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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

212 lines
7.1 KiB
Python

# Copyright (c) Microsoft. All rights reserved.
import asyncio
from collections.abc import AsyncIterable
from typing import Any
from agent_framework import (
AgentResponse,
AgentResponseUpdate,
AgentSession,
BaseAgent,
Content,
Message,
Role,
normalize_messages,
)
"""
Custom Agent Implementation Example
This sample demonstrates implementing a custom agent by extending BaseAgent class,
showing the minimal requirements for both streaming and non-streaming responses.
"""
class EchoAgent(BaseAgent):
"""A simple custom agent that echoes user messages with a prefix.
This demonstrates how to create a fully custom agent by extending BaseAgent
and implementing the required run() method with stream support.
"""
echo_prefix: str = "Echo: "
def __init__(
self,
*,
name: str | None = None,
description: str | None = None,
echo_prefix: str = "Echo: ",
**kwargs: Any,
) -> None:
"""Initialize the EchoAgent.
Args:
name: The name of the agent.
description: The description of the agent.
echo_prefix: The prefix to add to echoed messages.
**kwargs: Additional keyword arguments passed to BaseAgent.
"""
super().__init__(
name=name,
description=description,
echo_prefix=echo_prefix, # type: ignore
**kwargs,
)
def run(
self,
messages: str | Message | list[str] | list[Message] | None = None,
*,
stream: bool = False,
session: AgentSession | None = None,
**kwargs: Any,
) -> "AsyncIterable[AgentResponseUpdate] | asyncio.Future[AgentResponse]":
"""Execute the agent and return a response.
Args:
messages: The message(s) to process.
stream: If True, return an async iterable of updates. If False, return an awaitable response.
session: The conversation session (optional).
**kwargs: Additional keyword arguments.
Returns:
When stream=False: An awaitable AgentResponse containing the agent's reply.
When stream=True: An async iterable of AgentResponseUpdate objects.
"""
if stream:
return self._run_stream(messages=messages, session=session, **kwargs)
return self._run(messages=messages, session=session, **kwargs)
async def _run(
self,
messages: str | Message | list[str] | list[Message] | None = None,
*,
session: AgentSession | None = None,
**kwargs: Any,
) -> AgentResponse:
"""Non-streaming implementation."""
# Normalize input messages to a list
normalized_messages = normalize_messages(messages)
if not normalized_messages:
response_message = Message(
role=Role.ASSISTANT,
contents=[Content.from_text(text="Hello! I'm a custom echo agent. Send me a message and I'll echo it back.")],
)
else:
# For simplicity, echo the last user message
last_message = normalized_messages[-1]
if last_message.text:
echo_text = f"{self.echo_prefix}{last_message.text}"
else:
echo_text = f"{self.echo_prefix}[Non-text message received]"
response_message = Message(role=Role.ASSISTANT, contents=[Content.from_text(text=echo_text)])
# Store messages in session state if provided
if session is not None:
stored = session.state.setdefault("memory", {}).setdefault("messages", [])
stored.extend(normalized_messages)
stored.append(response_message)
return AgentResponse(messages=[response_message])
async def _run_stream(
self,
messages: str | Message | list[str] | list[Message] | None = None,
*,
session: AgentSession | None = None,
**kwargs: Any,
) -> AsyncIterable[AgentResponseUpdate]:
"""Streaming implementation."""
# Normalize input messages to a list
normalized_messages = normalize_messages(messages)
if not normalized_messages:
response_text = "Hello! I'm a custom echo agent. Send me a message and I'll echo it back."
else:
# For simplicity, echo the last user message
last_message = normalized_messages[-1]
if last_message.text:
response_text = f"{self.echo_prefix}{last_message.text}"
else:
response_text = f"{self.echo_prefix}[Non-text message received]"
# Simulate streaming by yielding the response word by word
words = response_text.split()
for i, word in enumerate(words):
# Add space before word except for the first one
chunk_text = f" {word}" if i > 0 else word
yield AgentResponseUpdate(
contents=[Content.from_text(text=chunk_text)],
role=Role.ASSISTANT,
)
# Small delay to simulate streaming
await asyncio.sleep(0.1)
# Store messages in session state if provided
if session is not None:
complete_response = Message(role=Role.ASSISTANT, contents=[Content.from_text(text=response_text)])
stored = session.state.setdefault("memory", {}).setdefault("messages", [])
stored.extend(normalized_messages)
stored.append(complete_response)
async def main() -> None:
"""Demonstrates how to use the custom EchoAgent."""
print("=== Custom Agent Example ===\n")
# Create EchoAgent
print("--- EchoAgent Example ---")
echo_agent = EchoAgent(
name="EchoBot", description="A simple agent that echoes messages with a prefix", echo_prefix="🔊 Echo: "
)
# Test non-streaming
print(f"Agent Name: {echo_agent.name}")
print(f"Agent ID: {echo_agent.id}")
query = "Hello, custom agent!"
print(f"\nUser: {query}")
result = await echo_agent.run(query)
print(f"Agent: {result.messages[0].text}")
# Test streaming
query2 = "This is a streaming test"
print(f"\nUser: {query2}")
print("Agent: ", end="", flush=True)
async for chunk in echo_agent.run(query2, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print()
# Example with sessions
print("\n--- Using Custom Agent with Session ---")
session = echo_agent.create_session()
# First message
result1 = await echo_agent.run("First message", session=session)
print("User: First message")
print(f"Agent: {result1.messages[0].text}")
# Second message in same thread
result2 = await echo_agent.run("Second message", session=session)
print("User: Second message")
print(f"Agent: {result2.messages[0].text}")
# Check conversation history
memory_state = session.state.get("memory", {})
messages = memory_state.get("messages", [])
if messages:
print(f"\nSession contains {len(messages)} messages in history")
else:
print("\nSession has no messages stored")
if __name__ == "__main__":
asyncio.run(main())