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https://github.com/dundas/thinkrun

ThinkRun — screen recorder for AI coding agents. Record a bug or session once and hand Claude Code or Cursor structured context it can act on. Real Chrome, your sessions. MCP + CLI.
https://github.com/dundas/thinkrun

ai-agent ai-coding-agent ai-screen-recorder ai-testing browser-automation bug-reporting chrome-extension claude-code cursor mcp mcp-server real-browser-mcp screen-recorder screen-recording

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ThinkRun — screen recorder for AI coding agents. Record a bug or session once and hand Claude Code or Cursor structured context it can act on. Real Chrome, your sessions. MCP + CLI.

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# ThinkRun — Screen Recorder for AI Coding Agents

Stop screenshotting bugs and typing paragraphs to explain them. ThinkRun records any browser session — or lets your agent record what it built — and turns it into **structured context your AI coding agent can act on**: every click, the console, network requests, your voice narration, and screenshots, all time-synced. Not just a screenshot. Not just a video.

Works with **Claude Code**, **Cursor**, **Cline**, **Windsurf**, and any MCP client.

## Install

```bash
npx @thinkrun/mcp
```

Or install the CLI globally:

```bash
npm install -g @thinkrun/cli
```

The CLI automatically installs the native host binary, which lets AI tools control Chrome.

## Add to your MCP config

```json
{
"mcpServers": {
"thinkrun": {
"command": "npx",
"args": ["@thinkrun/mcp"]
}
}
}
```

Config location:
- **Claude Code**: `~/.claude/settings.json`
- **Cursor**: `.cursor/mcp.json`
- **Cline**: VS Code settings → Cline MCP Servers

Then ask your agent: *"navigate to localhost:3000 and screenshot the checkout page"* — or record the bug yourself and hand the agent the result.

## Why ThinkRun

Other tools give your agent eyes. ThinkRun gives it **structured sight** — context it can parse in one pass instead of you re-explaining the page.

| | ThinkRun | Playwright MCP |
|---|---|---|
| Records a session into structured, replayable context | ✅ | ❌ drive-only |
| Turns a recording into an AI-ready report (clicks, console, network) | ✅ | ❌ |
| Shareable link, also as LLM-ready Markdown / JSON | ✅ | ❌ |
| Uses your real Chrome — your cookies & sessions | ✅ | ❌ headless only |
| Runs alongside your browser, no profile conflict | ✅ | ❌ fights for Chrome profile |

## Agent skills

Skills for Claude Code, Cursor, Codex, and Gemini CLI are in `.claude/skills/`, `.cursor/skills/`, `.codex/skills/`, and `.gemini/skills/`.

## Manual binary install

If you need to install the native host without the CLI, download the binary for your platform from the [latest release](https://github.com/dundas/thinkrun/releases/latest) and run:

```bash
chmod +x thinkbrowse-host-* && ./thinkbrowse-host-* --install
```

> The native host binary keeps the `thinkbrowse-host` filename for now — it's the same signed binary, unchanged by the rename.

## FAQ

### How do I give Claude Code (or Cursor) access to my browser?
Add ThinkRun as an MCP server — `npx @thinkrun/mcp` — and your agent can drive and read a real Chrome session, your cookies and logins included. See "Add to your MCP config" above.

### How do I show Cursor a UI bug instead of pasting a screenshot?
Record the bug with ThinkRun. Instead of a flat image, your agent gets a structured artifact — the steps you took, the console errors, the failing network requests, and the DOM state at the moment it broke — so it fixes the actual problem instead of guessing from a picture.

### Is there an MCP server that uses my real Chrome cookies and sessions?
Yes. ThinkRun runs against your real Chrome via a native host, so authenticated pages just work — no headless re-login, no separate profile, no fighting your browser for the Chrome profile.

### How do I record a browser session for an AI coding agent?
Run `npx @thinkrun/mcp` (or `npm i -g @thinkrun/cli`) and record any session — or let the agent record what it built. ThinkRun turns it into an AI-ready report (clicks, console, network, narration, screenshots, all time-synced), available as a shareable link and as LLM-ready Markdown/JSON.

### How do I give my AI coding agent context about what went wrong?
Hand it a ThinkRun recording. The structured "what happened" — actions, first bad signal, expected vs actual, and the element state — is exactly the context an agent needs to land a fix on the first try.

## More

- [thinkrun.ai](https://thinkrun.ai)
- [Privacy Policy](https://thinkrun.ai/privacy)