("MODEL_NAME") or "openai/gpt-5-nano",
-                function_calling_llm=details.get('function_calling_llm', {}).get("model") or os.environ.get("MODEL_NAME") or "openai/gpt-5-nano",
-                max_iter=details.get('max_iter', 15),
-                max_rpm=details.get('max_rpm'),
-                max_execution_time=details.get('max_execution_time'),
-                verbose=details.get('verbose', True),
-                cache=details.get('cache', True),
-                system_template=details.get('system_template'),
-                prompt_template=details.get('prompt_template'),
-                response_template=details.get('response_template'),
-                reflect_llm=details.get('reflect_llm', {}).get("model") or os.environ.get("MODEL_NAME") or "openai/gpt-5-nano",
-                min_reflect=details.get('min_reflect', 1),
-                max_reflect=details.get('max_reflect', 3),
+                llm=llm_model,
+                reflection=details.get('reflection', False),
             )
             
             if self.agent_callback:
@@ -834,32 +847,47 @@ class AgentsGenerator:
             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():
-                description_filled = task_details['description'].format(topic=topic)
-                expected_output_filled = task_details['expected_output'].format(topic=topic)
-
-                task = PraisonTask(
-                    description=description_filled,
-                    expected_output=expected_output_filled,
+            agent_tasks = details.get('tasks', {})
+            
+            # If no tasks defined, auto-generate one from instructions/backstory
+            if not agent_tasks:
+                # Use instructions or backstory as the task description
+                task_description = details.get('instructions') or backstory_filled
+                auto_task = PraisonTask(
+                    description=task_description,
+                    expected_output="Complete the assigned task successfully.",
                     agent=agent,
-                    tools=tools_list,  # Pass the same tools list to the task
-                    async_execution=task_details.get('async_execution', False),
-                    context=[],
-                    config=task_details.get('config', {}),
-                    output_json=task_details.get('output_json'),
-                    output_pydantic=task_details.get('output_pydantic'),
-                    output_file=task_details.get('output_file', ""),
-                    callback=task_details.get('callback'),
-                    create_directory=task_details.get('create_directory', False)
                 )
+                tasks.append(auto_task)
+                tasks_dict[f"{role}_auto_task"] = auto_task
+                self.logger.debug(f"Auto-generated task for agent {role_filled}")
+            else:
+                for task_name, task_details in agent_tasks.items():
+                    description_filled = task_details['description'].format(topic=topic)
+                    expected_output_filled = task_details['expected_output'].format(topic=topic)
 
-                self.logger.debug(f"Created task {task_name} with tools: {task.tools}")
-                
-                if self.task_callback:
-                    task.callback = self.task_callback
+                    task = PraisonTask(
+                        description=description_filled,
+                        expected_output=expected_output_filled,
+                        agent=agent,
+                        tools=tools_list,  # Pass the same tools list to the task
+                        async_execution=task_details.get('async_execution', False),
+                        context=[],
+                        config=task_details.get('config', {}),
+                        output_json=task_details.get('output_json'),
+                        output_pydantic=task_details.get('output_pydantic'),
+                        output_file=task_details.get('output_file', ""),
+                        callback=task_details.get('callback'),
+                        create_directory=task_details.get('create_directory', False)
+                    )
 
-                tasks.append(task)
-                tasks_dict[task_name] = task
+                    self.logger.debug(f"Created task {task_name} with tools: {task.tools}")
+                    
+                    if self.task_callback:
+                        task.callback = self.task_callback
+
+                    tasks.append(task)
+                    tasks_dict[task_name] = task
 
         # Set up task contexts
         for role, details in config['roles'].items():
@@ -876,16 +904,14 @@ class AgentsGenerator:
             agents = Agents(
                 agents=list(agents.values()),
                 tasks=tasks,
-                verbose=True,
                 process="hierarchical",
-                manager_llm=config.get('manager_llm') or os.environ.get("MODEL_NAME") or "openai/gpt-5-nano",
+                manager_llm=config.get('manager_llm') or os.environ.get("MODEL_NAME") or "gpt-4o-mini",
                 memory=memory
             )
         else:
             agents = Agents(
                 agents=list(agents.values()),
                 tasks=tasks,
-                verbose=True,
                 memory=memory
             )
 

src/praisonai/praisonai/deploy.py

@@ -57,7 +57,7 @@ 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==3.5.2 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==3.5.3 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/praisonai/inbuilt_tools/__init__.py

@@ -6,22 +6,43 @@ CREWAI_AVAILABLE = importlib.util.find_spec("crewai") is not None
 AUTOGEN_AVAILABLE = importlib.util.find_spec("autogen") is not None
 PRAISONAI_TOOLS_AVAILABLE = False
 
+# Guard against recursive imports
+_importing_autogen_tools = False
+_autogen_tools_module = None
+
 # Lazy import helper - only imports when actually needed
 def _get_autogen_tools():
     """Lazy import autogen_tools only when needed."""
-    global PRAISONAI_TOOLS_AVAILABLE
+    global PRAISONAI_TOOLS_AVAILABLE, _importing_autogen_tools, _autogen_tools_module
+    
+    # Return cached module if already imported
+    if _autogen_tools_module is not None:
+        return _autogen_tools_module
+    
+    # Prevent recursive import
+    if _importing_autogen_tools:
+        return None
+    
     if CREWAI_AVAILABLE or AUTOGEN_AVAILABLE:
         try:
+            _importing_autogen_tools = True
             from . import autogen_tools
+            _autogen_tools_module = autogen_tools
             PRAISONAI_TOOLS_AVAILABLE = True
             return autogen_tools
         except ImportError:
             pass
+        finally:
+            _importing_autogen_tools = False
     return None
 
 # For backward compatibility, provide __getattr__ for lazy access
 def __getattr__(name):
     """Lazy load autogen_tools exports on demand."""
+    # Avoid recursion during import
+    if _importing_autogen_tools:
+        raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+    
     tools_module = _get_autogen_tools()
     if tools_module and hasattr(tools_module, name):
         return getattr(tools_module, name)

src/praisonai/praisonai/version.py

@@ -1 +1 @@
-__version__ = "3.5.2"
+__version__ = "3.5.3"

src/praisonai/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.10.1",
+    "praisonaiagents>=0.10.2",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",

src/praisonai/uv.lock

@@ -4276,7 +4276,7 @@ requires-dist = [
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.1.0" },
     { name = "praisonai-tools", marker = "extra == 'autogen-v4'", specifier = ">=0.1.0" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.1.0" },
-    { name = "praisonaiagents", specifier = ">=0.10.1" },
+    { name = "praisonaiagents", specifier = ">=0.10.2" },
     { name = "pyautogen", marker = "extra == 'autogen'", specifier = "==0.2.29" },
     { name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
     { name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -4324,7 +4324,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.10.1"
+version = "0.10.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -4333,9 +4333,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/02/20/d7c02b4dc2da4c4427046b60286858288c44c5cae25ad08c3fc0727f8ebb/praisonaiagents-0.10.1.tar.gz", hash = "sha256:6d21ce2cbf5f0a4f56798f731a79d703536e1dc8706f1b00915d772e4d4084ea", size = 945938, upload-time = "2026-01-08T21:08:39.706Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/61/08/2829365c7cbb70fb7a4397ae39176785d346cd26fd5789657a2859d03204/praisonaiagents-0.10.2.tar.gz", hash = "sha256:ca62cfdcffc7702b66e205ad1ea82363707db9e06bad8818b8b4d1de875a447c", size = 945964, upload-time = "2026-01-08T21:47:37.191Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/53/d6/b381217a998847756ad9b423fc0cb0c2134e09971abfd333698abf809fae/praisonaiagents-0.10.1-py3-none-any.whl", hash = "sha256:6202ed7a3a46d299719a8b0470b48fcbac9f0989ac9fc19406065749ca5f6527", size = 923192, upload-time = "2026-01-08T21:08:37.808Z" },
+    { url = "https://files.pythonhosted.org/packages/27/58/791f31cb690c70ac507a393729530d83eee6af2fc5a3f55651bfbf40f7b9/praisonaiagents-0.10.2-py3-none-any.whl", hash = "sha256:0a5b1ce58a08f5cc377c6175c78e15925edce9a30d57e28c7f7cc50a64d61a3b", size = 923193, upload-time = "2026-01-08T21:47:35.032Z" },
 ]
 
 [[package]]