GitHub - andalabx/ember: Lightweight headless browser for AI agents

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Open source, lightweight headless browser for AI agents.

pip install ember-browser

No Docker. No API key to 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

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"

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

ember config --save-dir ./research/    # persists across sessions
ember config                           # show current settings
ember config --clear-save-dir          # clear it

EMBER_SAVE_DIR=./out ember url https://example.com

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

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

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}'

{
  "mcpServers": {
    "ember": {
      "command": "ember",
      "args": ["mcp"]
    }
  }
}

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: ...