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: GitHub Stars
Evaluation: Web Voyager Benchmark
Web Voyager Benchmark Results
The benchmark tests 643 tasks across Google, GitHub, Wikipedia, and 12 other real sites. Tasks include form submission, multi-page navigation, and search operations.
Top performers:
- Browser-Use: 89.1%
- Skyvern 2.0: 85.85%
- Agent-E: 73.1%
- WebVoyager: 57.1%
Comparing the tests:
Each team modified the benchmark differently, making direct score comparisons difficult.
Browser-Use tested 586 tasks after removing 55 outdated ones. Removed tasks included Apple products no longer available, expired flight dates, and recipes deleted from source websites. Tests ran on local machines using GPT-4o for evaluation. Technical changes: migrated from OpenAI API to LangChain, rewrote system prompts.
Skyvern ran 635 tasks in Skyvern Cloud using async cloud browsers. Removed 8 tasks with invalid answers. Updated 2023/2024 dates in flight/hotel tasks to 2025. Cloud testing exposes agents to bot detection and CAPTCHA that local testing avoids. Full test recordings available at eval.skyvern.com showing each action and decision.
Agent-E tested the complete 643-task dataset without modifications. Used DOM parsing only – no vision models or screenshots. Comparison baseline: original WebVoyager agent, not GPT-4o evaluation. Performance dropped on sites with dynamic forms where the DOM structure changes after user input (dropdowns revealing new fields based on selections).
Autonomous Web Agents and Copilots
Tools that navigate websites and complete multi-step tasks with minimal guidance.
General-Purpose Autonomous Agents
AgenticSeek: Replace Manus AI with a local alternative that doesn’t send your browsing data to the cloud. Install it on your machine, describe what you need (“extract all product prices from this page”), and it handles the clicking and data collection. Python-based, runs entirely self-hosted.
Auto-GPT: does more than just web browsing – it also handles file operations and code execution. You can deploy it through a browser interface or command line. When you give it a task like “research competitor pricing and save to spreadsheet,” it figures out which websites to visit, what data to grab, and how to organize the output.
AgentGPT: Configure agents directly in your browser without touching code. Create specialized agents like “ResearchGPT” or “DataGPT” that break down your goals into steps. The platform handles the orchestration – you just describe what you want accomplished. Self-hostable if you don’t want to use their hosted version.
SuperAGI: Framework for building custom autonomous agents. Comes with templates for common workflows, but you can extend it with your own logic. Handles browser automation as one component of larger workflows. Deploy locally or push to cloud infrastructure.
Nanobrowser: Chrome extension approach – install it, then control agents from your browser toolbar. Good for quick tasks like “extract all emails from this page” or “fill out this form with data from my spreadsheet.” Doesn’t scale beyond a few tabs, but requires zero server setup.
OpenManus: The open source answer to Manus’s commercial service. Runs browser tasks that take hours or days – like monitoring a site for price changes or waiting for a product to come back in stock. Deploy locally with Python and Docker, keep it running in the background.
Computer-Use Agents
Desktop automation that controls browsers as one piece of broader computer workflows.
OpenInterpreter: Terminal-based agent that executes Python, JavaScript, and shell scripts based on what you type. Ask it to “scrape this site and analyze the data in pandas,” and it writes the scraping code, runs it, then does the analysis. Browser automation integrates with file system access and data processing.
UI-TARS: Research framework from academia. Takes screenshots of your desktop, analyzes them with vision models, then generates commands to control GUI elements. Built for testing new approaches to desktop automation, not production use.
AutoBrowser MCP: Connects Claude’s “Computer Use” API to Chrome. Claude sees your browser screen, decides what to click, and executes the action. Runs as a Chrome extension plus a local server. The vision model handles layout interpretation.
Open Operator: Browser-Use the team’s answer to OpenAI’s Operator. Provides language models with direct access to Chrome via a simplified DOM view. Run it fully autonomous, or enable approval mode where you confirm each action before it executes. Install via Python or browser extension.
Web Navigation Agents
Focus specifically on multi-step website workflows.
Agent-E: Reads page HTML to find clickable elements and navigation paths. Uses “DOM Distillation” to strip pages down to essential interactive elements, plus “Skill Harvesting” to remember successful patterns. Scored 73.1% on WebVoyager benchmark using pure text – no vision models. Struggles when dropdown menus dynamically reveal new options.
AutoWebGLM: Simplifies HTML before feeding it to the language model. Complex pages get reduced to core navigation elements and form fields. Uses reinforcement learning to improve navigation decisions over time. Runs self-hosted via Python.
Vision-Based Navigation Agents
Combine screenshots with text analysis to interpret visual page layout.
Autogen WebSurfer Extension: Plug into Microsoft’s AutoGen framework to add web browsing. Requires Playwright installation. The framework lets you create agent teams – one agent searches while another processes results, and a third interacts with you. Good if you’re already using AutoGen, the framework overhead isn’t worth it.
Skyvern: Three-phase system: planner breaks tasks into steps, actor executes them, validator confirms success. Takes screenshots to identify buttons and forms visually. This approach handles JavaScript-heavy sites where the DOM changes after page load. Scored 85.85% on WebVoyager. Deploy self-hosted or use their managed cloud.
WebVoyager: Academic research prototype, not production-ready. Combines screenshot analysis with text extraction to test vision-based navigation theories. Scored 57.1% on its own benchmark. Useful for understanding the research direction, not for actual automation work.
LiteWebAgent: Vision language model with memory and planning. Controls Chrome through DevTools Protocol. Maintains context across page loads, remembering what it saw on previous pages when making navigation decisions. Python framework, self-hosted deployment.
Frameworks that let LLMs or users send commands to browsers without autonomous task planning.
Natural Language to Web Action
LaVague: you say, “Click the green button.” LaVague finds it and clicks it. Handles element identification across different page layouts. Good for repetitive tasks where you know exactly what you want but don’t want to write selectors. Python-based, runs self-hosted.
ZeroStep: Turns conversational instructions into Playwright test code. You describe the action in plain English, it generates the Playwright commands. Speeds up test writing if you’re already using Playwright. Node.js CLI tool.
LLM-Browser Bridges
Connect language models directly to browser controls.
Browser-Use: Takes messy DOM and restructures it for LLMs. Strips out irrelevant elements, labels interactive components, and provides control interfaces. This is what let Browser-Use hit 89.1% on WebVoyager. Available as a Python library or API, deploy self-hosted or use their cloud.
Browserless: Remote Chrome instances you control via REST or WebSocket. Spin up hundreds of browsers in the cloud without managing infrastructure. Each browser runs headless, so no GUI overhead. Use their hosted API or Docker for self-hosting.
ZeroStep (Playwright AI): AI layer on top of Playwright. Write prompts instead of selectors. Combines Playwright’s reliability with LLM flexibility for identifying elements. Requires Node.js and Playwright installation.
Task-specific tools, where you initiate each job individually.
Browser Automation Extensions
PulsarRPA: Chrome extension for data extraction. Point it at a table or list, show it what to extract, it handles the rest. Includes backend for scheduling and storing results. Good for non-technical users who need to pull data regularly.
VimGPT: Experimental: GPT-4 Vision controls your browser through Vimium keyboard shortcuts. The model sees screenshots and generates keyboard commands. Interesting research project, not practical for real work. Requires Vimium extension plus Python backend.
AI Scrapers and Crawlers
Crawl4AI: Crawler that uses LLMs to decide what’s important on a page. Instead of grabbing everything, it identifies relevant content based on your goal. Python-based, integrates with standard scraping libraries.
FireCrawl: Converts websites into clean Markdown or JSON. Handles navigation, JavaScript rendering, and content extraction. Output is structured for feeding into LLM context windows. Node.js library or CLI.
GPT-crawler: Crawls a site and outputs training data for custom GPTs. Point it at documentation or a knowledge base, it extracts content and formats it for fine-tuning. Python CLI tool.
ScrapeGraphAI: Builds knowledge graphs from crawled content. Good for documentation sites where you need to understand relationships between concepts. Outputs structured summaries or fact graphs. Python deployment.
AutoScraper: Learn-by-example scraper. Show it one page with the data you want, and it figures out the pattern and applies it to similar pages. Lightweight Python library for simple extraction tasks.
LLM Scraper: Send a page to an LLM and ask, “extract all product prices” or “find contact information.” The model interprets your intent and pulls relevant data. Flexible but more expensive than rule-based scrapers. Python-based.
AI Search Tools
BingGPT: Chat interface that combines Bing search with GPT responses. Ask questions, get answers with sources. Desktop application, not browser-based.
BraveGPT: AI rowser extension that adds GPT responses to Brave Search results. See both traditional search results and an AI summary side-by-side. Overlays directly onto search pages.
Web Control Frameworks for Developers
Low-level libraries for programmatic browser control.
Testing Frameworks
Playwright: Microsoft’s cross-browser automation. Supports Chromium, Firefox, WebKit. Built-in waits, network interception, and mobile emulation. Available in JavaScript, Python, .NET, and Java. Industry standard for modern web testing.
Selenium: The original browser automation framework. Works across all major browsers. Larger ecosystem but older architecture. Language bindings for Python, Java, C#, Ruby, more. WebDriver protocol standard.
taiko: ThoughtWorks framework with readable syntax. Good for functional testing where test readability matters. Node.js only.
Automation Libraries
Puppeteer: Google’s library for controlling Chrome/Chromium. High-level API for screenshots, PDF generation, and scraping. Node.js ecosystem works with TypeScript. Standard choice for headless Chrome automation.
Browser-Use: Listed earlier as LLM bridge, but also works as a developer automation library. Converts DOM into sa tructured format, handles navigation and interaction. Python library with API option.
What Makes These Web Agents Different
Browser-Use scored 89.1% on WebVoyager tests (after removing 55 outdated tasks), while Agent-E reached 73.1% on the full dataset. Browser-Use uses autonomous task planning with LangChain integration. Agent-E parses DOM structure directly without vision models, which runs faster but struggles when websites use dynamic dropdowns or reveal new options based on user choices.
Autonomy Levels
Fully autonomous agents like 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 tools like 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 need to update instructions manually.
Manual coding frameworks like Playwright and Selenium require explicit code for every click, form fill, and navigation. Tests run identically each time until the site changes an element ID or class name. Then selectors break and you rewrite the code.
How They Interpret Pages
Vision-based processing: Skyvern 2.0, WebVoyager, and VimGPT capture screenshots and send them to vision models like GPT-4V. They identify buttons and forms by looking at the rendered page.
Skyvern 2.0 actually uses a planner-actor-validator loop. The planner breaks down complex tasks into smaller goals, the actor executes them, and the validator confirms whether each goal succeeded. This three-phase approach helped Skyvern jump from 45% (single-prompt version) to 68.7% (with planner) to 85.85% (with validator checking if actions actually worked).
Vision processing 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 the code for clickable elements, input fields, and navigation links.
Agent-E uses “DOM Distillation” to reduce complex pages to essential elements, plus “Skill Harvesting” to remember and reuse successful interaction patterns. It beat the multimodal WebVoyager agent (which uses vision) on sites like Huggingface, Apple, and Amazon using only text. But Agent-E’s planning goes out of sync when websites dynamically reveal new options – like dropdown menus that change based on your selections.
DOM parsing costs less and runs faster. Browser-Use’s 89.1% accuracy comes partly from LangChain integration and updated prompts, not just skipping vision calls. But DOM approaches struggle when sites use shadow DOM, obfuscated class names, or heavy JavaScript 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 like proxy rotation and cookie management, limiting effectiveness on sites with bot detection.
Agent-E and WebVoyager only do web navigation. Agent-E achieved 73.1% overall on the full 643-task WebVoyager dataset, beating the multimodal WebVoyager agent’s 57.1%. Strong performance on sites like Wolfram (95.7%), Google Search (90.7%), and Google Maps (87.8%). Weak on dynamic sites: only 27.3% on Booking.com and 35.7% on Google Flights where dropdown menus and form fields change based on user selections.
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 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. Their benchmark actually ran in Skyvern Cloud (not local machines) to test real-world conditions with async cloud browsers and realistic IP addresses. Most benchmarks run on safe local IPs with good browser fingerprints, which doesn’t match production reality.
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 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. Agent-E reached 73.1% using text-only DOM parsing, beating WebVoyager’s 57.1% multimodal approach. But production sites with Cloudflare or DataDome will block agents without proper anti-detection.
Important benchmark context: Browser-Use and Agent-E ran tests locally with safe IP addresses. Skyvern specifically ran their tests in cloud infrastructure to match real production conditions, where you face bot detection, browser fingerprinting, and CAPTCHA challenges. The benchmark tests themselves run on cooperative sites without aggressive bot protection, so real-world success rates will be lower than these numbers suggest.
Benchmark sources
- Browser-Use
- Skyvern 2.0
- Agent-E
- WebVoyager
Principal Analyst
Cem Dilmegani
Principal Analyst
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.
