Python: [BREAKING] Moved to a single get_response and run API (#3379)

* WIP * big update to new ResponseStream model * fixed tests and typing * fixed tests and typing * fixed tools typevar import * fix * mypy fix * mypy fixes and some cleanup * fix missing quoted names * and client * fix imports agui * fix anthropic override * fix agui * fix ag ui * fix import * fix anthropic types * fix mypy * refactoring * updated typing * fix 3.11 * fixes * redid layering of chat clients and agents * redid layering of chat clients and agents * Fix lint, type, and test issues after rebase - Add @overload decorators to AgentProtocol.run() for type compatibility - Add missing docstring params (middleware, function_invocation_configuration) - Fix TODO format (TD002) by adding author tags - Fix broken observability tests from upstream: - Replace non-existent use_instrumentation with direct instantiation - Replace non-existent use_agent_instrumentation with AgentTelemetryLayer mixin - Fix get_streaming_response to use get_response(stream=True) - Add AgentInitializationError import - Update streaming exception tests to match actual behavior * Fix AgentExecutionException import error in test_agents.py - Replace non-existent AgentExecutionException with AgentRunException * Fix test import and asyncio deprecation issues - Add 'tests' to pythonpath in ag-ui pyproject.toml for utils_test_ag_ui import - Replace deprecated asyncio.get_event_loop().run_until_complete with asyncio.run * Fix azure-ai test failures - Update _prepare_options patching to use correct class path - Fix test_to_azure_ai_agent_tools_web_search_missing_connection to clear env vars * Convert ag-ui utils_test_ag_ui.py to conftest.py - Move test utilities to conftest.py for proper pytest discovery - Update all test imports to use conftest instead of utils_test_ag_ui - Remove old utils_test_ag_ui.py file - Revert pythonpath change in pyproject.toml * fix: use relative imports for ag-ui test utilities * fix agui * Rename Bare*Client to Raw*Client and BaseChatClient - Renamed BareChatClient to BaseChatClient (abstract base class) - Renamed BareOpenAIChatClient to RawOpenAIChatClient - Renamed BareOpenAIResponsesClient to RawOpenAIResponsesClient - Renamed BareAzureAIClient to RawAzureAIClient - Added warning docstrings to Raw* classes about layer ordering - Updated README in samples/getting_started/agents/custom with layer docs - Added test for span ordering with function calling * Fix layer ordering: FunctionInvocationLayer before ChatTelemetryLayer This ensures each inner LLM call gets its own telemetry span, resulting in the correct span sequence: chat -> execute_tool -> chat Updated all production clients and test mocks to use correct ordering: - ChatMiddlewareLayer (first) - FunctionInvocationLayer (second) - ChatTelemetryLayer (third) - BaseChatClient/Raw...Client (fourth) * Remove run_stream usage * Fix conversation_id propagation * Python: Add BaseAgent implementation for Claude Agent SDK (#3509) * Added ClaudeAgent implementation * Updated streaming logic * Small updates * Small update * Fixes * Small fix * Naming improvements * Updated imports * Addressed comments * Updated package versions * Update Claude agent connector layering * fix test and plugin * Store function middleware in invocation layer * Fix telemetry streaming and ag-ui tests * Remove legacy ag-ui tests folder * updates * Remove terminate flag from FunctionInvocationContext, use MiddlewareTermination instead - Remove terminate attribute from FunctionInvocationContext - Add result attribute to MiddlewareTermination to carry function results - FunctionMiddlewarePipeline.execute() now lets MiddlewareTermination propagate - _auto_invoke_function captures context.result in exception before re-raising - _try_execute_function_calls catches MiddlewareTermination and sets should_terminate - Fix handoff middleware to append to chat_client.function_middleware directly - Update tests to use raise MiddlewareTermination instead of context.terminate - Add middleware flow documentation in samples/concepts/tools/README.md - Fix ag-ui to use FunctionMiddlewarePipeline instead of removed create_function_middleware_pipeline * fix: remove references to removed terminate flag in purview tests, add type ignore * fix: move _test_utils.py from package to test folder * fix: call get_final_response() to trigger context provider notification in streaming test * fix: correct broken links in tools README * docs: clarify default middleware behavior in summary table * fix: ensure inner stream result hooks are called when using map()/from_awaitable() * Fix mypy type errors * Address PR review comments on observability.py - Remove TODO comment about unconsumed streams, add explanatory note instead - Remove redundant _close_span cleanup hook (already called in _finalize_stream) - Clarify behavior: cleanup hooks run after stream iteration, if stream is not consumed the span remains open until garbage collected * Remove gen_ai.client.operation.duration from span attributes Duration is a metrics-only attribute per OpenTelemetry semantic conventions. It should be recorded to the histogram but not set as a span attribute. * Remove duration from _get_response_attributes, pass directly to _capture_response Duration is a metrics-only attribute. It's now passed directly to _capture_response instead of being included in the attributes dict that gets set on the span. * Remove redundant _close_span cleanup hook in AgentTelemetryLayer _finalize_stream already calls _close_span() in its finally block, so adding it as a separate cleanup hook is redundant. * Use weakref.finalize to close span when stream is garbage collected If a user creates a streaming response but never consumes it, the cleanup hooks won't run. Now we register a weak reference finalizer that will close the span when the stream object is garbage collected, ensuring spans don't leak in this scenario. * Fix _get_finalizers_from_stream to use _result_hooks attribute Renamed function to _get_result_hooks_from_stream and fixed it to look for the _result_hooks attribute which is the correct name in ResponseStream class. * Add missing asyncio import in test_request_info_mixin.py * Fix leftover merge conflict marker in image_generation sample * Update integration tests * Fix integration tests: increase max_iterations from 1 to 2 Tests with tool_choice options require at least 2 iterations: 1. First iteration to get function call and execute the tool 2. Second iteration to get the final text response With max_iterations=1, streaming tests would return early with only the function call/result but no final text content. * Fix duplicate function call error in conversation-based APIs When using conversation_id (for Responses/Assistants APIs), the server already has the function call message from the previous response. We should only send the new function result message, not all messages including the function call which would cause a duplicate ID error. Fix: When conversation_id is set, only send the last message (the tool result) instead of all response.messages. * Add regression test for conversation_id propagation between tool iterations Port test from PR #3664 with updates for new streaming API pattern. Tests that conversation_id is properly updated in options dict during function invocation loop iterations. * Fix tool_choice=required to return after tool execution When tool_choice is 'required', the user's intent is to force exactly one tool call. After the tool executes, return immediately with the function call and result - don't continue to call the model again. This fixes integration tests that were failing with empty text responses because with tool_choice=required, the model would keep returning function calls instead of text. Also adds regression tests for: - conversation_id propagation between tool iterations (from PR #3664) - tool_choice=required returns after tool execution * Document tool_choice behavior in tools README - Add table explaining tool_choice values (auto, none, required) - Explain why tool_choice=required returns immediately after tool execution - Add code example showing the difference between required and auto - Update flow diagram to show the early return path for tool_choice=required * Fix tool_choice=None behavior - don't default to 'auto' Remove the hardcoded default of 'auto' for tool_choice in ChatAgent init. When tool_choice is not specified (None), it will now not be sent to the API, allowing the API's default behavior to be used. Users who want tool_choice='auto' can still explicitly set it either in default_options or at runtime. Fixes #3585 * Fix tool_choice=none should not remove tools In OpenAI Assistants client, tools were not being sent when tool_choice='none'. This was incorrect - tool_choice='none' means the model won't call tools, but tools should still be available in the request (they may be used later in the conversation). Fixes #3585 * Add test for tool_choice=none preserving tools Adds a regression test to ensure that when tool_choice='none' is set but tools are provided, the tools are still sent to the API. This verifies the fix for #3585. * Fix tool_choice=none should not remove tools in all clients Apply the same fix to OpenAI Responses client and Azure AI client: - OpenAI Responses: Remove else block that popped tool_choice/parallel_tool_calls - Azure AI: Remove tool_choice != 'none' check when adding tools When tool_choice='none', the model won't call tools, but tools should still be sent to the API so they're available for future turns. Also update README to clarify tool_choice=required supports multiple tools. Fixes #3585 * Keep tool_choice even when tools is None Move tool_choice processing outside of the 'if tools' block in OpenAI Responses client so tool_choice is sent to the API even when no tools are provided. * Update test to match new parallel_tool_calls behavior Changed test_prepare_options_removes_parallel_tool_calls_when_no_tools to test_prepare_options_preserves_parallel_tool_calls_when_no_tools to reflect that parallel_tool_calls is now preserved even when no tools are present, consistent with the tool_choice behavior. * Fix ChatMessage API and Role enum usage after rebase - Update ChatMessage instantiation to use keyword args (role=, text=, contents=) - Fix Role enum comparisons to use .value for string comparison - Add created_at to AgentResponse in error handling - Fix AgentResponse.from_updates -> from_agent_run_response_updates - Fix DurableAgentStateMessage.from_chat_message to convert Role enum to string - Add Role import where needed * Fix additional ChatMessage API and method name changes - Fix ChatMessage usage in workflow files (use text= instead of contents= for strings) - Fix AgentResponse.from_updates -> from_agent_run_response_updates in workflow files - Fix test files for ChatMessage and Role enum usage * Fix remaining ChatMessage API usage in test files * Fix more ChatMessage and Role API changes in source and test files - Fix ChatMessage in _magentic.py replan method - Fix Role enum comparison in test assertions - Fix remaining test files with old ChatMessage syntax * Fix ChatMessage and Role API changes across packages - Add Role import where missing - Fix ChatMessage signature: positional args to keyword args (role=, text=, contents=) - Fix Role enum comparisons: .role.value instead of .role string - Fix FinishReason enum usage in ag-ui event converters - Rename AgentResponse.from_updates to from_agent_run_response_updates in ag-ui Fixes API compatibility after Types API Review improvements merge * Fix ChatMessage and Role API changes in github_copilot tests * Fix ChatMessage and Role API changes in redis and github_copilot packages - Fix redis provider: Role enum comparison using .value - Fix redis tests: ChatMessage signature and Role comparisons - Fix github_copilot tests: ChatMessage signature and Role comparisons - Update docstring examples in redis chat message store * Fix ChatMessage and Role API changes in devui package - Fix executor: ChatMessage signature change - Fix conversations: Role enum to string conversion in two places - Fix tests: ChatMessage signatures and Role comparisons * Fix ChatMessage and Role API changes in a2a and lab packages - Fix a2a tests: Role comparisons and ChatMessage signatures - Fix lab tau2 source: Role enum comparison in flip_messages, log_messages, sliding_window - Fix lab tau2 tests: ChatMessage signatures and Role comparisons * Remove duplicate test files from ag-ui/tests (tests are in ag_ui_tests) * Fix ChatMessage and Role API changes across packages After rebasing on upstream/main which merged PR #3647 (Types API Review improvements), fix all packages to use the new API: - ChatMessage: Use keyword args (role=, text=, contents=) instead of positional args - Role: Compare using .value attribute since it's now an enum Packages fixed: - ag-ui: Fixed Role value extraction bugs in _message_adapters.py - anthropic: Fixed ChatMessage and Role comparisons in tests - azure-ai: Fixed Role comparison in _client.py - azure-ai-search: Fixed ChatMessage and Role in source/tests - bedrock: Fixed ChatMessage signatures in tests - chatkit: Fixed ChatMessage and Role in source/tests - copilotstudio: Fixed ChatMessage and Role in tests - declarative: Fixed ChatMessage in _executors_agents.py - mem0: Fixed ChatMessage and Role in source/tests - purview: Fixed ChatMessage in source/tests * Fix mypy errors for ChatMessage and Role API changes - durabletask: Use str() fallback in role value extraction - core: Fix ChatMessage in _orchestrator_helpers.py to use keyword args - core: Add type ignore for _conversation_state.py contents deserialization - ag-ui: Fix type ignore comments (call-overload instead of arg-type) - azure-ai-search: Fix get_role_value type hint to accept Any - lab: Move get_role_value to module level with Any type hint * Improve CI test timeout configuration - Increase job timeout from 10 to 15 minutes - Reduce per-test timeout to 60s (was 900s/300s) - Add --timeout_method thread for better timeout handling - Add --timeout-verbose to see which tests are slow - Reduce retries from 3 to 2 and delay from 10s to 5s This ensures individual test timeouts are shorter than the job timeout, providing better visibility when tests hang. With 60s timeout and 2 retries, worst case per test is ~180s. * Fix ChatMessage API usage in docstrings and source - Fix ChatMessage positional args in docstrings: _serialization.py, _threads.py, _middleware.py - Fix ChatMessage in tau2 runner.py - Fix role comparison in _orchestrator_helpers.py to use .value - Fix role comparison in _group_chat.py docstring example - Fix role assertions in test_durable_entities.py to use .value * Revert tool_choice/parallel_tool_calls changes - must be removed when no tools OpenAI API requires tool_choice and parallel_tool_calls to only be present when tools are specified. Restored the logic that removes these options when there are no tools. - Restored check in _chat_client.py to remove tool_choice and parallel_tool_calls when no tools present - Restored same logic in _responses_client.py - Reverted test to expect the correct behavior * fixed issue in tests * fix: resolve merge conflict markers in ag-ui tests * fix: restructure ag-ui tests and fix Role/FinishReason to use string types * fix: streaming function invocation and middleware termination - Refactor streaming function invocation to use get_final_response() on inner streams - Fix MiddlewareTermination to accept result parameter for passing results - Fix _AutoHandoffMiddleware to use MiddlewareTermination instead of context.terminate - Fix AgentMiddlewareLayer.run() to properly forward function/chat middleware - Remove duplicate middleware registration in AgentMiddlewareLayer.__init__ - Fix exception handling in _auto_invoke_function to properly capture termination - Fix mypy errors in core package - Update tests to use stream=True parameter for unified run API * fix all tests command * Refactor integration tests to use pytest fixtures - Merge testutils.py into conftest.py for azurefunctions integration tests - Merge dt_testutils.py into conftest.py for durabletask integration tests - Convert all integration tests to use fixtures instead of direct imports (fixes ModuleNotFoundError with --import-mode=importlib) - Add sample_helper fixture for azurefunctions tests - Add agent_client_factory and orchestration_helper fixtures for durabletask - Integration tests now skip with descriptive messages when services unavailable - Restructure devui tests into tests/devui/ with proper conftest.py - Add test organization guidelines to CODING_STANDARD.md - Remove __init__.py from test directories per pytest best practices * Fix pytest_collection_modifyitems to only skip integration tests The hook was skipping all tests in the test session, not just integration tests. Now it only skips items in the integration_tests directory. * Fix mem0 tests failing on Python 3.13 Use patch.object on the imported module instead of @patch with string path to ensure the mock takes effect regardless of import timing. * fix mem0 * another attempt for mem0 * fix for mem0 * fix mem0 * Increase worker initialization wait time in durabletask tests Increase from 2 to 8 seconds to allow time for: - Python startup and module imports - Azure OpenAI client creation - Agent registration with DTS worker - Worker connection to DTS This helps prevent test failures in CI where the first tests may run before the worker is fully ready to process requests. * Fix streaming test to use ResponseStream with finalizer The _consume_stream method now expects a ResponseStream that can provide a final AgentResponse via get_final_response(). Update the test to use ResponseStream with AgentResponse.from_updates as the finalizer. * Fix MockToolCallingAgent to use new ResponseStream API and update samples * small updates to run_stream to run * fix sub workflow * temp fix for az func test --------- Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com>
2026-06-16 21:04:09 +08:00 · 2026-02-05 21:09:58 +01:00
parent d1205896a1
commit 3dc59c83b5
372 changed files with 11583 additions and 9465 deletions
@@ -18,7 +18,7 @@ from azure.identity.aio import AzureCliCredential
 from pydantic import Field

 """
-Agent-Level and Run-Level Middleware Example
+Agent-Level and Run-Level MiddlewareTypes Example

 This sample demonstrates the difference between agent-level and run-level middleware:

@@ -107,7 +107,7 @@ async def debugging_middleware(
    """Run-level debugging middleware for troubleshooting specific runs."""
    print("[Debug] Debug mode enabled for this run")
    print(f"[Debug] Messages count: {len(context.messages)}")
-    print(f"[Debug] Is streaming: {context.is_streaming}")
+    print(f"[Debug] Is streaming: {context.stream}")

    # Log existing metadata from agent middleware
    if context.metadata:
@@ -163,7 +163,7 @@ async def function_logging_middleware(

 async def main() -> None:
    """Example demonstrating agent-level and run-level middleware."""
-    print("=== Agent-Level and Run-Level Middleware Example ===\n")
+    print("=== Agent-Level and Run-Level MiddlewareTypes Example ===\n")

    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
@@ -18,7 +18,7 @@ from azure.identity.aio import AzureCliCredential
 from pydantic import Field

 """
-Chat Middleware Example
+Chat MiddlewareTypes Example

 This sample demonstrates how to use chat middleware to observe and override
 inputs sent to AI models. Chat middleware intercepts chat requests before they reach
@@ -31,8 +31,8 @@ the underlying AI service, allowing you to:
 The example covers:
 - Class-based chat middleware inheriting from ChatMiddleware
 - Function-based chat middleware with @chat_middleware decorator
- Middleware registration at agent level (applies to all runs)
- Middleware registration at run level (applies to specific run only)
+- MiddlewareTypes registration at agent level (applies to all runs)
+- MiddlewareTypes registration at run level (applies to specific run only)
 """


@@ -137,7 +137,7 @@ async def security_and_override_middleware(
 async def class_based_chat_middleware() -> None:
    """Demonstrate class-based middleware at agent level."""
    print("\n" + "=" * 60)
-    print("Class-based Chat Middleware (Agent Level)")
+    print("Class-based Chat MiddlewareTypes (Agent Level)")
    print("=" * 60)

    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
@@ -161,7 +161,7 @@ async def class_based_chat_middleware() -> None:
 async def function_based_chat_middleware() -> None:
    """Demonstrate function-based middleware at agent level."""
    print("\n" + "=" * 60)
-    print("Function-based Chat Middleware (Agent Level)")
+    print("Function-based Chat MiddlewareTypes (Agent Level)")
    print("=" * 60)

    async with (
@@ -191,7 +191,7 @@ async def function_based_chat_middleware() -> None:
 async def run_level_middleware() -> None:
    """Demonstrate middleware registration at run level."""
    print("\n" + "=" * 60)
-    print("Run-level Chat Middleware")
+    print("Run-level Chat MiddlewareTypes")
    print("=" * 60)

    async with (
@@ -204,14 +204,14 @@ async def run_level_middleware() -> None:
        ) as agent,
    ):
        # Scenario 1: Run without any middleware
-        print("\n--- Scenario 1: No Middleware ---")
+        print("\n--- Scenario 1: No MiddlewareTypes ---")
        query = "What's the weather in Tokyo?"
        print(f"User: {query}")
        result = await agent.run(query)
        print(f"Response: {result.text if result.text else 'No response'}")

        # Scenario 2: Run with specific middleware for this call only (both enhancement and security)
-        print("\n--- Scenario 2: With Run-level Middleware ---")
+        print("\n--- Scenario 2: With Run-level MiddlewareTypes ---")
        print(f"User: {query}")
        result = await agent.run(
            query,
@@ -223,7 +223,7 @@ async def run_level_middleware() -> None:
        print(f"Response: {result.text if result.text else 'No response'}")

        # Scenario 3: Security test with run-level middleware
-        print("\n--- Scenario 3: Security Test with Run-level Middleware ---")
+        print("\n--- Scenario 3: Security Test with Run-level MiddlewareTypes ---")
        query = "Can you help me with my secret API key?"
        print(f"User: {query}")
        result = await agent.run(
@@ -235,7 +235,7 @@ async def run_level_middleware() -> None:

 async def main() -> None:
    """Run all chat middleware examples."""
-    print("Chat Middleware Examples")
+    print("Chat MiddlewareTypes Examples")
    print("========================")

    await class_based_chat_middleware()
@@ -20,7 +20,7 @@ from azure.identity.aio import AzureCliCredential
 from pydantic import Field

 """
-Class-based Middleware Example
+Class-based MiddlewareTypes Example

 This sample demonstrates how to implement middleware using class-based approach by inheriting
 from AgentMiddleware and FunctionMiddleware base classes. The example includes:
@@ -95,7 +95,7 @@ class LoggingFunctionMiddleware(FunctionMiddleware):

 async def main() -> None:
    """Example demonstrating class-based middleware."""
-    print("=== Class-based Middleware Example ===")
+    print("=== Class-based MiddlewareTypes Example ===")

    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
@@ -12,7 +12,7 @@ from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential

 """
-Decorator Middleware Example
+Decorator MiddlewareTypes Example

 This sample demonstrates how to use @agent_middleware and @function_middleware decorators
 to explicitly mark middleware functions without requiring type annotations.
@@ -52,22 +52,22 @@ def get_current_time() -> str:
@agent_middleware  # Decorator marks this as agent middleware - no type annotations needed
 async def simple_agent_middleware(context, next):  # type: ignore - parameters intentionally untyped to demonstrate decorator functionality
    """Agent middleware that runs before and after agent execution."""
-    print("[Agent Middleware] Before agent execution")
+    print("[Agent MiddlewareTypes] Before agent execution")
    await next(context)
-    print("[Agent Middleware] After agent execution")
+    print("[Agent MiddlewareTypes] After agent execution")


@function_middleware  # Decorator marks this as function middleware - no type annotations needed
 async def simple_function_middleware(context, next):  # type: ignore - parameters intentionally untyped to demonstrate decorator functionality
    """Function middleware that runs before and after function calls."""
-    print(f"[Function Middleware] Before calling: {context.function.name}")  # type: ignore
+    print(f"[Function MiddlewareTypes] Before calling: {context.function.name}")  # type: ignore
    await next(context)
-    print(f"[Function Middleware] After calling: {context.function.name}")  # type: ignore
+    print(f"[Function MiddlewareTypes] After calling: {context.function.name}")  # type: ignore


 async def main() -> None:
    """Example demonstrating decorator-based middleware."""
-    print("=== Decorator Middleware Example ===")
+    print("=== Decorator MiddlewareTypes Example ===")

    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
@@ -10,7 +10,7 @@ from azure.identity.aio import AzureCliCredential
 from pydantic import Field

 """
-Exception Handling with Middleware
+Exception Handling with MiddlewareTypes

 This sample demonstrates how to use middleware for centralized exception handling in function calls.
 The example shows:
@@ -54,7 +54,7 @@ async def exception_handling_middleware(

 async def main() -> None:
    """Example demonstrating exception handling with middleware."""
-    print("=== Exception Handling Middleware Example ===")
+    print("=== Exception Handling MiddlewareTypes Example ===")

    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
@@ -16,7 +16,7 @@ from azure.identity.aio import AzureCliCredential
 from pydantic import Field

 """
-Function-based Middleware Example
+Function-based MiddlewareTypes Example

 This sample demonstrates how to implement middleware using simple async functions instead of classes.
 The example includes:
@@ -80,7 +80,7 @@ async def logging_function_middleware(

 async def main() -> None:
    """Example demonstrating function-based middleware."""
-    print("=== Function-based Middleware Example ===")
+    print("=== Function-based MiddlewareTypes Example ===")

    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
@@ -17,7 +17,7 @@ from azure.identity.aio import AzureCliCredential
 from pydantic import Field

 """
-Middleware Termination Example
+MiddlewareTypes Termination Example

 This sample demonstrates how middleware can terminate execution using the `context.terminate` flag.
 The example includes:
@@ -40,7 +40,7 @@ def get_weather(


 class PreTerminationMiddleware(AgentMiddleware):
-    """Middleware that terminates execution before calling the agent."""
+    """MiddlewareTypes that terminates execution before calling the agent."""

    def __init__(self, blocked_words: list[str]):
        self.blocked_words = [word.lower() for word in blocked_words]
@@ -79,7 +79,7 @@ class PreTerminationMiddleware(AgentMiddleware):


 class PostTerminationMiddleware(AgentMiddleware):
-    """Middleware that allows processing but terminates after reaching max responses across multiple runs."""
+    """MiddlewareTypes that allows processing but terminates after reaching max responses across multiple runs."""

    def __init__(self, max_responses: int = 1):
        self.max_responses = max_responses
@@ -109,7 +109,7 @@ class PostTerminationMiddleware(AgentMiddleware):

 async def pre_termination_middleware() -> None:
    """Demonstrate pre-termination middleware that blocks requests with certain words."""
-    print("\n--- Example 1: Pre-termination Middleware ---")
+    print("\n--- Example 1: Pre-termination MiddlewareTypes ---")
    async with (
        AzureCliCredential() as credential,
        AzureAIAgentClient(credential=credential).as_agent(
@@ -136,7 +136,7 @@ async def pre_termination_middleware() -> None:

 async def post_termination_middleware() -> None:
    """Demonstrate post-termination middleware that limits responses across multiple runs."""
-    print("\n--- Example 2: Post-termination Middleware ---")
+    print("\n--- Example 2: Post-termination MiddlewareTypes ---")
    async with (
        AzureCliCredential() as credential,
        AzureAIAgentClient(credential=credential).as_agent(
@@ -170,7 +170,7 @@ async def post_termination_middleware() -> None:

 async def main() -> None:
    """Example demonstrating middleware termination functionality."""
-    print("=== Middleware Termination Example ===")
+    print("=== MiddlewareTypes Termination Example ===")
    await pre_termination_middleware()
    await post_termination_middleware()

@@ -1,7 +1,8 @@
 # Copyright (c) Microsoft. All rights reserved.

 import asyncio
-from collections.abc import AsyncIterable, Awaitable, Callable
+import re
+from collections.abc import Awaitable, Callable
 from random import randint
 from typing import Annotated

@@ -9,16 +10,19 @@ from agent_framework import (
    AgentResponse,
    AgentResponseUpdate,
    AgentRunContext,
+    ChatContext,
    ChatMessage,
-    Content,
+    ChatResponse,
+    ChatResponseUpdate,
+    ResponseStream,
+    Role,
    tool,
 )
-from agent_framework.azure import AzureAIAgentClient
-from azure.identity.aio import AzureCliCredential
+from agent_framework.openai import OpenAIResponsesClient
 from pydantic import Field

 """
-Result Override with Middleware (Regular and Streaming)
+Result Override with MiddlewareTypes (Regular and Streaming)

 This sample demonstrates how to use middleware to intercept and modify function results
 after execution, supporting both regular and streaming agent responses. The example shows:
@@ -26,7 +30,7 @@ after execution, supporting both regular and streaming agent responses. The exam
 - How to execute the original function first and then modify its result
 - Replacing function outputs with custom messages or transformed data
 - Using middleware for result filtering, formatting, or enhancement
- Detecting streaming vs non-streaming execution using context.is_streaming
+- Detecting streaming vs non-streaming execution using context.stream
 - Overriding streaming results with custom async generators

 The weather override middleware lets the original weather function execute normally,
@@ -45,10 +49,8 @@ def get_weather(
    return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."


-async def weather_override_middleware(
-    context: AgentRunContext, next: Callable[[AgentRunContext], Awaitable[None]]
-) -> None:
-    """Middleware that overrides weather results for both streaming and non-streaming cases."""
+async def weather_override_middleware(context: ChatContext, next: Callable[[ChatContext], Awaitable[None]]) -> None:
+    """Chat middleware that overrides weather results for both streaming and non-streaming cases."""

    # Let the original agent execution complete first
    await next(context)
@@ -57,56 +59,159 @@ async def weather_override_middleware(
    if context.result is not None:
        # Create custom weather message
        chunks = [
-            "Weather Advisory - ",
            "due to special atmospheric conditions, ",
            "all locations are experiencing perfect weather today! ",
            "Temperature is a comfortable 22°C with gentle breezes. ",
            "Perfect day for outdoor activities!",
        ]

-        if context.is_streaming:
-            # For streaming: create an async generator that yields chunks
-            async def override_stream() -> AsyncIterable[AgentResponseUpdate]:
-                for chunk in chunks:
-                    yield AgentResponseUpdate(contents=[Content.from_text(text=chunk)])
+        if context.stream and isinstance(context.result, ResponseStream):
+            index = {"value": 0}

-            context.result = override_stream()
+            def _update_hook(update: ChatResponseUpdate) -> ChatResponseUpdate:
+                for content in update.contents or []:
+                    if not content.text:
+                        continue
+                    content.text = f"Weather Advisory: [{index['value']}] {content.text}"
+                    index["value"] += 1
+                return update
+
+            context.result.with_update_hook(_update_hook)
        else:
-            # For non-streaming: just replace with the string message
-            custom_message = "".join(chunks)
-            context.result = AgentResponse(messages=[ChatMessage("assistant", [custom_message])])
+            # For non-streaming: just replace with a new message
+            current_text = context.result.text or ""
+            custom_message = f"Weather Advisory: [0] {''.join(chunks)} Original message was: {current_text}"
+            context.result = ChatResponse(messages=[ChatMessage(role=Role.ASSISTANT, text=custom_message)])
+
+
+async def validate_weather_middleware(context: ChatContext, next: Callable[[ChatContext], Awaitable[None]]) -> None:
+    """Chat middleware that simulates result validation for both streaming and non-streaming cases."""
+    await next(context)
+
+    validation_note = "Validation: weather data verified."
+
+    if context.result is None:
+        return
+
+    if context.stream and isinstance(context.result, ResponseStream):
+
+        def _append_validation_note(response: ChatResponse) -> ChatResponse:
+            response.messages.append(ChatMessage(role=Role.ASSISTANT, text=validation_note))
+            return response
+
+        context.result.with_finalizer(_append_validation_note)
+    elif isinstance(context.result, ChatResponse):
+        context.result.messages.append(ChatMessage(role=Role.ASSISTANT, text=validation_note))
+
+
+async def agent_cleanup_middleware(
+    context: AgentRunContext, next: Callable[[AgentRunContext], Awaitable[None]]
+) -> None:
+    """Agent middleware that validates chat middleware effects and cleans the result."""
+    await next(context)
+
+    if context.result is None:
+        return
+
+    validation_note = "Validation: weather data verified."
+
+    state = {"found_prefix": False}
+
+    def _sanitize(response: AgentResponse) -> AgentResponse:
+        found_prefix = state["found_prefix"]
+        found_validation = False
+        cleaned_messages: list[ChatMessage] = []
+
+        for message in response.messages:
+            text = message.text
+            if text is None:
+                cleaned_messages.append(message)
+                continue
+
+            if validation_note in text:
+                found_validation = True
+                text = text.replace(validation_note, "").strip()
+                if not text:
+                    continue
+
+            if "Weather Advisory:" in text:
+                found_prefix = True
+                text = text.replace("Weather Advisory:", "")
+
+            text = re.sub(r"\[\d+\]\s*", "", text)
+
+            cleaned_messages.append(
+                ChatMessage(
+                    role=message.role,
+                    text=text.strip(),
+                    author_name=message.author_name,
+                    message_id=message.message_id,
+                    additional_properties=message.additional_properties,
+                    raw_representation=message.raw_representation,
+                )
+            )
+
+        if not found_prefix:
+            raise RuntimeError("Expected chat middleware prefix not found in agent response.")
+        if not found_validation:
+            raise RuntimeError("Expected validation note not found in agent response.")
+
+        cleaned_messages.append(ChatMessage(role=Role.ASSISTANT, text=" Agent: OK"))
+        response.messages = cleaned_messages
+        return response
+
+    if context.stream and isinstance(context.result, ResponseStream):
+
+        def _clean_update(update: AgentResponseUpdate) -> AgentResponseUpdate:
+            for content in update.contents or []:
+                if not content.text:
+                    continue
+                text = content.text
+                if "Weather Advisory:" in text:
+                    state["found_prefix"] = True
+                    text = text.replace("Weather Advisory:", "")
+                text = re.sub(r"\[\d+\]\s*", "", text)
+                content.text = text
+            return update
+
+        context.result.with_update_hook(_clean_update)
+        context.result.with_finalizer(_sanitize)
+    elif isinstance(context.result, AgentResponse):
+        context.result = _sanitize(context.result)


 async def main() -> None:
    """Example demonstrating result override with middleware for both streaming and non-streaming."""
-    print("=== Result Override Middleware Example ===")
+    print("=== Result Override MiddlewareTypes Example ===")

    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
-    async with (
-        AzureCliCredential() as credential,
-        AzureAIAgentClient(credential=credential).as_agent(
-            name="WeatherAgent",
-            instructions="You are a helpful weather assistant. Use the weather tool to get current conditions.",
-            tools=get_weather,
-            middleware=[weather_override_middleware],
-        ) as agent,
-    ):
-        # Non-streaming example
-        print("\n--- Non-streaming Example ---")
-        query = "What's the weather like in Seattle?"
-        print(f"User: {query}")
-        result = await agent.run(query)
-        print(f"Agent: {result}")
+    agent = OpenAIResponsesClient(
+        middleware=[validate_weather_middleware, weather_override_middleware],
+    ).as_agent(
+        name="WeatherAgent",
+        instructions="You are a helpful weather assistant. Use the weather tool to get current conditions.",
+        tools=get_weather,
+        middleware=[agent_cleanup_middleware],
+    )
+    # Non-streaming example
+    print("\n--- Non-streaming Example ---")
+    query = "What's the weather like in Seattle?"
+    print(f"User: {query}")
+    result = await agent.run(query)
+    print(f"Agent: {result}")

-        # Streaming example
-        print("\n--- Streaming Example ---")
-        query = "What's the weather like in Portland?"
-        print(f"User: {query}")
-        print("Agent: ", end="", flush=True)
-        async for chunk in agent.run_stream(query):
-            if chunk.text:
-                print(chunk.text, end="", flush=True)
+    # Streaming example
+    print("\n--- Streaming Example ---")
+    query = "What's the weather like in Portland?"
+    print(f"User: {query}")
+    print("Agent: ", end="", flush=True)
+    response = agent.run(query, stream=True)
+    async for chunk in response:
+        if chunk.text:
+            print(chunk.text, end="", flush=True)
+    print("\n")
+    print(f"Final Result: {(await response.get_final_response()).text}")


 if __name__ == "__main__":
@@ -16,9 +16,9 @@ session data, etc.) to tools and sub-agents.

 Patterns Demonstrated:

-1. **Pattern 1: Single Agent with Middleware & Closure** (Lines 130-180)
+1. **Pattern 1: Single Agent with MiddlewareTypes & Closure** (Lines 130-180)
   - Best for: Single agent with multiple tools
-   - How: Middleware stores kwargs in container, tools access via closure
+   - How: MiddlewareTypes stores kwargs in container, tools access via closure
   - Pros: Simple, explicit state management
   - Cons: Requires container instance per agent

@@ -28,7 +28,7 @@ Patterns Demonstrated:
   - Pros: Automatic, works with nested delegation, clean separation
   - Cons: None - this is the recommended pattern for hierarchical agents

-3. **Pattern 3: Mixed - Hierarchical with Middleware** (Lines 250-300)
+3. **Pattern 3: Mixed - Hierarchical with MiddlewareTypes** (Lines 250-300)
   - Best for: Complex scenarios needing both delegation and state management
   - How: Combines automatic kwargs propagation with middleware processing
   - Pros: Maximum flexibility, can transform/validate context at each level
@@ -36,7 +36,7 @@ Patterns Demonstrated:

 Key Concepts:
 - Runtime Context: Session-specific data like API tokens, user IDs, tenant info
- Middleware: Intercepts function calls to access/modify kwargs
+- MiddlewareTypes: Intercepts function calls to access/modify kwargs
 - Closure: Functions capturing variables from outer scope
 - kwargs Propagation: Automatic forwarding of runtime context through delegation chains
 """
@@ -56,7 +56,7 @@ class SessionContextContainer:
        context: FunctionInvocationContext,
        next: Callable[[FunctionInvocationContext], Awaitable[None]],
    ) -> None:
-        """Middleware that extracts runtime context from kwargs and stores in container.
+        """MiddlewareTypes that extracts runtime context from kwargs and stores in container.

        This middleware runs before tool execution and makes runtime context
        available to tools via the container instance.
@@ -68,7 +68,7 @@ class SessionContextContainer:

        # Log what we captured (for demonstration)
        if self.api_token or self.user_id:
-            print("[Middleware] Captured runtime context:")
+            print("[MiddlewareTypes] Captured runtime context:")
            print(f"  - API Token: {'[PRESENT]' if self.api_token else '[NOT PROVIDED]'}")
            print(f"  - User ID: {'[PRESENT]' if self.user_id else '[NOT PROVIDED]'}")
            print(f"  - Session Metadata Keys: {list(self.session_metadata.keys())}")
@@ -140,7 +140,7 @@ async def send_notification(
 async def pattern_1_single_agent_with_closure() -> None:
    """Pattern 1: Single agent with middleware and closure for runtime context."""
    print("\n" + "=" * 70)
-    print("PATTERN 1: Single Agent with Middleware & Closure")
+    print("PATTERN 1: Single Agent with MiddlewareTypes & Closure")
    print("=" * 70)
    print("Use case: Single agent with multiple tools sharing runtime context")
    print()
@@ -234,7 +234,7 @@ async def pattern_1_single_agent_with_closure() -> None:

    print(f"\nAgent: {result4.text}")

-    print("\n✓ Pattern 1 complete - Middleware & closure pattern works for single agents")
+    print("\n✓ Pattern 1 complete - MiddlewareTypes & closure pattern works for single agents")


 # Pattern 2: Hierarchical agents with automatic kwargs propagation
@@ -353,7 +353,7 @@ async def pattern_2_hierarchical_with_kwargs_propagation() -> None:


 class AuthContextMiddleware:
-    """Middleware that validates and transforms runtime context."""
+    """MiddlewareTypes that validates and transforms runtime context."""

    def __init__(self) -> None:
        self.validated_tokens: list[str] = []
@@ -387,7 +387,7 @@ async def protected_operation(operation: Annotated[str, Field(description="Opera
 async def pattern_3_hierarchical_with_middleware() -> None:
    """Pattern 3: Hierarchical agents with middleware processing at each level."""
    print("\n" + "=" * 70)
-    print("PATTERN 3: Hierarchical with Middleware Processing")
+    print("PATTERN 3: Hierarchical with MiddlewareTypes Processing")
    print("=" * 70)
    print("Use case: Multi-level validation/transformation of runtime context")
    print()
@@ -433,7 +433,7 @@ async def pattern_3_hierarchical_with_middleware() -> None:
    )

    print(f"\n[Validation Summary] Validated tokens: {len(auth_middleware.validated_tokens)}")
-    print("✓ Pattern 3 complete - Middleware can validate/transform context at each level")
+    print("✓ Pattern 3 complete - MiddlewareTypes can validate/transform context at each level")


 async def main() -> None:
@@ -14,7 +14,7 @@ from azure.identity.aio import AzureCliCredential
 from pydantic import Field

 """
-Shared State Function-based Middleware Example
+Shared State Function-based MiddlewareTypes Example

 This sample demonstrates how to implement function-based middleware within a class to share state.
 The example includes:
@@ -88,7 +88,7 @@ class MiddlewareContainer:

 async def main() -> None:
    """Example demonstrating shared state function-based middleware."""
-    print("=== Shared State Function-based Middleware Example ===")
+    print("=== Shared State Function-based MiddlewareTypes Example ===")

    # Create middleware container with shared state
    middleware_container = MiddlewareContainer()
@@ -14,7 +14,7 @@ from azure.identity import AzureCliCredential
 from pydantic import Field

 """
-Thread Behavior Middleware Example
+Thread Behavior MiddlewareTypes Example

 This sample demonstrates how middleware can access and track thread state across multiple agent runs.
 The example shows:
@@ -48,13 +48,13 @@ async def thread_tracking_middleware(
    context: AgentRunContext,
    next: Callable[[AgentRunContext], Awaitable[None]],
 ) -> None:
-    """Middleware that tracks and logs thread behavior across runs."""
+    """MiddlewareTypes that tracks and logs thread behavior across runs."""
    thread_messages = []
    if context.thread and context.thread.message_store:
        thread_messages = await context.thread.message_store.list_messages()

-    print(f"[Middleware pre-execution] Current input messages: {len(context.messages)}")
-    print(f"[Middleware pre-execution] Thread history messages: {len(thread_messages)}")
+    print(f"[MiddlewareTypes pre-execution] Current input messages: {len(context.messages)}")
+    print(f"[MiddlewareTypes pre-execution] Thread history messages: {len(thread_messages)}")

    # Call next to execute the agent
    await next(context)
@@ -64,12 +64,12 @@ async def thread_tracking_middleware(
    if context.thread and context.thread.message_store:
        updated_thread_messages = await context.thread.message_store.list_messages()

-    print(f"[Middleware post-execution] Updated thread messages: {len(updated_thread_messages)}")
+    print(f"[MiddlewareTypes post-execution] Updated thread messages: {len(updated_thread_messages)}")


 async def main() -> None:
    """Example demonstrating thread behavior in middleware across multiple runs."""
-    print("=== Thread Behavior Middleware Example ===")
+    print("=== Thread Behavior MiddlewareTypes Example ===")

    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.