Prompt
What should I use to manage OpenAI, Anthropic, and Gemini from one layer?
Latest observation
If you want one abstraction layer for OpenAI, Anthropic, and Gemini, the best options are:
1. LiteLLM
Best overall choice for most teams.
- Supports OpenAI, Anthropic, Gemini, and many others
- Gives you an OpenAI-compatible API, so you can switch providers with minimal code changes
- Good for:
- model routing / fallbacks
- cost tracking
- retries
- load balancing
- proxy/server mode
Use it if: you want a practical, production-friendly unifier.
2. LangChain / LangGraph
Good if you also want agent workflows, tools, memory, and orchestration.
- Can call multiple model providers
- More focused on building apps/agents than just provider unification
- Heavier abstraction than LiteLLM
Use it if: you’re building an agentic app and want orchestration too.
3. Vellum / Portkey / Helicone
These are more about LLM ops / gateway / observability than pure abstraction.
- Portkey: AI gateway with routing, retries, fallback, observability
- Helicone: strong observability/monitoring, can sit in front of providers
- Vellum: more workflow/eval/LLMOps-oriented
Use them if: you care about monitoring, governance, and prompt management.
My recommendation
- Just one layer to unify APIs: LiteLLM
- Need orchestration/agents: LangChain + LiteLLM
- Need production gateway + observability: Portkey or Helicone
If you want, I can also give you a feature-by-feature comparison table or a recommended setup for Python/TypeScript.
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (2 observations 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.
Want this interpreted for your brand?
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