🚅 LiteLLM

LiteLLM AI Gateway

Open Source AI Gateway for 100+ LLMs. Self-hosted. Enterprise-ready. Call any LLM in OpenAI format.

LiteLLM Proxy Server (AI Gateway) | Hosted Proxy | Enterprise Tier | Website

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What is LiteLLM

LiteLLM is an open source AI Gateway that gives you a single, unified interface to call 100+ LLM providers — OpenAI, Anthropic, Gemini, Bedrock, Azure, and more — using the OpenAI format.

Use it as a Python SDK for direct library integration, or deploy the AI Gateway (Proxy Server) as a centralized service for your team or organization.

Jump to LiteLLM Proxy (LLM Gateway) Docs
Jump to Supported LLM Providers

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Why LiteLLM

Managing LLM calls across providers gets complicated fast — different SDKs, auth patterns, request formats, and error types for every model. LiteLLM removes that friction:

• Unified API — one interface for 100+ LLMs, no provider-specific SDK juggling
• Drop-in OpenAI compatibility — swap providers without rewriting your code
• Production-ready gateway — virtual keys, spend tracking, guardrails, load balancing, and an admin dashboard out of the box
• 8ms P95 latency at 1k RPS (benchmarks)

OSS Adopters

Netflix

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Features

LLMs - Call 100+ LLMs (Python SDK + AI Gateway)

All Supported Endpoints - /chat/completions, /responses, /embeddings, /images, /audio, /batches, /rerank, /a2a, /messages and more.

Python SDK

uv add litellm

from litellm import completion
import os

os.environ["OPENAI_API_KEY"] = "your-openai-key"
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key"

# OpenAI
response = completion(model="openai/gpt-4o", messages=[{"role": "user", "content": "Hello!"}])

# Anthropic  
response = completion(model="anthropic/claude-sonnet-4-20250514", messages=[{"role": "user", "content": "Hello!"}])

AI Gateway (Proxy Server)

Getting Started - E2E Tutorial - Setup virtual keys, make your first request

uv tool install 'litellm[proxy]'
litellm --model gpt-4o

import openai

client = openai.OpenAI(api_key="anything", base_url="http://0.0.0.0:4000")
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

Docs: LLM Providers

Agents - Invoke A2A Agents (Python SDK + AI Gateway)

Supported Providers - LangGraph, Vertex AI Agent Engine, Azure AI Foundry, Bedrock AgentCore, Pydantic AI

Python SDK - A2A Protocol

from litellm.a2a_protocol import A2AClient
from a2a.types import SendMessageRequest, MessageSendParams
from uuid import uuid4

client = A2AClient(base_url="http://localhost:10001")

request = SendMessageRequest(
    id=str(uuid4()),
    params=MessageSendParams(
        message={
            "role": "user",
            "parts": [{"kind": "text", "text": "Hello!"}],
            "messageId": uuid4().hex,
        }
    )
)
response = await client.send_message(request)

AI Gateway (Proxy Server)

Step 1. Add your Agent to the AI Gateway — set protocolVersion to 1.0 or 0.3 per agent

Step 2. Call Agent via A2A SDK (requires a2a-sdk>=1.1.0)

import httpx
from a2a.client import A2ACardResolver, ClientConfig, ClientFactory
from a2a.types import Message, Part, Role, SendMessageRequest
from a2a.utils.constants import TransportProtocol
from uuid import uuid4

base_url = "http://localhost:4000/a2a/my-agent"  # LiteLLM proxy + agent name
headers = {"Authorization": "Bearer <your-master-key>"}    # LiteLLM master key or a virtual key

async with httpx.AsyncClient(headers=headers, timeout=60.0) as http_client:
    resolver = A2ACardResolver(httpx_client=http_client, base_url=base_url)
    agent_card = await resolver.get_agent_card()
    config = ClientConfig(
        httpx_client=http_client,
        streaming=False,
        supported_protocol_bindings=[TransportProtocol.JSONRPC, TransportProtocol.HTTP_JSON],
    )
    client = ClientFactory(config).create(agent_card)

    request = SendMessageRequest(
        message=Message(
            message_id=uuid4().hex,
            role=Role.ROLE_USER,
            parts=[Part(text="Hello!")],
        )
    )
    async for event in client.send_message(request):
        populated = event.ListFields()
        if populated and populated[0][0].name in ("message", "msg"):
            print("".join(getattr(p, "text", "") or "" for p in populated[0][1].parts))

Docs: A2A Agent Gateway

MCP Tools - Connect MCP servers to any LLM (Python SDK + AI Gateway)

Python SDK - MCP Bridge

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from litellm import experimental_mcp_client
import litellm

server_params = StdioServerParameters(command="python", args=["mcp_server.py"])

async with stdio_client(server_params) as (read, write):
    async with ClientSession(read, write) as session:
        await session.initialize()

        # Load MCP tools in OpenAI format
        tools = await experimental_mcp_client.load_mcp_tools(session=session, format="openai")

        # Use with any LiteLLM model
        response = await litellm.acompletion(
            model="gpt-4o",
            messages=[{"role": "user", "content": "What's 3 + 5?"}],
            tools=tools
        )

AI Gateway - MCP Gateway

Step 1. Add your MCP Server to the AI Gateway

Step 2. Call MCP tools via /chat/completions

curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
  -H 'Authorization: Bearer <your-master-key>' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "Summarize the latest open PR"}],
    "tools": [{
      "type": "mcp",
      "server_url": "litellm_proxy/mcp/github",
      "server_label": "github_mcp",
      "require_approval": "never"
    }]
  }'

Use with Cursor IDE

{
  "mcpServers": {
    "LiteLLM": {
      "url": "http://localhost:4000/mcp/",
      "headers": {
        "x-litellm-api-key": "Bearer <your-master-key>"
      }
    }
  }
}

For MCP OAuth, an upstream may advertise dynamic client registration but refuse requests with HTTP 401 or 403. If the provider requires a pre-registered OAuth app, configure its credentials.client_id and, when required, credentials.client_secret on the MCP server. This skips dynamic registration in the gateway sign-in flow. The provider must approve the app for MCP access; reaching its authorization page does not establish that login or tool calls will succeed

Docs: MCP Gateway

Agents - Run Claude Code, Codex, OpenCode or Deep Agents on any model (Python SDK)

Python SDK - Agents

import litellm
from litellm import Harness, sandbox

result = litellm.agent(
    Harness.CLAUDE_CODE,  # or Harness.CODEX, Harness.OPENCODE, Harness.DEEPAGENTS
    "Find why tests/test_router.py is flaky and fix it.",
    sandbox=sandbox.local("./repo"),
    model="litellm_proxy/claude-sonnet-4-5",  # a model group on your AI Gateway
)

print(result.text, result.cost, [f.path for f in result.files])

Set LITELLM_PROXY_API_BASE and LITELLM_PROXY_API_KEY and every model call the agent makes goes through your AI Gateway, tagged harness,claude_code. Drop the litellm_proxy/ prefix to call a provider directly. Install starlette uvicorn plus the agent's CLI (claude, codex or opencode), or deepagents langchain-litellm for Deep Agents.

Docs: Agent Harnesses

Supported Providers (Website Supported Models | Docs)

Provider	/chat/completions	/messages	/responses	/embeddings	/image/generations	/audio/transcriptions	/audio/speech	/moderations	/batches	/rerank
Abliteration (abliteration)	✅
AI/ML API (aiml)	✅	✅	✅	✅	✅
AI21 (ai21)	✅	✅	✅
AI21 Chat (ai21_chat)	✅	✅	✅
Aleph Alpha	✅	✅	✅
Amazon Nova	✅	✅	✅
Anthropic (anthropic)	✅	✅	✅						✅
Anthropic Text (anthropic_text)	✅	✅	✅						✅
Anyscale	✅	✅	✅
AssemblyAI (assemblyai)	✅	✅	✅			✅
Auto Router (auto_router)	✅	✅	✅
AWS - Bedrock (bedrock)	✅	✅	✅	✅						✅
AWS - Sagemaker (sagemaker)	✅	✅	✅	✅
Azure (azure)	✅	✅	✅	✅	✅	✅	✅	✅	✅
Azure AI (azure_ai)	✅	✅	✅	✅	✅	✅	✅	✅	✅
Azure Text (azure_text)	✅	✅	✅			✅	✅	✅	✅
Baseten (baseten)	✅	✅	✅
Bytez (bytez)	✅	✅	✅
Cerebras (cerebras)	✅	✅	✅
Clarifai (clarifai)	✅	✅	✅
Cloudflare AI Workers (cloudflare)	✅	✅	✅
Codestral (codestral)	✅	✅	✅
Cognition (cognition)	✅	✅	✅
Cohere (cohere)	✅	✅	✅	✅						✅
Cohere Chat (cohere_chat)	✅	✅	✅
CometAPI (cometapi)	✅	✅	✅	✅
CompactifAI (compactifai)	✅	✅	✅
Custom (custom)	✅	✅	✅
Custom OpenAI (custom_openai)	✅	✅	✅			✅	✅	✅	✅
Dashscope (dashscope)	✅	✅	✅	✅						✅
Databricks (databricks)	✅	✅	✅
DataRobot (datarobot)	✅	✅	✅
Deepgram (deepgram)	✅	✅	✅			✅
DeepInfra (deepinfra)	✅	✅	✅
Deepseek (deepseek)	✅	✅	✅
Eden AI (edenai)	✅	✅	✅	✅	✅	✅	✅
ElevenLabs (elevenlabs)	✅	✅	✅			✅	✅
Empower (empower)	✅	✅	✅
Fal AI (fal_ai)	✅	✅	✅		✅
Featherless AI (featherless_ai)	✅	✅	✅
Fireworks AI (fireworks_ai)	✅	✅	✅
FriendliAI (friendliai)	✅	✅	✅
Galadriel (galadriel)	✅	✅	✅
GitHub Copilot (github_copilot)	✅	✅	✅	✅
GitHub Models (github)	✅	✅	✅
Google - PaLM	✅	✅	✅
Google - Vertex AI (vertex_ai)	✅	✅	✅	✅	✅
Google AI Studio - Gemini (gemini)	✅	✅	✅
GradientAI (gradient_ai)	✅	✅	✅
Groq AI (groq)	✅	✅	✅
Heroku (heroku)	✅	✅	✅
Hosted VLLM (hosted_vllm)	✅	✅	✅
Huggingface (huggingface)	✅	✅	✅	✅						✅
Hyperbolic (hyperbolic)	✅	✅	✅
IBM - Watsonx.ai (watsonx)	✅	✅	✅	✅
Infinity (infinity)				✅
Jina AI (jina_ai)				✅
Lambda AI (lambda_ai)	✅	✅	✅
Lemonade (lemonade)	✅	✅	✅
LiteLLM Proxy (litellm_proxy)	✅	✅	✅	✅	✅
Llamafile (llamafile)	✅	✅	✅
LM Studio (lm_studio)	✅	✅	✅
Maritalk (maritalk)	✅	✅	✅
Meta - Llama API (meta_llama)	✅	✅	✅
Mistral AI API (mistral)	✅	✅	✅	✅
ModelScope (modelscope)	✅	✅	✅		✅
Moonshot (moonshot)	✅	✅	✅
Morph (morph)	✅	✅	✅
Nebius AI Studio (nebius)	✅	✅	✅	✅
NLP Cloud (nlp_cloud)	✅	✅	✅
Novita AI (novita)	✅	✅	✅
Nscale (nscale)	✅	✅	✅
Nvidia NIM (nvidia_nim)	✅	✅	✅
OCI (oci)	✅	✅	✅
Ollama (ollama)	✅	✅	✅	✅
Ollama Chat (ollama_chat)	✅	✅	✅
Oobabooga (oobabooga)	✅	✅	✅			✅	✅	✅	✅
OpenAI (openai)	✅	✅	✅	✅	✅	✅	✅	✅	✅
OpenAI-like (openai_like)				✅
OpenRouter (openrouter)	✅	✅	✅
OVHCloud AI Endpoints (ovhcloud)	✅	✅	✅
Perplexity AI (perplexity)	✅	✅	✅
Petals (petals)	✅	✅	✅
Pinstripes (pinstripes)	✅	✅	✅
Predibase (predibase)	✅	✅	✅
Qianwen AI Platform (qwen_ai_platform)	✅	✅	✅	✅	✅					✅
QwenCloud (qwencloud)	✅	✅	✅	✅	✅					✅
Recraft (recraft)					✅
Replicate (replicate)	✅	✅	✅
Sagemaker Chat (sagemaker_chat)	✅	✅	✅
Sail (sail)	✅	✅	✅
Sambanova (sambanova)	✅	✅	✅
Snowflake (snowflake)	✅	✅	✅
Text Completion Codestral (text-completion-codestral)	✅	✅	✅
Text Completion OpenAI (text-completion-openai)	✅	✅	✅			✅	✅	✅	✅
Together AI (together_ai)	✅	✅	✅
Topaz (topaz)	✅	✅	✅
Triton (triton)	✅	✅	✅
V0 (v0)	✅	✅	✅
Vercel AI Gateway (vercel_ai_gateway)	✅	✅	✅
VLLM (vllm)	✅	✅	✅
Volcengine (volcengine)	✅	✅	✅
Voyage AI (voyage)				✅
WandB Inference (wandb)	✅	✅	✅
Watsonx Text (watsonx_text)	✅	✅	✅
xAI (xai)	✅	✅	✅
Xinference (xinference)				✅

Read the Docs

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Get Started

You can use LiteLLM through either the Proxy Server or Python SDK. Both give you a unified interface to access multiple LLMs (100+ LLMs). Choose the option that best fits your needs:

	LiteLLM AI Gateway	LiteLLM Python SDK
Use Case	Central service (LLM Gateway) to access multiple LLMs	Use LiteLLM directly in your Python code
Who Uses It?	Gen AI Enablement / ML Platform Teams	Developers building LLM projects
Key Features	Centralized API gateway with authentication and authorization, multi-tenant cost tracking and spend management per project/user, per-project customization (logging, guardrails, caching), virtual keys for secure access control, admin dashboard UI for monitoring and management	Direct Python library integration in your codebase, Router with retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - Router, application-level load balancing and cost tracking, exception handling with OpenAI-compatible errors, observability callbacks (Lunary, MLflow, Langfuse, etc.)

Stable Release: Use docker images with the -stable tag. These have undergone 12 hour load tests, before being published. More information about the release cycle here

Support for more providers. Missing a provider or LLM Platform, raise a feature request.

Deploy on AWS or GCP with Terraform

Run the LiteLLM proxy as a production-ready componentized stack (gateway, backend, UI on separate services; managed Postgres + Redis + object store) using the published Terraform modules. Both modules are on the public Terraform Registry — no auth needed.

AWS — ECS Fargate + Aurora + ElastiCache + ALB

— opens an in-browser shell, already authenticated to your AWS account. Once inside, run:

git clone https://github.com/BerriAI/litellm.git
cd litellm/terraform/litellm/aws/examples/default
cp terraform.tfvars.example terraform.tfvars   # edit region/tenant/env
terraform init && terraform apply

Module page →

Or call the module from your own root config:

# main.tf
terraform {
  required_version = ">= 1.6.0"
  required_providers {
    aws = { source = "hashicorp/aws", version = "~> 5.60" }
  }
}

provider "aws" {
  region = "us-west-2"
}

module "litellm" {
  source  = "BerriAI/litellm/aws"
  version = "~> 1.89"

  region = "us-west-2"
  azs    = ["us-west-2a", "us-west-2b"]
  tenant = "acme"
  env    = "prod"

  # Production: provide an ACM cert. Without one, set allow_plaintext_alb = true
  # (dev/trial only).
  # acm_certificate_arn = "arn:aws:acm:us-west-2:111122223333:certificate/..."
  allow_plaintext_alb = true
}

output "litellm_url" {
  value = module.litellm.alb_dns_name
}

terraform init
terraform apply

Provider API keys live in AWS Secrets Manager; reference ARNs via gateway_extra_secrets. Full input list and architecture diagram on the registry page.

GCP — Cloud Run + Cloud SQL + Memorystore + HTTPS LB

Real 1-click. Opens Cloud Shell, clones this repo, and walks you through terraform apply via a built-in DeployStack tutorial — pick the project, the tutorial sets up the Artifact Registry remote repo, writes terraform.tfvars from your answers, and runs apply.

Module page →

To call the module from your own config instead, Cloud Run can't pull from ghcr.io directly, so first set up a one-time Artifact Registry remote repo backed by GHCR:

gcloud artifacts repositories create litellm \
  --location=us-central1 \
  --repository-format=docker \
  --mode=remote-repository \
  --remote-docker-repo=https://ghcr.io \
  --project=my-gcp-project

Then:

# main.tf
terraform {
  required_version = ">= 1.6.0"
  required_providers {
    google      = { source = "hashicorp/google",      version = "~> 6.10" }
    google-beta = { source = "hashicorp/google-beta", version = "~> 6.10" }
  }
}

provider "google"      { project = "my-gcp-project"; region = "us-central1" }
provider "google-beta" { project = "my-gcp-project"; region = "us-central1" }

module "litellm" {
  source  = "BerriAI/litellm/google"
  version = "~> 1.89"

  project_id = "my-gcp-project"
  region     = "us-central1"
  tenant     = "acme"
  env        = "prod"

  # Replace my-gcp-project with your GCP project ID (same value as project_id above).
  image_registry = "us-central1-docker.pkg.dev/my-gcp-project/litellm/berriai"

  # Production: provide DNS already pointing at the LB IP for Google-managed certs.
  # Without one, set allow_plaintext_lb = true (dev/trial only).
  # lb_domains         = ["proxy.example.com"]
  allow_plaintext_lb = true
}

output "litellm_url" {
  value = module.litellm.load_balancer_url
}

terraform init
terraform apply

Provider API keys live in Secret Manager; reference resource IDs (e.g. projects/my-gcp-project/secrets/openai-api-key) via gateway_extra_secrets. Full input list and architecture diagram on the registry page.

Both stacks include

• The full componentized split (gateway / backend / UI as independent services)
• Managed Postgres (writer + reader) and Redis
• Versioned object store for proxy state + file uploads
• An auto-generated LITELLM_MASTER_KEY in your cloud's secret manager
• A one-off migration job that runs prisma migrate deploy before the proxy starts
• The same proxy_config surface as the Helm chart — pass YAML as a typed map

The Terraform modules live at terraform/litellm/aws/ and terraform/litellm/gcp/ in this repo; the registry entries are read-only mirrors updated on each release.

Run in Developer Mode

Services

1. Setup .env file in root
2. Run dependent services docker-compose up db prometheus

Backend

1. Run make bootstrap
2. Start proxy backend: uv run python litellm/proxy/proxy_cli.py

Frontend

1. Navigate to ui/litellm-dashboard (dependencies were already installed w/ make bootstrap)
2. Start dashboard: npm run dev

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).

---

Enterprise

For companies that need better security, user management and professional support

Get an Enterprise License Talk to founders

This covers:

• ✅ Features under the LiteLLM Commercial License:
• ✅ Feature Prioritization
• ✅ Custom Integrations
• ✅ Professional Support - Dedicated discord + slack
• ✅ Custom SLAs
• ✅ Secure access with Single Sign-On

Contributing

We welcome contributions to LiteLLM! Whether you're fixing bugs, adding features, or improving documentation, we appreciate your help.

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.

Code Quality / Linting

LiteLLM follows the Google Python Style Guide.

Our automated checks include:

• Ruff for formatting, linting, and code quality
• basedpyright for type checking
• Circular import detection
• Import safety checks

All these checks must pass before your PR can be merged.

Support / talk with founders

• Schedule Demo 👋
• Community Discord 💭
• Community Slack 💭
• Our emails ✉️ ishaan@berri.ai / krrish@berri.ai

Contributors