Intelligent Assistant

Build a comprehensive AI assistant using SkinFlow's multi-agent capabilities.

Overview

This example demonstrates how to create an intelligent assistant that can handle complex tasks, maintain context, and use various tools.

Features

• Multi-Agent Coordination: Uses specialized agents for different types of tasks
• Context Management: Maintains conversation history and user preferences
• Tool Integration: Leverages custom tools for enhanced capabilities
• Memory System: Persistent storage for long-term learning

Quick Start

Setup

cd examples/intelligent-agent
npm install
cp env.example .env
# Edit .env with your API keys

Run the Example

node demo/complete-demo.js

Architecture

The intelligent assistant consists of:

1. Main Framework: Core orchestration and task management

2. Specialized Agents:

   • Planning Agent: Task decomposition and strategy
   • Research Agent: Information gathering and analysis
   • Programming Agent: Code generation and technical tasks
   • Content Agent: Creative and writing tasks

3. Tool System:

   • Web Search: Information retrieval
   • Data Analysis: Processing and insights
   • File Operations: Document management

Usage Examples

Basic Assistant

import { createMultiAgentFramework } from 'skingflow'

const framework = await createMultiAgentFramework({
  llm: {
    provider: 'openai',
    apiKey: process.env.OPENAI_API_KEY,
    model: 'gpt-4'
  },
  memory: {
    storage: {
      type: 'memory'
    }
  }
})

// Simple request
const result = await framework.processRequest(
  "Help me plan a vacation to Japan",
  { userId: 'user123' }
)

Advanced Assistant with Tools

const framework = await createMultiAgentFramework({
  llm: {
    provider: 'openai',
    apiKey: process.env.OPENAI_API_KEY,
    model: 'gpt-4'
  },
  tools: {
    customTools: [
      {
        name: 'web_search',
        description: 'Search the web for current information',
        parameters: {
          type: 'object',
          properties: {
            query: { type: 'string', description: 'Search query' }
          },
          required: ['query']
        },
        handler: async (params) => {
          // Implement web search logic
          return `Search results for: ${params.query}`
        }
      }
    ]
  }
})

Key Components

1. Framework Configuration

• LLM provider setup
• Memory system configuration
• Tool registration
• Agent coordination

2. Agent System

• Task decomposition
• Agent selection
• Parallel processing
• Result aggregation

3. Memory Management

• Short-term context
• Long-term storage
• User preferences
• Learning patterns

Best Practices

1. Start Simple: Begin with basic requests before adding complexity
2. Monitor Performance: Track response times and success rates
3. Handle Errors: Implement proper error handling and fallbacks
4. Optimize Prompts: Fine-tune agent prompts for better results
5. Use Tools Wisely: Leverage tools to extend capabilities

Common Use Cases

• Customer Support: Automated helpdesk and FAQ handling
• Content Creation: Blog posts, articles, creative writing
• Research Assistance: Information gathering and analysis
• Task Management: Planning and organization help
• Learning Companion: Educational support and tutoring

Troubleshooting

Common Issues

1. API Rate Limits: Implement retry logic and rate limiting
2. Memory Issues: Configure appropriate memory storage
3. Tool Failures: Add error handling for external services
4. Agent Coordination: Monitor agent interactions and conflicts

Performance Tips

• Use streaming for better user experience
• Implement caching for repeated requests
• Monitor resource usage and scale accordingly
• Test with various input types and complexities

Next Steps

• Explore Custom Tools for extending functionality
• Learn about Memory Management for persistent storage
• Check Production Deployment for scaling

Related Examples

• Quick Start - Basic framework usage
• Content Creation - Automated content generation
• Data Analysis - Intelligent data processing