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Overview

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

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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 virtual 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
  • 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 /mcp
  • Uses shared types from @bytebot/shared package

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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 supporting Anthropic Claude, OpenAI GPT, and Google Gemini models
  • WebSocket Support for real-time updates
  • Computer Use API Client to control the desktop
  • Prisma ORM for database operations
  • Tool definitions for computer actions (mouse, keyboard, screenshots)
Responsibilities:
  • Interprets natural language requests
  • Plans sequences of computer actions
  • Manages task state and progress
  • Handles errors and retries
  • Provides real-time task updates via WebSocket

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3. Web Task Interface

The user interface for interacting with your AI agent:
  • Next.js 15 Application with TypeScript for type safety
  • Embedded VNC Viewer to watch the desktop in action
  • Task Management UI with status badges
  • WebSocket Connections for live updates
  • Reusable components for consistent UI
  • API utilities for streamlined server communication
Features:
  • Task creation and management interface
  • Desktop tab for direct manual control
  • Real-time desktop viewer with takeover mode
  • Task history and status tracking
  • Responsive design for all devices

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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

1

User Input

User describes a task in natural language via the chat UI
2

Task Creation

Agent service creates a task record and adds it to the processing queue
3

AI Planning

The LLM analyzes the task and generates a plan of computer actions
4

Action Execution

Agent sends computer actions to bytebotd via REST API or MCP
5

Desktop Automation

bytebotd executes actions (mouse, keyboard, screenshots) on the desktop
6

Result Processing

Agent receives results, updates task status, and continues or completes
7

User Feedback

Results and status updates are sent back to the user in real-time

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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
Default configuration is for development. For production:
  • Enable authentication on all APIs
  • Use HTTPS/WSS for all connections
  • Implement network policies
  • Rotate credentials regularly

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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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Production Deployment

Services: All services on dedicated hardware
Scale: Single instance (1 agent, 1 desktop)
Use Case: Business automation
Resources: 8GB+ RAM, 4+ CPU cores

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Enterprise Deployment

Services: Kubernetes orchestration
Scale: Single instance with high availability
Use Case: Organization-wide automation
Resources: Dedicated nodes

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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. Allocate sufficient resources to containers
  2. Limit concurrent tasks to prevent overload
  3. Monitor resource usage regularly
  4. Use LiteLLM proxy for provider flexibility

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Next Steps

Agent System

Learn about the AI agent capabilities

Desktop Environment

Explore the virtual desktop environment

API Reference

Integrate with your applications

Deployment Guide

Deploy your own instance
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