Multi-LLM Integration

Warden is designed to seamlessly integrate with multiple large language models (LLMs), giving developers the flexibility to use the best model for each task. With built-in support for top AI providers and the ability to add custom models, Warden ensures a versatile and scalable AI framework.

Supported AI Providers

Warden supports integration with the following popular LLMs:

1. OpenAI

• Models: GPT-3, GPT-4, and more.
• Usage: Ideal for high-quality, contextually accurate conversations.
• Features: Supports dynamic API key management for easy setup.

2. GPT-4-Free

• Free-to-use alternative to OpenAI's GPT-4.
• Usage: Useful for testing and non-commercial applications.

3. Claude (Anthropic)

• Models: Claude and Claude 2.
• Usage: Known for concise and reliable responses.

4. DeepSeek

• Usage: Customizable and specialized for niche tasks or domains.
• Features: Tailored capabilities for domain-specific knowledge.

5. Grok (xAI)

• Usage: Cutting-edge model built for real-time, task-specific intelligence.
• Features: Simplifies integration with Grok APIs.

Key Features of Multi-LLM Support

1. Dynamic Model Selection

Warden allows you to dynamically choose which LLM to use for specific tasks. For example:

agent.SetModel("OpenAI-GPT4")
response := agent.Respond("What's the weather today?")

2. Easy API Key Management

Manage API keys for different LLM providers securely:

• Set keys in environment variables:

  export OPENAI_API_KEY=your_openai_api_key
  

• Access them within your application for seamless integration.

3. Custom Model Support

Adding a custom LLM is straightforward. Define a new agent or model handler:

type CustomLLM struct {}

func (c *CustomLLM) Respond(input string) string {
    // Custom response logic
    return "This is a custom model response."
}

Register the custom model in your application logic:

agent.SetModel("CustomLLM")

Example: Using Multiple LLMs

Here’s an example of how to leverage multiple LLMs within Warden:

agent1 := NewAgent("OpenAI-GPT4")
agent2 := NewAgent("Claude")

response1 := agent1.Respond("Tell me about AI.")
response2 := agent2.Respond("Explain quantum mechanics.")

fmt.Println("Response from GPT-4:", response1)
fmt.Println("Response from Claude:", response2)

This approach enables you to use the strengths of different models for specific tasks.

Benefits of Multi-LLM Integration

1. Flexibility:

   • Choose the right model for the right task.
   • Experiment with different LLMs for optimization.

2. Scalability:

   • Add new models easily without modifying the core application.

3. Cost Optimization:

   • Use free or low-cost models for non-critical tasks while reserving premium models for high-priority tasks.

4. Enhanced Capabilities:

   • Combine the expertise of multiple models for more robust applications.

Conclusion

The multi-LLM integration feature makes Warden a powerful framework for AI development. Whether you’re working with OpenAI, Claude, or your own custom model, Warden ensures a seamless and secure connection to the LLMs of your choice.