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Decision brief
litellm is a Python SDK and Proxy Server that facilitates the interaction with over 100 LLM APIs, offering features such as cost tracking, guardrails, load balancing, and logging.
Good fit when
- When you need to integrate multiple LLM (Language Learning Modelling) APIs into your application across different providers like Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, Hugging
Avoid when
- If your project only requires interaction with a single LLM API and basic functionalities, litellm may be overkill.
- Pricing:
- freemium - While the core functionality is provided free, specific extended features might require a paid plan.
- Requirements:
- Requires Docker
Observed Jul 11, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- 2 low (2 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install litellmHow it fits your stack(33)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Alternative
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Depends on
Related
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Similar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides an AI gateway to interact with over 100 LLM APIs, including cost tracking, guardrails, load balancing, and logging.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 1, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 1, 2026
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 1, 2026
- MCP server
- No MCP server detected
Source: repo_scan · Aug 1, 2026
- Languages
- python, javascript
Source: github.language+package.json+pyproject.toml · Aug 1, 2026
Categories
Graph entities
Tags
README
Verify Docker Image Signatures
All LiteLLM Docker images published to GHCR are signed with cosign. Every release is signed with the same key introduced in commit 0112e53.
Verify using the pinned commit hash (recommended):
A commit hash is cryptographically immutable, so this is the strongest way to ensure you are using the original signing key:
cosign verify \
--key https://raw.githubusercontent.com/BerriAI/litellm/0112e53046018d726492c814b3644b7d376029d0/cosign.pub \
ghcr.io/berriai/litellm:<release-tag>
Verify using a release tag (convenience):
Tags are protected in this repository and resolve to the same key. This option is easier to read but relies on tag protection rules:
cosign verify \
--key https://raw.githubusercontent.com/BerriAI/litellm/<release-tag>/cosign.pub \
ghcr.io/berriai/litellm:<release-tag>
Replace <release-tag> with the version you are deploying (e.g. v1.83.0-stable).
Quick Start for Contributors
This requires uv to be installed.
git clone https://github.com/BerriAI/litellm.git
cd litellm
make install-dev # Install development dependencies
make format # Format your code
make lint # Run all linting checks
make test-unit # Run unit tests
make format-check # Check formatting only
For detailed contributing guidelines, see CONTRIBUTING.md.
📖 Contributing to documentation? The LiteLLM docs have moved to a separate repository: BerriAI/litellm-docs. Please open doc PRs there. Docs are served at docs.litellm.ai.
For agents
This page has a .md twin and JSON over the API.