Files
agent-framework/python/samples/02-agents/conversations/redis_history_provider.py
Eduard van Valkenburg 67ce1baecf Python: fix reasoning model workflow handoff and history serialization (#4083)
* fix: strip function_call and text_reasoning from cross-agent workflow handoff

When a reasoning model (e.g. gpt-5-mini) runs as Agent 1 in a workflow, its
response includes text_reasoning items (with server-scoped IDs like rs_XXXX)
and function_call items. Forwarding these to Agent 2 in a fresh conversation
caused API errors because the reasoning/call IDs are scoped to the original
stored response context.

Changes:
- Strip 'function_call', 'text_reasoning', 'function_approval_request', and
  'function_approval_response' from handoff messages in _agent_executor.py
- Keep 'function_result' so the actual tool output content is preserved for
  the next agent's context
- Update unit tests to reflect that function_result messages survive handoff
  (messages grow from 2→3: user, tool(result), assistant(summary))
- Fix incorrect test assertions in test_function_invocation_stop_clears_*
  that assumed the client layer updates session.service_session_id
- Also fixed _extract_function_calls to search all messages with call_id
  deduplication, and the error-limit stop path to submit function_call_output
  items before halting (via tool_choice=none cleanup call)

Relates to: https://github.com/microsoft/agent-framework/issues/4047

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fix: reasoning model workflow handoff and history serialization

Fixes multiple related issues when using reasoning models (gpt-5-mini,
gpt-5.2) in multi-agent workflows that chain agents via from_response
or replay full conversation history via AgentExecutorRequest.

## Reasoning items always emitted on output_item.added

When a reasoning model produces encrypted or hidden reasoning (no
visible text), the Responses API still fires a reasoning output item
without any reasoning_text.delta events. Previously no text_reasoning
Content was emitted in that case, making it invisible to downstream
logic. Both the non-streaming (_parse_response_from_openai) and
streaming (output_item.added) paths now always emit at least one
text_reasoning Content — with empty text if no content is available —
so co-occurrence detection and serialization guards work reliably.

## Reasoning items only serialized when paired with a function_call

The Responses API only accepts reasoning items in input when they
directly preceded a function_call in the original response. Sending a
reasoning item that preceded a text response (no tool call) causes:
  "reasoning was provided without its required following item"
_prepare_message_for_openai now checks has_function_call per message
and skips text_reasoning serialization when there is no accompanying
function_call.

## summary field is an array, not an object

The reasoning item summary field sent to the Responses API must be an
array of objects ([{"type": "summary_text", "text": ...}]), not a
single object. Fixed _prepare_content_for_openai accordingly.

## service_session_id cleared when explicit history is provided

When a workflow coordinator replays a full conversation (including
function calls from a previous agent run) back to an executor via
AgentExecutorRequest or from_response, the executor's session still
held a service_session_id (previous_response_id) from the prior run.
The API then received the same function-call items twice — once from
previous_response_id (server-stored) and once from the explicit input —
causing: "Duplicate item found with id fc_...".

AgentExecutor.run (when should_respond=True) and from_response now
reset self._session.service_session_id = None before running so that
explicit input is the sole source of conversation context.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* small improvements in text reasoning

* refactor: add reset_service_session to AgentExecutorRequest for explicit history replay

Replace the implicit 'always clear service_session_id when should_respond=True'
with an explicit opt-in field on AgentExecutorRequest.

The old approach used should_respond=True as a proxy for 'full history replay',
but that conflates two distinct intents:
- Orchestrations group chat sends should_respond=True with an empty/single-message
  list (not a full replay) — unnecessarily clearing service_session_id.
- HITL / feedback coordinators send the full prior conversation and truly need
  a fresh service session ID to avoid duplicate-item API errors.

Changes:
- Add AgentExecutorRequest.reset_service_session: bool = False
- AgentExecutor.run only clears service_session_id when this flag is True
- AgentExecutor.from_response unchanged (always clears; always full conversation)
- Set reset_service_session=True in all full-history-replay call sites:
  agents_with_HITL.py, azure_chat_agents_tool_calls_with_feedback.py,
  autogen-migration round-robin coordinator, tau2 runner
- Update _FullHistoryReplayCoordinator test helper to pass the flag

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* comment update

* fixes from feedback

* fix test

* reverted changes to agent executor

* fix: remove reset_service_session from tau2 runner

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* two other reverts

* fix sample

---------

Co-authored-by: Giles Odigwe <79032838+giles17@users.noreply.github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-02-19 21:02:20 +00:00

266 lines
8.2 KiB
Python

# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from uuid import uuid4
from agent_framework import AgentSession
from agent_framework.openai import OpenAIChatClient
from agent_framework.redis import RedisHistoryProvider
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
"""
Redis History Provider Session Example
This sample demonstrates how to use Redis as a history provider for session
management, enabling persistent conversation history storage across sessions
with Redis as the backend data store.
"""
# Default Redis URL for local Redis Stack.
# Override via the REDIS_URL environment variable for remote or authenticated instances.
REDIS_URL = os.getenv("REDIS_URL", "redis://localhost:6379")
async def example_manual_memory_store() -> None:
"""Basic example of using Redis history provider."""
print("=== Basic Redis History Provider Example ===")
# Create Redis history provider
redis_provider = RedisHistoryProvider(
source_id="redis_basic_chat",
redis_url=REDIS_URL,
)
# Create agent with Redis history provider
agent = OpenAIChatClient().as_agent(
name="RedisBot",
instructions="You are a helpful assistant that remembers our conversation using Redis.",
context_providers=[redis_provider],
)
# Create session
session = agent.create_session()
# Have a conversation
print("\n--- Starting conversation ---")
query1 = "Hello! My name is Alice and I love pizza."
print(f"User: {query1}")
response1 = await agent.run(query1, session=session)
print(f"Agent: {response1.text}")
query2 = "What do you remember about me?"
print(f"User: {query2}")
response2 = await agent.run(query2, session=session)
print(f"Agent: {response2.text}")
print("Done\n")
async def example_user_session_management() -> None:
"""Example of managing user sessions with Redis."""
print("=== User Session Management Example ===")
user_id = "alice_123"
session_id = f"session_{uuid4()}"
# Create Redis history provider for specific user session
redis_provider = RedisHistoryProvider(
source_id=f"redis_{user_id}",
redis_url=REDIS_URL,
max_messages=10, # Keep only last 10 messages
)
# Create agent with history provider
agent = OpenAIChatClient().as_agent(
name="SessionBot",
instructions="You are a helpful assistant. Keep track of user preferences.",
context_providers=[redis_provider],
)
# Start conversation
session = agent.create_session(session_id=session_id)
print(f"Started session for user {user_id}")
# Simulate conversation
queries = [
"Hi, I'm Alice and I prefer vegetarian food.",
"What restaurants would you recommend?",
"I also love Italian cuisine.",
"Can you remember my food preferences?",
]
for i, query in enumerate(queries, 1):
print(f"\n--- Message {i} ---")
print(f"User: {query}")
response = await agent.run(query, session=session)
print(f"Agent: {response.text}")
print("Done\n")
async def example_conversation_persistence() -> None:
"""Example of conversation persistence across application restarts."""
print("=== Conversation Persistence Example ===")
# Phase 1: Start conversation
print("--- Phase 1: Starting conversation ---")
redis_provider = RedisHistoryProvider(
source_id="redis_persistent_chat",
redis_url=REDIS_URL,
)
agent = OpenAIChatClient().as_agent(
name="PersistentBot",
instructions="You are a helpful assistant. Remember our conversation history.",
context_providers=[redis_provider],
)
session = agent.create_session()
# Start conversation
query1 = "Hello! I'm working on a Python project about machine learning."
print(f"User: {query1}")
response1 = await agent.run(query1, session=session)
print(f"Agent: {response1.text}")
query2 = "I'm specifically interested in neural networks."
print(f"User: {query2}")
response2 = await agent.run(query2, session=session)
print(f"Agent: {response2.text}")
# Serialize session state
serialized = session.to_dict()
# Phase 2: Resume conversation (simulating app restart)
print("\n--- Phase 2: Resuming conversation (after 'restart') ---")
restored_session = AgentSession.from_dict(serialized)
# Continue conversation - agent should remember context
query3 = "What was I working on before?"
print(f"User: {query3}")
response3 = await agent.run(query3, session=restored_session)
print(f"Agent: {response3.text}")
query4 = "Can you suggest some Python libraries for neural networks?"
print(f"User: {query4}")
response4 = await agent.run(query4, session=restored_session)
print(f"Agent: {response4.text}")
print("Done\n")
async def example_session_serialization() -> None:
"""Example of session state serialization and deserialization."""
print("=== Session Serialization Example ===")
redis_provider = RedisHistoryProvider(
source_id="redis_serialization_chat",
redis_url=REDIS_URL,
)
agent = OpenAIChatClient().as_agent(
name="SerializationBot",
instructions="You are a helpful assistant.",
context_providers=[redis_provider],
)
session = agent.create_session()
# Have initial conversation
print("--- Initial conversation ---")
query1 = "Hello! I'm testing serialization."
print(f"User: {query1}")
response1 = await agent.run(query1, session=session)
print(f"Agent: {response1.text}")
# Serialize session state
serialized = session.to_dict()
print(f"\nSerialized session state: {serialized}")
# Deserialize session state (simulating loading from database/file)
print("\n--- Deserializing session state ---")
restored_session = AgentSession.from_dict(serialized)
# Continue conversation with restored session
query2 = "Do you remember what I said about testing?"
print(f"User: {query2}")
response2 = await agent.run(query2, session=restored_session)
print(f"Agent: {response2.text}")
print("Done\n")
async def example_message_limits() -> None:
"""Example of automatic message trimming with limits."""
print("=== Message Limits Example ===")
# Create provider with small message limit
redis_provider = RedisHistoryProvider(
source_id="redis_limited_chat",
redis_url=REDIS_URL,
max_messages=3, # Keep only 3 most recent messages
)
agent = OpenAIChatClient().as_agent(
name="LimitBot",
instructions="You are a helpful assistant with limited memory.",
context_providers=[redis_provider],
)
session = agent.create_session()
# Send multiple messages to test trimming
messages = [
"Message 1: Hello!",
"Message 2: How are you?",
"Message 3: What's the weather?",
"Message 4: Tell me a joke.",
"Message 5: This should trigger trimming.",
]
for i, query in enumerate(messages, 1):
print(f"\n--- Sending message {i} ---")
print(f"User: {query}")
response = await agent.run(query, session=session)
print(f"Agent: {response.text}")
print("Done\n")
async def main() -> None:
"""Run all Redis history provider examples."""
print("Redis History Provider Examples")
print("=" * 50)
print("Prerequisites:")
print("- Redis server running (set REDIS_URL env var or default localhost:6379)")
print("- OPENAI_API_KEY environment variable set")
print("=" * 50)
# Check prerequisites
if not os.getenv("OPENAI_API_KEY"):
print("ERROR: OPENAI_API_KEY environment variable not set")
return
try:
# Run all examples
await example_manual_memory_store()
await example_user_session_management()
await example_conversation_persistence()
await example_session_serialization()
await example_message_limits()
print("All examples completed successfully!")
except Exception as e:
print(f"Error running examples: {e}")
raise
if __name__ == "__main__":
asyncio.run(main())