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Overview

The Bytebot Agent System transforms a simple desktop container into an intelligent, autonomous computer user. By combining Claude AI with structured task management, it can understand natural language requests and execute complex workflows just like a human would. Bytebot Agent Architecture

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How the AI Agent Works

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The Brain: Multi-Model AI Integration

At the heart of Bytebot is a flexible AI integration that supports multiple models. Choose the AI that best fits your needs: Anthropic Claude (Default):
  • Best for complex reasoning and visual understanding
  • Excellent at following detailed instructions
  • Superior performance on desktop automation tasks
OpenAI GPT Models:
  • Fast and reliable for general automation
  • Strong code understanding and generation
  • Cost-effective for routine tasks
Google Gemini:
  • Efficient for high-volume tasks
  • Good balance of speed and capability
  • Excellent multilingual support
The agent with any model:
  1. Understands Context: Processes your natural language requests with full conversation history
  2. Plans Actions: Breaks down complex tasks into executable computer actions
  3. Adapts in Real-time: Adjusts its approach based on what it sees on screen
  4. Learns from Feedback: Improves task execution through conversation

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Conversation Flow

1

You Describe a Task

“Research competitors for my SaaS product and create a comparison table”
2

AI Plans the Approach

The AI model understands the request and plans: open browser → search → visit sites → extract data → create document
3

Executes Actions

The agent controls the desktop: clicking, typing, taking screenshots, reading content
4

Provides Updates

Real-time status updates and asks for clarification when needed
5

Delivers Results

Completes the task and provides the output (files, screenshots, summaries)

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Task Management System

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Task Lifecycle

Tasks move through a structured lifecycle:

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Task Properties

Each task contains:
  • Description: What needs to be done
  • Priority: Urgent, High, Medium, or Low
  • Status: Current state in the lifecycle
  • Type: Immediate or Scheduled
  • History: All messages and actions taken

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Smart Task Processing

The agent processes tasks intelligently:
  1. Priority Queue: Urgent tasks run first
  2. Error Recovery: Automatically retries failed actions
  3. Human in the Loop: Asks for help when stuck
  4. Context Preservation: Maintains conversation history across sessions

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Real-world Capabilities

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What the Agent Can Do

Web Automation

  • Browse websites
  • Fill out forms
  • Extract data
  • Download files
  • Monitor changes

Document Work

  • Create documents
  • Edit spreadsheets
  • Generate reports
  • Organize files
  • Convert formats

Email & Communication

  • Access webmail through browser
  • Read and extract information
  • Fill contact forms
  • Navigate communication portals
  • Handle verification flows

Data Processing

  • Extract from PDFs
  • Process CSV files
  • Create visualizations
  • Generate summaries
  • Transform data

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Technical Architecture

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Core Components

  1. NestJS Agent Service
    • Integrates with multiple AI provider APIs (Anthropic, OpenAI, Google)
    • Handles WebSocket connections
    • Coordinates with desktop API
  2. Message System
    • Structured conversation format
    • Supports text and images
    • Maintains full context
    • Enables rich interactions
  3. Database Schema 
    Tasks: id, description, status, priority, timestamps
Messages: id, task_id, role, content, timestamps
Summaries: id, task_id, content, parent_id
  4. Computer Action Bridge
    • Translates AI decisions to desktop actions
    • Handles screenshots and feedback
    • Manages action timing
    • Provides error handling

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API Endpoints

Key endpoints for programmatic control: 
// Create a new task
POST /tasks
{
  "description": "Your task description",
  "priority": "HIGH",
  "type": "IMMEDIATE"
}

// Get task status
GET /tasks/:id

// Send a message
POST /tasks/:id/messages
{
  "content": "Additional instructions"
}

// Get task history
GET /tasks/:id/messages

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Chat UI Features

The web interface provides:

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Real-time Interaction

  • Live chat with the AI agent
  • Instant status updates
  • Progress indicators
  • Error notifications

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Visual Feedback

  • Embedded desktop viewer
  • Screenshot history
  • Action replay
  • Task timeline

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Task Management

  • Create and prioritize tasks
  • View active and completed tasks
  • Export conversation logs
  • Manage task queues

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Security & Privacy

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Data Isolation

  • All processing happens in your infrastructure
  • No data sent to external services (except your chosen AI provider API)
  • Conversations stored locally
  • Complete audit trail

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Access Control

  • Configurable authentication
  • API key management
  • Network isolation options

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Extending the Agent

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Integration Points

  • External API calls via the Agent API
  • Custom AI prompts for specialized workflows
  • MCP protocol support for tool integration

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Best Practices

  1. Clear Instructions: Be specific about desired outcomes
  2. Break Down Complex Tasks: Use multiple smaller tasks for better results
  3. Provide Context: Include relevant files or URLs
  4. Monitor Progress: Watch the desktop view for real-time feedback
  5. Review Results: Verify outputs meet requirements

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Troubleshooting

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Next Steps

Quick Start

Get your agent running

API Reference

Integrate with your apps

Use Cases

See what’s possible

Best Practices

Optimize your workflows