Overview
LiteLLM can use TokenLab in two ways:- use TokenLab as an OpenAI-compatible endpoint behind LiteLLM
- use LiteLLM’s gateway when your team also needs its virtual keys, aliases, or logs
https://api.tokenlab.sh/v1.
If your application needs Claude Messages or Gemini fields, use an integration that keeps that API format.
Install
Proxy Configuration
Create alitellm-config.yaml like this:
Call LiteLLM Through OpenAI SDK
Direct Python Usage
If you are using LiteLLM as a Python library instead of the proxy, keep the same TokenLab base URL:Best Practices
Prefer custom_openai for TokenLab
Prefer custom_openai for TokenLab
Treat TokenLab as an OpenAI-compatible endpoint unless you have a very specific reason to build a more complex provider mapping.
Use LiteLLM when you need one more gateway layer
Use LiteLLM when you need one more gateway layer
LiteLLM makes sense when your own platform wants virtual keys, extra model-selection policy, or centralized logs in front of TokenLab.
Keep native-provider expectations realistic
Keep native-provider expectations realistic
OpenAI-compatible translation layers are great for broad compatibility, but they are not the right place to promise every provider-native feature.
Troubleshooting
Connection errors
Connection errors
- Verify
api_baseis exactlyhttps://api.tokenlab.sh/v1 - Make sure LiteLLM can reach TokenLab over the public internet
- If you run the proxy locally, verify the OpenAI client points to your LiteLLM port instead of TokenLab directly
Authentication errors
Authentication errors
- Check that LiteLLM is reading the right
OPENAI_API_KEY - Confirm the TokenLab key starts with
sk- - Confirm the key is active in TokenLab dashboard
Model not found
Model not found
- Verify the TokenLab model name in
custom_openai/<model> - Keep your LiteLLM
model_namealias separate from the real TokenLab model id