Merge pull request #103 from Fr4nc3/main

fix creating tasks

Author: Fr4nc3

src/backend/app_kernel.py

@@ -115,18 +115,18 @@ async def input_task_endpoint(input_task: InputTask, request: Request):
     if not input_task.session_id:
         input_task.session_id = str(uuid.uuid4())
 
-    # Fix 2: Don't try to set user_id on InputTask directly since it doesn't have that field
-    # Instead, include it in the JSON we'll pass to the planner
-    
     try:
-        # Create just the planner agent instead of all agents
+        # Create all agents instead of just the planner agent
+        # This ensures other agents are created first and the planner has access to them
         kernel, memory_store = await initialize_runtime_and_context(input_task.session_id, user_id)
-        planner_agent = await AgentFactory.create_agent(
-            agent_type=AgentType.PLANNER, 
-            session_id=input_task.session_id, 
+        agents = await AgentFactory.create_all_agents(
+            session_id=input_task.session_id,
             user_id=user_id
         )
 
+        # Get the planner agent from the created agents
+        planner_agent = agents[AgentType.PLANNER]
+        
         # Convert input task to JSON for the kernel function, add user_id here
         input_task_data = input_task.model_dump()
         input_task_data["user_id"] = user_id
@@ -137,8 +137,11 @@ async def input_task_endpoint(input_task: InputTask, request: Request):
             KernelArguments(input_task_json=input_task_json)
         )
 
+        print(f"Result: {result}")
         # Get plan from memory store
         plan = await memory_store.get_plan_by_session(input_task.session_id)
+
+        print(f"Plan: {plan}")
 
         if not plan or not plan.id:
             # If plan not found by session, try to extract plan ID from result
@@ -190,7 +193,6 @@ async def input_task_endpoint(input_task: InputTask, request: Request):
         raise HTTPException(status_code=400, detail="Error creating plan")
 
 
-
 @app.post("/human_feedback")
 async def human_feedback_endpoint(human_feedback: HumanFeedback, request: Request):
 

src/backend/kernel_agents/agent_factory.py

@@ -152,29 +152,37 @@ async def create_agent(
         agent_type_str = cls._agent_type_strings.get(agent_type, agent_type.value.lower())
         tools = await cls._load_tools_for_agent(kernel, agent_type_str)
 
-        # Build the agent definition (functions schema) if tools exist
+        # Build the agent definition (functions schema)
         definition = None
         client = None
+        
         try:
             client = config.get_ai_project_client()
         except Exception as client_exc:
             logger.error(f"Error creating AIProjectClient: {client_exc}")
-            raise
+            if agent_type == AgentType.GROUP_CHAT_MANAGER:
+                logger.info(f"Continuing with GroupChatManager creation despite AIProjectClient error")
+            else:
+                raise
+                
         try:
-            if tools:
-                # Create the agent definition using the AIProjectClient (project-based pattern)
+            # Create the agent definition using the AIProjectClient (project-based pattern)
+            # For GroupChatManager, create a definition with minimal configuration
+            if client is not None:
                 definition = await client.agents.create_agent(
                     model=config.AZURE_OPENAI_DEPLOYMENT_NAME,
                     name=agent_type_str,
                     instructions=system_message,
                     temperature=temperature,
                     response_format=None  # Add response_format if required
                 )
+                logger.info(f"Successfully created agent definition for {agent_type_str}")
         except Exception as agent_exc:
-            logger.error(f"Error creating agent definition with AIProjectClient: {agent_exc}")
-            raise
-        if definition is None:
-            raise RuntimeError("Failed to create agent definition from Azure AI Project. Check your Azure configuration, permissions, and network connectivity.")
+            logger.error(f"Error creating agent definition with AIProjectClient for {agent_type_str}: {agent_exc}")
+            if agent_type == AgentType.GROUP_CHAT_MANAGER:
+                logger.info(f"Continuing with GroupChatManager creation despite definition error")
+            else:
+                raise
 
         # Create the agent instance using the project-based pattern
         try:
@@ -215,7 +223,7 @@ async def create_agent(
         cls._agent_cache[session_id][agent_type] = agent
 
         return agent
-        
+
     @classmethod
     async def create_azure_ai_agent(
         cls,
@@ -272,7 +280,7 @@ async def _load_tools_for_agent(cls, kernel: Kernel, agent_type: str) -> List[Ke
         """Load tools for an agent from the tools directory.
         
         This tries to load tool configurations from JSON files. If that fails,
-        it creates a simple helper function as a fallback.
+        it returns an empty list for agents that don't need tools.
         
         Args:
             kernel: The semantic kernel instance
@@ -286,9 +294,20 @@ async def _load_tools_for_agent(cls, kernel: Kernel, agent_type: str) -> List[Ke
             tools = BaseAgent.get_tools_from_config(kernel, agent_type)
             logger.info(f"Successfully loaded {len(tools)} tools for {agent_type}")
             return tools
+        except FileNotFoundError:
+            # No tool configuration file found - this is expected for some agents
+            logger.info(f"No tools defined for agent type '{agent_type}'. Returning empty list.")
+            return []
         except Exception as e:
-            logger.warning(f"Failed to load tools for {agent_type}, using fallback: {e}")
+            logger.warning(f"Error loading tools for {agent_type}: {e}")
 
+            # Return an empty list for agents without tools rather than attempting a fallback
+            # Special handling for group_chat_manager which typically doesn't need tools
+            if "group_chat_manager" in agent_type:
+                logger.info(f"No tools needed for {agent_type}. Returning empty list.")
+                return []
+                
+            # For other agent types, try to create a simple fallback tool
             try:
                 # Use PromptTemplateConfig to create a simple tool
                 from semantic_kernel.prompt_template.prompt_template_config import PromptTemplateConfig
@@ -319,7 +338,7 @@ async def _load_tools_for_agent(cls, kernel: Kernel, agent_type: str) -> List[Ke
                 return [function]
             except Exception as fallback_error:
                 logger.error(f"Failed to create fallback tool for {agent_type}: {fallback_error}")
-                # Return an empty list if everything fails
+                # Return an empty list if everything fails - the agent can still function without tools
                 return []
 
     @classmethod
@@ -343,16 +362,56 @@ async def create_all_agents(
         if session_id in cls._agent_cache and len(cls._agent_cache[session_id]) == len(cls._agent_classes):
             return cls._agent_cache[session_id]
 
-        # Create each agent type
+        # Create each agent type in two phases
+        # First, create all agents except PlannerAgent and GroupChatManager
         agents = {}
-        for agent_type in cls._agent_classes.keys():
+        planner_agent_type = AgentType.PLANNER
+        group_chat_manager_type = AgentType.GROUP_CHAT_MANAGER
+        
+        # Initialize cache for this session if it doesn't exist
+        if session_id not in cls._agent_cache:
+            cls._agent_cache[session_id] = {}
+        
+        # Phase 1: Create all agents except planner and group chat manager
+        for agent_type in [at for at in cls._agent_classes.keys() 
+                           if at != planner_agent_type and at != group_chat_manager_type]:
             agents[agent_type] = await cls.create_agent(
                 agent_type=agent_type,
                 session_id=session_id,
                 user_id=user_id,
                 temperature=temperature
             )
-            
+        
+        # Create agent name to instance mapping for the planner
+        agent_instances = {}
+        for agent_type, agent in agents.items():
+            agent_name = cls._agent_type_strings.get(agent_type).replace("_", "") + "Agent"
+            agent_name = agent_name[0].upper() + agent_name[1:]  # Capitalize first letter
+            agent_instances[agent_name] = agent
+        
+        # Log the agent instances for debugging
+        logger.debug(f"Created {len(agent_instances)} agent instances for planner: {', '.join(agent_instances.keys())}")
+        
+        # Phase 2: Create the planner agent with agent_instances
+        planner_agent = await cls.create_agent(
+            agent_type=planner_agent_type,
+            session_id=session_id,
+            user_id=user_id,
+            temperature=temperature,
+            agent_instances=agent_instances  # Pass agent instances to the planner
+        )
+        agents[planner_agent_type] = planner_agent
+        
+        # Phase 3: Create group chat manager with all agents including the planner
+        group_chat_manager = await cls.create_agent(
+            agent_type=group_chat_manager_type,
+            session_id=session_id,
+            user_id=user_id,
+            temperature=temperature,
+            available_agents=agent_instances  # Pass all agents to group chat manager
+        )
+        agents[group_chat_manager_type] = group_chat_manager
+        
         return agents
 
     @classmethod

src/backend/kernel_agents/planner_agent.py

@@ -54,6 +54,7 @@ def __init__(
         config_path: Optional[str] = None,
         available_agents: List[str] = None,
         agent_tools_list: List[str] = None,
+        agent_instances: Optional[Dict[str, BaseAgent]] = None,
         client=None,
         definition=None,
     ) -> None:
@@ -70,6 +71,7 @@ def __init__(
             config_path: Optional path to the configuration file
             available_agents: List of available agent names for creating steps
             agent_tools_list: List of available tools across all agents
+            agent_instances: Dictionary of agent instances available to the planner
             client: Optional client instance (passed to BaseAgent)
             definition: Optional definition instance (passed to BaseAgent)
         """
@@ -96,6 +98,7 @@ def __init__(
                                                      "ProductAgent", "ProcurementAgent", 
                                                      "TechSupportAgent", "GenericAgent"]
         self._agent_tools_list = agent_tools_list or []
+        self._agent_instances = agent_instances or {}
 
         # Create the Azure AI Agent for planning operations
         # This will be initialized in async_init
@@ -138,6 +141,11 @@ async def handle_input_task(self, kernel_arguments: KernelArguments) -> str:
 
         # Generate a structured plan with steps
         plan, steps = await self._create_structured_plan(input_task)
+
+        print(f"Plan created: {plan}")
+
+        print(f"Steps created: {steps}")
+
 
         if steps:
             # Add a message about the created plan
@@ -280,26 +288,34 @@ async def _create_structured_plan(self, input_task: InputTask) -> Tuple[Plan, Li
             if self._azure_ai_agent is None:
                 raise RuntimeError("Failed to initialize Azure AI Agent for planning")
 
-            # Get response from the Azure AI Agent
-            # Based on the method signature, invoke takes only named arguments, not positional ones
+            # Log detailed information about the instruction being sent
             logging.info(f"Invoking PlannerAgent with instruction length: {len(instruction)}")
 
-            # Create kernel arguments
+            # Create kernel arguments - make sure we explicitly emphasize the task
             kernel_args = KernelArguments()
-            kernel_args["input"] = instruction
+            kernel_args["input"] = f"TASK: {input_task.description}\n\n{instruction}"
 
+            print(f"Kernel arguments: {kernel_args}")
+
             # Call invoke with proper keyword arguments
             response_content = ""
 
             # Use keyword arguments instead of positional arguments
-            # Based on the method signature, we need to pass 'arguments' and possibly 'kernel'
-            async_generator = self._azure_ai_agent.invoke(arguments=kernel_args)
+            # Set a lower temperature to ensure consistent results
+            async_generator = self._azure_ai_agent.invoke(
+                arguments=kernel_args,
+                settings={
+                    "temperature": 0.0
+                }
+            )
 
             # Collect the response from the async generator
             async for chunk in async_generator:
                 if chunk is not None:
                     response_content += str(chunk)
 
+            print(f"Response content: {response_content}")
+            
             # Debug the response
             logging.info(f"Response content length: {len(response_content)}")
             logging.debug(f"Response content first 500 chars: {response_content[:500]}")
@@ -358,6 +374,10 @@ async def _create_structured_plan(self, input_task: InputTask) -> Tuple[Plan, Li
                 summary = parsed_result.summary_plan_and_steps
                 human_clarification_request = parsed_result.human_clarification_request
 
+                # Log potential mismatches between task and plan for debugging
+                if "onboard" in input_task.description.lower() and "marketing" in initial_goal.lower():
+                    logging.warning(f"Potential mismatch: Task was about onboarding but plan goal mentions marketing: {initial_goal}")
+                    
                 # Log the steps and agent assignments for debugging
                 for i, step in enumerate(steps_data):
                     logging.info(f"Step {i+1} - Agent: {step.agent}, Action: {step.action}")
@@ -469,7 +489,7 @@ async def _create_structured_plan(self, input_task: InputTask) -> Tuple[Plan, Li
 
             await self._memory_store.add_plan(error_plan)
             return error_plan, []
-            
+    
     async def _create_fallback_plan_from_text(self, input_task: InputTask, text_content: str) -> Tuple[Plan, List[Step]]:
         """Create a plan from unstructured text when JSON parsing fails.
         
@@ -574,7 +594,44 @@ def _generate_instruction(self, objective: str) -> str:
         agents_str = ", ".join(self._available_agents)
 
         # Create list of available tools
-        tools_str = "\n".join(self._agent_tools_list) if self._agent_tools_list else "Various specialized tools"
+        # If _agent_tools_list is empty but we have agent instances available elsewhere,
+        # we should retrieve tools directly from agent instances
+        tools_str = ""
+        if hasattr(self, '_agent_instances') and self._agent_instances:
+            # Extract tools from agent instances
+            agent_tools_sections = []
+            
+            # Process each agent to get their tools
+            for agent_name, agent in self._agent_instances.items():
+                if hasattr(agent, '_tools') and agent._tools:
+                    # Create a section header for this agent
+                    agent_tools_sections.append(f"### {agent_name} Tools ###")
+                    
+                    # Add each tool from this agent
+                    for tool in agent._tools:
+                        if hasattr(tool, 'name') and hasattr(tool, 'description'):
+                            tool_desc = f"Agent: {agent_name} - Function: {tool.name} - {tool.description}"
+                            agent_tools_sections.append(tool_desc)
+                    
+                    # Add a blank line after each agent's tools
+                    agent_tools_sections.append("")
+            
+            # Join all sections
+            if agent_tools_sections:
+                tools_str = "\n".join(agent_tools_sections)
+                # Log the tools for debugging
+                logging.debug(f"Generated tools list from agent instances with {len(agent_tools_sections)} entries")
+            else:
+                tools_str = "Various specialized tools (No tool details available from agent instances)"
+                logging.warning("No tools found in agent instances")
+        elif self._agent_tools_list:
+            # Fall back to the existing tools list if available
+            tools_str = "\n".join(self._agent_tools_list)
+            logging.debug(f"Using existing agent_tools_list with {len(self._agent_tools_list)} entries")
+        else:
+            # Default fallback
+            tools_str = "Various specialized tools"
+            logging.warning("No tools information available for planner instruction")
 
         # Build the instruction, avoiding backslashes in f-string expressions
         objective_part = f"Your objective is:\n{objective}" if objective else "When given an objective, analyze it and create a plan to accomplish it."