OpenBrowser

Saved Cookies and Scheduled Tasks is available in the cloud-hosted version. Join the waitlist for early access: https://openbrowser.me :

Saved-Login-scheduled-workflows-720p.mp4

Automating Walmart Product Scraping:

OpenbrowserAI-Top40Walmart.mp4

OpenBrowserAI Automatic Flight Booking:

OpenBrowserAI.-.Automatic.Flight.Booking.mp4

OpenBrowserAI Automatic Form Filling:

Automatic.Form.Filling.Demo.mp4

AI-powered browser automation using CodeAgent and CDP (Chrome DevTools Protocol)

OpenBrowser is a framework for intelligent browser automation. It combines direct CDP communication with a CodeAgent architecture, where the LLM writes Python code executed in a persistent namespace, to navigate, interact with, and extract information from web pages autonomously.

Documentation
Key Features
Installation
Quick Start
Configuration
Supported LLM Providers
Claude Code Plugin
Codex
OpenCode
OpenClaw
MCP Server
Benchmark: Token Efficiency
CLI Usage
Project Structure
Backend and Frontend Deployment
Testing
Research: Reinforcement Fine-Tuning for Browser Agents
Contributing
License
Contact

Documentation

Full documentation: https://docs.openbrowser.me

Key Features

CodeAgent Architecture - LLM writes Python code in a persistent Jupyter-like namespace for browser automation
Raw CDP Communication - Direct Chrome DevTools Protocol for maximum control and speed
Vision Support - Screenshot analysis for visual understanding of pages
12+ LLM Providers - OpenAI, Anthropic, Google, Groq, AWS Bedrock, Azure OpenAI, Ollama, and more
MCP Server - Model Context Protocol support for Claude Desktop integration
CLI Daemon - Persistent browser daemon with -c flag for direct code execution from Bash
Video Recording - Record browser sessions as video files

Installation

Quick install (macOS / Linux)

curl -fsSL https://raw.githubusercontent.com/billy-enrizky/openbrowser-ai/main/install.sh | sh

Quick install (Windows PowerShell)

irm https://raw.githubusercontent.com/billy-enrizky/openbrowser-ai/main/install.ps1 | iex

Detects uv, pipx, or pip and installs OpenBrowser automatically.

Install to ~/.local/bin without sudo:

curl -fsSL https://raw.githubusercontent.com/billy-enrizky/openbrowser-ai/main/install.sh | sh -s -- --local

Homebrew (macOS / Linux)

brew tap billy-enrizky/openbrowser
brew install openbrowser-ai

pip

pip install openbrowser-ai

uv (recommended)

uv pip install openbrowser-ai

uvx (zero install)

Run directly without installing -- uvx downloads and caches the package automatically:

# MCP server mode
uvx openbrowser-ai --mcp

# CLI daemon mode
uvx openbrowser-ai -c "await navigate('https://example.com')"

pipx

pipx install openbrowser-ai

From source

git clone https://github.com/billy-enrizky/openbrowser-ai.git
cd openbrowser-ai
uv pip install -e ".[agent]"

Optional Dependencies

pip install openbrowser-ai[agent]      # LLM agent support (langgraph, langchain, litellm)
pip install openbrowser-ai[all]        # All LLM providers
pip install openbrowser-ai[anthropic]  # Anthropic Claude
pip install openbrowser-ai[groq]       # Groq
pip install openbrowser-ai[ollama]     # Ollama (local models)
pip install openbrowser-ai[aws]        # AWS Bedrock
pip install openbrowser-ai[azure]      # Azure OpenAI
pip install openbrowser-ai[video]      # Video recording support

No separate browser install needed. OpenBrowser auto-detects any installed Chromium-based browser (Chrome, Edge, Brave, Chromium) and uses it directly. If none is found and uvx is available, Chromium is installed automatically on first run. To pre-install manually (requires uvx): openbrowser-ai install

Quick Start

Basic Usage

import asyncio
from openbrowser import CodeAgent, ChatGoogle

async def main():
    agent = CodeAgent(
        task="Go to google.com and search for 'Python tutorials'",
        llm=ChatGoogle(model="gemini-3-flash"),
    )

    result = await agent.run()
    print(f"Result: {result}")

asyncio.run(main())

With Different LLM Providers

from openbrowser import CodeAgent, ChatOpenAI, ChatAnthropic, ChatGoogle

# OpenAI
agent = CodeAgent(task="...", llm=ChatOpenAI(model="gpt-5.2"))

# Anthropic
agent = CodeAgent(task="...", llm=ChatAnthropic(model="claude-sonnet-4-6"))

# Google Gemini
agent = CodeAgent(task="...", llm=ChatGoogle(model="gemini-3-flash"))

Using Browser Session Directly

import asyncio
from openbrowser import BrowserSession, BrowserProfile

async def main():
    profile = BrowserProfile(
        headless=True,
        viewport_width=1920,
        viewport_height=1080,
    )
    
    session = BrowserSession(browser_profile=profile)
    await session.start()
    
    await session.navigate_to("https://example.com")
    screenshot = await session.screenshot()
    
    await session.stop()

asyncio.run(main())

Configuration

Environment Variables

# Google (recommended)
export GOOGLE_API_KEY="..."

# OpenAI
export OPENAI_API_KEY="sk-..."

# Anthropic
export ANTHROPIC_API_KEY="sk-ant-..."

# Groq
export GROQ_API_KEY="gsk_..."

# AWS Bedrock
export AWS_ACCESS_KEY_ID="..."
export AWS_SECRET_ACCESS_KEY="..."
export AWS_DEFAULT_REGION="us-west-2"

# Azure OpenAI
export AZURE_OPENAI_API_KEY="..."
export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"

BrowserProfile Options

from openbrowser import BrowserProfile

profile = BrowserProfile(
    headless=True,
    viewport_width=1280,
    viewport_height=720,
    disable_security=False,
    extra_chromium_args=["--disable-gpu"],
    record_video_dir="./recordings",
    proxy={
        "server": "http://proxy.example.com:8080",
        "username": "user",
        "password": "pass",
    },
)

Supported LLM Providers

Provider	Class	Models
Google	`ChatGoogle`	gemini-3-flash, gemini-3-pro
OpenAI	`ChatOpenAI`	gpt-5.2, o4-mini, o3
Anthropic	`ChatAnthropic`	claude-sonnet-4-6, claude-opus-4-6
Groq	`ChatGroq`	llama-4-scout, qwen3-32b
AWS Bedrock	`ChatAWSBedrock`	anthropic.claude-sonnet-4-6, amazon.nova-pro
AWS Bedrock (Anthropic)	`ChatAnthropicBedrock`	Claude models via Anthropic Bedrock SDK
Azure OpenAI	`ChatAzureOpenAI`	Any Azure-deployed model
OpenRouter	`ChatOpenRouter`	Any model on openrouter.ai
DeepSeek	`ChatDeepSeek`	deepseek-chat, deepseek-r1
Cerebras	`ChatCerebras`	llama-4-scout, qwen-3-235b
Ollama	`ChatOllama`	llama-4-scout, deepseek-r1 (local)
OCI	`ChatOCIRaw`	Oracle Cloud GenAI models
Browser-Use	`ChatBrowserUse`	External LLM service

Claude Code Plugin

Install OpenBrowser as a Claude Code plugin:

# Add the marketplace (one-time)
claude plugin marketplace add billy-enrizky/openbrowser-ai

# Install the plugin
claude plugin install openbrowser@openbrowser-ai

This installs the MCP server and 6 built-in skills:

Skill	Description
`web-scraping`	Extract structured data, handle pagination
`form-filling`	Fill forms, login flows, multi-step wizards
`e2e-testing`	Test web apps by simulating user interactions
`page-analysis`	Analyze page content, structure, metadata
`accessibility-audit`	Audit pages for WCAG compliance
`file-download`	Download files (PDFs, CSVs) using browser session

See plugin/README.md for detailed tool parameter documentation.

Codex

OpenBrowser works with OpenAI Codex via native skill discovery.

Quick Install

Tell Codex:

Fetch and follow instructions from https://raw.githubusercontent.com/billy-enrizky/openbrowser-ai/refs/heads/main/.codex/INSTALL.md

Manual Install

# Clone the repository
git clone https://github.com/billy-enrizky/openbrowser-ai.git ~/.codex/openbrowser

# Symlink skills for native discovery
mkdir -p ~/.agents/skills
ln -s ~/.codex/openbrowser/plugin/skills ~/.agents/skills/openbrowser

# Restart Codex

Then configure the MCP server in your project (see MCP Server below).

Detailed docs: .codex/INSTALL.md

OpenCode

OpenBrowser works with OpenCode.ai via plugin and skill symlinks.

Quick Install

Tell OpenCode:

Fetch and follow instructions from https://raw.githubusercontent.com/billy-enrizky/openbrowser-ai/refs/heads/main/.opencode/INSTALL.md

Manual Install

# Clone the repository
git clone https://github.com/billy-enrizky/openbrowser-ai.git ~/.config/opencode/openbrowser

# Create directories
mkdir -p ~/.config/opencode/plugins ~/.config/opencode/skills

# Symlink plugin and skills
ln -s ~/.config/opencode/openbrowser/.opencode/plugins/openbrowser.js ~/.config/opencode/plugins/openbrowser.js
ln -s ~/.config/opencode/openbrowser/plugin/skills ~/.config/opencode/skills/openbrowser

# Restart OpenCode

Then configure the MCP server in your project (see MCP Server below).

Detailed docs: .opencode/INSTALL.md

OpenClaw

OpenClaw supports OpenBrowser via the CLI daemon. Install OpenBrowser, then use openbrowser-ai -c from the Bash tool:

openbrowser-ai -c "await navigate('https://example.com')"
openbrowser-ai -c "print(await evaluate('document.title'))"

The daemon starts automatically on first use and persists variables across calls.

For OpenClaw plugin documentation, see docs.openclaw.ai/tools/plugin.

MCP Server

OpenBrowser includes an MCP (Model Context Protocol) server that exposes browser automation as tools for AI assistants like Claude. Listed on the MCP Registry as me.openbrowser/openbrowser-ai. No external LLM API keys required -- the MCP client provides the intelligence.

Quick Setup

Claude Code: add to your project's .mcp.json:

{
  "mcpServers": {
    "openbrowser": {
      "command": "uvx",
      "args": ["openbrowser-ai", "--mcp"]
    }
  }
}

Claude Desktop: add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "openbrowser": {
      "command": "uvx",
      "args": ["openbrowser-ai", "--mcp"],
      "env": {
        "OPENBROWSER_HEADLESS": "true"
      }
    }
  }
}

Run directly:

uvx openbrowser-ai --mcp

Tool

The MCP server exposes a single execute_code tool that runs Python code in a persistent namespace with browser automation functions. The LLM writes Python code to navigate, interact, and extract data, returning only what was explicitly requested.

Available functions (all async, use await):

Category	Functions
Navigation	`navigate(url, new_tab)`, `go_back()`, `wait(seconds)`
Interaction	`click(index)`, `input_text(index, text, clear)`, `scroll(down, pages, index)`, `send_keys(keys)`, `upload_file(index, path)`
Dropdowns	`select_dropdown(index, text)`, `dropdown_options(index)`
Tabs	`switch(tab_id)`, `close(tab_id)`
JavaScript	`evaluate(code)`: run JS in page context, returns Python objects
Downloads	`download_file(url, filename)`: download a file using browser cookies, `list_downloads()`: list downloaded files
State	`browser.get_browser_state_summary()`: get page metadata and interactive elements
CSS	`get_selector_from_index(index)`: get CSS selector for an element
Completion	`done(text, success)`: signal task completion

Pre-imported libraries: json, csv, re, datetime, asyncio, Path, requests, numpy, pandas, matplotlib, BeautifulSoup

Configuration

Environment Variable	Description	Default
`OPENBROWSER_HEADLESS`	Run browser without GUI	`false`
`OPENBROWSER_ALLOWED_DOMAINS`	Comma-separated domain whitelist	(none)
`OPENBROWSER_COMPACT_DESCRIPTION`	Minimal tool description (~500 tokens)	`false`
`OPENBROWSER_MAX_OUTPUT`	Max output characters per execution	`10000`

Benchmark: Token Efficiency

CLI Benchmark: 4-Way Comparison (6 Tasks, N=3 runs)

Four CLI tools compared with a single Bash tool each. Claude Sonnet 4.6 on Bedrock. Randomized order. All achieve 100% accuracy.

CLI Tool	Duration (mean +/- std)	Tool Calls	Bedrock API Tokens	Response Chars
openbrowser-ai	84.8 +/- 10.9s	15.3 +/- 2.3	36,010 +/- 6,063	9,452 +/- 472
browser-use	106.0 +/- 9.5s	20.7 +/- 6.4	77,123 +/- 33,354	36,241 +/- 12,940
agent-browser	99.0 +/- 6.8s	25.0 +/- 4.0	90,107 +/- 3,698	56,009 +/- 39,733
playwright-cli	118.3 +/- 21.4s	25.7 +/- 8.1	94,130 +/- 35,982	84,065 +/- 49,713

openbrowser-ai uses 2.1-2.6x fewer tokens than all competitors via Python code batching and compact DOM representation.

Per-Task Token Usage

Task	openbrowser-ai	browser-use	playwright-cli	agent-browser
fact_lookup	2,504	4,710	16,857	9,676
form_fill	7,887	15,811	31,757	19,226
multi_page_extract	2,354	2,405	8,886	8,117
search_navigate	16,539	47,936	27,779	44,367
deep_navigation	2,178	3,747	4,705	5,534
content_analysis	4,548	2,515	4,147	3,189

openbrowser-ai wins 5 of 6 tasks. The advantage is largest on complex pages (search_navigate: 2.9x fewer tokens than browser-use) where code batching avoids repeated page state dumps.

Cost per Benchmark Run (6 Tasks)

Model	openbrowser-ai	browser-use	playwright-cli	agent-browser
Claude Sonnet 4.6 ($3/$15 per M)	$0.12	$0.24	$0.29	$0.27
Claude Opus 4.6 ($5/$25 per M)	$0.24	$0.45	$0.56	$0.51

Raw results are in benchmarks/e2e_4way_cli_results.json. Full 4-way comparison with methodology.

E2E LLM Benchmark: MCP Server Comparison (6 Tasks, N=5 runs)

MCP Server	Pass Rate	Duration (mean +/- std)	Tool Calls	Bedrock API Tokens
Playwright MCP (Microsoft)	100%	62.7 +/- 4.8s	9.4 +/- 0.9	158,787
Chrome DevTools MCP (Google)	100%	103.4 +/- 2.7s	19.4 +/- 0.5	299,486
OpenBrowser MCP	100%	77.0 +/- 6.7s	13.8 +/- 2.0	50,195

OpenBrowser uses 3.2x fewer tokens than Playwright and 6.0x fewer than Chrome DevTools. MCP response sizes: Playwright 1,132,173 chars, Chrome DevTools 1,147,244 chars, OpenBrowser 7,853 chars -- a 144x difference.

Full MCP comparison with methodology

CLI Usage

# Run a browser automation task with an LLM agent
uvx openbrowser-ai -p "Search for Python tutorials on Google"

# Execute code directly via persistent daemon
uvx openbrowser-ai -c "await navigate('https://example.com')"
uvx openbrowser-ai -c "print(await evaluate('document.title'))"

# Daemon management
uvx openbrowser-ai daemon start     # Start daemon (auto-starts on first -c call)
uvx openbrowser-ai daemon stop      # Stop daemon and browser
uvx openbrowser-ai daemon status    # Show daemon info
uvx openbrowser-ai daemon restart   # Restart daemon

# Install browser
uvx openbrowser-ai install

# Run MCP server
uvx openbrowser-ai --mcp

The -c flag connects to a persistent browser daemon over a Unix socket (localhost TCP on Windows). Variables persist across calls while the daemon is running. The daemon starts automatically on first use and shuts down after 10 minutes of inactivity.

Project Structure

openbrowser-ai/
├── .claude-plugin/            # Claude Code marketplace config
├── .codex/                    # Codex integration
│   └── INSTALL.md
├── .opencode/                 # OpenCode integration
│   ├── INSTALL.md
│   └── plugins/openbrowser.js
├── plugin/                    # Plugin package (skills + MCP config)
│   ├── .claude-plugin/
│   ├── .mcp.json
│   └── skills/                # 6 browser automation skills
├── src/openbrowser/
│   ├── __init__.py            # Main exports
│   ├── cli.py                 # CLI commands
│   ├── config.py              # Configuration
│   ├── actor/                 # Element interaction
│   ├── agent/                 # LangGraph agent
│   ├── browser/               # CDP browser control
│   ├── code_use/              # Code agent + shared executor
│   ├── daemon/                # Persistent browser daemon (Unix socket)
│   ├── dom/                   # DOM extraction
│   ├── llm/                   # LLM providers
│   ├── mcp/                   # MCP server
│   └── tools/                 # Action registry
├── benchmarks/                # MCP benchmarks and E2E tests
│   ├── playwright_benchmark.py
│   ├── cdp_benchmark.py
│   ├── openbrowser_benchmark.py
│   └── e2e_published_test.py
└── tests/                     # Test suite

Testing

# Run unit tests
pytest tests/

# Run with verbose output
pytest tests/ -v

# E2E test the MCP server against the published PyPI package
uv run python benchmarks/e2e_published_test.py

Benchmarks

Run individual MCP server benchmarks (JSON-RPC stdio, 5-step Wikipedia workflow):

uv run python benchmarks/openbrowser_benchmark.py   # OpenBrowser MCP
uv run python benchmarks/playwright_benchmark.py     # Playwright MCP
uv run python benchmarks/cdp_benchmark.py            # Chrome DevTools MCP

Raw results are in benchmarks/e2e_4way_cli_results.json. See full comparison for methodology.

Backend and Frontend Deployment

The project includes a FastAPI backend and a Next.js frontend, both containerized with Docker.

Prerequisites

Docker and Docker Compose
A .env file in the project root with POSTGRES_PASSWORD and any LLM API keys (see backend/env.example)

Local Development (Docker Compose)

# Start backend + PostgreSQL (frontend runs locally)
docker-compose -f docker-compose.dev.yml up --build

# In a separate terminal, start the frontend
cd frontend && npm install && npm run dev

Service	URL	Description
Backend	http://localhost:8000	FastAPI + WebSocket + VNC
Frontend	http://localhost:3000	Next.js dev server
PostgreSQL	localhost:5432	Chat persistence
VNC	ws://localhost:6080	Live browser view

The dev compose mounts backend/app/ and src/ as volumes for hot-reload. API keys are loaded from backend/.env via env_file. The POSTGRES_PASSWORD is read from the root .env file.

Full Stack (Docker Compose)

# Start all services (backend + frontend + PostgreSQL)
docker-compose up --build

This builds and runs both the backend and frontend containers together with PostgreSQL.

Backend

The backend is a FastAPI application in backend/ with a Dockerfile at backend/Dockerfile. It includes:

REST API on port 8000
WebSocket endpoint at /ws for real-time agent communication
VNC support (Xvfb + x11vnc + websockify) for live browser viewing on ports 6080-6090
Kiosk security: Openbox window manager, Chromium enterprise policies, X11 key grabber daemon
Health check at /health

# Build the backend image
docker build -f backend/Dockerfile -t openbrowser-backend .

# Run standalone
docker run -p 8000:8000 -p 6080:6080 \
  --env-file backend/.env \
  -e VNC_ENABLED=true \
  -e AUTH_ENABLED=false \
  --shm-size=2g \
  openbrowser-backend

Frontend

The frontend is a Next.js application in frontend/ with a Dockerfile at frontend/Dockerfile.

# Build the frontend image
cd frontend && docker build -t openbrowser-frontend .

# Run standalone
docker run -p 3000:3000 \
  -e NEXT_PUBLIC_API_URL=http://localhost:8000 \
  -e NEXT_PUBLIC_WS_URL=ws://localhost:8000/ws \
  openbrowser-frontend

Environment Variables

Key environment variables for the backend (see backend/env.example for the full list):

Variable	Description	Default
`GOOGLE_API_KEY`	Google/Gemini API key	(required)
`DEFAULT_LLM_MODEL`	Default model for agents	`gemini-3-flash-preview`
`AUTH_ENABLED`	Enable Cognito JWT auth	`false`
`VNC_ENABLED`	Enable VNC browser viewing	`true`
`DATABASE_URL`	PostgreSQL connection string	(optional)
`POSTGRES_PASSWORD`	PostgreSQL password (root `.env`)	(required for compose)

Research: Reinforcement Fine-Tuning for Browser Agents

Beyond the framework, we conducted two independent research studies on improving browser agents through reinforcement learning, both using the FormFactory benchmark (1,250 form-filling tasks across 8 domains) and OpenBrowser's browser execution environment.

Study 1: Browser-in-the-Loop (Autoregressive RL)

We investigated whether reinforcement learning can improve a language model's ability to fill web forms beyond what supervised learning achieves.

Method: Two-phase pipeline -- SFT on Qwen3-8B with QLoRA (992 demonstrations), then online GRPO with live browser execution rewards (composite: 40% submission success + 40% field accuracy + 20% execution completeness)
Result: GRPO achieves 9.1% higher average reward than SFT alone on held-out validation (p=0.007, Wilcoxon signed-rank test). Improvement comes specifically from better form submission, not field filling.
Key finding: SFT is a prerequisite -- without it, the base model generates unstructured text and earns zero reward across all attempts.
Paper: ResearchGate DOI: 10.13140/RG.2.2.24922.71360
Models: Qwen3-8B-FormFactory-SFT-LoRA, Qwen3-8B-FormFactory-GRPO-LoRA

Study 2: Diffusion Language Models for Web Action Planning

We investigated whether diffusion language models -- which generate text by iteratively denoising an entire sequence in parallel rather than left-to-right -- can learn web action planning.

Models tested: ReFusion 8B (masked diffusion with causal LM backbone) and FS-DFM 1.3B (pure discrete flow matching)
Result: After SFT, diffusion models solve 60-69% of tasks vs. 100% for the AR baseline. Token-level RL is universally fragile (2/16 comparisons improve, both insignificant). Sequence-level RL succeeds: MDPO pushes ReFusion to 91.9% (+31.4pp) and ESPO pushes FS-DFM to 87.1% (+18.6pp).
Key finding: The appropriate RL formulation is architecture-dependent. ELBO-based optimization (ESPO) produces concentrated distributions across architectures, while per-step trajectory methods produce multimodal distributions.
Paper: ResearchGate DOI: 10.13140/RG.2.2.11500.94088
Models: 10 trained models on HuggingFace including ReFusion-8B-MDPO, FS-DFM-1.3B-ESPO-mu8, and more

Reproducing RL Experiments

All training code is in infra/training/. Training runs on a single NVIDIA A10G GPU (24GB VRAM) via Anyscale.

# Study 1: Autoregressive RL (Qwen3-8B)
# SFT phase -- QLoRA fine-tuning on 992 FormFactory demonstrations (2-4 hours)
python infra/training/finetuning/sft_trainer.py

# Online GRPO phase -- browser-in-the-loop reward (4-8 hours per epoch)
# Requires headless Chromium + FormFactory forms server
python infra/training/shared/formfactory_server.py &   # Start form server
python infra/training/finetuning/online_grpo_trainer.py

# Evaluate SFT and GRPO checkpoints on val/test splits
python infra/training/finetuning/eval_sft.py

# Study 2: Diffusion LM RL (ReFusion 8B, FS-DFM 1.3B)
# SFT phase
python infra/training/flow_matching/fsdfm_sft_trainer.py    # FS-DFM SFT
python infra/training/flow_matching/flow_sft_trainer.py      # ReFusion SFT

# Sequence-level RL (best results)
python infra/training/flow_matching/espo_fsdfm_trainer.py    # ESPO on FS-DFM
python infra/training/flow_matching/espo_refusion_trainer.py # ESPO on ReFusion
python infra/training/flow_matching/mdpo_fsdfm_trainer.py    # MDPO on FS-DFM
python infra/training/flow_matching/mdpo_refusion_trainer.py # MDPO on ReFusion

# Submit jobs to Anyscale cloud
python infra/training/anyscale/submit_job.py --config infra/training/anyscale/online_grpo_job.yaml

# Push trained checkpoints to HuggingFace
python infra/training/anyscale/push_checkpoints_to_hf.py

# Serve trained model locally via vLLM or Ollama
python infra/training/serving/serve_vllm.py
python infra/training/serving/export_gguf.py   # Export to GGUF for Ollama

Reward function (in infra/training/shared/reward_functions.py): composite score = 0.4 * task completion + 0.4 * field accuracy + 0.2 * execution completeness. Online reward (online_reward.py) launches headless Chromium, executes the model's action plan, and computes the score from live browser state.

Contributing

Contributions are welcome! Please:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

Email: billy.suharno@gmail.com
GitHub: @billy-enrizky
Repository: github.com/billy-enrizky/openbrowser-ai
Documentation: https://docs.openbrowser.me

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OpenBrowser

Table of Contents

Documentation

Key Features

Installation

Quick install (macOS / Linux)

Quick install (Windows PowerShell)

Homebrew (macOS / Linux)

pip

uv (recommended)

uvx (zero install)

pipx

From source

Optional Dependencies

Quick Start

Basic Usage

With Different LLM Providers

Using Browser Session Directly

Configuration

Environment Variables

BrowserProfile Options

Supported LLM Providers

Claude Code Plugin

Codex

Quick Install

Manual Install

OpenCode

Quick Install

Manual Install

OpenClaw

MCP Server

Quick Setup

Tool

Configuration

Benchmark: Token Efficiency

CLI Benchmark: 4-Way Comparison (6 Tasks, N=3 runs)

Per-Task Token Usage

Cost per Benchmark Run (6 Tasks)

E2E LLM Benchmark: MCP Server Comparison (6 Tasks, N=5 runs)

CLI Usage

Project Structure

Testing

Benchmarks

Backend and Frontend Deployment

Prerequisites

Local Development (Docker Compose)

Full Stack (Docker Compose)

Backend

Frontend

Environment Variables

Research: Reinforcement Fine-Tuning for Browser Agents

Study 1: Browser-in-the-Loop (Autoregressive RL)

Study 2: Diffusion Language Models for Web Action Planning

Reproducing RL Experiments

Contributing

License

Contact