LiteLLM is an open-source library that provides a unified API to call 100+ LLM providers — OpenAI, Anthropic, Cohere, Replicate, Azure, AWS Bedrock, and more — with a single interface.
The Problem LiteLLM Solves
A CTO at a SaaS company had their entire backend hardcoded to OpenAI's API. When they needed to add Anthropic as a fallback, it required rewriting 40+ files. With LiteLLM, switching between providers is a one-line change.
Key Features:
- Unified API — Same interface for OpenAI, Anthropic, Cohere, and 100+ providers
- Load Balancing — Route requests across multiple providers and API keys
- Fallbacks — Automatic failover when a provider is down
- Cost Tracking — Track spend per model, per user, per team
- Proxy Server — Drop-in OpenAI-compatible proxy for any LLM
Quick Start
pip install litellm
from litellm import completion
# OpenAI
response = completion(model="gpt-4", messages=["role": "user"])
# Anthropic — same interface!
response = completion(model="claude-3-opus-20240229", messages=["role": "user"])
# Cohere — still the same!
response = completion(model="command-r-plus", messages=["role": "user"])
Proxy Server
Run a local OpenAI-compatible proxy:
litellm --model claude-3-opus-20240229
Now point any OpenAI SDK client to http://localhost:4000 and it works with Claude, Gemini, or any supported model.
Load Balancing & Fallbacks
from litellm import Router
router = Router(
model_list=[
"model_name": "gpt-4",
"model_name": "gpt-4",
],
fallbacks=[{"gpt-4": ["claude-3-opus-20240229"]}]
)
response = router.completion(model="gpt-4", messages=["role": "user"])
Why Teams Switch to LiteLLM
- Vendor independence — never locked into one provider
- Cost optimization — route to cheapest model per use case
- Reliability — automatic failover across providers
- Observability — built-in spend tracking and logging
Check out the LiteLLM documentation to get started.
Building AI-powered applications? I create data extraction and web scraping tools. Check out my Apify actors or email spinov001@gmail.com for custom solutions.
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