This open-source AI browser provides a privacy-focused platform for automating complex web tasks, serving as an alternative to commercial solutions like ChatGPT Atlas and Microsoft CoPilot Edge. It empowers users with local processing capabilities for research, analysis, and automation, enhancing productivity while safeguarding data.

How It Works

The core approach leverages multi-agent automation, where specialized AI agents collaborate autonomously to tackle intricate web-based tasks. Processing occurs entirely locally on the user's machine, ensuring privacy and enabling offline functionality when integrated with local models via Ollama. The platform's extensibility allows compatibility with over 100 AI models through various providers like OpenAI, Claude, Gemini, and Llama, facilitated by LiteLLM.

Quick Start & Requirements

• Primary install/run command: Download executables for macOS or Windows from the releases page.
• Non-default prerequisites: macOS 10.15+ or Windows 10 (64-bit)+, 8GB RAM (16GB recommended), 2GB free disk space. AI provider credentials (API keys or sign-in) are required.
• Links: Download, Docs, Community.

Highlighted Details

• Multi-Agent Automation: Specialized AI agents work together to autonomously handle complex web tasks.
• Privacy-First: All processing occurs locally, supporting complete offline operation with local models.
• Extensible: Compatible with 100+ AI models via OpenAI, Claude, Gemini, Llama, and more through LiteLLM.
• Use Cases: Supports literature reviews, data collection, competitive intelligence, market research, product comparisons, talent sourcing, and lead generation.

Maintenance & Community

Support and discussions are available via the Discord community. Bug reporting and feature requests can be submitted through GitHub Issues. Contribution guidelines are detailed in the project's documentation.

Licensing & Compatibility

The project is released under the permissive BSD-3-Clause License, generally allowing for commercial use and integration into closed-source projects.

Limitations & Caveats

The README does not explicitly detail limitations. However, performance is dependent on user hardware, and advanced local model integration requires technical setup. The multi-agent system's complexity might introduce a learning curve for intricate task configurations.