DashClaw

<div align="center">
  <img src="public/images/logo-circular.png" alt="DashClaw" width="240" />
  <h1>DashClaw</h1>
  <p><strong>Decision Infrastructure for AI agents.</strong></p>
  <p>Stop agents before they make expensive mistakes.</p>
  <p><sub>Try it in 10 seconds</sub></p>
  <pre><code>npx dashclaw-demo</code></pre>
  <p><sub>No setup. Opens Decision Replay automatically.</sub></p>

  <img src="public/images/demo-gif2.gif" alt="DashClaw Demo" width="1000" />

<br />
<p><strong>Works with:</strong></p>
<p>LangChain • CrewAI • OpenClaw • OpenAI • Anthropic • AutoGen • Claude Code • Codex • Gemini CLI • Custom agents</p>
  <br />
  <p>Intercept decisions. Enforce policies. Record evidence.</p>
  <br />
  <p><strong>Agent &rarr; DashClaw &rarr; External Systems</strong></p>
  <p>DashClaw sits between your agents and your external systems. It evaluates policies before an agent action executes and records verifiable evidence of every decision.</p>
  <br />
  <p><a href="https://dashclaw.io/demo">View Live Demo</a></p>

  <a href="https://dashclaw.io"><img src="https://img.shields.io/badge/website-dashclaw.io-orange?style=flat-square" alt="Website" /></a>
  <a href="https://dashclaw.io/docs"><img src="https://img.shields.io/badge/docs-SDK%20%26%20API-blue?style=flat-square" alt="Docs" /></a>
  <a href="https://github.com/ucsandman/DashClaw/stargazers"><img src="https://img.shields.io/github/stars/ucsandman/DashClaw?style=flat-square&color=yellow" alt="GitHub stars" /></a>
  <a href="https://github.com/ucsandman/DashClaw/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-MIT-green?style=flat-square" alt="License" /></a>
  <a href="https://www.npmjs.com/package/dashclaw"><img src="https://img.shields.io/npm/v/dashclaw?style=flat-square&color=orange" alt="npm" /></a>
  <a href="https://pypi.org/project/dashclaw/"><img src="https://img.shields.io/pypi/v/dashclaw?style=flat-square&color=orange" alt="PyPI" /></a>

</div>

<br />

## Deploy

[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Fucsandman%2FDashClaw&env=DATABASE_URL,DASHCLAW_API_KEY,ENCRYPTION_KEY,NEXTAUTH_SECRET,NEXTAUTH_URL,CRON_SECRET,DASHCLAW_LOCAL_ADMIN_PASSWORD&envDescription=Required%20DashClaw%20configuration.%20See%20.env.example%20for%20details.&envLink=https%3A%2F%2Fgithub.com%2Fucsandman%2FDashClaw%2Fblob%2Fmain%2F.env.example&project-name=my-dashclaw&repository-name=my-dashclaw&products=%5B%7B%22type%22%3A%22integration%22%2C%22integrationSlug%22%3A%22neon%22%2C%22productSlug%22%3A%22neon%22%2C%22protocol%22%3A%22storage%22%7D%5D&skippable-integrations=1)

**$0 to deploy** — Vercel free tier + Neon free tier. Click the button, add the Neon integration when prompted, fill in the environment variables, and you're live. Database schema is created automatically during the build — no manual migration step required.

### After deploy

1. **Open your app** — Visit `https://your-app.vercel.app` and sign in.
2. **Copy the snippet** — Mission Control shows a ready-to-run code example with your API key and base URL pre-filled.
3. **Run it** — `node --env-file=.env demo.js` and watch governance happen.

#### Optional

- **Live decision stream** — Create a free [Upstash Redis](https://upstash.com) instance and add `UPSTASH_REDIS_REST_URL` and `UPSTASH_REDIS_REST_TOKEN` in Vercel env vars. Without this, Mission Control uses in-memory events (fine for getting started, but won't persist across serverless invocations).
- **Verify at /setup** — Open `https://your-app.vercel.app/setup` to confirm all systems are green.

---

## Connect Your Agent

**Three ways to get governed — pick what fits your workflow:**

### Option 1: Install the skill (30 seconds)

Give your AI agent the `dashclaw-platform-intelligence` skill and it instruments itself — no code changes, no manual wiring. The agent registers with DashClaw, sets up guard checks, records decisions, and starts tracking assumptions automatically.

```bash
# Download the skill into your agent's skill directory
cp -r public/downloads/dashclaw-platform-intelligence .claude/skills/
```

Set two environment variables and your agent is governed on its next run:
```bash
export DASHCLAW_BASE_URL=https://your-dashclaw-instance.com
export DASHCLAW_API_KEY=your_api_key
```

This is the fastest path. We gave our own OpenClaw agent the skill and it put itself on DashClaw in one conversation.

### Option 2: Drop in Claude Code hooks (zero-code)

Govern every Bash, Edit, Write, and MultiEdit call Claude Code makes — no SDK instrumentation needed:

```bash
cp hooks/dashclaw_pretool.py  .claude/hooks/
cp hooks/dashclaw_posttool.py .claude/hooks/
```

Set `DASHCLAW_BASE_URL`, `DASHCLAW_API_KEY`, and `DASHCLAW_HOOK_MODE=enforce`. Every tool call becomes a governed, replayable decision record. See [hooks/README.md](hooks/README.md) for the full guide.

### Option 3: Use the SDK (full control)

For custom agents where you want precise control over what gets governed:

```bash
npm install dashclaw    # Node.js
pip install dashclaw    # Python
```

The 4-step governance loop — Guard, Record, Verify, Outcome — is covered in the [Quickstart](#quickstart) below.

For framework-specific step-by-step guides (Claude Code, OpenAI Agents SDK, LangGraph, CrewAI), visit [`/connect`](https://dashclaw.io/connect) on your DashClaw instance.

---

## What is DashClaw?

DashClaw is not observability. It is **control before execution**.

AI agents generate actions from goals and context. They do not follow deterministic code paths. Therefore debugging alone is insufficient. **Agents require governance.**

DashClaw provides decision infrastructure to:
* Intercept risky agent actions.
* Enforce policy checks before execution.
* Require human approval (HITL) for sensitive operations.
* Record verifiable decision evidence to detect reasoning drift.
* Track agent learning velocity — the only platform that measures whether your agents are getting better or worse over time.

---

## ⚡ See DashClaw stop an agent from deleting production data

Run DashClaw instantly with **one command**.

```bash
npx dashclaw-demo
```

What happens:
1. A local DashClaw demo runtime starts automatically.
2. A demo agent attempts a **high-risk production deploy**.
3. DashClaw intercepts the decision and **blocks the action before execution**.
4. Your browser opens directly to the **Decision Replay** showing the governance trail.

No repo clone. No environment variables. No configuration. Just one command.

---

### What you’ll see

- 🔴 High risk score (85)
- 🛑 Policy requires approval before deploy
- 🧠 Assumptions recorded by the agent
- 📊 Full decision timeline with outcome

![DashClaw Decision Replay](public/images/screenshots/Replay.png)

---

## Platform Overview

<div align="center">

**Mission Control** — Real-time strategic posture, decision timeline, and intervention feed.

<img src="public/images/screenshots/Mission Control.png" alt="Mission Control" width="1000" />

<br /><br />

**Approval Queue** — Human-in-the-loop intervention with risk scores and one-click Allow / Deny.

<img src="public/images/screenshots/Approvals.png" alt="Approval Queue" width="1000" />

<br /><br />

**Guard Policies** — Declarative rules that govern agent behavior before actions execute.

<img src="public/images/screenshots/policies.png" alt="Guard Policies" width="1000" />

<br /><br />

**Drift Detection** — Statistical behavioral drift analysis with critical alerts when agents deviate from baselines.

<img src="public/images/screenshots/Assumptions.png" alt="Drift Detection" width="1000" />

</div>

---

## 🏗️ First Real Agent

**Fastest**: Install the [dashclaw-platform-intelligence skill](#option-1-install-the-skill-30-seconds) and let your agent instrument itself.

**Hands-on**: Use the **OpenAI Governed Agent Starter** to see the SDK in a real customer communication workflow:

```bash
cd examples/openai-governed-agent
npm install

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