What is your AI harness that lets you switch LLM models easily?

To enable easy switching between LLM models, I recommend implementing a lightweight abstraction layer that decouples your application code from specific model providers. Here's how to design it effectively:

Core Principles

1. ·Unified Interface: Define a simple contract (like an LLM abstract class with a complete method) that all providers must implement.
2. ·Configuration-Driven: Specify models and providers in a YAML/JSON config file (e.g., settings.yaml) or environment variables.
3. ·Provider Adapters: Create thin wrappers that translate the unified interface to each provider's API (OpenAI, Anthropic, HuggingFace, etc.).
4. ·Lazy Initialization: Load models only when first used to optimize resource usage.

Implementation Options

• ·

  Custom Solution: For full control, build a minimal router/factory system like the Python example above, with:

  • ·A base LLM class
  • ·Provider-specific adapters
  • ·Configuration loading
  • ·Optional caching and retries
• ·

  Off-the-Shelf Tools: For quicker setup, consider:

  • ·LangChain: Provides pre-built LLM abstractions
  • ·LiteLLM: Normalizes APIs across providers
  • ·Ollama/vLLM: Local OpenAI-compatible servers

Key Features to Include

• ·Standardized request/response formats
• ·Error handling and retries
• ·Cost tracking per provider
• ·Easy model switching via config changes

Production Considerations

• ·Secret management for API keys
• ·Rate limiting and fallback models
• ·Observability (logging, metrics)
• ·Testing against mock LLMs

This approach lets you swap models in minutes by editing a config file, without touching application code. The abstraction handles provider differences behind a consistent interface.