- orchestrate_v3(user_id, message, context): classifies intent, returns
(agent_name, agent_instance) — caller drives execution
- orchestrate_v3_stream(user_id, message, context): yields (agent_name, token)
pairs; first yield is always (agent_name, "") as a domain-detection signal
- ChatAgent.handle_stream(): default implementation yields handle() result as
one chunk; subclasses override for true token-level streaming
- Fix stale test_orchestrator.py assertions that expected a JSON final frame
that orchestrate_stream never emitted
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- ChatAgent.__init__: adds tool_results: list[dict] = []
- _tool_loop: wraps execution in a result collector; populates
self.tool_results with raw execute_on_client dicts after each run
- _tool_loop_stream: streaming variant — uses ainvoke for tool-call
iterations, llm.astream() for the final answer; same result capture
- ws_context.py: adds _tool_result_collector ContextVar +
set/clear helpers; execute_on_client appends to collector when set
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Introduced new API keys for Anthropic and Google in .env.example and settings.py
- Updated llm.py to retrieve API keys directly from settings
- Modified deploy.yaml to streamline code checkout and improve deployment process
- Replaced direct instantiation of ChatOpenAI with a centralized get_llm function in CheckpointAgent, NoteAgent, ProjectAgent, and TaskAgent.
- Introduced a new llm.py module to handle LLM model instantiation and API key management.
- Updated settings.py to include LLM_MODEL and LLM_ROUTER_MODEL configurations.
- Modified orchestrator.py to use get_router_llm for intent classification.
- Updated requirements.txt to include litellm for LLM management.
- Adjusted tests to mock get_llm instead of ChatOpenAI directly.
- Updated `TestModuleSingletons` in `test_execution_plan.py` to reflect new agent templates and playbook names.
- Changed assertions in playbook tests to match updated templates and agents.
- Introduced `test_storage.py` to cover the storage layer, including encryption, BlobStore, and VectorStore functionalities.
- Added tests for S3 interactions, ensuring upload, download, delete, and list operations work as expected.
- Implemented mock tests for Pinecone and Qdrant vector stores to validate upsert, search, and delete operations.
