servo-fetch

<div align="center"> <h1 align="center">servo-fetch</h1> <p align="center">A self-contained browser engine that fetches, renders, and extracts web content as Markdown, JSON, or screenshots — no Chromium, no API key, no setup.</p> <p> <a href="https://github.com/konippi/servo-fetch/actions"><img src="https://github.com/konippi/servo-fetch/workflows/CI/badge.svg" alt="CI"></a> <a href="https://crates.io/crates/servo-fetch"><img src="https://img.shields.io/crates/v/servo-fetch.svg" alt="crates.io"></a> <img src="https://img.shields.io/badge/Rust-1.86.0-blue?color=fc8d62&logo=rust" alt="MSRV"> <img src="https://img.shields.io/badge/license-MIT%2FApache--2.0-blue.svg" alt="MIT OR Apache-2.0"> </p> <img src="assets/demo.gif" alt="servo-fetch demo" width="900"> </div>

servo-fetch embeds the Servo browser engine. It executes JavaScript, computes CSS layout, captures screenshots with a software renderer, and extracts clean content — available as a CLI, a Rust library, and a Python SDK.

# CLI
servo-fetch "https://example.com"                          # clean Markdown
servo-fetch "https://example.com" --format png -o page.png # PNG screenshot

// Rust
let md = servo_fetch::markdown("https://example.com")?;

# Python
page = servo_fetch.fetch("https://example.com")
print(page.markdown)

Why servo-fetch

• Zero dependencies — single binary, no Chromium, no API key
• Real JS execution — SpiderMonkey runs JavaScript, parallel CSS engine computes layout
• Layout- and visibility-aware extraction — strips navbars, sidebars, footers by rendered position, plus cookie banners, modals, and CSS-hidden content (opacity:0, aria-hidden, sr-only)
• Schema-driven JSON — declarative CSS-selector schema pulls structured data
• Parallel batch fetch — multiple URLs fetched concurrently
• Site crawling — BFS link traversal with robots.txt, same-site scope, and rate limiting
• URL discovery — sitemap-based URL mapping without rendering (fast, lightweight)
• Screenshots without GPU — software renderer captures PNG/full-page screenshots anywhere
• Accessibility tree — AccessKit integration with roles, names, and bounding boxes

Performance and quality

Apple M3 Pro, versus Playwright (the typical AI-agent stack):

Benchmark	servo-fetch	playwright:optimized
Time — static-small	~231 ms	~645 ms
Time — spa-heavy	~331 ms	~798 ms
Memory (peak RSS)	51–64 MB	300–328 MB

Extraction quality: mean word-F1 0.819 vs Readability's 0.728 across eight page-type fixtures, with without[] boilerplate removal at 95.0% vs 78.6%. Direct-binary engine peers (chrome-headless-shell, Lightpanda, curl) are opt-in.

Methodology, three-axis breakdown, per-fixture F1, and raw JSON: benchmarks/README.md + benchmarks/results/.

Install

Interface	Install	Docs
CLI	curl -fsSL https://raw.githubusercontent.com/konippi/servo-fetch/main/install.sh | sh	CLI docs
Rust	cargo add servo-fetch	Library docs
Python	pip install servo-fetch	Python docs

<details> <summary><b>CLI install alternatives</b></summary>

cargo binstall servo-fetch-cli   # prebuilt binary
cargo install servo-fetch-cli    # build from source

Or download from GitHub Releases.

Linux — install runtime deps and use xvfb-run on headless servers:

sudo apt install -y libegl1 libfontconfig1 libfreetype6
xvfb-run --auto-servernum servo-fetch "https://example.com"

Windows — cargo binstall does not copy sidecar files (cargo-binstall#353), so the installed servo-fetch.exe fails at startup with a missing libEGL.dll. Download the .zip from Releases instead — it bundles libEGL.dll and libGLESv2.dll.

macOS — no extra setup needed.

</details>

Quick Start

CLI

servo-fetch "https://example.com"                          # Markdown (default)
servo-fetch "https://example.com" --format json            # Structured JSON
servo-fetch "https://example.com" --format png -o page.png # PNG screenshot
servo-fetch "https://example.com" --js "document.title"    # Run JavaScript
servo-fetch "https://example.com" --schema schema.json     # Schema-driven JSON
servo-fetch URL1 URL2 URL3                                 # Parallel batch
servo-fetch "https://example.com" --output page.md         # Save to a single file
servo-fetch URL1 URL2 --output-dir ./out/                  # Save each URL to its own file
servo-fetch crawl "https://docs.example.com" --limit 20    # Crawl a site
servo-fetch crawl URL --output-dir ./pages/                # Save each crawled page to its own file
servo-fetch map "https://example.com"                      # Discover URLs via sitemap
servo-fetch mcp                                            # MCP server (stdio)
servo-fetch serve                                          # HTTP API server

Full CLI reference → servo-fetch-cli

Rust

cargo add servo-fetch

// URL → Markdown in one line
let md = servo_fetch::markdown("https://example.com")?;

// Fetch with options
use servo_fetch::{fetch, FetchOptions};
use std::time::Duration;

let page = fetch(FetchOptions::new("https://example.com").timeout(Duration::from_secs(60)))?;
println!("{}", page.html);
let md = page.markdown()?;

// Crawl a site
servo_fetch::crawl_each(
    servo_fetch::CrawlOptions::new("https://docs.example.com")
        .limit(100)
        .user_agent("MyBot/1.0"),
    |result| match &result.outcome {
        Ok(page) => println!("{}: {} chars", result.url, page.content.len()),
        Err(e) => eprintln!("{}: {e}", result.url),
    },
)?;

// Discover URLs via sitemap (no rendering)
let urls = servo_fetch::map(
    servo_fetch::MapOptions::new("https://example.com").limit(1000),
)?;
for u in &urls {
    println!("{}", u.url);
}

Full API reference → servo-fetch

Python

pip install servo-fetch

import servo_fetch

page = servo_fetch.fetch("https://example.com")
print(page.markdown)

# Schema extraction
from servo_fetch import Schema, Field
schema = Schema(
    base_selector=".product",
    fields=[
        Field(name="title", selector="h2", type="text"),
        Field(name="price", selector=".price", type="text"),
    ],
)
page = servo_fetch.fetch("https://shop.example.com", schema=schema)
print(page.extracted)

Full API reference → bindings/python

MCP Server

Built-in Model Context Protocol server with six tools: fetch, batch_fetch, crawl, map, screenshot, and execute_js.

{
  "mcpServers": {
    "servo-fetch": {
      "command": "servo-fetch",
      "args": ["mcp"]
    }
  }
}

Streamable HTTP: servo-fetch mcp --port 8080

Full MCP tool reference → servo-fetch-cli README

HTTP API

REST endpoints for containerized deployments and HTTP clients:

servo-fetch serve                            # 127.0.0.1:3000
servo-fetch serve --host 0.0.0.0 --port 80   # expose to network

curl -X POST http://127.0.0.1:3000/v1/fetch \
  -H 'content-type: application/json' \
  -d '{"url":"https://example.com"}'

Endpoints: GET /health, GET /version, POST /v1/fetch, POST /v1/batch_fetch, POST /v1/screenshot, POST /v1/execute_js, POST /v1/crawl, POST /v1/map.

Full HTTP API reference → servo-fetch-cli README

Docker

Multi-arch image on GitHub Container Registry (linux/amd64, linux/arm64):

docker run --rm -p 3000:3000 ghcr.io/konippi/servo-fetch:latest
curl -X POST http://127.0.0.1:3000/v1/fetch \
  -H 'content-type: application/json' \
  -d '{"url":"https://example.com"}'

Runs as non-root (UID 1001). Images are signed with cosign (keyless) and published with SLSA provenance and SBOM attestations.

Agent Skills

servo-fetch ships with an Agent Skills package for AI coding agents:

npx skills add https://github.com/konippi/servo-fetch/tree/main/skills/servo-fetch

Security

servo-fetch blocks all private and reserved IP ranges (RFC 6890), strips credentials from URLs, disables HTTP redirects to prevent SSRF bypass, and sanitizes all output against terminal escape injection (CVE-2021-42574). See SECURITY.md for details.

Limitations

• Sites behind login walls or CAPTCHAs are not supported.

Contributing

See CONTRIBUTING.md for development setup, commit conventions, and PR guidelines.

License

MIT OR Apache-2.0