We tested proprietary web agents, remote browsers, and benchmarked 8 MCP servers across web search and browser automation tasks.
Below are 30+ open-source web agents that enable AI to navigate, interact with, and extract data from the web, including browsing, authentication, and crawling.
- Autonomous web agents and copilots:
- Web automation & scraping toolkits:
- Agent enablement tools:
- Web control frameworks & libraries for developers:
Open-source web agents: Accuracy benchmark
Evaluation: Web Voyager Benchmark
WebVoyager benchmark runs 643 task instances across Google, GitHub, Wikipedia, and 12 other real-world sites. Tasks include form submission, multi-page navigation, and search operations.
Methodology:
- 643 task instances across Google, GitHub, Wikipedia, and other sites
- Success is measured by comparing agent outputs to standard outputs using GPT-4o and GPT-4o-mini
Modified benchmark implementations:
- Browser-Use switched from raw OpenAI calls to LangChain and updated prompts. They removed 55 tasks with outdated data (missing Apple site information, expired flight listings).
- Skyvern ran tests using Skyvern 2.0 with GPT-4V vision processing.
- Agent-E adjusted DOM interaction methods and prompt structure. Their benchmark compared against the original WebVoyager agent, not GPT-4o evaluation.
Autonomous Web Agents and Copilots
Tools that navigate websites and complete multi-step tasks with minimal guidance.
General-purpose autonomous agents (Web-capable)
LLM-based agents that operate websites with little to no oversight.
- AgenticSeek– GitHub (91k stars) – Runs locally and handles web browsing, form completion, and data extraction. Works as an alternative to Manus AI without cloud dependencies.
- Interaction: Text.
- Deployment: Python (self-hosted).
- Auto-GPT– GitHub (76k stars) –Autonomous agent that executes web tasks alongside file operations and code execution. Deploy agents through the browser interface or CLI.
- Interaction: Text.
- Deployment: Python CLI app.
- AgentGPT – GitHub (34k stars) – Web-based platform for configuring autonomous agents in your browser. Create task-specific agents (like “ResearchGPT”) that break down goals into executable steps.
- Interaction: Text (chat interface).
- Deployment: Browser app (self-hostable).
- SuperAGI Github (~15.9k stars) Modular platform for building autonomous agents with browser workflow support. Includes agent templates and deployment options.
- Interaction: Text.
- Deployment: Python framework (self-hosted or cloud)
- Nanobrowser – GitHub (7.1k stars) – Chrome extension that executes multi-agent workflows using natural language. Handles on-page tasks like spreadsheet data extraction, form filling, and navigation.
- Interaction: Text.
- Deployment: Browser extension (Chrome).
- OpenManus – GitHub (600+ stars) – Executes long-running browser tasks locally. Alternative to the commercial Manus service.
- Interaction: Text.
- Deployment: Local deployment via Python and Docker.
Computer-use Agents
Desktop automation systems that control browsers as part of broader computer workflows.
- OpenInterpreter – GitHub (59k+ stars) – CLI agent that runs code and automates browser tasks from terminal commands. Executes Python, JavaScript, and shell scripts based on natural language input.
- Interaction: Text (CLI + browser control).
- Deployment: Python CLI (local or Docker).
- UI-TARS – GitHub (6.5k stars) – Multimodal agent that controls desktop interfaces through screenshot analysis and symbolic commands. Research framework for GUI automation.
- Interaction: Vision + structured input.
- Deployment: Python (research framework).
- AutoBrowser MCP– GitHub (2.5k stars) – Claude-powered Chrome automation via the “Computer Use” API.
- Interaction: Vision + Text.
- Deployment: Chrome extension + local server.
- Open Operatorby Browser-Use team – GitHub (1.7k stars) –Gives LLMs direct Chrome control through simplified DOM interface. Runs autonomously or with feedback loops where you approve each action.
- Interaction: Text + DOM (LLM-controlled)
- Deployment: Python, Browser extension (self-hosted)
Read more: Open Operator: Free alternative to OpenAI’s Operator.
Web navigation agents
Agents that execute multi-step website workflows by analyzing page structure.
- Agent-E – GitHub (1.1k stars) – DOM-aware automation that parses HTML structure to identify interactive elements and navigation paths.
- Interaction: Text (DOM-based parsing).
- Deployment: Python app (with optional UI).
- AutoWebGLM – Github (800+ stars) – Uses HTML simplification and reinforcement learning for navigation decisions. Reduces complex pages to essential elements before planning actions.
- Interaction: Text (HTML structure + LLM).
- Deployment: Python (self-hosted).
Vision-based web navigation agents (Multimodal)
Agents that process webpage screenshots alongside text and DOM to interpret visual layout.
- Autogen extension WebSurfer – GitHub (46k stars, part of Microsoft’s AutoGen framework) – Creates multi-agent teams with web browsing capability. Requires Playwright installation. One agent searches while another handles user interaction.
- Interaction: Vision + Text.
- Deployment: Python library (AutoGen plugin).
- Skyvern – GitHub (13.6k stars) – Automates browser workflows using computer vision and LLMs. Processes screenshots to identify buttons, forms, and navigation elements.
- Interaction: Vision + Text.
- Deployment: Self-hosted server (or managed cloud).
- WebVoyager – GitHub (800+ stars) – Academic research prototype that combines screenshot analysis with text extraction. Developed for testing vision-based navigation approaches.
- LiteWebAgent –Github (90+ stars) – VLM-based agent with memory and planning capabilities. Controls browsers through Chrome DevTools Protocol.
- Interaction: Vision + text.
- Deployment: Python-based framework (self-hosted).
Agent enablement tools
Frameworks that let LLMs or users send commands to browsers without autonomous task planning.
Natural Language to Web Action Tools
Convert natural language instructions into browser commands.
- LaVague – GitHub (6k+ stars) – Translates prompts like “click the green button” into browser actions. Handles element identification and interaction.
- Interaction: Natural language to browser commands
- Deployment: Python (self-hosted).
- ZeroStep – GitHub (300+ stars) – Converts text prompts into Playwright test steps for UI automation.
- Interaction: Text prompts to Playwright scripts
- Deployment: Node.js CLI.
LLM-browser bridges
Enable language models to control browsers through developer protocols. Provide execution capability without task planning.
- Browser-Use – GitHub (63k stars) – Converts DOM into LLM-friendly format with control interfaces.
- Interaction: Text (DOM-level).
- Deployment: Python library / API (self-hosted or cloud).
- Browserless – GitHub (10.3k stars) – Cloud-hosted/headless Chrome via REST/WebSocket APIs for remote browser control.
- Interaction: Text (HTTP/WebSocket).
- Deployment: Hosted API or Docker.
- ZeroStep (Playwright AI) – GitHub (311 stars) – AI-powered UI testing framework using Playwright + prompt-based instruction.
- Interaction: Text.
- Deployment: Node.js + Playwright.
Web automation & scraping toolkits
Tools for specific automation tasks like data extraction or form filling. You initiate each job rather than setting high-level goals.
LLM-powered web RPA and browser extensions
Browser tools that automate clicking, typing, and scraping through prompts or workflow recordings.
- PulsarRPA – GitHub (800+ stars) – An AI browser automation tool for data extraction tasks.
- Interaction: Text.
- Deployment: Chrome extension + backend.
- VimGPT – GitHub (2.7k stars) – A project that uses GPT-4 Vision to control a browser via the Vimium extension. It interprets the rendered web page as an image, allowing the language model to generate appropriate keyboard commands for navigation and interaction.
- Interaction: Vision + Text.
- Deployment: Browser plugin (Vimium) plus Python
AI web scrapers and crawlers
Extract and structure website data using LLMs or parsing rules.
- Crawl4AI – GitHub (46k stars) – An open-source web crawler that integrates LLMs for smarter navigation and extraction.
- Interaction: Text.
- Deployment: Python.
- FireCrawl – GitHub (40k stars) – An open API and tool for turning websites into Markdown or JSON. Crawls web pages and converts the content (text, links, etc.) into structured data that LLMs can parse.
- Interaction: Text.
- Deployment: Node.js library/CLI.
- GPT-crawler – GitHub (21k stars) – Crawl a site to generate knowledge files to create your own custom GPT from a URL.
- Interaction: Text.
- Deployment: Python CLI tool.
- ScrapeGraphAI – GitHub (20k stars) – A Python-based AI scraper that builds a “knowledge graph” from website content. Best for crawling documentation or articles and outputting a structured summary or graph of facts.
- Interaction: Text.
- Deployment: Python.
- AutoScraper – GitHub (6.8k stars) – Lightweight web Scraper for Python.
- Interaction: Text (prompt + examples).
- Deployment: Python library (self-hosted).
- LLM Scraper – GitHub (5k stars) – A web scraping tool that uses an LLM to parse content. Instead of static HTML parsing, it asks an LLM what data to pull from a page (based on user intent) and how to format it.
- Interaction: Text.
- Deployment: Python.
AI web search tools
Combine search engines with LLM processing for conversational search.
- BingGPT – GitHub (9.2k stars) – An AI-powered search chat app that leverages Bing Search + GPT to provide direct Q&A from the web.
- Interaction: Text (chat-based).
- Deployment: Application deployment (desktop).
- BraveGPT – GitHub (150+ stars) – Integrates GPT responses with Brave Search results, overlaying contextual LLM output directly onto SERPs.
- Interaction: Text
- Deployment: Browser extension
Web control frameworks for developers
Low-level libraries for browser automation, testing, and scraping.
Web testing and UI automation frameworks
Simulate user interactions for application testing across browsers.
- Playwright – GitHub (73k stars) – Microsoft-backed browser automation framework supporting Chromium, Firefox, and WebKit. Enables powerful cross-browser automation with built-in waits, context isolation, and more.
- Interaction: Code-based (JavaScript, Python, .NET, Java).
- Deployment: Multi-language SDKs + CLI tools.
- Selenium – GitHub (32k stars) – A browser automation framework for cross-browser UI automation and testing that lets developers simulate real user behavior across browsers.
- Interaction: Code-based (multi-language: Python, Java, C#, etc.).
- Deployment: WebDriver server + language bindings.
- taiko – GitHub (3k stars) – A Node.js framework by ThoughtWorks for browser automation with readable syntax. Great for functional testing and scripting UI flows.
- Interaction: Code-based (JavaScript).
- Deployment: Node.js environment.
Web control and automation libraries
Programmatic browser control for scraping, automation, and AI integration.
- Puppeteer – GitHub (91k stars) – A Node.js library for controlling headless Chrome or Chromium. Offers a high-level API to automate screenshots and scraping.
- Interaction: Code-based (JavaScript/TypeScript).
- Deployment: Node.js app or script.
- Browser-Use (Also LLM-Bridge) – GitHub (63k stars) – A developer-friendly framework that converts the DOM into a structured format suitable for LLMs. Offers control interfaces for navigating and interacting with web pages programmatically.
- Interaction: Text-based (DOM-level abstraction).
- Deployment: Python library/API (self-hosted).
What Makes These Web Agents Different
Browser-Use scored 89% on WebVoyager benchmark tests, while Agent-E reached 73%. Browser use relies on autonomous task planning. You describe a goal, and it breaks down the steps. Agent-E parses DOM structure directly, which is faster but requires clearer page markup.
Autonomy levels
Fully autonomous: Browser-Use, Skyvern, and Agent-E accept high-level goals (“find cheapest Paris flight”) and plan their own navigation steps. They adapt to unexpected elements like cookie banners or captchas. However, each decision requires an LLM call, increasing both cost and response time.
Step-by-step guidance: LaVague and ZeroStep execute specific commands (“click search button,” “enter text in field 2”). Faster execution since they skip planning overhead. But if a site redesigns its layout, you’ll need to update your instructions manually.
Manual coding: Playwright and Selenium require you to write explicit code for every click, form fill, and navigation. Your tests run each time identically until the site changes an element ID or class name. Then your selectors break and you rewrite the code.
How they interpret pages
Vision-based processing
Skyvern, WebVoyager, and VimGPT capture screenshots and send them to vision models like GPT-4V. They identify buttons and forms the same way you do, by looking at the rendered page.
This works on JavaScript-heavy sites where the DOM rebuilds after page load. But GPT-4V charges per image token, making each page view 10-20x more expensive than reading HTML. Vision models also add 2-3 seconds per page compared to DOM parsing.
DOM parsing
Browser-Use and Agent-E read page HTML directly. They scan for clickable elements, input fields, and navigation links in the code.
DOM parsing costs less and runs faster. Browser-Use’s 89% accuracy comes partly from skipping expensive vision calls. But it struggles when sites use shadow DOM, obfuscated class names, or heavy JavaScript DOM manipulation.
Combined approach
LiteWebAgent and AutoWebGLM parse DOM for structure, then use vision to verify what users actually see. More accurate than DOM alone, cheaper than pure vision, but you’re running two systems per page.
Specialization
Auto-GPT and AgenticSeek handle web browsing alongside file operations and code execution. They lack web-specific features such as proxy rotation and cookie management, which limits their effectiveness on sites with bot detection.
Agent-E and WebVoyager only do web navigation. They include optimizations for multi-page forms, session handling, and authentication flows. Agent-E’s 73% score reflects this focus, as it handles complex checkout processes that general agents fail.
Crawl4AI and FireCrawl extract data and convert pages to Markdown or JSON. They don’t fill forms or click through workflows. Use them when you need content in a structured format, not when you need to complete multi-step tasks.
Playwright and Selenium automate browser testing. They produce identical results across runs, essential for regression tests. But this determinism means they can’t adapt. When a site changes, your test suite breaks.
Deployment options
Local execution: AgenticSeek, Nanobrowser, and OpenInterpreter run on your machine. Your browsing data stays local, and you avoid API costs. But a typical workstation handles 5-10 concurrent browser instances before CPU/RAM maxes out.
Cloud APIs: Browserless provides remote Chrome instances via REST or WebSocket. You can spin up hundreds of parallel sessions with automatic proxy rotation. Each request adds 100-300ms latency compared to local browsers, and your traffic routes through their servers unless you self-host with Docker.
Flexible deployment: Skyvern runs locally during development, then deploys to cloud for production. This flexibility contributed to its 86% benchmark score, which was tested in both environments.
Integration patterns
AutoGen’s WebSurfer requires adopting Microsoft’s entire multi-agent framework. You get built-in agent orchestration and memory management, but you can’t easily integrate it with existing systems.
Browser-Use and Playwright work as standalone libraries. Drop them into any Python or Node.js project. But you’ll build your own agent coordination, error handling, and result storage.
Nanobrowser and BraveGPT install as Chrome extensions. No server setup required, add to the browser and start. Can’t scale beyond a few concurrent tabs, and they don’t integrate with backend automation pipelines.
Production considerations
Skyvern and Browserless include residential proxy support, randomized mouse movements, and browser fingerprint rotation. These features prevent IP bans and CAPTCHA triggers on protected sites.
WebVoyager and AutoWebGLM focus on navigation algorithms rather than anti-detection. Their 59-73% benchmark scores demonstrate solid path-finding logic. But production sites with Cloudflare or DataDome will block them within minutes.
The benchmark tests run on cooperative sites without bot protection. Real-world success requires both navigation intelligence and evasion capabilities, which explains why some tools with lower benchmark scores outperform higher-scoring alternatives on protected sites.
Benchmark sources
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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