* 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
Microsoft Agent Framework Python Weather Agent sample (M365 Agents SDK)
This sample demonstrates a simple Weather Forecast Agent built with the Python Microsoft Agent Framework, exposed through the Microsoft 365 Agents SDK compatible endpoints. The agent accepts natural language requests for a weather forecast and responds with a textual answer. It supports multi-turn conversations to gather required information.
Prerequisites
- Python 3.11+
- uv for fast dependency management
- devtunnel
- Microsoft 365 Agents Toolkit for playground/testing
- Access to OpenAI or Azure OpenAI with a model like
gpt-4o-mini
Configuration
Set the following environment variables:
# Common
export PORT=3978
export USE_ANONYMOUS_MODE=True # set to false if using auth
# OpenAI
export OPENAI_API_KEY="..."
export OPENAI_CHAT_MODEL_ID="..."
Installing Dependencies
From the repository root or the sample folder:
uv sync
Running the Agent Locally
# Activate environment first if not already
source .venv/bin/activate # (Windows PowerShell: .venv\Scripts\Activate.ps1)
# Run the weather agent demo
python m365_agent_demo/app.py
The agent starts on http://localhost:3978. Health check: GET /api/health.
QuickStart using Agents Playground
-
Install (if not already):
winget install agentsplayground -
Start the Python agent locally:
python m365_agent_demo/app.py -
Start the playground:
agentsplayground -
Chat with the Weather Agent.
QuickStart using WebChat (Azure Bot)
To test via WebChat you can provision an Azure Bot and point its messaging endpoint to your agent.
-
Create an Azure Bot (choose Client Secret auth for local tunneling).
-
Create a
.envfile in this sample folder with the following (replace placeholders):# Authentication / Agentic configuration USE_ANONYMOUS_MODE=False CONNECTIONS__SERVICE_CONNECTION__SETTINGS__CLIENTID="<client-id>" CONNECTIONS__SERVICE_CONNECTION__SETTINGS__CLIENTSECRET="<client-secret>" CONNECTIONS__SERVICE_CONNECTION__SETTINGS__TENANTID="<tenant-id>" CONNECTIONS__SERVICE_CONNECTION__SETTINGS__SCOPES=https://graph.microsoft.com/.default AGENTAPPLICATION__USERAUTHORIZATION__HANDLERS__AGENTIC__SETTINGS__TYPE=AgenticUserAuthorization AGENTAPPLICATION__USERAUTHORIZATION__HANDLERS__AGENTIC__SETTINGS__SCOPES=https://graph.microsoft.com/.default AGENTAPPLICATION__USERAUTHORIZATION__HANDLERS__AGENTIC__SETTINGS__ALTERNATEBLUEPRINTCONNECTIONNAME=https://graph.microsoft.com/.default -
Host dev tunnel:
devtunnel host -p 3978 --allow-anonymous -
Set the bot Messaging endpoint to:
https://<tunnel-host>/api/messages -
Run your local agent:
python m365_agent_demo/app.py -
Use "Test in WebChat" in Azure Portal.
Federated Credentials or Managed Identity auth types typically require deployment to Azure App Service instead of tunneling.
Troubleshooting
- 404 on
/api/messages: Ensure you are POSTing and using the correct tunnel URL. - Empty responses: Check model key / quota and ensure environment variables are set.
- Auth errors when anonymous disabled: Validate MSAL config matches your Azure Bot registration.