fix: updated the agent framework to latest version

src/backend/pyproject.toml

@@ -7,7 +7,7 @@ requires-python = ">=3.11"
 dependencies = [
     "azure-ai-evaluation==1.11.0",
     "azure-ai-inference==1.0.0b9",
-    "azure-ai-projects==2.0.0b3",
+    "azure-ai-projects==2.0.0",
     "azure-cosmos==4.9.0",
     "azure-identity==1.24.0",
     "azure-monitor-events-extension==0.1.0",
@@ -32,8 +32,9 @@ dependencies = [
     "mcp==1.26.0",
     "werkzeug==3.1.5",
     "azure-core==1.38.0",
-    "agent-framework-azure-ai==1.0.0b260130",
-    "agent-framework-core==1.0.0b260130",
+    "agent-framework-azure-ai==1.0.0rc4",
+    "agent-framework-core==1.0.0rc4",
+    "agent-framework-orchestrations==1.0.0b260311",
     "urllib3==2.6.3",
     "protobuf==5.29.6",
     "cryptography==46.0.5",

src/backend/v4/callbacks/response_handlers.py

@@ -8,7 +8,7 @@
 import re
 from typing import Any
 
-from agent_framework import ChatMessage
+from agent_framework import Message
 
 from v4.config.settings import connection_config
 from v4.models.messages import (
@@ -64,22 +64,22 @@ def _extract_tool_calls_from_contents(contents: list[Any]) -> list[AgentToolCall
 
 def agent_response_callback(
     agent_id: str,
-    message: ChatMessage,
+    message: Message,
     user_id: str | None = None,
 ) -> None:
     """
-    Final (non-streaming) agent response callback using agent_framework ChatMessage.
+    Final (non-streaming) agent response callback using agent_framework Message.
     """
     agent_name = getattr(message, "author_name", None) or agent_id or "Unknown Agent"
     role = getattr(message, "role", "assistant")
 
-    # FIX: Properly extract text from ChatMessage
-    # ChatMessage has a .text property that concatenates all TextContent items
+    # FIX: Properly extract text from Message
+    # Message has a .text property that concatenates all TextContent items
     text = ""
-    if isinstance(message, ChatMessage):
+    if isinstance(message, Message):
         text = message.text  # Use the property directly
     else:
-        # Fallback for non-ChatMessage objects
+        # Fallback for non-Message objects
         text = str(getattr(message, "text", ""))
 
     text = clean_citations(text or "")
@@ -125,8 +125,8 @@ async def streaming_agent_response_callback(
         # If text is None, don't fall back to str(update) as that would show object repr
         # Just skip if there's no actual text content
         if chunk_text is None:
-            # Check if update is a ChatMessage
-            if isinstance(update, ChatMessage):
+            # Check if update is a Message
+            if isinstance(update, Message):
                 chunk_text = update.text or ""
             elif hasattr(update, "content"):
                 chunk_text = str(update.content) if update.content else ""

src/backend/v4/magentic_agents/common/lifecycle.py

@@ -5,8 +5,7 @@
 from typing import Any, Optional
 
 from agent_framework import (
-    ChatAgent,
-    HostedMCPTool,
+    Agent,
     MCPStreamableHTTPTool,
 )
 
@@ -46,8 +45,8 @@ def __init__(
     ) -> None:
         self._stack: AsyncExitStack | None = None
         self.mcp_cfg: MCPConfig | None = mcp
-        self.mcp_tool: HostedMCPTool | None = None
-        self._agent: ChatAgent | None = None
+        self.mcp_tool: MCPStreamableHTTPTool | None = None
+        self._agent: Agent | None = None
         self.team_service: TeamService | None = team_service
         self.team_config: TeamConfiguration | None = team_config
         self.client: Optional[AgentsClient] = None
@@ -155,9 +154,9 @@ def get_chat_client(self) -> AzureAIClient:
         """
         if (
             self._agent
-            and self._agent.chat_client
+            and self._agent.client
         ):
-            return self._agent.chat_client  # type: ignore
+            return self._agent.client  # type: ignore
         chat_client = AzureAIClient(
             project_endpoint=self.project_endpoint,
             agent_name=self.agent_name,

src/backend/v4/magentic_agents/foundry_agent.py

@@ -3,13 +3,12 @@
 import logging
 from typing import List, Optional
 
-from agent_framework import (ChatAgent, ChatMessage, HostedCodeInterpreterTool,
-                             Role)
+from agent_framework import (Agent, Message, ChatOptions)
 from agent_framework_azure_ai import \
     AzureAIClient  # Provided by agent_framework
 from azure.ai.projects.models import (
     PromptAgentDefinition,
-    AzureAISearchAgentTool,
+    AzureAISearchTool,
     AzureAISearchToolResource,
     AISearchIndexResource,
 )
@@ -92,17 +91,13 @@ def _is_azure_search_requested(self) -> bool:
         return False
 
     async def _collect_tools(self) -> List:
-        """Collect tool definitions for ChatAgent (MCP path only)."""
+        """Collect tool definitions for Agent (MCP path only)."""
         tools: List = []
 
-        # Code Interpreter (only in MCP path per incompatibility note)
+        # Code Interpreter is now handled server-side via AzureAIClient agent definition.
+        # HostedCodeInterpreterTool was removed in rc4.
         if self.enable_code_interpreter:
-            try:
-                code_tool = HostedCodeInterpreterTool()
-                tools.append(code_tool)
-                self.logger.info("Added Code Interpreter tool.")
-            except Exception as ie:
-                self.logger.error("Code Interpreter tool creation failed: %s", ie)
+            self.logger.info("Code Interpreter requested — handled server-side by AzureAIClient.")
 
         # MCP Tool (from base class)
         if self.mcp_tool:
@@ -121,7 +116,7 @@ async def _create_azure_search_enabled_client(self) -> Optional[AzureAIClient]:
 
         This uses the AIProjectClient.agents.create_version() approach with:
         - PromptAgentDefinition for agent configuration
-        - AzureAISearchAgentTool with AzureAISearchToolResource for search capability
+        - AzureAISearchTool with AzureAISearchToolResource for search capability
         - AISearchIndexResource for index configuration with project_connection_id
 
         Requirements:
@@ -167,7 +162,7 @@ async def _create_azure_search_enabled_client(self) -> Optional[AzureAIClient]:
             top_k,
         )
 
-        # Create agent using create_version with PromptAgentDefinition and AzureAISearchAgentTool
+        # Create agent using create_version with PromptAgentDefinition and AzureAISearchTool
         # This approach matches the Knowledge Mining Solution Accelerator pattern
         try:
             enhanced_instructions = (
@@ -181,7 +176,7 @@ async def _create_azure_search_enabled_client(self) -> Optional[AzureAIClient]:
                     model=self.model_deployment_name,
                     instructions=enhanced_instructions,
                     tools=[
-                        AzureAISearchAgentTool(
+                        AzureAISearchTool(
                             azure_ai_search=AzureAISearchToolResource(
                                 indexes=[
                                     AISearchIndexResource(
@@ -253,37 +248,39 @@ async def _after_open(self) -> None:
                     )
 
                 # In Azure Search raw tool path, tools/tool_choice are handled server-side.
-                self._agent = ChatAgent(
+                self._agent = Agent(
                     id=self.get_agent_id(),
-                    chat_client=chat_client,
+                    client=chat_client,
                     instructions=self.agent_instructions,
                     name=self.agent_name,
                     description=self.agent_description,
-                    tool_choice="required",  # Force usage
-                    temperature=temp,
-                    model_id=self.model_deployment_name,
-                    default_options={"store": False},  # Client-managed conversation to avoid stale tool call IDs across rounds
+                    default_options=ChatOptions(
+                        store=False,
+                        tool_choice="required",
+                        temperature=temp,
+                    ),
                 )
             else:
                 # MCP path (also used by RAI agent which has no tools)
                 self.logger.info("Initializing agent in MCP mode.")
                 tools = await self._collect_tools()
-                self._agent = ChatAgent(
+                self._agent = Agent(
                     id=self.get_agent_id(),
-                    chat_client=self.get_chat_client(),
+                    client=self.get_chat_client(),
                     instructions=self.agent_instructions,
                     name=self.agent_name,
                     description=self.agent_description,
                     tools=tools if tools else None,
-                    tool_choice="auto" if tools else "none",
-                    temperature=temp,
-                    model_id=self.model_deployment_name,
-                    default_options={"store": False},  # Client-managed conversation to avoid stale tool call IDs across rounds
+                    default_options=ChatOptions(
+                        store=False,
+                        tool_choice="auto" if tools else "none",
+                        temperature=temp,
+                    ),
                 )
-            self.logger.info("Initialized ChatAgent '%s'", self.agent_name)
+            self.logger.info("Initialized Agent '%s'", self.agent_name)
 
         except Exception as ex:
-            self.logger.error("Failed to initialize ChatAgent: %s", ex)
+            self.logger.error("Failed to initialize Agent: %s", ex)
             raise
 
         # Register agent globally
@@ -305,9 +302,9 @@ async def invoke(self, prompt: str):
         if not self._agent:
             raise RuntimeError("Agent not initialized; call open() first.")
 
-        messages = [ChatMessage(role=Role.USER, text=prompt)]
+        messages = [Message(role="user", text=prompt)]
 
-        async for update in self._agent.run_stream(messages):
+        async for update in self._agent.run(messages, stream=True):
             yield update
 
     # -------------------------