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Overview

Bytebot is a self-hosted AI desktop agent built with a modular, containerized architecture. It combines a Linux desktop environment with Claude AI to create an autonomous computer user that can perform tasks through natural language instructions.

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System Architecture

The system consists of four main components that work together:

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1. Bytebot Desktop Container

The foundation of the system - a containerized Linux desktop that provides:

• Ubuntu 22.04 LTS base for stability and compatibility
• XFCE4 Desktop for a lightweight, responsive UI
• bytebotd Daemon - The automation service built on nutjs that executes computer actions
• Pre-installed Applications: Firefox ESR, Thunderbird, text editors, and development tools
• VNC & noVNC for remote desktop access

Key Features:

• Runs completely isolated from your host system
• Consistent environment across different platforms
• Can be customized with additional software
• Accessible via REST API on port 9990
• MCP SSE endpoint available at /sse

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2. AI Agent Service

The brain of the system - orchestrates tasks using an LLM:

• NestJS Framework for robust, scalable backend
• LLM Integration via OpenAI API for understanding and planning
• WebSocket Support for real-time updates
• Computer Use API Client to control the desktop

Responsibilities:

• Interprets natural language requests
• Plans sequences of computer actions
• Manages task state and progress
• Handles errors and retries

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3. Web Task Interface

The user interface for interacting with your AI agent:

• Next.js Application with TypeScript for type safety
• Embedded VNC Viewer to watch the desktop in action
• Task Management UI for tracking progress
• WebSocket Connections for live updates

Features:

• Intuitive task interface
• Visual feedback of desktop actions
• Task history and status
• Export conversation logs

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4. PostgreSQL Database

Persistent storage for the agent system:

• Tasks Table: Stores task details, status, and metadata
• Messages Table: Stores AI conversation history
• Prisma ORM for type-safe database access

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Data Flow

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Task Execution Flow

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Communication Protocols

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Security Architecture

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Isolation Layers

1. Container Isolation

   • Each desktop runs in its own Docker container
   • No access to host filesystem by default
   • Network isolation with explicit port mapping

2. Process Isolation

   • bytebotd runs as non-root user
   • Separate processes for different services
   • Resource limits enforced by Docker

3. Network Security

   • Services only accessible from localhost by default
   • Can be configured with authentication
   • HTTPS/WSS for external connections

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API Security

• Desktop API: No authentication by default (localhost only). Supports REST and MCP.
• Agent API: Can be secured with API keys
• Database: Password protected, not exposed externally
• VNC Access: Optional password protection

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Deployment Patterns

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Single User (Development)

Services: All on one machine
Scale: 1 instance each
Use Case: Personal automation, development
Resources: 4GB RAM, 2 CPU cores

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Team Deployment

Services: Separate agent and desktop hosts
Scale: Multiple desktop containers
Use Case: Shared automation platform
Resources: 8GB+ RAM, 4+ CPU cores

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Enterprise Deployment

Services: Kubernetes orchestration
Scale: Auto-scaling based on load
Use Case: Organization-wide automation
Resources: Dedicated cluster

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Extension Points

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Custom Tools

Add specialized software to the desktop:

FROM bytebot/desktop:latest
RUN apt-get update && apt-get install -y \
    your-custom-tools

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AI Integrations

Extend agent capabilities:

• Custom tools for the LLM
• Additional AI models
• Specialized prompts
• Domain-specific knowledge

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Performance Considerations

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Resource Usage

• Desktop Container: ~1GB RAM idle, 2GB+ active
• Agent Service: ~256MB RAM
• UI Service: ~128MB RAM
• Database: ~256MB RAM

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Optimization Tips

1. Use lightweight desktop environments
2. Limit concurrent tasks
3. Monitor resource usage
4. Scale horizontally for more capacity

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Next Steps