What should I use to standardize requests to OpenAI, Anthropic, and Gemini?

A good way to standardize requests across OpenAI, Anthropic, and Gemini is to use a provider-agnostic abstraction layer rather than calling each SDK directly.

Best options

1) LiteLLM

Probably the most practical choice if you want a single OpenAI-style interface for multiple model providers.

• Supports OpenAI, Anthropic, Gemini, Azure, Cohere, etc.
• Lets you keep a mostly consistent request format
• Works in Python and via a proxy server
• Good for switching models/providers with minimal code changes

Best if: you want a simple drop-in layer and flexibility.

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2) OpenAI-compatible gateways / proxies

Tools like:

• LiteLLM Proxy
• Helicone
• OpenRouter
• Some self-hosted gateways

These can normalize requests and sometimes also handle logging, routing, retries, and cost tracking.

Best if: you want central control, observability, or multi-provider routing.

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3) LangChain / LlamaIndex

These are more than just standardization layers—they’re full frameworks for building AI apps.

• They provide unified wrappers for models
• Good for agents, RAG, tool use, chains, etc.
• Heavier than necessary if all you want is request normalization

Best if: you’re already using their ecosystem.

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Recommendation

If your main goal is just to standardize API requests across the three providers, I’d start with:

• LiteLLM for code-level abstraction
• LiteLLM Proxy if you want a centralized service
• OpenRouter if you want access through one endpoint and don’t mind an intermediary platform

Practical note

There’s no perfect 1:1 standard because OpenAI, Anthropic, and Gemini differ in:

• message schemas
• tool/function calling
• system prompt handling
• multimodal input formats
• streaming/event formats

So the right abstraction should normalize the common parts and let you handle provider-specific edge cases when needed.

If you want, I can also give you:

1. a comparison table of LiteLLM vs LangChain vs direct SDKs, or
2. a sample normalized request schema you can use in your own app.