AgenticCrawler

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Browser automation agent that acts and observes. Navigate, click, and extract — or inspect network traffic, debug console errors, profile performance, and audit accessibility. One Rust binary.

Single Rust binary. Full DevTools observability. 42 tools. 25 LLM providers. MCP server built-in.

Why acrawl?

Most browser agents stop at navigate and click. acrawl goes further: the agent can also inspect network requests, analyze console errors, measure page performance, audit accessibility, and intercept network calls — the full DevTools surface, available as first-class agent tools.

It ships as a single Rust binary. No Python runtime, no Node runtime, no Docker. Drop it into any server or CI pipeline and describe a goal; the agent figures out what to visit, what to click, what to inspect, and when it's done.

• One binary, zero runtimes. cargo build --release produces a self-contained executable. No Python, no Node runtime — just Rust and a Chromium download for browser automation.
• Acts and observes. The agent has the full DevTools surface as first-class tools: inspect network requests with timing, analyze and deduplicate console logs, stream WebSocket messages, measure page performance (TTFB, resource breakdown), audit cookies and browser storage, measure JS/CSS coverage, run axe-core WCAG accessibility audits, and intercept or mock network calls. No other agent framework exposes this.
• Deterministic where you can, AI where you must. Define loops, conditionals, and parallel branches as JSON scripts — executed without any LLM calls. Fall back to the agent when pages behave unexpectedly. Best of both worlds.
• No code required. Describe the goal in plain English. The agent plans, navigates, and extracts.
• Smart fetching. Static pages are served over HTTP (fast). When JavaScript or interaction is needed, acrawl detects JS framework markers (__next_data__, __nuxt, __vue, ng-app, React roots), auth redirects, and short <noscript> bodies — then transparently escalates to a headless browser.
• Sub-agent parallelism. Fork child agents onto separate browser tabs with independent state and step budgets. A URL-claiming registry prevents siblings from crawling the same page twice.
• MCP client and server. Extend the agent with custom tools via Model Context Protocol servers. Or flip it: acrawl mcp exposes 38 browser and DevTools tools plus run_goal to Claude Code, Cursor, VS Code, Zed, and 13 other clients.
• 25 LLM providers. Anthropic, OpenAI, Google Gemini, DeepSeek, AWS Bedrock, Azure OpenAI, Vertex AI, GitHub Copilot, Groq, Mistral, xAI, Cohere, Alibaba DashScope, OpenRouter, and more. Or bring your own via any OpenAI-compatible endpoint.

How does it compare?

vs. AI web agents and scraping tools

Notes:

• browser-use (85k+ GitHub stars): Python + Playwright, DOM + screenshots, supports GPT/Claude/Gemini/Ollama via LiteLLM, 89.1% WebVoyager. No single binary — requires Python and pip install. Every action calls an LLM: 2-5s/step, ~$0.02-0.30/task. Cloud tier adds stealth; self-hosted is bare Playwright.
• Stagehand (Browserbase, 21k+ stars): TypeScript + CDP (v3), mixes deterministic Playwright with AI primitives (act(), extract(), observe()). Action caching reuses successful clicks without re-calling the LLM. Requires Node and, for production, Browserbase cloud hosting.
• Skyvern (21k+ stars, Apache 2.0): vision-first (screenshot-only, no DOM), handles legacy portals and government forms that DOM tools struggle with. No-code cloud UI available. Each step costs vision-model tokens — ~$0.10-0.50/task. 85.85% WebVoyager.
• Firecrawl (82k+ stars): managed scraping API. Returns LLM-ready Markdown, JSON extraction, site-wide crawl. Not an agentic tool — minimal multi-step interaction. Ships an official MCP server. Per-page pricing from $19/month.
• Playwright MCP (Microsoft, 29k+ stars): MCP server that exposes browser control via the accessibility tree. Sub-100ms actions, zero vision tokens. Drives an LLM client's browser rather than having its own reasoning — no autonomous goal navigation. Used in GitHub Copilot Agent.

vs. native LLM provider browsing

Most AI providers offer some form of browsing, but it is designed for conversational information retrieval, not programmatic web automation. Key constraints:

Notes:

• ChatGPT Agent (OpenAI, July 2025): runs in a sandboxed cloud virtual machine with its own Chromium instance. Can browse, click, and fill forms but pauses for user confirmation on sensitive actions (purchases, logins). Uses two modes: a fast text browser for research queries and a visual browser for interaction. Cannot run code in the browser, install extensions, or access your local file system. Susceptible to prompt injection. Available to Plus/Pro/Business subscribers.
• ChatGPT Atlas (OpenAI, October 2025): a full Chromium browser with ChatGPT integrated as a sidebar + agent. Agent mode drives the same sandboxed cloud VM as ChatGPT Agent; core limitations are identical.
• Claude Computer Use (Anthropic API, beta since October 2024): screenshot + mouse/keyboard API for any desktop application, not just browsers. Vision-only — no DOM access. Developers must provide and manage the entire computing environment (typically a Docker container with Xvfb + Firefox). Not a ready-to-use binary. Requires significant infrastructure to operate in production.
• Claude in Chrome (Anthropic Chrome extension, beta November 2025+): lets Claude operate within your existing Chrome session using your real cookies and logins. Available to Max plan subscribers. Not an open API — no programmatic control. Good for interactive personal tasks; not suitable for batch automation.
• Gemini / Deep Research (Google): browsing is grounded via Google Search API calls, not a live browser session. Deep Research synthesizes across many searches but cannot interact with pages (click, fill forms, navigate dynamically). Project Mariner (experimental computer use) is a separate, limited research preview.
• Copilot / Edge (Microsoft): Edge's Copilot Mode uses Bing retrieval with some ability to navigate pages. Real-world tests show high latency (6+ minutes for multi-page comparison tasks) and frequent interruptions for user confirmation. Not a developer API.

Quick Start

Install

Linux / macOS (x64 / ARM64):

curl -fsSL https://raw.githubusercontent.com/Mingye-Lu/AgenticCrawler/main/install.sh | bash

Windows (x64, PowerShell):

irm https://raw.githubusercontent.com/Mingye-Lu/AgenticCrawler/main/install.ps1 | iex