DAA AI

🚀 FULL IMPLEMENTATION - This is the complete, production-ready implementation of the DAA AI module, not a placeholder.

AI integration layer for the Decentralized Autonomous Agents (DAA) system, providing Claude AI integration via QuDAG MCP (Model Context Protocol) for intelligent decision making and task automation.

Overview

DAA AI enables autonomous agents to leverage Claude's advanced AI capabilities through QuDAG's MCP integration, providing:

• Claude Integration: Direct integration with Anthropic's Claude models
• Agent Management: Spawn and manage specialized AI agents
• Task Execution: Automated task processing with AI reasoning
• Tool Integration: MCP-based tool usage for extended capabilities
• Memory Management: Persistent memory for agent learning and context

Features

Core AI Capabilities

• Claude API Integration: Native support for Claude 3 models
• MCP Protocol: QuDAG MCP integration for tool usage
• Agent Spawning: Create specialized agents (Researcher, Coder, Analyst)
• Task Automation: AI-powered task execution and reasoning
• Memory System: Persistent memory for agent context and learning

Agent Types

• Researcher: Information gathering and analysis
• Coder: Code generation and software development
• Analyst: Data analysis and pattern recognition
• Coordinator: Multi-agent task coordination
• Specialist: Domain-specific expertise

Tool Integration

• Code Execution: Execute code via MCP tools
• File Operations: File system operations
• Web Search: Internet research capabilities
• Data Analysis: Statistical and analytical tools

Usage

Basic Setup

use daa_ai::{AISystem, AIConfig, agents::AgentType};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Configure AI system
    let config = AIConfig {
        claude: claude::ClaudeConfig {
            api_key: "your-anthropic-api-key".to_string(),
            model: "claude-3-opus-20240229".to_string(),
            ..Default::default()
        },
        ..Default::default()
    };

    // Initialize AI system
    let mut ai_system = AISystem::new(config).await?;
    ai_system.initialize().await?;

    // Spawn an AI agent
    let agent_id = ai_system.spawn_agent(
        AgentType::Researcher,
        Some(vec!["web_search".to_string(), "analysis".to_string()]),
        None,
    ).await?;

    println!("Spawned agent: {}", agent_id);
    Ok(())
}

Task Execution

use daa_ai::tasks::Task;

// Create a task
let task = Task {
    id: uuid::Uuid::new_v4().to_string(),
    task_type: "research".to_string(),
    description: "Research recent developments in quantum computing".to_string(),
    parameters: serde_json::json!({
        "topic": "quantum computing",
        "time_range": "last_6_months",
        "sources": ["arxiv", "nature", "science"]
    }),
};

// Execute task with agent
let result = ai_system.execute_task(&agent_id, task).await?;
println!("Task result: {:?}", result);

Tool Usage

use std::collections::HashMap;

// Use a tool via MCP
let mut parameters = HashMap::new();
parameters.insert("query".to_string(), serde_json::json!("latest AI research"));
parameters.insert("limit".to_string(), serde_json::json!(10));

let tool_result = ai_system.use_tool(
    &agent_id,
    "web_search",
    parameters,
).await?;

println!("Tool result: {:?}", tool_result);

Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│    AISystem     │    │  ClaudeClient   │    │   MCPClient     │
│                 │    │                 │    │                 │
│ - Agent Mgmt    │◄──►│ - API Calls     │◄──►│ - Tool Calls    │
│ - Task Coord    │    │ - Model Config  │    │ - Protocol Mgmt │
│ - Memory Mgmt   │    │ - Response Parse│    │ - Connection    │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│  AgentManager   │    │  TaskManager    │    │  ToolRegistry   │
│                 │    │                 │    │                 │
│ - Agent Spawn   │    │ - Task Queue    │    │ - Tool Catalog  │
│ - Capabilities  │    │ - Execution     │    │ - MCP Tools     │
│ - Lifecycle     │    │ - Results       │    │ - Custom Tools  │
└─────────────────┘    └─────────────────┘    └─────────────────┘

Features

The crate supports several feature flags:

• default: Includes MCP and Claude features
• mcp: Enables QuDAG MCP integration
• claude: Claude API support (always enabled)
• rules-integration: Integration with DAA Rules engine
• database: Persistent storage for agents and tasks
• full: All features enabled

[dependencies]
daa-ai = { version = "0.1.0", features = ["full"] }

Configuration

Claude Configuration

use daa_ai::claude::ClaudeConfig;

let claude_config = ClaudeConfig {
    api_key: std::env::var("ANTHROPIC_API_KEY")?,
    model: "claude-3-opus-20240229".to_string(),
    endpoint: "https://api.anthropic.com".to_string(),
    timeout: 60,
};

MCP Configuration

use daa_ai::MCPClientConfig;

let mcp_config = MCPClientConfig {
    server_url: "http://localhost:3000".to_string(),
    timeout: 30,
    max_connections: 10,
    retry_attempts: 3,
    available_tools: vec![
        "code_execution".to_string(),
        "file_operations".to_string(),
        "web_search".to_string(),
    ],
};

Integration with DAA System

With DAA Rules

#[cfg(feature = "rules-integration")]
use daa_ai::rules_integration::RulesIntegration;

// AI agents can leverage rules for decision making
let rules_integration = RulesIntegration::new(rules_engine);
ai_system.add_rules_integration(rules_integration).await?;

With DAA Economy

// AI agents can interact with the economic system
let economic_task = Task {
    id: uuid::Uuid::new_v4().to_string(),
    task_type: "economic_analysis".to_string(),
    description: "Analyze token distribution patterns".to_string(),
    parameters: serde_json::json!({
        "token": "rUv",
        "time_period": "30_days",
        "metrics": ["distribution", "velocity", "concentration"]
    }),
};

License

MIT OR Apache-2.0