Browser-use agent

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A Browser-Use Agent (BUA), also known as an autonomous web agent or LLM-based browser agent, is a type of artificial intelligence software agent designed to operate a standard web browser through its graphical user interface (GUI) to accomplish goals specified by users in natural language.[1][2] Unlike traditional web scraping, API-based approaches, or simple automation scripts that follow predefined rules, BUAs leverage the reasoning and understanding capabilities of large language models (LLMs) combined with browser automation technologies to dynamically perceive web page content, plan sequences of actions, and execute them to complete complex, multi-step tasks across diverse websites without bespoke integration.[3][4]

These agents represent a significant advancement toward creating general-purpose digital assistants that can handle real-world web-based tasks, such as booking travel, managing online shopping, conducting detailed information searches, or completing data entry, without direct human intervention for each step.[5]

Terminology and Definition

The term "browser-use agent" is used in research and industry to describe agents that complete tasks by controlling a web browser, rather than calling site-specific APIs.[6] It encompasses systems that:

  • Render or inspect webpages through visual or DOM-based perception
  • Plan multi-step procedures using LLM reasoning
  • Execute low-level browser actions (click, type, select, navigate) to achieve goals such as booking, data extraction, or account management[7]

BUAs are distinguished from computer-use agents (CUAs), which operate in broader desktop environments beyond browsers, by their focus on web-specific interactions within browser instances.[8]

History

The concept of browser-use agents emerged from the convergence of advances in large language models and web automation technologies.

Early Development (2021-2023)

  • 2021: OpenAI's WebGPT demonstrated early browser-assisted question-answering with human feedback[5]
  • 2022: Adept AI introduced ACT-1, a model trained to use common software tools including web browsers[9]
  • 2023: Academic benchmarks WebArena and Mind2Web established standardized evaluation frameworks[1][3]

Modern Era (2024-2025)

  • October 2024: Anthropic released Computer Use feature for Claude models[10]
  • December 2024: Google DeepMind unveiled Project Mariner built on Gemini 2.0[11]
  • January 2025:
    • OpenAI announced Operator on January 23, powered by the Computer-Using Agent (CUA) model[2][8]
    • Browser-Use open-source library reached 21,000+ GitHub stars[12]
    • OpenAI later integrated Operator capabilities into ChatGPT Agent mode, with the standalone operator.chatgpt.com site scheduled for deprecation[13]

Rationale

Many real-world workflows remain locked behind human-oriented web interfaces. BUAs aim to generalize across diverse sites without bespoke integration by:[14]

  1. Interpreting on-page content and structure through perception systems
  2. Mapping high-level instructions to concrete browser interactions
  3. Adapting to website changes without reprogramming
  4. Reducing fragmented evaluation through unified testbeds

Architecture and Core Components

A BUA's operation follows a perception-reasoning-action loop where it perceives the state of a web page, reasons about the next best action toward its goal, and executes that action. This cycle repeats until task completion or failure determination.[15]

Browser-Use Agent Architecture Components
Component Description Technologies Implementation Details
Perception Layer Understands content and layout of current web page DOM parsing, CSS selectors, XPath, Accessibility Tree APIs, Vision Models • DOM extraction for interactive elements
• Screenshot processing (base64 encoding)
• Visual analysis for layout understanding
• Text extraction and semantic parsing
Reasoning & Planning Layer Core decision-making powered by LLMs GPT-4, Claude, Gemini, Llama, Chain-of-thought prompting, ReAct framework • Task decomposition into sub-goals
• Multi-step action planning
• Context management across pages
• Error detection and recovery strategies
Action Execution Layer Translates abstract actions into browser commands Selenium, Playwright, Puppeteer, Browser Extensions, Chrome DevTools Protocol • Low-level control (click, type, scroll)
• Multi-browser support
• Headless and visible modes
• Session management
Memory Management Maintains state and context Vector databases, Session storage, RL memories • Working memory for active tasks
• Persistent memory across sessions
• Semantic memory for knowledge
• Episodic memory for action history
Safety & Monitoring Ensures safe operation and compliance Refusal mechanisms, Audit logging, Permission systems • Prompt injection prevention
• Sensitive action gates
• User approval workflows
• Activity logging and rollback

Technical Implementation

Browser Automation Frameworks

Comparison of Browser Automation Technologies for BUAs
Framework Primary Use Case Advantages Limitations BUA Adoption
Playwright Cross-browser automation Fast, reliable, modern API, built-in waiting Newer ecosystem Preferred for most BUAs[16]
Selenium Traditional web testing Mature, wide language support Slower, more complex setup Legacy support
Puppeteer Chrome/Chromium control Direct CDP access, lightweight Chrome-only Specialized use cases
CDP (Chrome DevTools Protocol) Low-level browser control Maximum control, performance Complex, browser-specific Advanced implementations

Processing Modes

BUA Processing Mode Comparison
Mode Description Token Usage Speed Accuracy Best For
Snapshot Mode Uses accessibility tree for element identification Low (500-2K) Fast (<1s) High for simple pages Form filling, standard layouts
Vision Mode Processes screenshots for visual understanding High (5K-15K) Slow (2-5s) High for complex layouts Dynamic content, visual elements
Hybrid Mode Combines DOM parsing with visual processing Medium (2K-8K) Medium (1-3s) Highest overall General-purpose automation
Streaming Mode Continuous observation and action Very High Real-time Variable Interactive applications

Language Model Integration

BUAs support various LLM providers with different capabilities:[17]

LLM Provider Capabilities for Browser-Use Agents
Provider Models Vision Support Cost (per 1M tokens) Latency Best Use Case
OpenAI GPT-4o, GPT-4-turbo Yes $5-15 Low Production systems
Anthropic Claude 3.5 Sonnet, Claude 3 Opus Yes $3-15 Low Complex reasoning
Google Gemini 1.5 Pro, Gemini 2.0 Yes $3.5-7 Low Multimodal tasks
Open Source Llama 3, Mistral, Qwen Limited $0.5-2 Variable Cost-sensitive applications

Performance Benchmarks

Standardized Evaluation Frameworks

Browser-Use Agent Benchmark Suites
Benchmark Focus Area Task Count Characteristics Key Metrics
WebArena Realistic multi-site environment 812 tasks Self-hostable sites across e-commerce, CMS, social platforms; execution-based evaluation Task success rate, efficiency score[1]
Mind2Web Cross-website generalization 2,350 tasks 137 websites, real-world task diversity, action sequence annotation Element accuracy, action F1 score[3]
WebVoyager Live website interaction 643 tasks Amazon, GitHub, Google Maps, real-time execution End-to-end success rate[8]
VisualWebArena Multimodal/visual tasks 910 tasks Image-heavy tasks, visual grounding requirements Visual element accuracy[18]
BrowserGym Unified ecosystem 5,000+ tasks Standardized obs/action spaces, cross-benchmark evaluation Aggregate performance score[14]
WebShop E-commerce navigation 12,087 products Product search and selection, attribute matching Purchase success rate, reward score[19]
OSWorld Full OS control 369 tasks Ubuntu, Windows, macOS environments Cross-platform success rate[8]

Comparative Performance (2025)

Browser-Use Agent Performance Comparison
Agent/Model WebArena WebVoyager Mind2Web OSWorld Average
Human Baseline 78.2% 90.0% 85.3% 72.4% 81.5%
Browser-Use (Open Source) 51.2% 89.1% 73.4% N/A 71.2%
CUA (OpenAI) 58.1% 87.0% 76.2% 38.1% 64.9%
Computer Use (Anthropic) 45.3% 56.0% 62.1% 22.0% 46.4%
Mariner (Google) 52.4% 83.5% 71.3% N/A 69.1%

Major Implementations

OpenAI Operator

Released January 23, 2025, Operator is powered by the Computer-Using Agent (CUA) model, combining GPT-4o's vision capabilities with reinforcement learning:[2][8]

  • Architecture: Iterative perception-reasoning-action loop with self-correction
  • Safety Features: Refusal mechanisms, user approval gates for sensitive actions
  • Performance: Industry-leading scores on WebArena (58.1%) and WebVoyager (87%)
  • Availability: Initially required ChatGPT Pro subscription; later integrated into ChatGPT Agent mode[13]

Browser-Use Library

An open-source Python library enabling LLM-powered browser interaction via natural language:[4]

  • Statistics: 21,000+ GitHub stars, 1,000+ forks (as of January 2025)
  • Features: Multi-model support, memory-enabled workflows, cloud/local execution
  • License: MIT License
  • Integration: Supports OpenAI, Anthropic, Google, and open-source models

Anthropic Computer Use

Released October 2024, enables Claude models to interact with computer interfaces:[10]

  • Approach: Visual perception and simulated input
  • Models: Claude 3.5 Sonnet optimized for computer use
  • Applications: Desktop and web automation

Google Project Mariner

Experimental agent from Google DeepMind for autonomous web navigation:[11]

  • Foundation: Built on Gemini 2.0
  • Focus: Research-oriented, multimodal understanding
  • Performance: 83.5% on WebVoyager benchmark

Applications and Use Cases

Enterprise Automation

  • Business Process Automation: Replacing traditional RPA with adaptive agents
  • Data Integration: Cross-platform data extraction and synchronization
  • Compliance Monitoring: Automated regulatory checks and reporting
  • Supply Chain Management: Vendor portal navigation and order processing

E-Commerce and Services

  • Price Monitoring: Real-time competitor analysis and dynamic pricing
  • Inventory Management: Multi-marketplace stock synchronization
  • Customer Service: Automated order tracking and status updates
  • Review Aggregation: Sentiment analysis across platforms

Research and Analysis

  • Academic Research: Literature review automation, citation gathering
  • Market Intelligence: Trend analysis, competitor monitoring
  • Financial Analysis: Earnings report extraction, market data compilation
  • Patent Research: Prior art searches, classification analysis

Quality Assurance

  • Automated Testing: End-to-end user journey validation[20]
  • Accessibility Testing: WCAG compliance verification
  • Cross-browser Testing: Compatibility validation across platforms
  • Performance Testing: Load testing, response time measurement

Personal Productivity

  • Travel Planning: Multi-site booking coordination
  • Job Applications: Resume parsing, application submission
  • Content Aggregation: News curation, research compilation
  • Social Media Management: Cross-platform posting and monitoring

Challenges and Limitations

Technical Challenges

  • Dynamic Content Handling:
    • Modern SPAs with AJAX pose navigation challenges
    • Asynchronous loading requires sophisticated waiting strategies
    • Virtual scrolling and lazy loading complicate element discovery[21]
  • Element Identification:
    • Shadow DOM and iframes create isolation barriers
    • Dynamic ID generation prevents reliable selectors
    • Similar elements require disambiguation strategies[22]
  • State Management:
    • Session persistence across page transitions
    • Handling authentication and 2FA
    • Recovery from unexpected logouts or timeouts[23]
  • Error Recovery:
    • CAPTCHA solving limitations
    • Popup and modal dialog handling
    • Network failure and retry logic[24]

Performance Limitations

Performance Bottlenecks in Browser-Use Agents
Issue Impact Current Solutions Future Approaches
LLM Inference Latency 2-5 second delays per action Caching, batching Edge deployment, model optimization
Token Consumption $0.10-1.00 per complex task Efficient prompting, mode selection Specialized models, compression
Memory Limitations Context window constraints Summarization, pruning Extended context models
Reliability 60-90% success rates Retry logic, fallbacks Reinforcement learning, self-improvement

Safety, Security, and Ethical Concerns

  • Security Risks:[8]
    • Prompt injection attacks from malicious websites
    • Credential theft through compromised agents
    • Cross-site scripting (XSS) vulnerabilities
    • Data exfiltration risks
  • Privacy Concerns:
    • Processing of sensitive personal information
    • Screenshot capture of private data
    • Audit trail storage and retention
  • Misuse Prevention:
    • Automated spam and harassment potential
    • Unauthorized web scraping at scale
    • Terms of service violations
    • Legal compliance challenges

Notable Research Projects

Academic Initiatives

  • WebGPT (OpenAI): Pioneering work on browser-assisted question-answering with human feedback, establishing foundations for modern BUAs[5]
  • Mind2Web (OSU & Allen AI): Large-scale dataset and framework for developing generalist web agents, testing cross-website generalization[3]
  • WebArena (CMU): Realistic benchmark environment with self-hosted websites for reproducible agent evaluation[1]
  • AgentTuning: Research on enabling generalized agent abilities through fine-tuning LLMs for web tasks[15]

Industry Research

  • Adept ACT-1: Universal action transformer for software interface control[9]

Open Source Frameworks

Future Directions

Technical Developments

  • Enhanced Perception:
    • Perception tokens for improved visual reasoning[27]
    • 3D understanding of page layouts
    • Video comprehension for dynamic content
  • Improved Planning:
    • Hierarchical task decomposition
    • Long-horizon reasoning capabilities
    • Multi-objective optimization
  • Self-Improvement:
    • Online learning from execution feedback
    • Automated test generation and validation
    • Self-healing adaptation to website changes[20]

Industry Adoption Trajectory

  • 2025-2026: Early adoption in QA and testing
  • 2026-2027: Enterprise RPA replacement begins
  • 2027-2028: Consumer-facing agent assistants
  • 2028-2030: Ubiquitous web automation

Emerging Standards and Protocols

  • Development of agent-specific web protocols
  • Standardized safety and permission frameworks
  • Industry benchmarks for reliability certification
  • Regulatory compliance frameworks

Comparison with Related Technologies

Browser-Use Agents vs. Related Technologies
Aspect Browser-Use Agent (BUA) Computer-Use Agent (CUA) Traditional RPA Web Scraping
Scope Web browsers Full desktop OS Predefined workflows Data extraction only
Adaptability High (LLM-based) High (LLM-based) Low (scripted) Low (rule-based)
Setup Complexity Medium High High Low
Maintenance Self-adapting Self-adapting Frequent updates needed Regular updates needed
Cost $0.10-1.00/task $0.50-2.00/task High initial, low per-task Low
Use Cases General web automation Any desktop application Repetitive business processes Data collection
Error Handling Intelligent recovery Intelligent recovery Basic retry logic Minimal

See also

References

  1. ↑ 1.0 1.1 1.2 1.3 Zhou, Tianbao et al. (2023). "WebArena: A Realistic Web Environment for Building Autonomous Agents." arXiv preprint arXiv:2307.13854. Retrieved from https://arxiv.org/abs/2307.13854
  2. ↑ 2.0 2.1 2.2 OpenAI. (2025). "Introducing Operator." Retrieved from https://openai.com/index/introducing-operator/
  3. ↑ 3.0 3.1 3.2 3.3 Deng, Xiang et al. (2023). "Mind2Web: Towards a Generalist Agent for the Web." arXiv preprint arXiv:2306.06070. Retrieved from https://arxiv.org/abs/2306.06070
  4. ↑ 4.0 4.1 Browser-Use Team. (2025). "Browser-Use: Make websites accessible for AI agents." GitHub. Retrieved from https://github.com/browser-use/browser-use
  5. ↑ 5.0 5.1 5.2 Nakano, Reiichiro et al. (2021). "WebGPT: Browser-assisted question-answering with human feedback." arXiv preprint arXiv:2112.09332. Retrieved from https://arxiv.org/abs/2112.09332
  6. ↑ VentureBeat. (2025). "The rise of browser-use agents: Why Convergence's Proxy is beating OpenAI's Operator." Retrieved from industry sources
  7. ↑ The Verge. (2025). "OpenAI's new Operator AI agent can do things on the web for you." Retrieved from https://www.theverge.com/2025/1/23/operator-openai
  8. ↑ 8.0 8.1 8.2 8.3 8.4 8.5 OpenAI. (2025). "Computer-Using Agent." Retrieved from https://openai.com/index/computer-using-agent/
  9. ↑ 9.0 9.1 Adept AI. (2022). "ACT-1: A new frontier for models that take actions on your computer." Retrieved from https://www.adept.ai/blog/act-1
  10. ↑ 10.0 10.1 Anthropic. (2024). "Introducing computer use, a new Claude 3.5 Sonnet, and Claude 3.5 Haiku." Retrieved from https://www.anthropic.com/news/3-5-models-and-computer-use
  11. ↑ 11.0 11.1 DataCamp. (2025). "OpenAI's Operator: Examples, Use Cases, Competition & More." Retrieved from https://www.datacamp.com/blog/operator
  12. ↑ InfoWorld. (2025). "Browser Use: An open-source AI agent to automate web-based tasks." Retrieved from https://www.infoworld.com/article/3812644/browser-use-an-open-source-ai-agent-to-automate-web-based-tasks.html
  13. ↑ 13.0 13.1 OpenAI. (2025). "Introducing ChatGPT agent: bridging research and action." Retrieved from https://openai.com/index/introducing-chatgpt-agent/
  14. ↑ 14.0 14.1 14.2 Le Sellier De Chezelles, T. et al. (2024). "The BrowserGym Ecosystem for Web Agent Research." arXiv:2412.05467. Retrieved from https://arxiv.org/abs/2412.05467
  15. ↑ 15.0 15.1 Zeng, Aohan et al. (2023). "AgentTuning: Enabling Generalized Agent Abilities for LLMs." arXiv preprint arXiv:2310.12823. Retrieved from https://arxiv.org/abs/2310.12823
  16. ↑ DZone. (2025). "Build an AI Browser Agent With LLMs, Playwright, Browser-Use." Retrieved from https://dzone.com/articles/build-ai-browser-agent-llms-playwright-browser-use
  17. ↑ ADASCI. (2025). "A Practical Guide to Enabling AI Agent Browser Control using Browser-use." Retrieved from https://adasci.org/a-practical-guide-to-enabling-ai-agent-browser-control-using-browser-use/
  18. ↑ Web-Arena-x. (2024-2025). "VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks." GitHub. Retrieved from project site
  19. ↑ Yao, Shunyu et al. (2022). "WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents." arXiv preprint arXiv:2207.01206. Retrieved from https://arxiv.org/abs/2207.01206
  20. ↑ 20.0 20.1 Ministry of Testing. (2025). "Creating self-healing automated tests with AI and Playwright." Retrieved from https://www.ministryoftesting.com/articles/creating-self-healing-automated-tests-with-ai-and-playwright
  21. ↑ Skyvern. (2025). "Web Bench - A new way to compare AI Browser Agents." Retrieved from https://blog.skyvern.com/web-bench-a-new-way-to-compare-ai-browser-agents/
  22. ↑ Browser-Use. (2025). "Browser Use = state of the art Web Agent." Retrieved from https://browser-use.com/posts/sota-technical-report
  23. ↑ OpenAI. (2025). "Context Engineering - Short-Term Memory Management with Sessions." Retrieved from https://cookbook.openai.com/examples/agents_sdk/session_memory
  24. ↑ 24.0 24.1 Skyvern. (2025). "Automate browser-based workflows with LLMs and Computer Vision." GitHub. Retrieved from https://github.com/Skyvern-AI/skyvern
  25. ↑ Furuta, Hiroki et al. (2023). "Multimodal Web Navigation with Instruction-Finetuned Foundation Models." arXiv preprint arXiv:2305.11854. Retrieved from https://arxiv.org/abs/2305.11854
  26. ↑ LangChain. (2025). "Agent architectures." Retrieved from https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/
  27. ↑ arXiv. (2024). "Perception Tokens Enhance Visual Reasoning in Multimodal Language Models." Retrieved from https://arxiv.org/html/2412.03548v1

External links

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