{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"Unified Python interface to call many LLM providers (OpenAI, Anthropic, Bedrock, Azure, VertexAI, Groq, etc.) in OpenAI format, plus an AI Gateway proxy server for centralized access with authentication, cost tracking, and routing.","skillfed_tags":["llm-abstraction","api-gateway","multi-provider"],"use_cases":["Switch between LLM providers without rewriting completion calls by changing the model string.","Build a centralized LLM gateway for your organization with virtual keys, cost tracking per user/project, and unified authentication.","Implement retry and fallback logic across multiple LLM deployments using the Router for resilience.","Connect MCP servers to any LLM via the experimental MCP bridge for tool-augmented completions.","Track LLM spend and usage across teams using the proxy's multi-tenant cost tracking and observability callbacks.","Invoke agent-to-agent (A2A) protocols from multiple agent platforms through a unified gateway."],"what_it_does":"unclecode-litellm is a pre-compromise fork that abstracts away differences between LLM provider APIs, letting you call models from many providers through a single OpenAI-compatible interface. It ships as both a Python SDK for direct integration into your code and an AI Gateway (proxy server) for centralized LLM access with authentication, virtual keys, cost tracking, and request routing.\n\nThe Python SDK handles completion calls, embeddings, image generation, audio transcription, and more across providers. The AI Gateway layer adds multi-tenant support, per-project customization (logging, guardrails, caching), and admin dashboards for monitoring. It also supports agent-to-agent (A2A) protocols, MCP (Model Context Protocol) tool bridging, and router-based retry and fallback logic across multiple deployments. Runtime dependencies include httpx, openai, pydantic, aiohttp, and tokenizers.","worth_installing":"Yes, with conditions. This is a pre-compromise fork, so verify that this fork's divergence from the main project meets your needs\u2014check whether all providers and features you require are present and functional. Install friction is low, maintenance is active, and the MIT license is permissive. No known vulnerabilities. Suitable for teams building multi-provider LLM applications or operating a centralized LLM gateway."},"id":"unclecode-litellm","links":{"html":"https://skillfed.io/packages/unclecode-litellm","md":"https://skillfed.io/packages/unclecode-litellm.md","pypi":"https://pypi.org/project/unclecode-litellm/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-24","license_spdx":"MIT","license_treatment":"permissive","name":"unclecode-litellm","python_support":"supports_current","summary":"Pre-compromise fork of litellm - Library to easily interface with LLM API providers"},"popularity":{"monthly_downloads":900309,"position":4776,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.81.13"}