Is the Browsegrab MCP server free to use?

Yes. Browsegrab is listed on AllMCPs as a free, open Model Context Protocol server you can install into Claude Desktop, Cursor, or any MCP-compatible client.

Is the Browsegrab MCP server free to use?

Yes. Browsegrab is listed on AllMCPs as a free, open Model Context Protocol server you can install into Claude Desktop, Cursor, or any MCP-compatible client.

Browsegrab

Token-efficient browser agent for local LLMs. Playwright + accessibility tree + MarkGrab.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Install Config Generator

Choose your client

claude_desktop_config.json

{
  "mcpServers": {
    "browsegrab": {
      "command": "npx",
      "args": [
        "-y",
        "browsegrab"
      ]
    }
  }
}

💡 Paste into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)

Documentation Overview

browsegrab

한국어 문서 · llms.txt

Token-efficient browser agent for local LLMs — Playwright + accessibility tree + MarkGrab, MCP native.

browsegrab is a lightweight browser automation library designed for local LLMs (8B-35B parameters). It combines Playwright's accessibility tree with MarkGrab's HTML-to-markdown conversion to achieve 5-8x fewer tokens per step compared to alternatives like browser-use.

Features

Token-efficient: ~500-1,500 tokens/step (vs 4,000-10,000 for browser-use)
Local LLM first: Optimized for vLLM, Ollama, and OpenAI-compatible endpoints
MCP native: Built-in MCP server with 8 browser automation tools
MarkGrab integration: HTML → clean markdown for content extraction
Accessibility tree + ref system: Stable element references (e1, e2, ...) without vision models
Success pattern caching: Zero LLM calls on repeated workflows
5-stage JSON parser: Robust action parsing for local LLM outputs
Minimal dependencies: Only playwright + httpx in core

Installation

Terminal

pip install browsegrab
playwright install chromium

With optional features:

Terminal

pip install browsegrab[mcp]      # MCP server support
pip install browsegrab[content]  # MarkGrab content extraction
pip install browsegrab[cli]      # CLI with rich output
pip install browsegrab[all]      # Everything

Quick Start

Python API

server.ts

from browsegrab import BrowseSession

async with BrowseSession() as session:
    # Navigate and get accessibility tree snapshot
    await session.navigate("https://example.com")
    snap = await session.snapshot()
    print(snap.tree_text)
    # - heading "Example Domain" [level=1]
    # - link "Learn more": [ref=e1]

    # Click using ref ID
    result = await session.click("e1")
    print(result.url)  # https://www.iana.org/help/example-domains

    # Type into search box
    await session.navigate("https://en.wikipedia.org")
    snap = await session.snapshot()
    await session.type("e4", "Python programming", submit=True)

    # Extract compressed content (AX tree + markdown)
    content = await session.extract_content()

CLI

bash

# Accessibility tree snapshot
browsegrab snapshot https://example.com

# JSON output
browsegrab snapshot https://example.com -f json

# Extract content (AX tree + markdown)
browsegrab extract https://en.wikipedia.org/wiki/Python

# Agentic browse (requires LLM endpoint)
browsegrab browse https://example.com "Find the about page"

MCP Server

bash

browsegrab-mcp  # Start MCP server (stdio)

Claude Desktop / Cursor / VS Code config:

config.json

{
  "mcpServers": {
    "browsegrab": {
      "command": "browsegrab-mcp"
    }
  }
}

8 MCP tools: browser_navigate, browser_click, browser_type, browser_snapshot, browser_scroll, browser_extract_content, browser_go_back, browser_wait

How It Works

Agent Browse Loop

mermaid

flowchart LR
    A["🌐 URL + Goal"] --> B["Navigate"]
    B --> C["AX Tree Snapshot\n~200–500 tokens"]
    C --> D{"LLM\nDecision"}
    D -->|"click / type / scroll"| E["Execute Action"]
    E --> C
    D -->|"goal reached"| F["Extract Content\n(MarkGrab)"]
    F --> G["✅ Result"]

Token Efficiency

browsegrab separates structure (accessibility tree) from content (MarkGrab markdown), sending only what the LLM needs:

mermaid

flowchart TD
    A["Raw HTML"] --> B["Accessibility Tree"]
    A --> C["MarkGrab Markdown"]
    B --> D["Structure: ~200–500 tokens\nInteractive elements with ref IDs"]
    C --> E["Content: ~300–800 tokens\nClean markdown · on-demand"]
    D --> F["Combined: ~500–1,300 tokens/step\n⚡ 5–8× fewer than browser-use"]
    E --> F

Token efficiency (measured)

Page	Interactive elements	Tokens	browser-use equivalent
example.com	1	~60	~500+
Wikipedia article	452	~1,254	~10,000+

Architecture

server.ts

browsegrab/
├── config.py                 # Dataclass configs (env var loading)
├── result.py                 # Result types (ActionResult, BrowseResult, ...)
├── session.py                # BrowseSession orchestrator
├── browser/
│   ├── manager.py            # Playwright lifecycle (async context manager)
│   ├── snapshot.py           # Accessibility tree + ref system
│   ├── selectors.py          # 4-strategy selector resolver
│   └── actions.py            # navigate, click, type, scroll, go_back, wait
├── dom/
│   ├── ref_map.py            # ref ID ↔ element bidirectional mapping
│   └── compress.py           # AX tree + MarkGrab → compressed context
├── llm/
│   ├── base.py               # LLMProvider ABC
│   ├── provider.py           # vLLM, Ollama, OpenAI-compatible
│   ├── prompt.py             # System prompts (~400 tokens)
│   └── parse.py              # 5-stage JSON fallback parser
├── agent/
│   ├── history.py            # Sliding window history compression
│   ├── cache.py              # Domain-based success pattern cache
│   └── loop_guard.py         # Duplicate action detection
├── __main__.py               # CLI (click)
└── mcp_server.py             # FastMCP server (8 tools)

Configuration

All settings via environment variables (BROWSEGRAB_* prefix):

bash

# Browser
BROWSEGRAB_BROWSER_HEADLESS=true
BROWSEGRAB_BROWSER_TIMEOUT_MS=30000

# LLM (for agentic browse)
BROWSEGRAB_LLM_PROVIDER=vllm          # vllm | ollama | openai
BROWSEGRAB_LLM_BASE_URL=http://localhost:8000/v1
BROWSEGRAB_LLM_MODEL=Qwen/Qwen3.5-32B-AWQ

# Agent
BROWSEGRAB_AGENT_MAX_STEPS=10
BROWSEGRAB_AGENT_ENABLE_CACHE=true

Part of the QuartzUnit Ecosystem

Library	Role
markgrab	Passive extraction (URL → markdown)
snapgrab	Passive capture (URL → screenshot)
docpick	Document OCR → structured JSON
browsegrab	Active automation (goal → browser actions → results)

Development

bash

git clone https://github.com/QuartzUnit/browsegrab.git
cd browsegrab
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
playwright install chromium

# Unit tests (no browser needed)
pytest tests/ -m "not e2e"

# Full suite including E2E
pytest tests/ -v

License

MIT

_{Part of the QuartzUnit ecosystem — composable Python libraries for data collection, extraction, search, and AI agent safety.}

Related MCP Servers

View all in Browser Automation View all alternatives

Frequently Asked Questions about Browsegrab

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "browsegrab": { "command": "npx", "args": ["-y", "Browsegrab"] } }

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Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/browsegrab?style=directory)](https://allmcps.com/mcp/browsegrab)

HTML Embed

<a href="https://allmcps.com/mcp/browsegrab"><img src="https://allmcps.com/api/badge/browsegrab?style=directory" alt="Browsegrab on AllMCPs" /></a>

Technical Specs & Signals

CategoryBrowser Automation

27Quality signal: Emerging · 27/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20

Documentation & tools11/30

Adoption & activity1/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

★ Featured

AllMCPs Server

The official MCP server for AllMCPs.com - submit and manage tools directly from your AI. The open directory for MCP servers. Connect Claude, Cursor, Windsurf, and AI agents to databases, tools, files, and APIs. Explore 3,181+ servers. AllMCPs is the premier, open directory for discovering, evaluating, and installing Model Context Protocol (MCP) servers to equip AI agents and LLMs with real-world superpowers.

Explore Server →

Own this project?

This directory is pre-filled from public sources. Claim via GitHub README, site badge, or DNS TXT to get the verified badge and attach your website.

Free dofollow backlink: after claiming, verify your product site and place a dofollow AllMCPs badge — we recheck it stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 📂 Browser Automation →Best MCP servers for Browser Automation →Alternatives to Browsegrab →Install in Claude Desktop Install in Cursor Install in VS Code

Browsegrab

Token-efficient browser agent for local LLMs. Playwright + accessibility tree + MarkGrab.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Install Config Generator

Choose your client

claude_desktop_config.json

{
  "mcpServers": {
    "browsegrab": {
      "command": "npx",
      "args": [
        "-y",
        "browsegrab"
      ]
    }
  }
}

💡 Paste into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)

Documentation Overview

browsegrab

한국어 문서 · llms.txt

Token-efficient browser agent for local LLMs — Playwright + accessibility tree + MarkGrab, MCP native.

Features

Token-efficient: ~500-1,500 tokens/step (vs 4,000-10,000 for browser-use)
Local LLM first: Optimized for vLLM, Ollama, and OpenAI-compatible endpoints
MCP native: Built-in MCP server with 8 browser automation tools
MarkGrab integration: HTML → clean markdown for content extraction
Accessibility tree + ref system: Stable element references (e1, e2, ...) without vision models
Success pattern caching: Zero LLM calls on repeated workflows
5-stage JSON parser: Robust action parsing for local LLM outputs
Minimal dependencies: Only playwright + httpx in core

Installation

Terminal

pip install browsegrab
playwright install chromium

With optional features:

Terminal

pip install browsegrab[mcp]      # MCP server support
pip install browsegrab[content]  # MarkGrab content extraction
pip install browsegrab[cli]      # CLI with rich output
pip install browsegrab[all]      # Everything

Quick Start

Python API

server.ts

from browsegrab import BrowseSession

async with BrowseSession() as session:
    # Navigate and get accessibility tree snapshot
    await session.navigate("https://example.com")
    snap = await session.snapshot()
    print(snap.tree_text)
    # - heading "Example Domain" [level=1]
    # - link "Learn more": [ref=e1]

    # Click using ref ID
    result = await session.click("e1")
    print(result.url)  # https://www.iana.org/help/example-domains

    # Type into search box
    await session.navigate("https://en.wikipedia.org")
    snap = await session.snapshot()
    await session.type("e4", "Python programming", submit=True)

    # Extract compressed content (AX tree + markdown)
    content = await session.extract_content()

CLI

bash

# Accessibility tree snapshot
browsegrab snapshot https://example.com

# JSON output
browsegrab snapshot https://example.com -f json

# Extract content (AX tree + markdown)
browsegrab extract https://en.wikipedia.org/wiki/Python

# Agentic browse (requires LLM endpoint)
browsegrab browse https://example.com "Find the about page"

MCP Server

bash

browsegrab-mcp  # Start MCP server (stdio)

Claude Desktop / Cursor / VS Code config:

config.json

{
  "mcpServers": {
    "browsegrab": {
      "command": "browsegrab-mcp"
    }
  }
}

8 MCP tools: browser_navigate, browser_click, browser_type, browser_snapshot, browser_scroll, browser_extract_content, browser_go_back, browser_wait

How It Works

Agent Browse Loop

mermaid

flowchart LR
    A["🌐 URL + Goal"] --> B["Navigate"]
    B --> C["AX Tree Snapshot\n~200–500 tokens"]
    C --> D{"LLM\nDecision"}
    D -->|"click / type / scroll"| E["Execute Action"]
    E --> C
    D -->|"goal reached"| F["Extract Content\n(MarkGrab)"]
    F --> G["✅ Result"]

Token Efficiency

browsegrab separates structure (accessibility tree) from content (MarkGrab markdown), sending only what the LLM needs:

mermaid

flowchart TD
    A["Raw HTML"] --> B["Accessibility Tree"]
    A --> C["MarkGrab Markdown"]
    B --> D["Structure: ~200–500 tokens\nInteractive elements with ref IDs"]
    C --> E["Content: ~300–800 tokens\nClean markdown · on-demand"]
    D --> F["Combined: ~500–1,300 tokens/step\n⚡ 5–8× fewer than browser-use"]
    E --> F

Token efficiency (measured)

Page	Interactive elements	Tokens	browser-use equivalent
example.com	1	~60	~500+
Wikipedia article	452	~1,254	~10,000+

Architecture

server.ts

browsegrab/
├── config.py                 # Dataclass configs (env var loading)
├── result.py                 # Result types (ActionResult, BrowseResult, ...)
├── session.py                # BrowseSession orchestrator
├── browser/
│   ├── manager.py            # Playwright lifecycle (async context manager)
│   ├── snapshot.py           # Accessibility tree + ref system
│   ├── selectors.py          # 4-strategy selector resolver
│   └── actions.py            # navigate, click, type, scroll, go_back, wait
├── dom/
│   ├── ref_map.py            # ref ID ↔ element bidirectional mapping
│   └── compress.py           # AX tree + MarkGrab → compressed context
├── llm/
│   ├── base.py               # LLMProvider ABC
│   ├── provider.py           # vLLM, Ollama, OpenAI-compatible
│   ├── prompt.py             # System prompts (~400 tokens)
│   └── parse.py              # 5-stage JSON fallback parser
├── agent/
│   ├── history.py            # Sliding window history compression
│   ├── cache.py              # Domain-based success pattern cache
│   └── loop_guard.py         # Duplicate action detection
├── __main__.py               # CLI (click)
└── mcp_server.py             # FastMCP server (8 tools)

Configuration

All settings via environment variables (BROWSEGRAB_* prefix):

bash

# Browser
BROWSEGRAB_BROWSER_HEADLESS=true
BROWSEGRAB_BROWSER_TIMEOUT_MS=30000

# LLM (for agentic browse)
BROWSEGRAB_LLM_PROVIDER=vllm          # vllm | ollama | openai
BROWSEGRAB_LLM_BASE_URL=http://localhost:8000/v1
BROWSEGRAB_LLM_MODEL=Qwen/Qwen3.5-32B-AWQ

# Agent
BROWSEGRAB_AGENT_MAX_STEPS=10
BROWSEGRAB_AGENT_ENABLE_CACHE=true

Part of the QuartzUnit Ecosystem

Library	Role
markgrab	Passive extraction (URL → markdown)
snapgrab	Passive capture (URL → screenshot)
docpick	Document OCR → structured JSON
browsegrab	Active automation (goal → browser actions → results)

Development

bash

git clone https://github.com/QuartzUnit/browsegrab.git
cd browsegrab
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
playwright install chromium

# Unit tests (no browser needed)
pytest tests/ -m "not e2e"

# Full suite including E2E
pytest tests/ -v

License

MIT

_{Part of the QuartzUnit ecosystem — composable Python libraries for data collection, extraction, search, and AI agent safety.}

Related MCP Servers

View all in Browser Automation View all alternatives

Frequently Asked Questions about Browsegrab

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "browsegrab": { "command": "npx", "args": ["-y", "Browsegrab"] } }

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

Live Dynamic SVG Preview

Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/browsegrab?style=directory)](https://allmcps.com/mcp/browsegrab)

HTML Embed

<a href="https://allmcps.com/mcp/browsegrab"><img src="https://allmcps.com/api/badge/browsegrab?style=directory" alt="Browsegrab on AllMCPs" /></a>

Technical Specs & Signals

CategoryBrowser Automation

27Quality signal: Emerging · 27/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20

Documentation & tools11/30

Adoption & activity1/15

Community engagement0/10

★ Featured

AllMCPs Server

Explore Server →

Own this project?

This directory is pre-filled from public sources. Claim via GitHub README, site badge, or DNS TXT to get the verified badge and attach your website.

Free dofollow backlink: after claiming, verify your product site and place a dofollow AllMCPs badge — we recheck it stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 📂 Browser Automation →Best MCP servers for Browser Automation →Alternatives to Browsegrab →Install in Claude Desktop Install in Cursor Install in VS Code