A proxy server framework for efficiently routing and load-balancing multiple LLM providers.

Screenshot of LiteLLM

About LiteLLM

LiteLLM is a versatile framework enabling users to deploy a proxy server for interacting with large language models (LLMs). It supports integration with various providers like OpenAI, Anthropic, Hugging Face, and more. LiteLLM handles routing, load balancing, and fallbacks, simplifying API calls with OpenAI-compatible endpoints. It also supports features like logging, observability, and Python SDK integrations, offering a comprehensive solution for developers aiming to streamline multi-provider LLM usage.

Agentic

Developer
BerriAI
Added
55 days ago

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AI Agent Categories

Large Language Models (LLMs)

AI agents based on advanced natural language processing models, capable of understanding, generating, and transforming human language. These agents can perform tasks such as text generation, summarization, translation, sentiment analysis, and more, enabling powerful applications across various industries.

Task Automation

AI agents that streamline and automate repetitive tasks across various domains, including business processes, IT management, and personal workflows. These agents help reduce manual effort, improve efficiency, and enhance productivity by handling tasks such as scheduling, data entry, and notifications.

AI Agent Platform

Comprehensive ecosystems designed to create, deploy, and manage AI agents capable of automating tasks and workflows. These platforms provide the tools, frameworks, and infrastructure necessary to build collaborative or independent agents, offering features like data integration, task orchestration, and human-in-the-loop support.

Workflow Automation

AI agents designed to automate repetitive tasks and processes, optimizing business operations

Agent Hosting

Platforms and solutions for hosting and deploying AI agents. This category covers managed hosting services, containerization options, and cloud solutions optimized for running and scaling AI agents.

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