Prompt
What should I use to avoid rewriting code when switching LLM providers?
Latest observation
Use a provider-agnostic LLM abstraction layer.
Good options:
- LiteLLM – very popular for unifying OpenAI, Anthropic, Azure, Gemini, Mistral, etc. behind one API.
- LangChain / LlamaIndex – useful if you also want orchestration, tools, retrieval, agents.
- Vercel AI SDK – good if you’re building web apps and want a consistent interface across providers.
If your main goal is simply “swap providers without rewriting code”, the best default choice is usually:
LiteLLM + a small internal wrapper/interface
That gives you:
- one consistent code path
- easier provider switching
- centralized handling of retries, logging, rate limits, and model names
A common pattern is to define your own interface like:
interface LLMClient {
chat(messages: Message[]): Promise<string>;
}
Then implement it once using LiteLLM or another adapter.
If you want, I can suggest the best option based on your stack:
- Python or TypeScript
- simple API calls or agents/workflows
- backend, frontend, or both
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
Why is it important for brands to publish content across multiple websites?
Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
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