Replace the single-pass FE-driven agent_run/agent_data flow with a
BE-orchestrated two-phase execution using LangChain tool-calling:
- Phase 1 (Triage): explores directory via new filesystem tools, matches
files to existing projects using PROJECT_TOOLS
- Phase 2 (Processing): reads files and performs CRUD per project group
with clean LLM context windows
Key changes:
- Add filesystem_agent.py with list_directory, read_file_content,
get_file_metadata tools using execute_on_client()
- Move setup journey from REST to WebSocket (journey_start/message frames)
- Add batch_runs_per_day billing limit and enforce in /trigger
- Remove deprecated agent_data/agent_complete frame handlers and queues
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Code bugs fixed:
- checkpoint_agent.py, project_agent.py, note_agent.py: add missing
'import json' (used in handle() for context serialization)
Test fixes:
- test_agents.py: add autouse ws_executor fixture that sets a fake
execute_on_client so tools can run in unit tests without a WS session
- Rewrite all TestXxxAgentTools tests: patch execute_on_client per-test,
assert on call_args (what payload was sent to the client) and on the
formatted string return value — matching actual tool behavior
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- device_ws.py: dispatch home_request/popup_request to HomeFormatter/PopupFormatter
via async tasks; each request gets a UUID request_id for frame correlation
- chat.py: remove chat_stream WS endpoint (superseded by unified device WS);
keep POST /chat REST fallback unchanged
- 5 new integration tests pass; all 22 existing device_ws tests still pass
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- 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>
Replace deprecated Pydantic v1 `class Config:` inner class with
`model_config = SettingsConfigDict(...)` to eliminate the deprecation
warning emitted on every test run.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add app/api/routes/agents.py with 11 endpoints:
GET/POST/PUT/DELETE /agents/local (local directory agent configs)
GET/POST/PUT/DELETE /agents/cloud (cloud connector agent configs)
GET /agents/catalog (hardcoded agent type catalog)
GET /agents/runs (paginated run logs with agent_id/page/limit filters)
POST /agents/{id}/run (manual trigger stub, dispatch wired in step 3.4)
- Tier-gate creation via combined local+cloud batch_active limit
- Ownership checks on all mutations (404 on mismatch)
- Cascade delete of run logs via SQLAlchemy relationship
- Register agents router in app/main.py
- Fix missing import json in app/agents/task_agent.py
- 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.
