BrowserOS is an open-source Chromium fork designed to bring AI agents directly into the browser experience, offering a privacy-first alternative to cloud-dependent tools like ChatGPT Atlas and Perplexity Comet. Unlike traditional browsers that treat AI as an external service, BrowserOS runs agents natively within the browser environment, allowing users to automate tasks—such as form filling, data scraping, or web navigation—without sending sensitive data to remote servers. Built for developers and privacy-conscious users, it leverages the familiar Chrome interface while integrating local LLMs via Ollama and LMStudio, ensuring complete control over data and model execution. This project is community-driven, transparent, and built on the foundations of Ungoogled Chromium and The Chromium Project.
BrowserOS addresses the growing frustration with outdated browser experiences that lack intelligent automation. It empowers users to replace manual, tab-heavy workflows with AI-driven actions that run locally, preserving privacy and reducing dependency on commercial AI platforms. Whether you’re automating repetitive web tasks or integrating LLMs into daily browsing, BrowserOS provides a secure, open-source foundation for the next generation of intelligent web interaction.
What You Get
- Native AI agent support - Run AI agents directly in-browser using your own API keys or local LLMs via Ollama and LMStudio, without data leaving your machine.
- Privacy-first architecture - All browsing history, AI interactions, and agent data are stored locally; no telemetry or cloud uploads by default.
- Chromium compatibility - Fully compatible with Chrome extensions, bookmarks, and user profiles—seamlessly import your existing Chrome data.
- Built-in AI ad blocker - Blocks ads using AI-powered detection that works across more scenarios than traditional filter lists.
- MCP server integration - Expose BrowserOS as an MCP (Model Control Protocol) server to control it programmatically from tools like claude-code or gemini-cli.
- Cross-platform desktop app - Available as native installers for macOS, Windows, and Linux (AppImage and Debian packages).
Common Use Cases
- Building a private AI-powered research assistant - Use BrowserOS with Ollama to run Llama 3 or Mistral locally, then automate web research tasks like extracting summaries from academic papers or comparing product specs across sites without exposing queries to third parties.
- Automating repetitive web tasks at scale - A freelance data analyst uses BrowserOS to auto-fill forms, extract pricing from e-commerce sites, and scrape product images—using local AI agents to validate data accuracy without uploading sensitive client information.
- Problem → Solution flow: Overwhelmed by 70+ tabs → AI agent auto-organizes and summarizes - Users struggling with tab overload can deploy BrowserOS agents to automatically group related tabs, summarize content from each, and generate action items—all running locally with no cloud dependency.
- DevOps teams managing browser automation pipelines - Teams use BrowserOS as a headless AI-aware browser for testing, scraping, and automation scripts that require real-time LLM reasoning (e.g., interpreting CAPTCHAs or dynamic content), leveraging its MCP server to integrate with CLI tools like claude-code.
Under The Hood
BrowserOS is a Chromium-based browser platform that merges AI capabilities with OS-level customization and extensibility. It presents a modular architecture designed to integrate browser extensions, server-side management, and native OS features into a unified system. The project emphasizes flexibility through custom patches and extension ecosystems.
Architecture
BrowserOS adopts a layered architecture with clear separation between core browser components, AI integration points, and user-facing modules. It leverages service-oriented design and factory patterns to manage extensions and configurations.
- Uses a layered architecture with well-defined modules for browser core, AI integration, and OS-level extensions
- Implements service-oriented components to handle extension lifecycle and server configuration
- Employs factory methods for dynamic creation of browser extensions and custom modules
- Supports modular builds that allow selective feature inclusion based on deployment needs
Tech Stack
Built primarily in C++ with TypeScript and Python, BrowserOS integrates deeply with Chromium’s codebase while extending functionality through AI-powered extensions and automation tools.
- Constructed using C++ as the primary language, with TypeScript for browser extensions and Python for build tools and automation
- Relies heavily on Chromium’s core components and integrates Python packages such as setuptools, click, typer, PyYAML, requests, boto3, and cryptography
- Utilizes Chromium’s GN build system alongside Python-based project management tools like pyproject.toml and requirements.txt
- Includes unit test files for core components and automated workflows for CLA management and issue tracking
Code Quality
The codebase reflects a moderate quality level with an emphasis on testability and structured error handling. While consistent patterns are present, some technical debt remains due to integration complexities.
- Emphasizes testability with unit tests and automated workflows for core modules
- Implements consistent error handling through widespread use of try/catch blocks across components
- Shows a balance between code clarity and structural complexity in its integration with Chromium’s architecture
- Demonstrates efforts to maintain code consistency, though some patching introduces architectural friction
What Makes It Unique
BrowserOS stands out by combining browser-based AI with OS-level extensibility, offering a novel approach to custom extension management and server integration.
- Custom Chromium patches enable OS-level features such as metrics tracking and server configuration without external dependencies
- Introduces a dedicated extension ecosystem for managing bundled and maintained browser extensions at the OS level
- Features custom appcast handling and centralized update mechanisms tailored for enterprise or developer-focused use cases
- Offers modular architecture that supports OS-specific builds and selective feature inclusion for diverse deployment scenarios