Bytebot: Open-Source AI Desktop Agent

An AI that has its own computer to complete tasks for you

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What is a Desktop Agent?

A desktop agent is an AI that has its own computer. Unlike browser-only agents or traditional RPA tools, Bytebot comes with a full virtual desktop where it can:

Use any application (browsers, email clients, office tools, IDEs)
Download and organize files with its own file system
Log into websites and applications using password managers
Read and process documents, PDFs, and spreadsheets
Complete complex multi-step workflows across different programs

Think of it as a virtual employee with their own computer who can see the screen, move the mouse, type on the keyboard, and complete tasks just like a human would.

Why Give AI Its Own Computer?

When AI has access to a complete desktop environment, it unlocks capabilities that aren't possible with browser-only agents or API integrations:

Complete Task Autonomy

Give Bytebot a task like "Download all invoices from our vendor portals and organize them into a folder" and it will:

Open the browser
Navigate to each portal
Handle authentication (including 2FA via password managers)
Download the files to its local file system
Organize them into a folder

Process Documents

Upload files directly to Bytebot's desktop and it can:

Read entire PDFs into its context
Extract data from complex documents
Cross-reference information across multiple files
Create new documents based on analysis
Handle formats that APIs can't access

Use Real Applications

Bytebot isn't limited to web interfaces. It can:

Use desktop applications like text editors, VS Code, or email clients
Run scripts and command-line tools
Install new software as needed
Configure applications for specific workflows

Quick Start

Deploy in 2 Minutes

Option 1: Railway (Easiest)

Just click and add your AI provider API key.

Option 2: Docker Compose

hljs language-bash

git clone https://github.com/bytebot-ai/bytebot.git
cd bytebot

# Add your AI provider key (choose one)
echo "ANTHROPIC_API_KEY=sk-ant-..." > docker/.env
# Or: echo "OPENAI_API_KEY=sk-..." > docker/.env
# Or: echo "GEMINI_API_KEY=..." > docker/.env

docker-compose -f docker/docker-compose.yml up -d

# Open http://localhost:9992

Full deployment guide →

How It Works

Bytebot consists of four integrated components:

Virtual Desktop: A complete Ubuntu Linux environment with pre-installed applications
AI Agent: Understands your tasks and controls the desktop to complete them
Task Interface: Web UI where you create tasks and watch Bytebot work
APIs: REST endpoints for programmatic task creation and desktop control

Key Features

Natural Language Tasks: Just describe what you need done
File Uploads: Drop files onto tasks for Bytebot to process
Live Desktop View: Watch Bytebot work in real-time
Takeover Mode: Take control when you need to help or configure something
Password Manager Support: Install 1Password, Bitwarden, etc. for automatic authentication
Persistent Environment: Install programs and they stay available for future tasks

Example Tasks

Basic Examples

hljs language-css

"Go to Wikipedia and create a summary of quantum computing"
"Research flights from NYC to London and create a comparison document"
"Take screenshots of the top 5 news websites"

Document Processing

hljs language-arduino

"Read the uploaded contracts.pdf and extract all payment terms and deadlines"
"Process these 5 invoice PDFs and create a summary report"
"Download and analyze the latest financial report and answer: What were the key risks mentioned?"

Multi-Application Workflows

hljs language-css

"Download last month's bank statements from our three banks and consolidate them"
"Check all our vendor portals for new invoices and create a summary report"
"Log into our CRM, export the customer list, and update records in the ERP system"

Programmatic Control

Create Tasks via API

hljs language-python

import requests

# Simple task
response = requests.post('http://localhost:9991/tasks', json={
    'description': 'Download the latest sales report and create a summary'
})

# Task with file upload
files = {'files': open('contracts.pdf', 'rb')}
response = requests.post('http://localhost:9991/tasks',
    data={'description': 'Review these contracts for important dates'},
    files=files
)

Direct Desktop Control

hljs language-bash

# Take a screenshot
curl -X POST http://localhost:9990/computer-use \
  -H "Content-Type: application/json" \
  -d '{"action": "screenshot"}'

# Click at specific coordinates
curl -X POST http://localhost:9990/computer-use \
  -H "Content-Type: application/json" \
  -d '{"action": "click_mouse", "coordinate": [500, 300]}'

Full API documentation →

Setting Up Your Desktop Agent

1. Deploy Bytebot

Use one of the deployment methods above to get Bytebot running.

2. Configure the Desktop

Use the Desktop tab in the UI to:

Install additional programs you need
Set up password managers for authentication
Configure applications with your preferences
Log into websites you want Bytebot to access

3. Start Giving Tasks

Create tasks in natural language and watch Bytebot complete them using the configured desktop.

Use Cases

Business Process Automation

Invoice processing and data extraction
Multi-system data synchronization
Report generation from multiple sources
Compliance checking across platforms

Development & Testing

Automated UI testing
Cross-browser compatibility checks
Documentation generation with screenshots
Code deployment verification

Research & Analysis

Competitive analysis across websites
Data gathering from multiple sources
Document analysis and summarization
Market research compilation

Architecture

Bytebot is built with:

Desktop: Ubuntu 22.04 with XFCE, Firefox, VS Code, and other tools
Agent: NestJS service that coordinates AI and desktop actions
UI: Next.js application for task management
AI Support: Works with Anthropic Claude, OpenAI GPT, Google Gemini
Deployment: Docker containers for easy self-hosting

Why Self-Host?

Data Privacy: Everything runs on your infrastructure
Full Control: Customize the desktop environment as needed
No Limits: Use your own AI API keys without platform restrictions
Flexibility: Install any software, access any systems

Advanced Features

Multiple AI Providers

Use any AI provider through our LiteLLM integration:

Azure OpenAI
AWS Bedrock
Local models via Ollama
100+ other providers

Enterprise Deployment

Deploy on Kubernetes with Helm:

hljs language-bash

# Clone the repository
git clone https://github.com/bytebot-ai/bytebot.git
cd bytebot

# Install with Helm
helm install bytebot ./helm \
  --set agent.env.ANTHROPIC_API_KEY=sk-ant-...

Enterprise deployment guide →

Community & Support

Discord: Join our community for help and discussions
Documentation: Comprehensive guides at docs.bytebot.ai
GitHub Issues: Report bugs and request features

Contributing

We welcome contributions! Whether it's:

🐛 Bug fixes
✨ New features
📚 Documentation improvements
🌐 Translations

Please:

Check existing issues first
Open an issue to discuss major changes
Submit PRs with clear descriptions
Join our Discord to discuss ideas

License

Bytebot is open source under the Apache 2.0 license.

Give your AI its own computer. See what it can do.

_{Built by Tantl Labs and the open source community}

bytebot

Bytebot: Open-Source AI Desktop Agent

What is a Desktop Agent?

Why Give AI Its Own Computer?

Complete Task Autonomy

Process Documents

Use Real Applications

Quick Start

Deploy in 2 Minutes

How It Works

Key Features

Example Tasks

Basic Examples

Document Processing

Multi-Application Workflows

Programmatic Control

Create Tasks via API

Direct Desktop Control

Setting Up Your Desktop Agent

1. Deploy Bytebot

2. Configure the Desktop

3. Start Giving Tasks

Use Cases

Business Process Automation

Development & Testing

Research & Analysis

Architecture

Why Self-Host?

Advanced Features

Multiple AI Providers

Enterprise Deployment

Community & Support

Contributing

License

Similar Packages

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Original Author

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