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AI Agent Overview

The Lillo Agent System provides a flexible, secure, and scalable framework for creating and managing AI agents. This document outlines the core concepts, architecture, and implementation details of the agent system.

Core Concepts

Agent Definition

An agent in Lillo is a configurable autonomous entity that can:

  • Maintain persistent state and memory

  • Handle secure configurations

  • Provide customizable capabilities

  • Manage platform-specific integrations

Key Components

1. Configuration Management

interface Agent {
  id: string;
  name: string;
  type: string;
  secureConfig?: SecureConfig;
  tokenConfig?: TokenConfig;
  systemIdentity?: SystemIdentity;
  characterConfig?: CharacterConfig;
  capabilities?: Record<string, boolean>;
  isActive: boolean;
  createdAt: Date;
  updatedAt: Date;
}

The configuration system supports:

  • Secure storage of sensitive data (API keys, tokens)

  • Platform-specific configurations

  • Character and personality definitions

  • Capability toggles

  • System identity management

2. State Management

The agent system maintains state through:

  • Persistent database storage

  • Redis-based KV store for temporary states

  • Creation state management for multi-step processes

  • TTL-based state cleanup

3. Security Layer

Security features include:

  • Encrypted storage of sensitive configurations

  • Role-based access control

  • Admin-only operations

  • Secure token management

Architecture

Data Flow

  1. Configuration Loading

    • Secure config retrieval

    • Decryption of sensitive data

    • Capability validation

  2. State Management

    • Creation state tracking

    • Session management

    • Temporary data storage

  3. Platform Integration

    • Telegram bot integration

    • Twitter API integration

    • Cross-platform message handling

Database Schema

CREATE TABLE agents (
  id UUID PRIMARY KEY,
  name VARCHAR(255) NOT NULL,
  type VARCHAR(255) NOT NULL,
  secure_config TEXT,
  token_config JSONB,
  system_identity JSONB,
  character_config JSONB,
  capabilities JSONB,
  is_active BOOLEAN DEFAULT true,
  admin_id VARCHAR(255),
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
  updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

Implementation

Agent Creation

// Example agent creation
const agent = await AgentConfigService.createAgent({
  name: "AgentName",
  type: "Telegram Bot",
  secureConfig: {
    telegram: {
      botToken: "BOT_TOKEN",
      botId: "BOT_ID",
      botUsername: "username"
    }
  },
  characterConfig: {
    bio: "Agent description",
    spice: "Personality traits",
    format: "Response format",
    language: "en"
  },
  capabilities: {
    chat: true,
    imageGeneration: false
  },
  isActive: true
});

State Management

// Example state management
await AgentStateManager.setState(userId, {
  step: AgentCreationStep.INIT,
  isActive: true
});

const state = await AgentStateManager.getState(userId);

Capabilities

Agents can be configured with various capabilities:

  1. Core Capabilities

    • Basic chat functionality

    • Command handling

    • State persistence

  2. Optional Capabilities

    • Image generation (DALL-E)

    • Market data integration

    • Weather information

    • Social media integration

Best Practices

  1. Configuration

    • Always encrypt sensitive data

    • Use environment variables for tokens

    • Validate configurations before activation

  2. State Management

    • Clean up temporary states

    • Handle state transitions gracefully

    • Implement proper error handling

  3. Security

    • Regular token rotation

    • Proper access control

    • Secure storage of credentials

Error Handling

The agent system implements comprehensive error handling:

  1. Configuration Errors

    • Missing required fields

    • Invalid configuration format

    • Encryption/decryption failures

  2. State Errors

    • Invalid state transitions

    • State retrieval failures

    • Cleanup failures

  3. Platform Errors

    • API integration failures

    • Token validation errors

    • Rate limiting handling

Future Considerations

  1. Scalability

    • Multiple agent support

    • Cross-agent communication

    • Distributed state management

  2. Extensions

    • Additional platform integrations

    • Enhanced capability system

    • Advanced state management

  3. Monitoring

    • Agent health checks

    • Performance monitoring

    • Usage analytics

Related Documentation

PreviousLillo Incentives ProgramNextMemory handling

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