Servo Fetch

servo-fetch A self-contained browser engine that fetches, renders, and extracts web content as Markdown, JSON, or screenshots — no Chromium, no API key, no setup.

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

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/README.md) + [benchmarks/results/](benchmarks/results/).

Install

| Interface | Install | Docs | | --------- | ------- | ---- | | CLI | curl -fsSL https://raw.githubusercontent.com/konippi/servo-fetch/main/install.sh \| sh | [CLI docs](crates/servo-fetch-cli/README.md) | | Rust | cargo add servo-fetch | [Library docs](crates/servo-fetch/README.md) | | Python | pip install servo-fetch | [Python docs](bindings/python/README.md) | | Node.js | npm install servo-fetch | [Node docs](bindings/node/README.md) |

CLI install alternatives

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.

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 server

Full CLI reference → [servo-fetch-cli](crates/servo-fetch-cli/README.md)

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](crates/servo-fetch/README.md)

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](bindings/python/README.md)

Node.js

npm install servo-fetch

import { 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](bindings/node/README.md)

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](crates/servo-fetch-cli/README.md)

Prefer in-process tools? Wrap the Python API as agent tools — see [bindings/python/examples/strands_agent.py](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](crates/servo-fetch-cli/README.md#http-api-server)

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](./SECURITY.md) for details.

Limitations

• Sites behind CAPTCHAs are not supported.

Contributing

See [CONTRIBUTING.md](./CONTRIBUTING.md) for development setup, commit conventions, and PR guidelines.

License

[MIT](./LICENSE-MIT) OR [Apache-2.0](./LICENSE-APACHE)

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: konippi
• Source: konippi/servo-fetch
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.