* 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
Sample Integration Tests
Integration tests that validate the Durable Agent Framework samples by running them against a Durable Task Scheduler (DTS) instance.
Setup
1. Create .env file
Copy .env.example to .env and fill in your Azure credentials:
cp .env.example .env
Required variables:
AZURE_OPENAI_ENDPOINTAZURE_OPENAI_CHAT_DEPLOYMENT_NAMEAZURE_OPENAI_API_KEY(optional if using Azure CLI authentication)RUN_INTEGRATION_TESTS(set totrue)ENDPOINT(default: http://localhost:8080)TASKHUB(default: default)
Optional variables (for streaming tests):
REDIS_CONNECTION_STRING(default: redis://localhost:6379)REDIS_STREAM_TTL_MINUTES(default: 10)
2. Start required services
Durable Task Scheduler:
docker run -d --name dts-emulator -p 8080:8080 -p 8082:8082 mcr.microsoft.com/dts/dts-emulator:latest
- Port 8080: gRPC endpoint (used by tests)
- Port 8082: Web dashboard (optional, for monitoring)
Redis (for streaming tests):
docker run -d --name redis -p 6379:6379 redis:latest
- Port 6379: Redis server endpoint
Running Tests
The tests automatically start and stop worker processes for each sample.
Run all sample tests
uv run pytest packages/durabletask/tests/integration_tests -v
Run specific sample
uv run pytest packages/durabletask/tests/integration_tests/test_01_single_agent.py -v
Run with verbose output
uv run pytest packages/durabletask/tests/integration_tests -sv
How It Works
Each test file uses pytest markers to automatically configure and start the worker process:
pytestmark = [
pytest.mark.sample("03_single_agent_streaming"),
pytest.mark.integration_test,
pytest.mark.requires_azure_openai,
pytest.mark.requires_dts,
pytest.mark.requires_redis,
]
Troubleshooting
Tests are skipped:
Ensure RUN_INTEGRATION_TESTS=true is set in your .env file.
DTS connection failed:
Check that the DTS emulator container is running: docker ps | grep dts-emulator
Redis connection failed:
Check that Redis is running: docker ps | grep redis
Missing environment variables:
Ensure your .env file contains all required variables from .env.example.
Tests timeout: Check that Azure OpenAI credentials are valid and the service is accessible.
If you see "DTS emulator is not available":
- Ensure Docker container is running:
docker ps | grep dts-emulator - Check port 8080 is not in use by another process
- Restart the container if needed
Azure OpenAI Errors
If you see authentication or deployment errors:
- Verify your
AZURE_OPENAI_ENDPOINTis correct - Confirm
AZURE_OPENAI_CHAT_DEPLOYMENT_NAMEmatches your deployment - If using API key, check
AZURE_OPENAI_API_KEYis valid - If using Azure CLI, ensure you're logged in:
az login
CI/CD
For automated testing in CI/CD pipelines:
- Use Docker Compose to start DTS emulator
- Set environment variables via CI/CD secrets
- Run tests with appropriate markers:
pytest -m integration_test