# Show HN: Ember – Lightweight headless browser for AI agents (17MB idle) > Source: > Published: 2026-07-09 11:32:52+00:00 ``` ███████╗███╗ ███╗██████╗ ███████╗██████╗ ██╔════╝████╗ ████║██╔══██╗██╔════╝██╔══██╗ █████╗ ██╔████╔██║██████╔╝█████╗ ██████╔╝ ██╔══╝ ██║╚██╔╝██║██╔══██╗██╔══╝ ██╔══██╗ ███████╗██║ ╚═╝ ██║██████╔╝███████╗██║ ██║ ╚══════╝╚═╝ ╚═╝╚═════╝ ╚══════╝╚═╝ ╚═╝ ``` **Open source, lightweight headless browser for AI agents.** ``` pip install ember-browser ``` *No Docker. No API key to start.* Most web tools for agents ship with Chromium (~281 MB) or require Docker just to get started. We needed something an agent could use on a VPS, a laptop, or a Raspberry Pi without thinking about it. ember runs at ~17 MB idle. It decides whether a page needs a browser — you just pass it a URL. | ember | Crawl4AI | Firecrawl OSS | Playwright | | |---|---|---|---|---| | Setup | `pip install` | `pip install` | Docker + Redis + Node | `pip` + browser install | | Package size | ~54 MB | ~200–350 MB | Thin client only | ~47 MB | | Browser binary | Lightpanda ~12 MB | Chromium ~281 MB | Chromium ~281 MB | Chromium ~281 MB | | Docker required | No | No | Yes | No | | API key required | No | No | No | No | | MCP server | Yes | No | Yes | Yes | | Search built-in | Yes | No | Yes | No | | Zero-infra self-host | Yes | Yes | No | Yes | ``` pip install ember-browser ember version # verify install ember # start the interactive session ember url https://example.com # or run a one-shot command ember serve # start the REST API ``` `ember` with no arguments opens a persistent session. Commands and a save guide are shown on startup — no need to type `help` first. ``` ███████╗███╗ ███╗██████╗ ███████╗██████╗ ██╔════╝████╗ ████║██╔══██╗██╔════╝██╔══██╗ █████╗ ██╔████╔██║██████╔╝█████╗ ██████╔╝ ██╔══╝ ██║╚██╔╝██║██╔══██╗██╔══╝ ██╔══██╗ ███████╗██║ ╚═╝ ██║██████╔╝███████╗██║ ██║ ╚══════╝╚═╝ ╚═╝╚═════╝ ╚══════╝╚═╝ ╚═╝ v0.1.0 lightweight headless browser for AI agents url scrape a page to markdown search web search crawl crawl a whole website map discover all URLs on a site interact control a browser with natural language extract pull structured data with an LLM batch scrape many URLs concurrently ─── saving results ─────────────────────────────────────────── one result url example.com -o page.md everything output ./research/ then all results auto-save last result save page.md after any command ember › url andausman.com ember › save page.md ember › output ./research/ # auto-save everything from here ember/research › search "python asyncio" -n 10 ember/research › crawl docs.example.com ember/research › output clear # stop auto-saving ember › quit ``` Every command works standalone too: ``` ember url https://example.com # scrape a page ember search "AI agents python" -n 10 # web search ember crawl https://docs.example.com --max-pages 20 # crawl a site ember map https://example.com # discover all URLs ember interact https://amazon.com \ --prompt "find a mechanical keyboard under $100" ember extract https://example.com/pricing \ --prompt "list all plans and prices as JSON" ``` All commands accept `-o` to save that run: ``` ember url https://example.com -o page.md ember search "python" -o results.json ember crawl https://docs.example.com -o ./pages/ # one .md per page ember map https://example.com -o urls.txt ember extract https://example.com -o data.json ``` Set a default save directory so you never need `-o` : ``` ember config --save-dir ./research/ # persists across sessions ember config # show current settings ember config --save-dir "" # clear it ``` Or use an environment variable for the current shell: ``` EMBER_SAVE_DIR=./out ember url https://example.com ``` In a session, the three ways to save: ``` ember › url example.com -o page.md # save just this run ember › save page.md # save the last result ember › output ./research/ # auto-save all results from now on # urls.txt — one URL per line, # = comment ember batch urls.txt # 5 concurrent by default ember batch urls.txt -c 20 -o ./pages/ # 20 parallel, save to dir python from emb.scrape import scrape_url, scrape_markdown from emb.search import search from emb.crawl import crawl from emb.map import map_url # Scrape a page → ScrapeResult result = scrape_url("https://example.com") print(result.markdown) # full page content as markdown print(result.title) # page title print(result.success) # True / False # Just the markdown text md = scrape_markdown("https://example.com") # Crawl a site result = crawl("https://docs.example.com", max_pages=20, max_depth=3) for page in result.pages: print(page.url, len(page.markdown)) # Discover URLs result = map_url("https://example.com", max_links=100) print(result.links) # list[str] # Search the web results = search("python asyncio tutorial", limit=5) for r in results: print(r.title, r.url) # Browser interaction with natural language from emb.interact import interact result = interact("https://example.com", prompt="click the login button") print(result.content) # what the agent did / saw # LLM-powered structured extraction from emb.agent import extract data = extract("https://example.com/pricing", prompt="list all plans and prices") print(data) # dict python import asyncio from emb.scrape import scrape_url_async async def main(): results = await asyncio.gather( scrape_url_async("https://example.com"), scrape_url_async("https://httpbin.org/get"), ) for r in results: print(r.url, r.success) asyncio.run(main()) ember serve # http://127.0.0.1:51251 ember serve --port 8080 # custom port EMBER_API_KEY=your-secret ember serve # require auth curl -X POST http://localhost:51251/scrape \ -H "Content-Type: application/json" \ -H "X-API-Key: your-secret" \ -d '{"url": "https://example.com"}' curl -X POST http://localhost:51251/search \ -H "Content-Type: application/json" \ -d '{"query": "AI agents", "limit": 5}' curl -X POST http://localhost:51251/crawl \ -H "Content-Type: application/json" \ -d '{"url": "https://docs.example.com", "max_pages": 10}' ``` Endpoints: `/scrape` `/search` `/crawl` `/map` `/interact` `/extract` `/agent` `/health` Add to your Hermes config, OpenClaw config, Mercury config, or any MCP-compatible host: ``` { "mcpServers": { "ember": { "command": "ember", "args": ["mcp"] } } } ``` Works with Hermes, OpenClaw, Mercury, and any MCP-compatible host. Available tools: `scrape` , `search_web` , `crawl_site` , `map_site` , `batch_scrape` , `interact_page` , `extract_data` . Once connected, your agent can use ember tools directly in conversation: ``` User: Summarise the latest posts on Hacker News Agent: [calls scrape("https://news.ycombinator.com")] → returns full page markdown with titles, scores, links Agent: Here are today's top stories on Hacker News: ... User: Find 5 articles about AI agents and scrape each one Agent: [calls search_web("AI agents 2025", limit=5)] → returns list of {title, url, description} Agent: [calls batch_scrape(["url1", "url2", ...])] → returns markdown for each page Agent: Here's a summary across all 5 articles: ... ``` Not every page needs a browser. ember knows the difference. **Tier 1 — trafilatura** handles ~89% of the web: blogs, news, documentation, docs sites, GitHub. Pure HTTP, no browser process, no memory overhead. **Tier 2 — Lightpanda** handles JavaScript-heavy pages, SPAs, and interactive content. It's a real browser engine written in Zig, built for machines rather than humans — 20 MB total. ember downloads and caches it automatically on first use, and only falls back to it when tier 1 produces thin content. Most requests never reach the browser. | State | RAM | |---|---| | Idle | ~17 MB | | Scraping a static page | ~20 MB | | Running the browser | ~140 MB | Firecrawl needs 4–8 GB in Docker. Crawl4AI imports at 171 MB before scraping anything. ember fits where your agent already runs. | Variable | Default | Description | |---|---|---| `EMBER_SAVE_DIR` | (none) | Default directory for saved results. Overrides `ember config --save-dir` for the current shell. | `EMBER_API_KEY` | (none) | Enables API key auth on the REST server (`X-API-Key` header). | `EMBER_PORT` | `51251` | Default port for `ember serve` . Overridden by `--port` flag. | `EMBER_INTERACT_PROVIDER` | `openai` | LLM provider for `interact` (`openai` , `anthropic` , `ollama` , etc.). | `EMBER_LLM_API_KEY` | (none) | API key for LLM-powered extraction. | `EMBER_LLM_BASE_URL` | `https://api.openai.com/v1` | LLM API endpoint for extraction. | `EMBER_LLM_MODEL` | `gpt-4o-mini` | Model used by `extract` . | `EMBER_LIGHTPANDA_PATH` | (auto) | Path to a custom Lightpanda binary. Skips auto-download if set. | [MIT](/andalabx/ember/blob/main/LICENSE) — open source forever.