base on Bytebot is a self-hosted AI desktop agent that automates computer tasks through natural language commands, operating within a containerized Linux desktop environment. <div align="center"> <img src="docs/images/bytebot-logo.png" width="500" alt="Bytebot Logo"> # Bytebot: Open-Source AI Desktop Agent <a href="https://trendshift.io/repositories/14624" target="_blank"><img src="https://trendshift.io/api/badge/repositories/14624" alt="bytebot-ai%2Fbytebot | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> **An AI that has its own computer to complete tasks for you** [](https://railway.com/deploy/bytebot?referralCode=L9lKXQ) [](https://github.com/bytebot-ai/bytebot/tree/main/docker) [](LICENSE) [](https://discord.com/invite/d9ewZkWPTP) [🌐 Website](https://bytebot.ai) • [📚 Documentation](https://docs.bytebot.ai) • [💬 Discord](https://discord.com/invite/d9ewZkWPTP) • [𝕏 Twitter](https://x.com/bytebot_ai) <!-- Keep these links. Translations will automatically update with the README. --> [Deutsch](https://zdoc.app/de/bytebot-ai/bytebot) | [Español](https://zdoc.app/es/bytebot-ai/bytebot) | [français](https://zdoc.app/fr/bytebot-ai/bytebot) | [日本語](https://zdoc.app/ja/bytebot-ai/bytebot) | [한국어](https://zdoc.app/ko/bytebot-ai/bytebot) | [Português](https://zdoc.app/pt/bytebot-ai/bytebot) | [Русский](https://zdoc.app/ru/bytebot-ai/bytebot) | [中文](https://zdoc.app/zh/bytebot-ai/bytebot) </div> --- https://github.com/user-attachments/assets/f271282a-27a3-43f3-9b99-b34007fdd169 https://github.com/user-attachments/assets/72a43cf2-bd87-44c5-a582-e7cbe176f37f ## 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)** [](https://railway.com/deploy/bytebot?referralCode=L9lKXQ) Just click and add your AI provider API key. **Option 2: Docker Compose** ```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 →](https://docs.bytebot.ai/quickstart) ## How It Works Bytebot consists of four integrated components: 1. **Virtual Desktop**: A complete Ubuntu Linux environment with pre-installed applications 2. **AI Agent**: Understands your tasks and controls the desktop to complete them 3. **Task Interface**: Web UI where you create tasks and watch Bytebot work 4. **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 ``` "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 ``` "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 ``` "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 ```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 ```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 →](https://docs.bytebot.ai/api-reference/introduction) ## 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](https://docs.bytebot.ai/deployment/litellm): - Azure OpenAI - AWS Bedrock - Local models via Ollama - 100+ other providers ### Enterprise Deployment Deploy on Kubernetes with Helm: ```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 →](https://docs.bytebot.ai/deployment/helm) ## Community & Support - **Discord**: [Join our community](https://discord.com/invite/d9ewZkWPTP) for help and discussions - **Documentation**: Comprehensive guides at [docs.bytebot.ai](https://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: 1. Check existing [issues](https://github.com/bytebot-ai/bytebot/issues) first 2. Open an issue to discuss major changes 3. Submit PRs with clear descriptions 4. Join our [Discord](https://discord.com/invite/d9ewZkWPTP) to discuss ideas ## License Bytebot is open source under the Apache 2.0 license. --- <div align="center"> **Give your AI its own computer. See what it can do.** [](https://railway.com/deploy/bytebot?referralCode=L9lKXQ) _{Built by [Tantl Labs](https://tantl.com) and the open source community} </div> ", Assign "at most 3 tags" to the expected json: {"id":"14624","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"