Python: Fix Azure AI sample errors (#4021)

* Python: Fix Azure AI sample errors

- azure_ai_with_application_endpoint: Add missing name to Agent constructor
- azure_ai_with_file_search: Fix resource path (parents[2] -> parents[3])
- azure_ai_with_openapi: Fix resource path (parents[2] -> parents[3])
- azure_ai_with_session: Use get_agent/get_session to reuse existing agent
  version and preserve conversation context across agent instances

* Python: Fix resource paths in azure_ai_agent samples

- azure_ai_with_file_search: Fix path to employees.pdf (parent.parent -> parents[3]/shared)
- azure_ai_with_openapi_tools: Fix path to weather.json/countries.json (parents[2] -> parents[3])

* fix V1 SDK hosted tools (FileSearchTool, etc.) silently dropped during agent creation

* fix: V2 file search sample uses correct SDK (AIProjectClient instead of AgentsClient)

The azure_ai/azure_ai_with_file_search.py sample incorrectly used the V1
AgentsClient for file/vector store operations. Replaced with V2 pattern:
AIProjectClient + get_openai_client() for file upload and vector store
management, matching the official Azure AI Projects SDK samples.

* fix: use context manager for file open in V2 file search sample

python/packages/azure-ai/agent_framework_azure_ai/_agent_provider.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import sys
-from collections.abc import Callable, MutableMapping, Sequence
+from collections.abc import Callable, Sequence
 from typing import Any, Generic, cast
 
 from agent_framework import (
@@ -25,7 +25,7 @@ from azure.core.credentials_async import AsyncTokenCredential
 from pydantic import BaseModel
 
 from ._chat_client import AzureAIAgentClient, AzureAIAgentOptions
-from ._shared import AzureAISettings, from_azure_ai_agent_tools, to_azure_ai_agent_tools
+from ._shared import AzureAISettings, to_azure_ai_agent_tools
 
 if sys.version_info >= (3, 13):
     from typing import Self, TypeVar  # type: ignore # pragma: no cover
@@ -238,13 +238,9 @@ class AzureAIAgentsProvider(Generic[OptionsCoT]):
         # Local MCP tools (MCPTool) are handled by Agent at runtime, not stored on the Azure agent
         normalized_tools = normalize_tools(tools)
         if normalized_tools:
-            # Only convert non-MCP tools to Azure AI format
-            non_mcp_tools: list[FunctionTool | MutableMapping[str, Any]] = []
-            for normalized_tool in normalized_tools:
-                if isinstance(normalized_tool, MCPTool):
-                    continue
-                if isinstance(normalized_tool, (FunctionTool, MutableMapping)):
-                    non_mcp_tools.append(normalized_tool)
+            # Collect all non-MCP tools for Azure AI agent creation.
+            # to_azure_ai_agent_tools handles FunctionTool, SDK Tool types (FileSearchTool, etc.), and dicts.
+            non_mcp_tools: list[Any] = [t for t in normalized_tools if not isinstance(t, MCPTool)]
             if non_mcp_tools:
                 # Pass run_options to capture tool_resources (e.g., for file search vector stores)
                 run_options: dict[str, Any] = {}
@@ -429,16 +425,10 @@ class AzureAIAgentsProvider(Generic[OptionsCoT]):
         """
         merged: list[ToolTypes] = []
 
-        # Convert hosted tools from agent definition
-        hosted_tools = from_azure_ai_agent_tools(agent_tools)
-        for hosted_tool in hosted_tools:
-            # Skip function tool dicts - they don't have implementations
-            # Skip OpenAPI tool dicts - they're defined on the agent, not needed at runtime
-            if isinstance(hosted_tool, dict):
-                tool_type = hosted_tool.get("type")
-                if tool_type == "function" or tool_type == "openapi":
-                    continue
-            merged.append(hosted_tool)
+        # Hosted tools (file_search, code_interpreter, bing_grounding, openapi, etc.)
+        # are already defined on the server agent and will be read back by the client
+        # at run time via agent_definition.tools. We skip them here to avoid sending
+        # them again at request time (which causes API errors like unknown vector_store_ids).
 
         # Add user-provided function tools and MCP tools
         if provided_tools:

python/packages/azure-ai/tests/test_agent_provider.py

@@ -437,7 +437,7 @@ def test_as_agent_with_hosted_tools(
     azure_ai_unit_test_env: dict[str, str],
     mock_agents_client: MagicMock,
 ) -> None:
-    """Test as_agent handles hosted tools correctly."""
+    """Test as_agent excludes hosted tools from local tools (they stay on the server agent)."""
     mock_code_interpreter = MagicMock()
     mock_code_interpreter.type = "code_interpreter"
 
@@ -456,9 +456,10 @@ def test_as_agent_with_hosted_tools(
     agent = provider.as_agent(mock_agent)
 
     assert isinstance(agent, Agent)
-    # Should have code_interpreter dict tool in the default_options tools
+    # Hosted tools (code_interpreter, file_search, etc.) are already on the server agent
+    # and should NOT be in local tools to avoid re-sending them at run time
     tools = agent.default_options.get("tools") or []
-    assert any(isinstance(t, dict) and t.get("type") == "code_interpreter" for t in tools)
+    assert not any(isinstance(t, dict) and t.get("type") == "code_interpreter" for t in tools)
 
 
 def test_as_agent_with_dict_function_tools_validates(

python/samples/02-agents/providers/azure_ai/azure_ai_with_application_endpoint.py

@@ -24,6 +24,7 @@ async def main() -> None:
         # /api/projects/<project-name>/applications/<application-name>/protocols
         AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project_client,
         Agent(
+            name="ApplicationAgent",
             client=AzureAIClient(
                 project_client=project_client,
             ),

python/samples/02-agents/providers/azure_ai/azure_ai_with_file_search.py

@@ -1,12 +1,12 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
+import contextlib
 import os
 from pathlib import Path
 
 from agent_framework.azure import AzureAIClient, AzureAIProjectAgentProvider
-from azure.ai.agents.aio import AgentsClient
-from azure.ai.agents.models import FileInfo, VectorStore
+from azure.ai.projects.aio import AIProjectClient
 from azure.identity.aio import AzureCliCredential
 
 """
@@ -25,27 +25,30 @@ USER_INPUTS = [
 
 async def main() -> None:
     """Main function demonstrating Azure AI agent with file search capabilities."""
-    file: FileInfo | None = None
-    vector_store: VectorStore | None = None
-
     async with (
         AzureCliCredential() as credential,
-        AgentsClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as agents_client,
-        AzureAIProjectAgentProvider(credential=credential) as provider,
+        AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project_client,
+        AzureAIProjectAgentProvider(project_client=project_client) as provider,
     ):
+        openai_client = project_client.get_openai_client()
+
         try:
-            # 1. Upload file and create vector store
-            pdf_file_path = Path(__file__).parents[2] / "shared" / "resources" / "employees.pdf"
+            # 1. Upload file and create vector store via OpenAI client
+            pdf_file_path = Path(__file__).parents[3] / "shared" / "resources" / "employees.pdf"
             print(f"Uploading file from: {pdf_file_path}")
 
-            file = await agents_client.files.upload_and_poll(file_path=str(pdf_file_path), purpose="assistants")
-            print(f"Uploaded file, file ID: {file.id}")
-
-            vector_store = await agents_client.vector_stores.create_and_poll(file_ids=[file.id], name="my_vectorstore")
+            vector_store = await openai_client.vector_stores.create(name="my_vectorstore")
             print(f"Created vector store, vector store ID: {vector_store.id}")
 
-            # 2. Create a client to access hosted tool factory methods
-            client = AzureAIClient(credential=credential)
+            with open(pdf_file_path, "rb") as f:
+                file = await openai_client.vector_stores.files.upload_and_poll(
+                    vector_store_id=vector_store.id,
+                    file=f,
+                )
+            print(f"Uploaded file, file ID: {file.id}")
+
+            # 2. Create a file search tool
+            client = AzureAIClient(project_client=project_client)
             file_search_tool = client.get_file_search_tool(vector_store_ids=[vector_store.id])
 
             # 3. Create an agent with file search capabilities using the provider
@@ -64,11 +67,9 @@ async def main() -> None:
                 response = await agent.run(user_input)
                 print(f"# Agent: {response.text}")
         finally:
-            # 5. Cleanup: Delete the vector store and file in case of earlier failure to prevent orphaned resources.
-            if vector_store:
-                await agents_client.vector_stores.delete(vector_store.id)
-            if file:
-                await agents_client.files.delete(file.id)
+            # 5. Cleanup: Delete the vector store (also deletes associated files)
+            with contextlib.suppress(Exception):
+                await openai_client.vector_stores.delete(vector_store.id)
 
 
 if __name__ == "__main__":

python/samples/02-agents/providers/azure_ai/azure_ai_with_openapi.py

@@ -20,7 +20,7 @@ Prerequisites:
 
 async def main() -> None:
     # Load the OpenAPI specification
-    resources_path = Path(__file__).parents[2] / "shared" / "resources" / "countries.json"
+    resources_path = Path(__file__).parents[3] / "shared" / "resources" / "countries.json"
 
     with open(resources_path) as f:
         openapi_countries = json.load(f)

python/samples/02-agents/providers/azure_ai/azure_ai_with_session.py

@@ -133,15 +133,14 @@ async def example_with_existing_session_id() -> None:
         if existing_session_id:
             print("\n--- Continuing with the same session ID in a new agent instance ---")
 
-            # Create a new agent instance from the same provider
-            second_agent = await provider.create_agent(
+            # Retrieve the same agent (reuses existing agent version on the service)
+            second_agent = await provider.get_agent(
                 name="BasicWeatherAgent",
-                instructions="You are a helpful weather agent.",
                 tools=get_weather,
             )
 
-            # Create a session with the existing ID
-            session = second_agent.create_session(service_session_id=existing_session_id)
+            # Attach the existing service session ID so conversation context is preserved
+            session = second_agent.get_session(service_session_id=existing_session_id)
 
             second_query = "What was the last city I asked about?"
             print(f"User: {second_query}")

python/samples/02-agents/providers/azure_ai_agent/azure_ai_with_file_search.py

@@ -35,7 +35,7 @@ async def main() -> None:
     ):
         try:
             # 1. Upload file and create vector store
-            pdf_file_path = Path(__file__).parent.parent / "resources" / "employees.pdf"
+            pdf_file_path = Path(__file__).parents[3] / "shared" / "resources" / "employees.pdf"
             print(f"Uploading file from: {pdf_file_path}")
 
             file = await agents_client.files.upload_and_poll(file_path=str(pdf_file_path), purpose="assistants")

python/samples/02-agents/providers/azure_ai_agent/azure_ai_with_openapi_tools.py

@@ -23,7 +23,7 @@ USER_INPUTS = [
 
 def load_openapi_specs() -> tuple[dict[str, Any], dict[str, Any]]:
     """Load OpenAPI specification files."""
-    resources_path = Path(__file__).parents[2] / "shared" / "resources"
+    resources_path = Path(__file__).parents[3] / "shared" / "resources"
 
     with open(resources_path / "weather.json") as weather_file:
         weather_spec = json.load(weather_file)