{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"LiteLLM Enterprise provides a unified Python SDK and self-hosted AI Gateway to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) through a single OpenAI-compatible interface.","skillfed_tags":["llm-gateway","multi-provider","openai-compatible"],"use_cases":["Build an LLM application that can switch between OpenAI, Anthropic, and other providers without rewriting code.","Deploy a centralized AI Gateway for your team so all services call LLMs through one proxy with unified auth and monitoring.","Track and control LLM spending across multiple providers using virtual keys and spend limits.","Integrate MCP tools or A2A agents into your LLM workflows via a single gateway.","Load-balance requests across multiple LLM providers to reduce latency and cost."],"what_it_does":"LiteLLM Enterprise is a gateway and SDK for calling multiple LLM providers through a single, OpenAI-compatible interface. Instead of managing separate SDKs, authentication patterns, and error handling for each provider, you write code once and swap providers by changing a model string. It can run as a Python library for direct integration or as a self-hosted proxy server that your team or organization calls over HTTP, with features like virtual keys, spend tracking, load balancing, and an admin dashboard.\n\nThe package is designed for production use: it handles the complexity of provider-specific quirks, request formatting, and error types transparently. It also supports agent protocols (A2A), MCP (Model Context Protocol) tool integration, and a range of endpoints beyond chat completions\u2014embeddings, image generation, audio, batches, and reranking. No runtime dependencies means installation is straightforward; the main constraint is that you need API keys for whichever providers you want to use.","worth_installing":"Yes, with conditions. The package is actively maintained with low install friction and solves a real problem for teams managing multiple LLM providers. However, the proprietary license is not clearly documented in the package metadata\u2014verify licensing terms before deploying in production or in open-source projects. If you need a multi-provider LLM abstraction and can accept the license terms, it is worth installing."},"id":"litellm-enterprise","links":{"html":"https://skillfed.io/packages/litellm-enterprise","md":"https://skillfed.io/packages/litellm-enterprise.md","pypi":"https://pypi.org/project/litellm-enterprise/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-12","license_spdx":"LicenseRef-Proprietary","license_treatment":"unclear","name":"litellm-enterprise","python_support":"supports_current","summary":"Package for LiteLLM Enterprise features"},"popularity":{"monthly_downloads":6428712,"position":1912,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.1.55"}