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Eduard van Valkenburg
1e350ea22f
* 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
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These are common instructions for setting up your environment for every sample in this directory. These samples illustrate the Durable extensibility for Agent Framework running in Azure Functions.
All of these samples are set up to run in Azure Functions. Azure Functions has a local development tool called CoreTools which we will set up to run these samples locally.
Environment Setup
1. Install dependencies and create appropriate services
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Install Azure Functions Core Tools 4.x
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Install Azurite storage emulator
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Create an Azure OpenAI resource. Note the Azure OpenAI endpoint, deployment name, and the key (or ensure you can authenticate with
AzureCliCredential). -
Install a tool to execute HTTP calls, for example the REST Client extension
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[Optionally] Create an Azure Function Python app to later deploy your app to Azure if you so desire.
2. Create and activate a virtual environment
Windows (PowerShell):
python -m venv .venv
.venv\Scripts\Activate.ps1
Linux/macOS:
python -m venv .venv
source .venv/bin/activate
3. Running the samples
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Inside each sample:
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Install Python dependencies – from the sample directory, run
pip install -r requirements.txt(or the equivalent in your active virtual environment). -
Copy
local.settings.json.templatetolocal.settings.json, then updateAZURE_OPENAI_ENDPOINTandAZURE_OPENAI_CHAT_DEPLOYMENT_NAMEfor Azure OpenAI authentication. The samples useAzureCliCredentialby default, so ensure you're logged in viaaz login.- Alternatively, you can use API key authentication by setting
AZURE_OPENAI_API_KEYand updating the code to useAzureOpenAIChatClient()without the credential parameter. - Keep
TASKHUB_NAMEset todefaultunless you plan to change the durable task hub name.
- Alternatively, you can use API key authentication by setting
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Run the command
func startfrom the root of the sample -
Follow each sample's README for scenario-specific steps, and use its
demo.httpfile (or provided curl examples) to trigger the hosted HTTP endpoints.
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