DashClaw sits between your agents and their actions. Every action goes through a policy check before it runs. The agent waits until the decision comes back.
What it does:
- Guard policies defined in YAML. Conditions, thresholds, actions (allow / block / require_approval). Versioned, testable in CI via simulation endpoint.
- Decision recording to Postgres before the action fires. Action, context, assumptions, policy applied, outcome. All org-scoped.
- Human-in-the-loop over SSE with Redis broker. 10-min replay window so approvals survive disconnects. Agent holds pending until decision or timeout.
- CLI approval channel. `dashclaw approve <id>` from a second terminal. No browser needed.
- Node and Python SDKs. Wraps existing calls. No framework lock-in.
How the guard check works in practice:
const decision = await claw.guard({ action_type: 'deploy', risk_score: 90 });
if (decision.decision === 'block') return;
Works with LangChain, CrewAI, OpenAI tools, Anthropic tools, Autogen, or custom agents.
- Quick Demo w/Docker: npx dashclaw-demo
- End to End Demo: cd examples/openai-governed-agent && npm install && node index.js
That runs a real agent, attempts a high-risk action, DashClaw intercepts it, and waits for your approval in the browser. Or you can run npm install -g @dashclaw/cli and approve it in the terminal via dashclaw approve <actionId>
Stack: Next.js 15 (JS, no TS), Neon Postgres, Redis, NextAuth, Vercel. MIT licensed.
Honest limitations: guard policies handle threshold and allowlist rules well. Contextual policies that reason over full conversation history are not there yet. Thinking about that as a separate eval layer rather than baking it into the guard runtime. Features in the Labs category are still in development, currently building out integrations and starting with Discord/Slack.
GitHub: https://github.com/ucsandman/DashClaw Demo (fixture data, no login): https://dashclaw.io
Happy to go deep on any part of the architecture.