LiteLLM: An open-source gateway for unified LLM access

The growing number of large language models (LLMs) from various providers—Anthropic, Google, Meta, Microsoft, Nvidia, OpenAI, and many others—has given developers a rich set of choices but also has introduced complexity. Each provider has its own API nuances and response formats, making it a challenge to switch models or support multiple back fends in one application. LiteLLM is an open-source project that tackles this fragmentation head-on by providing a unified interface (and gateway) to call more than 100 LLM APIs using a single, consistent format.

In essence, LiteLLM acts as a “universal remote” for LLMs, allowing developers to integrate a diverse set of models as if they were calling OpenAI’s API, regardless of the underlying model provider.

Since its launch, LiteLLM has quickly gained traction in the AI developer community. The project’s GitHub repository (maintained by BerriAI, a team backed by Y Combinator) has garnered over 20,000 stars and 2,600 forks, reflecting widespread interest. Part of this popularity stems from the real-world needs it addresses. Organizations including Netflix, Lemonade, and Rocket Money have adopted LiteLLM to provide day-zero access to new models with minimal overhead. By standardizing how developers interface with LLM providers, LiteLLM promises faster integration of the latest models and smoother operations across an ever-evolving LLM ecosystem.

In this article, we’ll discuss LiteLLM’s origins and goals, dive into its core functionality and key features, and examine how it simplifies LLM usage through practical examples. We’ll also discuss the enterprise edition of LiteLLM for commercial use and compare it to a few alternative solutions.

Project overview – LiteLLM

LiteLLM is designed as a universal adapter for LLM APIs, allowing developers to interact with various providers through a standardized interface. The project supports leading LLM providers including Anthropic, AWS Bedrock, AWS SageMaker, Azure OpenAI, DeepSeek, Google Vertex AI, OpenAI, and Ollama.

The project is built around two core components: the Python SDK and the Proxy Server. The Python SDK provides developers with an easy-to-use library for integrating multiple LLMs into their applications. Meanwhile, the Proxy Server acts as a production-grade gateway for managing LLM usage at scale. It offers centralized cost tracking, access control, and real-time monitoring of API calls.

The motivation behind LiteLLM is to simplify the development of multi-LLM applications and reduce the friction for platform teams in managing multiple model providers. According to the maintainers, LiteLLM simplifies model access, spend tracking, and fallbacks across more than 100 large language models.

In practical terms, LiteLLM aims to save time and effort for development teams. Instead of writing custom integration code for each new model API or waiting for vendor-specific SDKs, developers can use LiteLLM’s unified SDK and Proxy Server to gain immediate compatibility.

What problem does LiteLLM solve?

Developers often face significant challenges when integrating multiple LLMs into their applications. One of the primary issues is API heterogeneity, as different providers have different input/output formats and authentication mechanisms, which can complicate development. Additionally, managing fallbacks to respond to provider outages or rate limits requires custom code that can be error-prone and time-consuming to implement.

Another common pain point is cost opacity. It becomes difficult to track spending accurately when using multiple LLMs across different projects or teams. Organizations risk exceeding budgets or failing to optimize costs effectively without proper tools.

LiteLLM addresses these challenges by providing a unified API that standardizes interactions across all supported providers. It also includes built-in features like automatic retries for failed requests and real-time cost analytics, making it easier for developers to focus on building applications rather than managing infrastructure.

A closer look at LiteLLM

LiteLLM is designed to be both flexible and powerful. At its core is the ability to translate all API calls into OpenAI’s familiar completion() syntax, regardless of the underlying provider. This means developers can switch between models without significantly changing their code base.

For example, if a developer wants to use Anthropic’s Claude 3 instead of OpenAI’s GPT-4 for a particular task, they need only to specify the model name in their request. LiteLLM handles the rest, including authentication and formatting.

In addition to the unified API, LiteLLM includes advanced features like dynamic fallbacks and structured outputs. Dynamic fallbacks allow requests to be routed to a backup model automatically if the primary model fails or becomes unavailable. Fallbacks ensure high availability even during provider outages. Structured outputs allow developers to validate responses using Pydantic schemas, reducing errors in downstream processing.

Here’s how you can use LiteLLM to call Anthropic’s Claude 3 using OpenAI’s format:

from litellm import completion

response = completion(
model='anthropic/claude-3',
messages=[{'role': 'user', 'content': 'Explain quantum computing'}]
)
print(response.choices[0].message.content) # Outputs Claude's response

For production environments, the LiteLLM Proxy Server can be deployed as a centralized gateway. This allows multiple teams or applications to share access to LLMs while maintaining control over costs and usage limits:

litellm --model openai/gpt-4 --api_key sk-xyz

Then clients can interact with the Proxy Server using standard OpenAI libraries:

import openai
client = openai.OpenAI(base_url='
client.chat.completions.create(model='gpt-4', messages=)

Key use cases for LiteLLM

LiteLLM offers several commercial-grade features that make it suitable for enterprise use cases. One of its most popular applications is multi-cloud LLM orchestration. Enterprises often use multiple providers to ensure redundancy or to optimize costs based on specific tasks. With LiteLLM, developers can distribute requests across different providers seamlessly:

response = completion(
model=['azure/gpt-4', 'aws-bedrock/claude-3'],
messages=[{'role': 'user', 'content': 'What are black holes?'}]
)

Another key feature for enterprises is cost governance. LiteLLM provides real-time cost analytics through the Proxy Server dashboard. Organizations can set monthly budgets for different teams or projects and monitor spending across all supported models. This level of transparency helps prevent budget overruns and ensures efficient resource allocation.

Audit compliance is another area where LiteLLM excels. The Proxy Server logs all input/output metadata securely, making it easier for organizations to meet regulatory requirements or conduct internal reviews.

Bottom line – LiteLLM

LiteLLM is more than just an open-source project—it’s a comprehensive solution for managing multi-provider LLM deployments at scale. LiteLLM empowers developers to build robust generative AI applications without worrying about infrastructure complexities by simplifying API interactions and adding powerful features like dynamic fallbacks and cost analytics.

LiteLLM’s combination of Python SDK and Proxy Server makes it suitable for both small teams experimenting with AI and large enterprises running mission-critical workloads. With active community support and continuous updates from BerriAI, LiteLLM is well-positioned to become the go-to choice for unified LLM access in the years ahead.