LiteLLM by LiteLLM | AI Agents

Agent framework

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By LiteLLM

LiteLLM is an innovative AI agent designed to streamline interactions with over 100 large language models (LLMs), providing a unified interface that simplifies the integration process for developers, data scientists, and businesses. This open-source toolkit is built to address the complexities of working with multiple LLMs by offering a robust set of features that enhance efficiency, scalability, and reliability.

Features

LiteLLM boasts a comprehensive set of features tailored to facilitate seamless integration and interaction with various large language models. Below is an overview of its key functionalities:

Feature	Description
Unified API Interface	Consistent interaction with multiple LLMs through a single API, reducing the need for understanding individual APIs and authentication mechanisms.
Seamless Integration	Quick implementation in Python projects with minimal code, enabling rapid development and testing.
Support for Multiple Models	Flexibility to switch between various models such as GPT-3 and GPT-Neo without extensive code alterations.
Error Handling	Standardized error management that maps common exceptions to their OpenAI equivalents for easier troubleshooting.
Robust Retry and Fallback Logic	Automatic retries and fallback to other providers in case of errors, ensuring service continuity.
Load Balancing and Cost Tracking	Tools for managing resource allocation and tracking expenses related to multiple LLMs.
Observability Features	Includes logging and callback support for real-time monitoring and debugging capabilities.
Unified OpenAI Format	Ensures consistent output formatting across all models, simplifying data parsing and processing.

Use Cases

LiteLLM can be utilized across a range of applications, including:

Application Development: Integrate LLMs seamlessly into applications, enhancing development efficiency.
Data Analysis: Utilize LLMs for tasks such as text generation and comprehension analysis.
Customer Support Automation: Automate responses to common queries, improving customer service efficiency.
Content Generation: Leverage advanced AI models to create high-quality written content.
Research and Development: Facilitate experimentation with different LLMs, expediting R&D processes.

How to get started

To get started with LiteLLM, users can access the open-source toolkit via its official GitHub repository. The repository includes comprehensive API documentation and a Python SDK to assist with integration into existing projects. Developers are encouraged to explore the toolkit's features and functionalities by downloading it from GitHub and reviewing the documentation provided.

LiteLLM Pricing Overview

The pricing for LiteLLM is structured around token-based costs, model-specific pricing, custom pricing options, and enterprise plans.

Token-Based Pricing

Costs are determined by the number of tokens processed in both input and output. Please refer to the LiteLLM API for details on cost per token.

Model-Specific Pricing

Each model has its own pricing, available through the model_cost function in the LiteLLM API.

Custom Pricing

Users can define their own pricing by setting input_cost_per_token and output_cost_per_token in the litellm_params.

Enterprise Plans

Enterprise Basic: $500/month (cloud or self-hosted)
Enterprise Premium: Custom pricing with enterprise support and custom SLAs

AWS Marketplace: LiteLLM LLM Gateway (Proxy Server)

LiteLLM Enterprise: $30,000/year for all features under the LiteLLM Enterprise License