LiteLLM
Overview
An open-source Python SDK and proxy server for managing authentication, load balancing, and spend tracking across 100+ LLMs using a unified OpenAI format.
Best For Professions:
LiteLLM is an open-source toolkit that streamlines interactions with over 100 large language models (LLMs) by providing a unified API in the OpenAI format. It offers both a Python SDK and a proxy server (LLM Gateway) to manage authentication, load balancing, and spend tracking across various LLM providers, including OpenAI, Azure OpenAI, Vertex AI, and Amazon Bedrock. LiteLLM supports features such as retry and fallback logic, rate limiting, and logging integrations with tools like Langfuse and OpenTelemetry. It is designed to simplify the integration of multiple LLMs into applications, ensuring consistent output formats and efficient resource management.
Product overview
Capabilities, pricing and fit
LiteLLM is best understood as an open-source LLM gateway rather than an agent framework. LiteLLM is the open-source AI gateway that puts your full AI stack behind one OpenAI-compatible key. Track and cap LLM spend, route to the right model, and self-host anywhere — even air-gapped. 140+ providers, 1,892 models.
Information as of July 30, 2026
What it can do
- Agent application development
LiteLLM is the open-source AI gateway that puts your full AI stack behind one OpenAI-compatible key. Track and cap LLM spend, route to the right model, and self-host anywhere — even air-gapped. 140+ providers, 1,892 models.[1]
Where it fits best
- Developers seeking a unified interface to interact with multiple LLM providers.[1]
Sources (1)
- LiteLLM — Open-Source AI Gateway & LLM ProxyOfficial site · checked 2026-07-30
Autonomy level
77/100
Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.
Comparisons
Custom Comparisons
Some of the use cases of LiteLLM:
- Developers seeking a unified interface to interact with multiple LLM providers.
- Implementing load balancing and fallback mechanisms across different LLMs.
- Tracking and managing API usage and spending across various projects.
- Integrating LLM functionalities into applications with consistent output formats.
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Pricing model:
Code access:
Popularity level: 83%
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