litellm-enterprise
Package for LiteLLM Enterprise features
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.9.
- API keys for at least one LLM provider must be configured via environment variables.
- Low install friction; pure Python wheel with no runtime dependencies.
License · maintenance · safety
LicenseRef-Proprietary (unclear) — 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.
last release 2026-08-12 (2 days) · last repo commit 2026-08-14 · 56,349 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 6,428,712 downloads/mo, #1,912 on PyPI
Alternatives
Verify before relying
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!"}]
)- 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.
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 on it.
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
Before you install
Low install friction; pure Python wheel with no runtime dependencies. Actively maintained with a recent release and substantial repository activity.
Requires Python >=3.9. API keys for at least one LLM provider must be configured via environment variables.
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!"}]
)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
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