Rework agent structure - solve foundry leaks - interop with foundry

Author: marktayl1

src/backend/app_config.py

@@ -1,15 +1,16 @@
 # app_config.py
-import os
 import logging
-from typing import Optional, List, Dict, Any
-from dotenv import load_dotenv
-from azure.identity import DefaultAzureCredential, ClientSecretCredential
-from azure.cosmos.aio import CosmosClient
+import os
+from typing import Any, Dict, List, Optional
+
 from azure.ai.projects.aio import AIProjectClient
-from semantic_kernel.kernel import Kernel
-from semantic_kernel.contents import ChatHistory
+from azure.cosmos.aio import CosmosClient
+from azure.identity import ClientSecretCredential, DefaultAzureCredential
+from dotenv import load_dotenv
 from semantic_kernel.agents.azure_ai.azure_ai_agent import AzureAIAgent
+from semantic_kernel.contents import ChatHistory
 from semantic_kernel.functions import KernelFunction
+from semantic_kernel.kernel import Kernel
 
 # Load environment variables from .env file
 load_dotenv()
@@ -189,94 +190,6 @@ def get_ai_project_client(self):
             logging.error("Failed to create AIProjectClient: %s", exc)
             raise
 
-    async def create_azure_ai_agent(
-        self,
-        agent_name: str,
-        instructions: str,
-        tools: Optional[List[KernelFunction]] = None,
-        client=None,
-        response_format=None,
-        temperature: float = 0.0,
-    ):
-        """
-        Creates a new Azure AI Agent with the specified name and instructions using AIProjectClient.
-        If an agent with the given name (assistant_id) already exists, it tries to retrieve it first.
-
-        Args:
-            kernel: The Semantic Kernel instance
-            agent_name: The name of the agent (will be used as assistant_id)
-            instructions: The system message / instructions for the agent
-            agent_type: The type of agent (defaults to "assistant")
-            tools: Optional tool definitions for the agent
-            tool_resources: Optional tool resources required by the tools
-            response_format: Optional response format to control structured output
-            temperature: The temperature setting for the agent (defaults to 0.0)
-
-        Returns:
-            A new AzureAIAgent instance
-        """
-        try:
-            # Get the AIProjectClient
-            if client is None:
-                client = self.get_ai_project_client()
-
-            # # ToDo: This is the fixed code but commenting it out as agent clean up is no happening yet
-            #  # and there are multiple versions of agents due to testing
-            # # First try to get an existing agent with this name as assistant_id
-            # try:
-            #     agent_id = None
-            #     agent_list = await client.agents.list_agents()
-            #     for agent in agent_list.data:
-            #         if agent.name == agent_name:
-            #             agent_id = agent.id
-            #             break
-            #             # If the agent already exists, we can use it directly
-            #             # Get the existing agent definition
-            #     existing_definition = await client.agents.get_agent(agent_id)
-            #     # Create the agent instance directly with project_client and existing definition
-            #     agent = AzureAIAgent(
-            #         client=client,
-            #         definition=existing_definition,
-            #         plugins=tools,
-            #     )
-
-            #     client.agents.list_agents()
-
-            #     return agent
-            # except Exception as e:
-            #     # The Azure AI Projects SDK throws an exception when the agent doesn't exist
-            #     # (not returning None), so we catch it and proceed to create a new agent
-            #     if "ResourceNotFound" in str(e) or "404" in str(e):
-            #         logging.info(
-            #             f"Agent with ID {agent_name} not found. Will create a new one."
-            #         )
-            #     else:
-            #         # Log unexpected errors but still try to create a new agent
-            #         logging.warning(
-            #             f"Unexpected error while retrieving agent {agent_name}: {str(e)}. Attempting to create new agent."
-            #         )
-
-            # Create the agent using the project client with the agent_name as both name and assistantId
-            agent_definition = await client.agents.create_agent(
-                model=self.AZURE_OPENAI_DEPLOYMENT_NAME,
-                name=agent_name,
-                instructions=instructions,
-                temperature=temperature,
-                response_format=response_format,
-            )
-
-            # Create the agent instance directly with project_client and definition
-            agent = AzureAIAgent(
-                client=client,
-                definition=agent_definition,
-                plugins=tools,
-            )
-
-            return agent
-        except Exception as exc:
-            logging.error("Failed to create Azure AI Agent: %s", exc)
-            raise
-
 
 # Create a global instance of AppConfig
 config = AppConfig()

src/backend/kernel_agents/agent_base.py

@@ -1,27 +1,21 @@
 import json
 import logging
 import os
-from typing import Any, Awaitable, Callable, Dict, List, Mapping, Optional, Union
+from abc import ABC, abstractmethod
+from typing import (Any, Awaitable, Callable, Dict, List, Mapping, Optional,
+                    Union)
 
 import semantic_kernel as sk
-from semantic_kernel.agents.azure_ai.azure_ai_agent import AzureAIAgent
-from semantic_kernel.functions import KernelFunction
-from semantic_kernel.functions.kernel_arguments import KernelArguments
-from semantic_kernel.functions.kernel_function_decorator import kernel_function
-from semantic_kernel.agents import AzureAIAgentThread
-
-
 # Import the new AppConfig instance
 from app_config import config
 from context.cosmos_memory_kernel import CosmosMemoryContext
 from event_utils import track_event_if_configured
-from models.messages_kernel import (
-    ActionRequest,
-    ActionResponse,
-    AgentMessage,
-    Step,
-    StepStatus,
-)
+from models.messages_kernel import (ActionRequest, ActionResponse,
+                                    AgentMessage, Step, StepStatus)
+from semantic_kernel.agents.azure_ai.azure_ai_agent import AzureAIAgent
+from semantic_kernel.functions import KernelFunction
+from semantic_kernel.functions.kernel_arguments import KernelArguments
+from semantic_kernel.functions.kernel_function_decorator import kernel_function
 
 # Default formatting instructions used across agents
 DEFAULT_FORMATTING_INSTRUCTIONS = "Instructions: returning the output of this function call verbatim to the user in markdown. Then write AGENT SUMMARY: and then include a summary of what you did."
@@ -65,6 +59,7 @@ def __init__(
             endpoint=None,  # Set as needed
             api_version=None,  # Set as needed
             token=None,  # Set as needed
+            model=config.AZURE_OPENAI_DEPLOYMENT_NAME,
             agent_name=agent_name,
             system_prompt=system_message,
             client=client,
@@ -79,7 +74,7 @@ def __init__(
         self._tools = tools
         self._system_message = system_message
         self._chat_history = [{"role": "system", "content": self._system_message}]
-        self._agent = None  # Will be initialized in async_init
+        # self._agent = None  # Will be initialized in async_init
 
         # Required properties for AgentGroupChat compatibility
         self.name = agent_name  # This is crucial for AgentGroupChat to identify agents
@@ -97,24 +92,6 @@ def default_system_message(agent_name=None) -> str:
         name = agent_name
         return f"You are an AI assistant named {name}. Help the user by providing accurate and helpful information."
 
-    async def async_init(self):
-        """Asynchronously initialize the agent after construction.
-
-        This method must be called after creating the agent to complete initialization.
-        """
-        logging.info(f"Initializing agent: {self._agent_name}")
-        # Create Azure AI Agent or fallback
-        if not self._agent:
-            self._agent = await config.create_azure_ai_agent(
-                agent_name=self._agent_name,
-                instructions=self._system_message,
-                tools=self._tools,
-            )
-        else:
-            logging.info(f"Agent {self._agent_name} already initialized.")
-        # Tools are registered with the kernel via get_tools_from_config
-        return self
-
     async def handle_action_request(self, action_request: ActionRequest) -> str:
         """Handle an action request from another agent or the system.
 
@@ -160,7 +137,7 @@ async def handle_action_request(self, action_request: ActionRequest) -> str:
             # thread = self.client.agents.get_thread(
             #     thread=step.session_id
             # )  # AzureAIAgentThread(thread_id=step.session_id)
-            async_generator = self._agent.invoke(
+            async_generator = self.invoke(
                 messages=f"{str(self._chat_history)}\n\nPlease perform this action",
                 thread=thread,
             )
@@ -264,3 +241,83 @@ def save_state(self) -> Mapping[str, Any]:
     def load_state(self, state: Mapping[str, Any]) -> None:
         """Load the state of this agent."""
         self._memory_store.load_state(state["memory"])
+
+    @classmethod
+    @abstractmethod
+    async def create(cls, **kwargs) -> "BaseAgent":
+        """Create an instance of the agent."""
+        pass
+
+    @staticmethod
+    async def _create_azure_ai_agent_definition(
+        agent_name: str,
+        instructions: str,
+        tools: Optional[List[KernelFunction]] = None,
+        client=None,
+        response_format=None,
+        temperature: float = 0.0,
+    ):
+        """
+        Creates a new Azure AI Agent with the specified name and instructions using AIProjectClient.
+        If an agent with the given name (assistant_id) already exists, it tries to retrieve it first.
+
+        Args:
+            kernel: The Semantic Kernel instance
+            agent_name: The name of the agent (will be used as assistant_id)
+            instructions: The system message / instructions for the agent
+            agent_type: The type of agent (defaults to "assistant")
+            tools: Optional tool definitions for the agent
+            tool_resources: Optional tool resources required by the tools
+            response_format: Optional response format to control structured output
+            temperature: The temperature setting for the agent (defaults to 0.0)
+
+        Returns:
+            A new AzureAIAgent definition or an existing one if found
+        """
+        try:
+            # Get the AIProjectClient
+            if client is None:
+                client = config.get_ai_project_client()
+
+            # # First try to get an existing agent with this name as assistant_id
+            try:
+                agent_id = None
+                agent_list = await client.agents.list_agents()
+                for agent in agent_list.data:
+                    if agent.name == agent_name:
+                        agent_id = agent.id
+                        break
+                # If the agent already exists, we can use it directly
+                # Get the existing agent definition
+                if agent_id is not None:
+                    logging.info(f"Agent with ID {agent_id} exists.")
+
+                    existing_definition = await client.agents.get_agent(agent_id)
+
+                    return existing_definition
+            except Exception as e:
+                # The Azure AI Projects SDK throws an exception when the agent doesn't exist
+                # (not returning None), so we catch it and proceed to create a new agent
+                if "ResourceNotFound" in str(e) or "404" in str(e):
+                    logging.info(
+                        f"Agent with ID {agent_name} not found. Will create a new one."
+                    )
+                else:
+                    # Log unexpected errors but still try to create a new agent
+                    logging.warning(
+                        f"Unexpected error while retrieving agent {agent_name}: {str(e)}. Attempting to create new agent."
+                    )
+
+            # Create the agent using the project client with the agent_name as both name and assistantId
+            agent_definition = await client.agents.create_agent(
+                model=config.AZURE_OPENAI_DEPLOYMENT_NAME,
+                name=agent_name,
+                instructions=instructions,
+                temperature=temperature,
+                response_format=response_format,
+            )
+
+            return agent_definition
+        except Exception as exc:
+            logging.error("Failed to create Azure AI Agent: %s", exc)
+            raise