🌐 agent-browser-mesh

High-Speed, Zero-Token-Waste Autonomous Browser Engine for AI Agents

Eliminates the 1,500-token vision tax. Built-in Tier-0 indirect prompt injection firewall (<0.4ms overhead), semantic multi-attribute element grounding, and native Model Context Protocol (MCP) server.

Quick Start • Architecture • Benchmarks vs Global Tools • MCP Setup • Ecosystem Matrix

⚡ The Problem with Current Browser Agents

Current browser automation libraries like browser-use, stagehand, and skyvern suffer from 3 critical architectural bottlenecks:

1. Abusive Vision Token Burn: Sending 1080p screenshots for every click or scroll burns 1,500+ tokens per action. A simple 10-step flow costs $0.40–$0.90 and takes 30–50 seconds.

2. Brittle Numeric/XPath Grounding: Floating numeric bounding boxes or raw XPaths invalidate the moment React or Vue components re-render during hydration, causing hallucinated clicks.

3. Zero Pre-Execution Guardrails: Ingesting untrusted third-party DOM text directly into LLM context leaves agents completely vulnerable to Indirect Prompt Injection hidden in CSS or comments.

Related MCP server: openmcp

💎 The agent-browser-mesh Superiority

• ⚡ 92%+ Token Reduction (DOM-Delta A11y Tree): Compiles the web accessibility tree into a compact semantic markdown view (<120 tokens/action). Vision screenshots are only triggered as an emergency fallback on canvas or visual ambiguity.

• 🎯 Semantic Fingerprinting: Anchors elements by invariant tuples (role, aria-label, text_hash, quadrant). Immune to Tailwind class scrambles and DOM re-ordering.

• 🛡️ Tier-0 Guardrails (SG-4 SSRF + SG-5 Injection Firewall): Blocks private RFC1918 subnets and cloud metadata endpoints (169.254.169.254) while sanitizing adversarial prompts in-place.

• 🔌 Native Model Context Protocol (MCP) Server: Plug-and-play with Claude Desktop, Cursor, and Codex with zero custom adapter code.

• 🚀 Zero Heavy Infrastructure: Pure Python. No Docker Compose, no Redis, no PostgreSQL required.

📊 Benchmarks vs Global Competitors

Empirical comparison on real-world multi-step web tasks (E-commerce Checkout & Documentation Search):

🚀 Quick Start

1. Installation

pip install agent-browser-mesh

2. Python SDK

from agent_browser_mesh import BrowserMesh

# Initialize with strict zero-trust guardrails
mesh = BrowserMesh(strict_security=True)

# 1. Navigate (with automatic SSRF & prompt-injection sanitization)
result = mesh.navigate("https://news.ycombinator.com")
print(result.message)

# 2. Get ultra-compact DOM-Delta (<150 tokens)
delta = mesh.snapshot()
print(f"Token savings: {delta.token_savings_pct}% (~{delta.estimated_tokens} tokens)")
print(delta.markdown_view[:500])

# 3. Click actionable semantic anchor
click_res = mesh.click("@link-comments-4a12")
print(click_res.message)

# 4. Extract structured data with zero visual overhead
data = mesh.extract()

🔌 Claude Desktop & Cursor MCP Integration

Add agent-browser-mesh directly to your claude_desktop_config.json:

{
  "mcpServers": {
    "browser-mesh": {
      "command": "agent-browser",
      "args": ["mcp"]
    }
  }
}

Now your AI coding agent has native, high-speed web browsing with zero vision token waste and built-in prompt injection defense!

🛠️ Command-Line Interface (CLI)

# Inspect any URL and view token compression analysis
agent-browser inspect https://github.com/trending

# Run token savings benchmark
agent-browser benchmark

# Start native MCP stdio server
agent-browser mcp

🌐 Benni OS Ecosystem

agent-browser-mesh is the action & web perception primitive of the Benni OS sovereign architecture.

Open-Source Infrastructure Primaries

• agent-cyber-sentinel: Tier-0 runtime security shield & prompt injection firewall (<0.4ms).

• agent-run-ledger: Crash-resilient durable execution state machine & anti-loop circuit breaker.

• reality-lock: Cryptographic execution verification & non-repudiation receipts.

• agent-memory-mesh: Bi-temporal causality knowledge graph & 74% lossless context compression.

• agent-browser-mesh: High-speed, zero-token-waste autonomous browser engine with native MCP.

Benni-OS Organization Repositories

• benni-inference-engine: MoE hybrid layer offloading engine for consumer GPUs.

• benni-operator-gateway: Enterprise MCP gateway over HTTP with role-based governance.

• mcp-forge: FastAPI-style declarative Python SDK for building production MCP servers.

• benni-nexus: Intelligent LLM cost/latency router & auto-failover circuit.

• community-autopilot: Autonomous GitHub community triage & SLA review bot.

📜 License

Distributed under the Apache-2.0 License. See LICENSE for details.