Building Multi-Model AI Agents: Combining GPT, Claude, and RAG

As AI development matures, we're moving beyond single-model solutions. The most powerful AI agents today combine multiple models, each handling what they do best. But building these multi-model agents traditionally required complex infrastructure, careful API management, and significant cost overhead.

In this post, we'll build a practical research assistant agent that combines GPT-4o, Claude, and Gemini Flash, demonstrating how to leverage each model's strengths while optimizing for cost and performance.

The Challenge with Single-Model Solutions

Most AI applications today rely on a single model for all tasks. This creates several problems:

• Cost inefficiency (using expensive models for simple tasks)
• Missed opportunities for specialized capabilities
• Lack of redundancy and reliability
• Higher latency than necessary

Building a Smart Research Agent

Let's build a research assistant agent that can process documents, extract insights, and answer questions intelligently. This agent will:

1. Process and understand documents (Claude 3.5 Haiku)
2. Perform deep analysis (GPT-4o)
3. Handle quick queries (Llama 3.2 Groq)

1. Document Processing with Claude

Claude excels at understanding large documents and maintaining context. Here's how we structure the RAG component:

# Example Wave configuration from future SDK
const researchAgent = await waveloom.run('xxx', {
  params: {
    documentProcessor: {
      model: "claude-3-haiku",
    },
  }
});

By using Claude Haiku for document processing, we get:

• Excellent context understanding
• Cost-effective processing
• Reliable document parsing

2. Deep Analysis with GPT-4o

For complex reasoning and synthesis, we route to GPT-4o:

const analysisNode = {
  model: "gpt-4o",
  systemPrompt: `You are analyzing research documents. 
                 Focus on extracting key insights and patterns.
                 Always provide evidence for your conclusions.`,
};

GPT-4o handles:

• Complex reasoning tasks
• Pattern recognition
• Detailed explanations

3. Quick Responses with Llama (Groq)

For rapid responses and simple queries, we utilize Llama 3.2, a super-fast model utilizing Groq Fast AI Inference.

const quickResponder = {
  model: "llama..."
};

Putting It All Together

Here's how we combine these models in Waveloom:

1. Document input triggers Claude for processing
2. Processed content stored in vector database
3. User queries routed based on complexity:
   • Simple queries → Llama
   • Complex analysis → GPT-4o
   • Document lookup → Claude 3.5 Haiku

Cost Optimization

Let's break down the cost efficiency, during the early access phase of Waveloom:

• Document processing: 0.10 credits (Claude Haiku)
• Deep analysis: 0.30 credits/query (GPT-4o)
• Quick queries: 0.15 credits (Llama)

Traditional approach (everything through GPT-4):

• All operations: 0.30 credits each
• 100 operations = 30 credits

Best Practices

1. Model Selection
   • Use models like Gemini Flash, Llama or DeepSeek for simple, factual queries
   • Route to Claude Haiku for document processing
   • Save GPT-4o or Sonnet for complex reasoning

Getting Started

You can build this exact agent in Waveloom:

1. Visual builder for quick setup
2. Built-in monitoring
3. Automatic scaling
4. Cost optimization included

Create your first multi-model agent with our visual builder. Get started with 50 credits and 80% off premium models during our early access phase!