This FAQ reflects the current repository state.
The strongest parts today are the runtime platform pieces and the stateful research foundation:
- app bootstrapping and control-plane APIs
- channels, providers, skills, MCP, automation, and the console
- project/workflow/task state
- claim/evidence graph
- experiment tracking, result-bundle ingestion, and blocker remediation
What is still incomplete is the higher-level research quality layer: evidence matrix scoring, stronger claim validators, richer external execution adapters, and submission packaging.
Provider types in code today are openai, anthropic, gemini, ollama, dashscope, deepseek, minimax, other, and custom.
console, telegram, discord, dingtalk, feishu, imessage, qq, and voice.
- working data:
~/.researchclaw - secrets:
~/.researchclaw.secret
The secret dir stores envs.json and providers.json.
The research workflow state is stored under ~/.researchclaw/research/state.json unless you override the research path.
The backend can start without prebuilt console assets. Build them once:
cd console
npm install
npm run buildSet RESEARCHCLAW_AUTOMATION_TOKEN on the server. Requests must send the same token via Authorization: Bearer <token>, x-researchclaw-token, or x-researchclaw-automation-token.
If the package is not installed editable yet, Python may not find researchclaw. Either run:
pip install -e ".[dev]"or:
PYTHONPATH=src pytest -qYes. The current console includes a dedicated Research page with:
- project dashboards
- workflow execution
- execution health
- recent blockers
- remediation drill-down and batch actions