ember

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pip install ember-browser

Why ember

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.

Quick start

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

CLI

Interactive session

ember with no arguments opens a persistent session. Startup shows a short quick start, and help shows the full guide.

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  v0.1.1  lightweight headless browser for AI agents

  Quick Start
  url example.com                          scrape one page
  search openai api                        search the web
  interact example.com -p "summarize"      control a page with AI
  output ./research                        change auto-save folder
  help                                     show the full guide
  quit                                     exit

  ✓ auto-save on → ember_results

ember › url andalabx.com
ember › help
ember › output ./research
ember › search "python asyncio" -n 10
ember › output clear
ember › quit

One-shot commands

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"

extract requires EMBER_LLM_API_KEY. interact --no-browser also uses the OpenAI-compatible LLM path, so it needs EMBER_LLM_API_KEY and optionally EMBER_LLM_BASE_URL. Use ember url when you want raw page content without an LLM.

Saving results

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

The CLI saves to ember_results/ by default. Set a different default save directory if you want:

ember config --save-dir ./research/    # persists across sessions
ember config                           # show current settings
ember config --clear-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 main save paths are:

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

Async batch scraping

# 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

On Windows, UTF-8 files with a BOM are supported.

Python API

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

Async

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())

REST API

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

MCP

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: ...

How it works

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.

Memory footprint

Firecrawl needs 4–8 GB in Docker. Crawl4AI imports at 171 MB before scraping anything. ember fits where your agent already runs.

Environment variables

License

MIT — open source forever.