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agent-framework/python/samples/05-end-to-end/m365-agent
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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
1e350ea22f · 2026-02-12 21:00:32 +00:00
History
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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

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

  1. Install (if not already):

    winget install agentsplayground
    
  2. Start the Python agent locally: python m365_agent_demo/app.py

  3. Start the playground: agentsplayground

  4. 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.

  1. Create an Azure Bot (choose Client Secret auth for local tunneling).

  2. Create a .env file 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
    
  3. Host dev tunnel:

    devtunnel host -p 3978 --allow-anonymous
    
  4. Set the bot Messaging endpoint to: https://<tunnel-host>/api/messages

  5. Run your local agent: python m365_agent_demo/app.py

  6. 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.

Further Reading