Which integrations are available for this server?

Integrates with Ollama to provide agentic browsing capabilities, allowing local LLMs to reason and perform multi-step browser automation tasks. Supports OpenAI-compatible endpoints to power the agent's reasoning engine for navigating websites and performing complex browser actions. Enables efficient content extraction and navigation of Wikipedia articles by converting HTML to clean markdown for LLM consumption.

How do I use browsegrab?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@browsegrab go to stripe.com, find their pricing page, and summarize the plans" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

browsegrab

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

pip install browsegrab
playwright install chromium

With optional features:

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

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

# 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

browsegrab-mcp  # Start MCP server (stdio)

Claude Desktop / Cursor / VS Code config:

{
  "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

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

Raw HTML
├── Structure: Accessibility tree → interactive elements → [ref=eN]
│   → ~200-500 tokens
└── Content: MarkGrab → clean markdown (on-demand)
    → ~300-800 tokens

Combined: ~500-1,300 tokens per step

Token efficiency (measured)

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

Architecture

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):

# 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:30000/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

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

browsegrab

browsegrab

Features

Installation

Quick Start

Python API

CLI

MCP Server

How It Works

Token efficiency (measured)

Architecture

Configuration

Part of the QuartzUnit Ecosystem

Development

License

Resources

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