servo-fetch
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, a Python SDK, and a Node.js 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").await?;# Python
page = servo_fetch.fetch("https://example.com")
print(page.markdown)// Node.js
import { fetch } from "servo-fetch";
const md = await fetch("https://example.com");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
Agent-ready — drop-in web tool for AI agents: a built-in MCP server, or wrap the Python API as a tool in any agent framework
Related MCP server: nab
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 |
| |
Rust |
| |
Python |
| |
Node.js |
|
cargo binstall servo-fetch-cli # prebuilt binary
cargo install servo-fetch-cli # build from sourceOr 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.
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 "https://example.com" --cookies cookies.txt # Send session cookies
servo-fetch "https://example.com" -H "X-Api-Key: KEY" # Custom request header
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 serverFull CLI reference → servo-fetch-cli
Rust
cargo add servo-fetch// URL → Markdown in one line (async by default; use `blocking::*` for sync)
let md = servo_fetch::markdown("https://example.com").await?;
// 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))).await?;
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),
},
).await?;
// Discover URLs via sitemap (no rendering)
let urls = servo_fetch::map(
&servo_fetch::MapOptions::new("https://example.com").limit(1000),
).await?;
for u in &urls {
println!("{}", u.url);
}Full API reference → servo-fetch
Python
pip install servo-fetchimport 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
Node.js
npm install servo-fetchimport { fetch, crawl } from "servo-fetch";
const md = await fetch("https://example.com");
for await (const page of crawl("https://docs.example.com", { limit: 50 })) {
if (page.ok) console.log(page.url, page.title);
}Or run the bundled CLI without installing:
npx servo-fetch "https://example.com"Full API reference → bindings/node
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
Prefer in-process tools? Wrap the Python API as agent tools — see bindings/python/examples/strands_agent.py.
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-fetchSecurity
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 CAPTCHAs are not supported.
Contributing
See CONTRIBUTING.md for development setup, commit conventions, and PR guidelines.
License
MIT OR Apache-2.0
Maintenance
Resources
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If you are the server author, to access and configure the admin panel.
Tools
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