FAQ from LiteLLM
What is LiteLLM?
A developer-first, open-source LLM interoperability layer that standardizes calls across 100+ models and 30+ providers—turning fragmented LLM APIs into one predictable, production-ready interface.
How to use LiteLLM?
Import litellm, configure your credentials, and invoke completion() or embedding() using plain English model names. Optional enhancements include load balancing, fallback chains, token usage tracking, and async streaming—all enabled via simple keyword arguments.
What LLM models does LiteLLM support?
Everything from proprietary models (GPT-4-turbo, Claude-3.5-Sonnet, Gemini 1.5 Pro) to open weights (Llama 3, Phi-3, Qwen2, Gemma 2) and local runtimes (Ollama, LM Studio, Text Generation WebUI)—with new integrations added weekly by the community.
Can LiteLLM be used for research purposes?
Absolutely. Researchers rely on LiteLLM for reproducible LLM benchmarking, ablation studies, prompt engineering at scale, and cross-model analysis—thanks to deterministic input/output formatting and standardized metrics.
Does LiteLLM have its own pricing?
No. LiteLLM is completely free and open-source (MIT licensed). You only pay for the underlying LLM APIs you choose—LiteLLM adds no markup, no hidden fees, and no telemetry by default.
What is the demo playground in LiteLLM?
An interactive, browser-based sandbox where you can write Python snippets, select multiple models simultaneously, compare outputs side-by-side, inspect timing and token counts, and export configurations—ideal for debugging, teaching, and stakeholder demos.