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
The WebVoyager benchmark tests web agents across 15 real-world websites with tasks like searching, clicking, navigating, and submitting forms.
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
Some vendors modified the WebVoyager benchmark setup for their evaluations:
- Browser-Use: Adjusted prompts and migrated from raw OpenAI calls to LangChain. Removed 55 tasks they considered unsolvable or outdated (missing Apple site data, outdated flight listings).
- Skyvern (Steel.dev): Retested using Skyvern 2.0 with GPT-4V.
- Agent-E (Emergence AI): Fine-tuned DOM-based interaction and prompt structure. Benchmarked against WebVoyager, not GPT-4o.
Autonomous Web Agents and Copilots
These tools autonomously navigate websites and perform multi-step tasks with minimal user guidance.
General-purpose autonomous agents (Web-capable)
LLM-based agents that operate websites with little to no oversight.
- AgenticSeek – GitHub (91k stars) – A local-first alternative to Manus AI. AgenticSeek can autonomously browse the internet, search, read, extract information, and fill out web forms.
- Interaction: Text.
- Deployment: Python (self-hosted).
- Auto-GPT – GitHub (76k stars) – General-purpose autonomous AI agent with web capabilities. Build, deploy, and run AI agents in your browser.
- Interaction: Text.
- Deployment: Python CLI app.
- Auto-GPT – GitHub (76k stars) – General-purpose autonomous AI agent with web capabilities. Build, deploy, and run AI agents in your browser.
- Interaction: Text.
- Deployment: Python CLI app.
- AgentGPT – GitHub (34k stars) – A web-based platform to configure and deploy autonomous agents directly in your browser. You can create a custom AI agent (e.g., “ResearchGPT”) that plans steps and executes a given goal online.
- Interaction: Text (chat interface).
- Deployment: Browser app (self-hostable).
- SuperAGI Github (~15.9k stars) A modular, open-source platform for building autonomous agents, including browser workflows.
- Interaction: Text.
- Deployment: Python framework (self-hosted or cloud)
- Nanobrowser – GitHub (7.1k stars) – An open source alternative to OpenAI’s Operator, local-first AI web agent that runs as a Chrome extension. It enables you to execute multi-agent workflows in your browser using natural language prompts. Can be used for on-page automation such as extracting data to a spreadsheet, clicking pages, and completing forms.
- Interaction: Text.
- Deployment: Browser extension (Chrome).
- OpenManus – GitHub (600+ stars) – An open-source alternative to Manus. A general AI agent that can execute long-running tasks across browsers.
- Interaction: Text.
- Deployment: Local deployment via Python and Docker.
Computer-use Agents
LLM-driven systems that simulate or control real desktop environments, including browsers. Not all are built exclusively for the web, but many can perform web automation as part of broader desktop workflows.
- OpenInterpreter – GitHub (59k+ stars) – CLI-based agent that interprets natural language to execute code, browse, and automate tasks across terminal and browser environments.
- Interaction: Text (CLI + browser control).
- Deployment: Python CLI (local or Docker).
- UI-TARS – GitHub (6.5k stars) – Multimodal GUI agent that combines perception, reasoning, and action to operate desktop interfaces via screenshots and symbolic control.
- 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 Operator by Browser-Use team – GitHub (1.7k stars) – A local-first agent built on Browser-Use that gives LLMs low-level control over Chrome through a simplified DOM interface. Runs autonomously or with user feedback loops.
- Interaction: Text + DOM (LLM-controlled)
- Deployment: Python, Browser extension (self-hosted)
Read more: Open Operator: Free alternative to OpenAI’s Operator.
Web navigation agents
Goal-oriented agents designed to plan and execute multi-step sequences across a website using text or DOM structure (e.g., log in → fill form → submit → confirm) by reasoning over page structure and flow.
- Agent-E – GitHub (1.1k stars) – DOM-aware browser automation agent.
- Interaction: Text (DOM-based parsing).
- Deployment: Python app (with optional UI).
- AutoWebGLM – Github (800+ stars) – An LLM-based web agent that leverages HTML simplification and reinforcement learning for better navigation.
- Interaction: Text (HTML structure + LLM).
- Deployment: Python (self-hosted).
Vision-based web navigation agents (Multimodal)
Goal-oriented agents that interpret visual representations of web pages (screenshots or rendered UI) in addition to text or DOM. Typically powered by Vision Language Models like GPT-4V or BLIP.
- Autogen extension WebSurfer – GitHub (46k stars, part of Microsoft’s AutoGen framework) – A multimodal agent that can search the web. With WebSurfer, you can create a group chat team that includes a WebSurfer agent and a user proxy agent for web browsing tasks. You need to install Playwright to build it.
- Interaction: Vision + Text.
- Deployment: Python library (AutoGen plugin).
- Skyvern – GitHub (13.6k stars) – An open-source AI agent that automates browser workflows using LLMs and computer vision.
- Interaction: Vision + Text.
- Deployment: Self-hosted server (or managed cloud).
- WebVoyager – GitHub (800+ stars) – A vision-enabled web agent from academic research that combines page text with screenshots to improve web navigation.
- Interaction: Multimodal (text + vision).
- Deployment: Research prototype (Python).
- LiteWebAgent – Github (90+ stars) – A VLM-based agent that combines memory, planning, and browser control using the Chrome DevTools Protocol.
- Interaction: Vision + text.
- Deployment: Python-based framework (self-hosted).
Agent enablement tools
Frameworks that let LLMs or users control browsers using natural language or structured UI, without full autonomy.
Natural Language to Web Action Tools
Agents that enable users (or LLMs) to control web interfaces using natural language instructions (e.g., “click the green submit button”).
- LaVague – GitHub (6k+ stars) – Maps natural language prompts (e.g., “click the green button”) to browser actions.
- Interaction: Text (natural language → browser command).
- Deployment: Python (self-hosted).
- ZeroStep – GitHub (300+ stars) – Converts text prompts into Playwright test steps for UI automation.
- Interaction: Text (prompt → Playwright script).
- Deployment: Node.js CLI.
LLM-browser bridges
Interfaces that enable language models to control browsers using developer protocols such as Playwright.
These tools provide execution capabilities, allowing LLMs to interact with web environments, but do not perform task planning or goal management.
- 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
These tools don’t perform multi-step tasks autonomously. Instead, they focus on automating specific web tasks (such as form filling or data extraction) with some degree of agency. You initiate each job or target site rather than having the tool autonomously complete goals.
LLM-powered web RPA and browser extensions
Browser tools (often extensions or apps) that let users or LLMs automate tasks like clicking, typing, or scraping, typically via prompts, recordings, or simple workflows.
- 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
Tools that crawl websites and extract structured data using LLMs or rule-based logic.
- 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
Tools that crawl websites and extract structured data using LLMs or rule-based logic.
- 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
Developer-centric libraries that expose low-level APIs to automate repetitive tasks, test web applications, or scrape web content.
Web testing and UI automation frameworks
Tools designed to simulate user interactions for testing web applications across browsers and devices. Best for regression and end-to-end testing.
- Playwright – GitHub (73k stars) – Microsoft-backed browser automation framework supporting Chromium, Firefox, and WebKit. Enables powerful automation across browsers 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
Developer-focused libraries that provide programmatic access to browser actions for tasks like scraping, automation, or integrating with AI systems.
- 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
The benchmark results show big performance gaps between web agents. These differences aren’t random – they reflect fundamental choices about what problems each tool tries to solve.
The biggest difference is how independently agents operate:
Fully autonomous agents like Browser-Use (89% accuracy), Skyvern (86%), and Agent-E (73%) work with minimal human guidance. You give them a high-level goal like “find the cheapest flight to Paris,” and they figure out the steps themselves – navigating search forms, comparing results, handling unexpected popups.
This autonomy is powerful but slower and more expensive. These agents spend time “thinking” about what to do next, which requires more LLM calls.
Semi-autonomous agents like LaVague and ZeroStep need more specific instructions for each step. Tell them “click the search button” rather than “find flights.” They’re faster and more predictable but can’t adapt when websites change unexpectedly.
Developer frameworks like Playwright and Selenium give you complete control but zero intelligence. You write code for every possible scenario. Rock-solid reliability for known workflows, useless when something unexpected happens.
How They “See” Websites
Vision-based agents (Skyvern, WebVoyager, VimGPT) use AI vision models to look at webpage screenshots, just like humans do. They see buttons, forms, and layouts visually instead of reading HTML code.
Why this matters: They handle dynamic, JavaScript-heavy sites better. When a website completely rebuilds its DOM after loading, vision-based agents don’t care – they just see what’s on screen.
The cost: Vision processing is expensive. Every page view requires calling GPT-4V or similar models, which adds up fast. They’re also slower than DOM-based alternatives.
DOM-based agents (Browser-Use, Agent-E) parse HTML structure directly. They read the page’s code and figure out what’s clickable, what’s a form field, and where to navigate.
Why this matters: Much faster and cheaper than vision processing. Browser-Use achieves 89% accuracy while keeping costs down by avoiding expensive vision model calls.
The limitation: Struggles with heavily obfuscated code, shadow DOM elements, or sites that extensively modify themselves with JavaScript after loading.
Hybrid approaches (LiteWebAgent, AutoWebGLM) use both methods – DOM for structure, vision for visual context. This balances cost and capability but adds complexity.
What They’re Built For
General-purpose agents like Auto-GPT (76k GitHub stars) and AgenticSeek (91k stars) handle web tasks plus file management, code execution, and other operations. They’re versatile but not optimized specifically for web navigation. They lack specialized anti-bot measures and web-specific optimizations.
Web navigation specialists like Agent-E and WebVoyager focus exclusively on multi-step web workflows. They’re optimized for form completion, authentication flows, and complex navigation. This focus explains Agent-E’s 73% success rate on difficult web tasks.
Data extraction tools like Crawl4AI (46k stars) and FireCrawl (40k stars) excel at scraping and converting web content to structured formats (Markdown, JSON). Great for gathering data, but they can’t handle interactive tasks like filling forms or clicking through multi-page workflows.
Testing frameworks like Playwright (73k stars) and Selenium (32k stars) ensure reliable, repeatable browser testing. They’re deterministic by design – great for consistent test results, terrible for handling unexpected page changes.
Where They Run
Local agents like AgenticSeek, Nanobrowser, and OpenInterpreter run entirely on your machine. Maximum privacy, no external dependencies, but limited scaling. You can’t easily run 250 concurrent tasks like cloud-based solutions can.
Cloud-hosted agents like Browserless run on remote servers with APIs. They scale horizontally, handle sophisticated proxy rotation, and include enterprise-grade anti-bot measures. The tradeoff: latency, internet dependency, and potential privacy concerns.
Hybrid options like Skyvern work both ways – develop locally, deploy to cloud for production. This flexibility helped Skyvern achieve 86% accuracy across different testing and production environments.
Integration Complexity
Framework-integrated agents like AutoGen’s WebSurfer work within larger ecosystems. You get powerful multi-agent orchestration and memory management, but you must adopt the entire framework.
Standalone libraries like Browser-Use (63k stars) and Playwright integrate into any existing codebase. More flexible but you build surrounding infrastructure yourself.
Browser extensions like Nanobrowser and BraveGPT run directly in Chrome. Zero server infrastructure needed, but limited scaling and can’t run headless or integrate with backend systems.
Production Readiness
Enterprise-grade agents implement sophisticated anti-bot evasion – proxy rotation, browser fingerprinting, human-behavior simulation. These features aren’t measured in academic benchmarks but matter hugely for real-world use. This is why commercial offerings often outperform open-source tools in production despite similar benchmark scores.
Research-focused agents like WebVoyager and AutoWebGLM prioritize algorithmic innovation over anti-detection. Their 59-73% benchmark performance shows strong navigation logic, but they may fail on production websites with aggressive bot protection.
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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