What it does
LiteLLM offers a Python SDK and an AI gateway for calling different LLM providers through a common interface. The gateway adds routing, usage tracking, and access controls around those calls.
Why it’s interesting
Provider integrations can spread throughout an application. A shared gateway gives teams a place to manage model access and fallback behavior while keeping client code simpler.
Real-world use case
Route requests from several internal tools through one gateway. Configure a backup model for provider failures and track which tool is consuming the most tokens.
What I can learn
- 01
Adapter patterns
translate provider-specific requests into a shared API.
- 02
Resilience
reason about retries, cooldowns, and fallback routes.
- 03
API gateway design
centralize access and usage policies.
One thing to explore
Model routing and fallbacks
Find the Router class and follow how a request selects a deployment. Identify where a failed call can take a different route.
Explore the source (opens in a new tab)10-Minute Challenge
Map a fallback path
Before you start: Git and a text editor, or browse router.py on GitHub. No provider account needed.
- Open litellm/router.py and search for fallback-related functions.
- Trace the path from a failed request to a backup deployment.
- Write down one condition that should trigger a fallback and one that should return an error.
git clone --depth=1 https://github.com/BerriAI/litellm.gitcd litellm# Open litellm/router.py in your editor.