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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
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2026-02-12 21:00:32 +00:00
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Chat Client Examples
This folder contains simple examples demonstrating direct usage of various chat clients.
Examples
| File | Description |
|---|---|
azure_assistants_client.py |
Direct usage of Azure Assistants Client for basic chat interactions with Azure OpenAI assistants. |
azure_chat_client.py |
Direct usage of Azure Chat Client for chat interactions with Azure OpenAI models. |
azure_responses_client.py |
Direct usage of Azure Responses Client for structured response generation with Azure OpenAI models. |
chat_response_cancellation.py |
Demonstrates how to cancel chat responses during streaming, showing proper cancellation handling and cleanup. |
azure_ai_chat_client.py |
Direct usage of Azure AI Chat Client for chat interactions with Azure AI models. |
openai_assistants_client.py |
Direct usage of OpenAI Assistants Client for basic chat interactions with OpenAI assistants. |
openai_chat_client.py |
Direct usage of OpenAI Chat Client for chat interactions with OpenAI models. |
openai_responses_client.py |
Direct usage of OpenAI Responses Client for structured response generation with OpenAI models. |
custom_chat_client.py |
Demonstrates how to create custom chat clients by extending the BaseChatClient class. Shows a EchoingChatClient implementation and how to integrate it with Agent using the as_agent() method. |
Environment Variables
Depending on which client you're using, set the appropriate environment variables:
For Azure clients:
AZURE_OPENAI_ENDPOINT: Your Azure OpenAI endpointAZURE_OPENAI_CHAT_DEPLOYMENT_NAME: The name of your Azure OpenAI chat deploymentAZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: The name of your Azure OpenAI responses deployment
For Azure AI client:
AZURE_AI_PROJECT_ENDPOINT: Your Azure AI project endpointAZURE_AI_MODEL_DEPLOYMENT_NAME: The name of your model deployment
For OpenAI clients:
OPENAI_API_KEY: Your OpenAI API keyOPENAI_CHAT_MODEL_ID: The OpenAI model to use for chat clients (e.g.,gpt-4o,gpt-4o-mini,gpt-3.5-turbo)OPENAI_RESPONSES_MODEL_ID: The OpenAI model to use for responses clients (e.g.,gpt-4o,gpt-4o-mini,gpt-3.5-turbo)
For Ollama client:
OLLAMA_HOST: Your Ollama server URL (defaults tohttp://localhost:11434if not set)OLLAMA_MODEL_ID: The Ollama model to use for chat (e.g.,llama3.2,llama2,codellama)
Note
: For Ollama, ensure you have Ollama installed and running locally with at least one model downloaded. Visit https://ollama.com/ for installation instructions.