browser-use/web-ui

🖥️ Run AI Agent in your browser.

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Summary Information

Updated 23 minutes ago
Added to GitGenius on September 3rd, 2026
Created on January 2nd, 2025
Open Issues & Pull Requests: 324 (+0)
GitHub issues: Enabled
Number of forks: 2,719
Total Stargazers: 16,316 (+0)
Total Subscribers: 128 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 8.2 hours
Mean response time: 8.6 days
90th percentile: 18.4 days
Tracked items: 365

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Only 4% of issues opened in the past year have been closed. Three people close 58% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 272
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 497 days
Stale 30+ days: 272
Stale 90+ days: 268

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

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Most active issues this week

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Detailed Description

web-ui is a browser-based interface for running AI agents that automate web interactions.

The tool addresses the challenge of making websites accessible to AI agents by providing a user-friendly web interface built on Gradio that exposes the capabilities of the underlying browser-use framework. It works by allowing an AI agent to control a browser instance through a visual interface, where users can configure the agent, monitor its actions, and maintain persistent browser sessions to observe the complete interaction history.

The project suits developers and non-technical users who want to deploy AI agents for web automation without building custom integrations. It is particularly valuable for scenarios requiring authentication persistence, since it supports connecting your own browser with existing login sessions, eliminating the need to re-authenticate for each task. The tool supports multiple LLM providers including Google, OpenAI, Azure OpenAI, Anthropic, DeepSeek, and Ollama, making it flexible across different AI model choices. Installation is straightforward through either local Python setup or Docker, with configuration managed through environment variables.

Development activity shows consistent engagement with the codebase through regular commits and active issue management. The project maintains responsiveness to user feedback and bug reports, addressing problems in a timely manner. Documentation is actively maintained to reflect current functionality and installation procedures. The maintainers demonstrate commitment to expanding LLM provider support and improving the feature set based on user needs.