Files
agent-framework/python/samples/02-agents/middleware/override_result_with_middleware.py
Eduard van Valkenburg 1e350ea22f Python: [BREAKING] PR2 — Wire context provider pipeline, remove old types, update all consumers (#3850)
* PR2: Wire context provider pipeline and update all internal consumers

- Replace AgentThread with AgentSession across all packages
- Replace ContextProvider with BaseContextProvider across all packages
- Replace context_provider param with context_providers (Sequence)
- Replace thread= with session= in run() signatures
- Replace get_new_thread() with create_session()
- Add get_session(service_session_id) to agent interface
- DurableAgentThread -> DurableAgentSession
- Remove _notify_thread_of_new_messages from WorkflowAgent
- Wire before_run/after_run context provider pipeline in RawAgent
- Auto-inject InMemoryHistoryProvider when no providers configured

* fix: update all tests for context provider pipeline, fix lazy-loaders, remove old test files

* refactor: update all sample files for context provider pipeline (AgentThread→AgentSession, ContextProvider→BaseContextProvider)

* fix: update remaining ag-ui references (client docstring, getting_started sample)

* fix: make get_session service_session_id keyword-only to avoid confusion with session_id

* refactor: rename _RunContext.thread_messages to session_messages

* refactor: remove _threads.py, _memory.py, and old provider files; migrate devui to use plain message lists

* rename: remove _new_ prefix from test files

* refactor: rewrite SlidingWindowChatMessageStore as SlidingWindowHistoryProvider(InMemoryHistoryProvider)

* fix: read full history from session state directly instead of reaching into provider internals

* fix: update stale .pyi stubs, sample imports, and README references for new provider types

* fix: remove stale message_store, _notify_thread_of_new_messages, and session_id.key references in samples

* refactor: merge context_providers and sessions sample folders into sessions, remove aggregate_context_provider

* refactor: UserInfoMemory stores state in session.state instead of instance attributes

* feat: add Pydantic BaseModel support to session state serialization

Pydantic models stored in session.state are now automatically serialized
via model_dump() and restored via model_validate() during to_dict()/from_dict()
round-trips. Models are auto-registered on first serialization; use
register_state_type() for cold-start deserialization.

Also export register_state_type as a public API.

* fix mem0

* Update sample README links and descriptions for session terminology

- Replace 'thread' with 'session' in sample descriptions across all READMEs
- Update file links for renamed samples (mem0_sessions, redis_sessions, etc.)
- Fix Threads section → Sessions section in main samples/README.md
- Update tools, middleware, workflows, durabletask, azure_functions READMEs
- Update architecture diagrams in concepts/tools/README.md
- Update migration guides (autogen, semantic-kernel)

* Fix broken Redis README link to renamed sample

* Fix Mem0 OSS client search: pass scoping params as direct kwargs

AsyncMemory (OSS) expects user_id/agent_id/run_id as direct kwargs,
while AsyncMemoryClient (Platform) expects them in a filters dict.
Adds tests for both client types.

Port of fix from #3844 to new Mem0ContextProvider.

* Fix rebase issues: restore missing _conversation_state.py and checkpoint decode logic

- Add back _conversation_state.py (encode/decode_chat_messages) lost in rebase
- Fix on_checkpoint_restore to decode cache/conversation with decode_chat_messages
- Fix on_checkpoint_restore to use decode_checkpoint_value for pending requests
- Add tests/workflow/__init__.py for relative import support
- Fix test_agent_executor checkpoint selection (checkpoints[1] not superstep)

* Add STORES_BY_DEFAULT ClassVar to skip redundant InMemoryHistoryProvider injection

Chat clients that store history server-side by default (OpenAI Responses API,
Azure AI Agent) now declare STORES_BY_DEFAULT = True. The agent checks this
during auto-injection and skips InMemoryHistoryProvider unless the user
explicitly sets store=False.

* Fix broken markdown links in azure_ai and redis READMEs

* Fix getting-started samples to use session API instead of removed thread/ContextProvider API

* updates to workflow as agent

* fix group chat import

* Rename Thread→Session throughout, fix service_session_id propagation, remove stale AGUIThread

- Fix: Propagate conversation_id from ChatResponse back to session.service_session_id
  in both streaming and non-streaming paths in _agents.py
- Rename AgentThreadException → AgentSessionException
- Remove stale AGUIThread from ag_ui lazy-loader
- Rename use_service_thread → use_service_session in ag-ui package
- Rename test functions from *_thread_* to *_session_*
- Rename sample files from *_thread* to *_session*
- Update docstrings and comments: thread → session
- Update _mcp.py kwargs filter: add 'session' alongside 'thread'
- Fix ContinuationToken docstring example: thread=thread → session=session
- Fix _clients.py docstring: 'Agent threads' → 'Agent sessions'

* Fix broken markdown links after thread→session file renames

* fix azure ai test
2026-02-12 21:00:32 +00:00

217 lines
8.3 KiB
Python

# Copyright (c) Microsoft. All rights reserved.
import asyncio
import re
from collections.abc import Awaitable, Callable
from random import randint
from typing import Annotated
from agent_framework import (
AgentContext,
AgentResponse,
AgentResponseUpdate,
ChatContext,
ChatResponse,
ChatResponseUpdate,
Message,
ResponseStream,
Role,
tool,
)
from agent_framework.openai import OpenAIResponsesClient
from pydantic import Field
"""
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:
- 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.stream
- Overriding streaming results with custom async generators
The weather override middleware lets the original weather function execute normally,
then replaces its result with a custom "perfect weather" message. For streaming responses,
it creates a custom async generator that yields the override message in chunks.
"""
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
@tool(approval_mode="never_require")
def get_weather(
location: Annotated[str, Field(description="The location to get the weather for.")],
) -> str:
"""Get the weather for a given location."""
conditions = ["sunny", "cloudy", "rainy", "stormy"]
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: ChatContext, call_next: Callable[[], Awaitable[None]]) -> None:
"""Chat middleware that overrides weather results for both streaming and non-streaming cases."""
# Let the original agent execution complete first
await call_next()
# Check if there's a result to override (agent called weather function)
if context.result is not None:
# Create custom weather message
chunks = [
"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.stream and isinstance(context.result, ResponseStream):
index = {"value": 0}
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_transform_hook(_update_hook)
else:
# For non-streaming: just replace with a new message
current_text = context.result.text if isinstance(context.result, ChatResponse) else ""
custom_message = f"Weather Advisory: [0] {''.join(chunks)} Original message was: {current_text}"
context.result = ChatResponse(messages=[Message(role=Role.ASSISTANT, text=custom_message)])
async def validate_weather_middleware(context: ChatContext, call_next: Callable[[], Awaitable[None]]) -> None:
"""Chat middleware that simulates result validation for both streaming and non-streaming cases."""
await call_next()
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(Message(role=Role.ASSISTANT, text=validation_note))
return response
context.result.with_finalizer(_append_validation_note)
elif isinstance(context.result, ChatResponse):
context.result.messages.append(Message(role=Role.ASSISTANT, text=validation_note))
async def agent_cleanup_middleware(context: AgentContext, call_next: Callable[[], Awaitable[None]]) -> None:
"""Agent middleware that validates chat middleware effects and cleans the result."""
await call_next()
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[Message] = []
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(
Message(
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(Message(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_transform_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 MiddlewareTypes Example ===")
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
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)
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__":
asyncio.run(main())