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.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
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.
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.
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
Related MCP server: BAP MCP
pip install browsegrab
playwright install chromiumWith 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] # Everythingfrom 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()# 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"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
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"]browsegrab separates structure (accessibility tree) from content (MarkGrab markdown), sending only what the LLM needs:
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 --> FPage | Interactive elements | Tokens | browser-use equivalent |
example.com | 1 | ~60 | ~500+ |
Wikipedia article | 452 | ~1,254 | ~10,000+ |
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)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:8000/v1
BROWSEGRAB_LLM_MODEL=Qwen/Qwen3.5-32B-AWQ
# Agent
BROWSEGRAB_AGENT_MAX_STEPS=10
BROWSEGRAB_AGENT_ENABLE_CACHE=trueLibrary | Role |
Passive extraction (URL → markdown) | |
Passive capture (URL → screenshot) | |
Document OCR → structured JSON | |
browsegrab | Active automation (goal → browser actions → results) |
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/ -vPart of the QuartzUnit ecosystem — composable Python libraries for data collection, extraction, search, and AI agent safety.
This server cannot be deployed
AI-powered browser automation — navigate, click, fill forms, and extract data from any website.
E2LLM gives your AI eyes and hands in a real browser: structured perception (SiFR) plus action.
Control a real browser from AI agents: navigate, click, type, screenshot, read the page.
Cloud browsers for AI agents: saved logins, residential IPs, live human takeover, 57 browser tools.
