litellm-enterprise
Package for LiteLLM Enterprise features
What it is and 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.
The 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—embeddings, 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.
Use it for:
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page — verify before relying
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.
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—verify 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.
Install
litellm-enterprise on PyPI
pip
pip install litellm-enterpriseuv
uv add litellm-enterprisepoetry
poetry add litellm-enterpriseInstalling litellm-enterprise
Before you install
Low install friction; pure Python wheel with no runtime dependencies. Actively maintained with a recent release and substantial repository activity.
License in practice
License treatment is unclear — the package declares LicenseRef-Proprietary with no raw license text provided. Review licensing terms before use in commercial or open-source contexts.
Quickstart
pip install litellm-enterprise
from litellm import completion
import os
os.environ["OPENAI_API_KEY"] = "your-key"
response = completion(
model="openai/gpt-4o",
messages=[{"role": "user", "content": "Hello!"}]
)
Requires Python >=3.9. API keys for at least one LLM provider must be configured via environment variables.
Verify before relying
- Whether the proprietary license permits commercial deployment or redistribution.
- Whether the enterprise package includes all features mentioned in the description or if some require additional licensing.
- Performance characteristics and latency claims under real-world conditions.
Package facts
| License | LicenseRef-Proprietary (unclear) |
| Python support | supports the current Python release (>=3.9) |
| Install friction | low — pure-Python wheel |
| Runtime dependencies | none |
| Maintenance | actively maintained — 2 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 6,428,712/month — #1,912 on PyPI (30-day window, as of 2026-08-14) |
| Known vulnerabilities | none known (OSV.dev, checked 2026-08-14) |
Evidence: litellm_enterprise-0.1.55-py3-none-any.whl
Tags
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