I’m releasing DeepLook, an open-source browser tool that turns public web pages into readable text and structured data for AI agents.
It runs real Chrome, renders the page, and gives your application the visible text together with its title, final URL, HTTP status, and browser observations. You can use the same reading capability from a local CLI, a self-hosted HTTP API, or a coding agent through MCP.
The original project code is licensed under Apache-2.0. You can inspect it, run it on your own infrastructure, and contribute improvements.
Research and coding agents often need the actual page behind a URL. A lightweight HTTP fetch works for many sources; other pages need JavaScript rendering before useful text appears.
DeepLook handles that browser-reading step. Give it one public URL and consume the result in your own workflow. Search, research planning, deciding which links to follow, and writing a final answer remain your application’s responsibility.
I wanted the boundary to be easy to understand: a page request, a structured result, and an explicit error when the page cannot be read.
You need Node.js 22.14+ and Google Chrome. Local use does not require a DeepLook account, an LLM API key, or a shared project token.
git clone https://github.com/sayhitogambit/DeepLook.git
cd DeepLook
npm ci
npm run build
node dist/cli.js doctor --json
node dist/cli.js open https://example.com
Use JSON when your application needs page metadata, or the versioned machine format when you need a consistent success/error envelope:
node dist/cli.js open https://example.com --json
node dist/cli.js open https://example.com --machine --timeout-ms 30000
These commands install from GitHub source. They do not depend on a public npm registry release. You can also build and install your own tarball with npm pack.
DeepLook includes a stdio MCP server. After building the project, configure your MCP host to launch it:
{
"mcpServers": {
"deeplook": {
"command": "node",
"args": ["/absolute/path/to/DeepLook/dist/cli.js", "mcp"]
}
}
}
Configuration field names vary between hosts. The command exposes deeplook_open, which accepts a URL and an optional timeout:
{"url":"https://example.com","timeoutMs":30000}
Successful calls return page text and a structured result. Failed calls return a typed error. Each MCP process allows one active read, and cancellation propagates to the browser operation.
Page content remains untrusted input. An agent must treat instructions found inside a page as source material, rather than instructions from its user or developer.
DeepLook-API wraps the browser CLI with an HTTP interface, authentication, account quotas, concurrency limits, and lifecycle management.
Its page-reading endpoint is:
POST /v1/open
Authorization: Bearer <your-own-browser-key>
Content-Type: application/json
{"url":"https://example.com","timeoutMs":30000}
The API returns the page object on success. It reports errors such as invalid input, unauthorized access, an exhausted quota, a busy browser, a timeout, or a page that returned a challenge instead of useful content.
The operator generates and manages the server’s credentials. Administrator credentials and client browser keys have separate roles. There is no founder-owned token for everyone to share, and the source does not provide access to my accounts or infrastructure.
The credential guide walks through creating your own secrets and keeping them outside Git. The CLI and MCP tool can also connect to your own API through private environment variables.
The project reads one public HTTP(S) URL per call. It uses stock Chrome and keeps its sandbox enabled. It checks outbound destinations, including redirects and page resources, and rejects private, local, and metadata addresses.
It also has clear limits:
Those boundaries matter when you build an agent around a browser. The technical reference documents the response contract, errors, deadlines, cancellation, and cleanup behavior.
The repositories include deterministic tests, tests with a real sandboxed browser, package-installation checks, and HTTP/container integration tests. They exercise practical failure cases as well as successful reads.
The README is available in English, Brazilian Portuguese, and Simplified Chinese. The website source also includes an optional double-opt-in newsletter with confirmation and unsubscribe links; self-hosters configure their own SMTP and private storage. The browser and API themselves do not require email credentials.
I’m opening the project early so developers can try real workflows, inspect the implementation, and help shape what comes next. Useful contributions include reproducible page-reading bugs, documentation improvements, platform testing, and agent integrations.
Start with the DeepLook repository, follow the getting-started guide, and tell me what you build.
This announcement was prepared with AI assistance and checked against the released project.