> Full docs index: https://docs.steel.dev/llms.txt

# Steel Documentation

> Steel is the open-source browser API for AI agents — managed cloud browsers with stealth,
> residential proxies, CAPTCHA solving, persistent profiles, session replays, and agent observability.
> Use Steel to launch cloud browsers, scrape content, and automate web tasks.

## Quick Reference

- Install: `npm install steel-sdk` (Node.js) or `pip install steel-sdk` (Python)
- CLI: `curl -sSf https://setup.steel.dev | sh`
- Auth header: `steel-api-key: <your-key>`
- Auth env var: `STEEL_API_KEY`
- API base URL: `https://api.steel.dev`
- WebSocket: `wss://connect.steel.dev?apiKey=<key>&sessionId=<id>`
- MCP server: `https://mcp.steel.dev/mcp` (OAuth sign-in, or `Authorization: Bearer <your-key>`)
- API reference: https://steel.apidocumentation.com/api-reference
- Brand design language (DESIGN.md): https://docs.steel.dev/DESIGN.md

## Agent Instructions

- For the simplest one-liner scrape, use the CLI:
  ```bash
  steel scrape https://example.com
  ```
- For simple scraping without a browser session, use the REST scrape endpoint:
  ```
  curl -X POST https://api.steel.dev/v1/scrape \
    -H "steel-api-key: YOUR_KEY" \
    -H "Content-Type: application/json" \
    -d '{"url": "https://example.com"}'
  ```
- For browser automation, connect Puppeteer or Playwright via WebSocket:
  ```js
  import Steel from 'steel-sdk';
  import puppeteer from 'puppeteer-core';

  const client = new Steel({ steelAPIKey: process.env.STEEL_API_KEY });
  const session = await client.sessions.create();
  const browser = await puppeteer.connect({
    browserWSEndpoint: `wss://connect.steel.dev?apiKey=${process.env.STEEL_API_KEY}&sessionId=${session.id}`,
  });
  // ... use browser ...
  await browser.close();
  await client.sessions.release(session.id);
  ```
- For agent-style browser control over plain HTTP, use the Commands API. Take a snapshot, then act on the refs in it:
  ```
  curl -X POST https://api.steel.dev/v1/sessions/SESSION_ID/commands \
    -H "steel-api-key: YOUR_KEY" \
    -H "Content-Type: application/json" \
    -d '{"commands": [{"command": "navigate", "url": "https://example.com"}, {"command": "snapshot", "interactive": true}]}'
  ```
  Then send `{"after": "<next from the response>", "commands": [{"command": "click", "target": "e1"}]}`. Docs: https://docs.steel.dev/overview/commands-api/overview
- To give an MCP client Steel browsers, add the hosted MCP server. In Claude Code:
  ```bash
  claude mcp add --transport http steel https://mcp.steel.dev/mcp
  ```
  The client signs in with OAuth on first use. Docs: https://docs.steel.dev/overview/mcp
- Python SDK:
  ```python
  from steel import Steel
  client = Steel(steel_api_key="YOUR_KEY")  # or set STEEL_API_KEY env var
  result = client.scrape(url="https://example.com")
  print(result.content.html)
  ```
- `client.scrape()` returns a `ScrapeResponse` with:
  - `result.content.html` — full HTML string
  - `result.content.markdown` — markdown version
  - `result.content.cleaned_html` — cleaned HTML
  - `result.content.readability` — readability text
  - `result.metadata.status_code` — HTTP status (int)
  - `result.metadata.title` — page title
  - `result.links` — list of extracted links
- Always release sessions when done: `client.sessions.release(sessionId)`
- Do NOT use `session.websocketUrl` directly — construct the WSS URL as shown above
- The Node SDK constructor param is `steelAPIKey` (not `apiKey`)
- The Python SDK constructor param is `steel_api_key` (not `api_key`)
- The Python package installs as `pip install steel-sdk` but imports as `from steel import Steel`
- `sessions.create()` accepts an optional `sessionId` (Node) / `session_id` (Python) UUID when you need the ID before the session exists; omit it and Steel generates one
- The Python `sessions.create()` session timeout param is `api_timeout` (not `timeout`, which is the HTTP request timeout)
- Any docs page is available as markdown by appending `.md` to its URL, for example `https://docs.steel.dev/overview/steel-cli.md`; AI user agents (Claude, Cursor, GPT) receive markdown automatically at the canonical URL
- `/llms-full.txt` concatenates every page into one file (large, roughly 230k tokens); prefer fetching the individual `.md` pages you need over reading the whole bundle