litellm

---

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

---

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

---

Features

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

Python SDK

uv add litellm

An independent litellm-core distribution provides the Python SDK with the same import litellm API and runtime dependencies. It has no optional extras, CLI entry points, or bundled dashboard. Install one SDK distribution per environment because litellm and litellm-core own overlapping Python files. Use litellm for the proxy, CLI, and optional extras

Build and install core from this checkout while its release integration is pending:

python scripts/build_core_distribution.py --out-dir dist/core
python -m pip install dist/core/litellm_core-*.whl

The builder requires Git, uv and the Rust build toolchain. It reads the release version from the root pyproject.toml, leaves source files unchanged, and produces a wheel and a self-contained sdist in dist/core. Run installation in a fresh environment without 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!"}]
)

Python SDK

Docs: Autorouter SDK

Install with uv add litellm and set OPENAI_API_KEY for the models below.

import asyncio
from litellm import Router

router = Router(model_list=[
    {"model_name": "fast", "litellm_params": {"model": "openai/gpt-6-luna"}},
    {"model_name": "capable", "litellm_params": {"model": "openai/gpt-6.1-sol"}},
    {"model_name": "auto", "litellm_params": {
        "model": "auto_router/complexity_router",
        "complexity_router_config": {
            "classifier_type": "heuristic",
            "tiers": {"SIMPLE": "fast", "MEDIUM": "fast",
                      "COMPLEX": "capable", "REASONING": "capable"},
            "return_raw_model_name": True,
        },
        "complexity_router_default_model": "capable",
    }},
])
response = asyncio.run(router.acompletion(
    model="auto", messages=[{"role": "user", "content": "What is 2+2?"}],
))
print(response.model, response.choices[0].message.content)

AI Gateway (Proxy Server)

Create an Auto Router, then call its model name. Replace auto and the URL below with yours, and set LITELLM_API_KEY to your virtual key.

curl http://localhost:4000/v1/chat/completions \
  -H "Authorization: Bearer $LITELLM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"auto","messages":[{"role":"user","content":"What is 2+2?"}]}'

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

Python SDK - MCP Bridge

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

Python SDK - Agents

import litellm
from litellm import Harness, sandbox

result = litellm.agent(
    Harness.CLAUDE_CODE,  # or Harness.CODEX, Harness.OPENCODE, Harness.PI, 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, opencode or pi), 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)	✅	✅	✅
CLF AI Gateway (clf_ai_gateway)	✅	✅	✅
Cloudflare AI Workers (cloudflare)	✅	✅	✅
Codestral (codestral)	✅	✅	✅
Cognition (cognition)	✅	✅	✅
Cohere (cohere)	✅	✅	✅	✅						✅
Cohere Chat (cohere_chat)	✅	✅	✅
CometAPI (cometapi)	✅	✅	✅	✅
CompactifAI (compactifai)	✅	✅	✅
CoralBricks (coralbricks)	✅	✅	✅
Custom (custom)	✅	✅	✅
Custom OpenAI (custom_openai)	✅	✅	✅			✅	✅	✅	✅
Dashscope (dashscope)	✅	✅	✅	✅						✅
Databricks (databricks)	✅	✅	✅
DataRobot (datarobot)	✅	✅	✅
Deepgram (deepgram)	✅	✅	✅			✅
DeepInfra (deepinfra)	✅	✅	✅
Deepseek (deepseek)	✅	✅	✅
Eden AI (edenai)	✅	✅	✅	✅	✅	✅	✅
ElevenLabs (elevenlabs)	✅	✅	✅			✅	✅
EmpirioLabs AI (empiriolabs)	✅		✅
Empower (empower)	✅	✅	✅
eu/jev Decisions API (eu_jev)
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)	✅	✅	✅
Nace (nace)
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)	✅	✅	✅
Parallel AI (parallel_ai)
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)	✅	✅	✅
ScaleDown (scaledown)	✅
Snowflake (snowflake)	✅	✅	✅
Strands Decider (strands_decider)
Text Completion Codestral (text-completion-codestral)	✅	✅	✅
Text Completion OpenAI (text-completion-openai)	✅	✅	✅			✅	✅	✅	✅
Together AI (together_ai)	✅	✅	✅
Topaz (topaz)	✅	✅	✅
Triton (triton)	✅	✅	✅
Typesafe Decisions API (typesafe)
V0 (v0)	✅	✅	✅
Vercel AI Gateway (vercel_ai_gateway)	✅	✅	✅
VLLM (vllm)	✅	✅	✅
Volcengine (volcengine)	✅	✅	✅
VoyageAI by MongoDB (voyage)				✅
WandB Inference (wandb)	✅	✅	✅
Watsonx Text (watsonx_text)	✅	✅	✅
xAI (xai)	✅	✅	✅
Xinference (xinference)				✅
ZeroGPU (zerogpu)	✅	✅	✅

Read the Docs

---

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:

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 the terraform-google-litellm module mirror at the latest stable release (a small download instead of the full litellm 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. Releases are signed with the key introduced in commit e82a3c6.

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 published signing key:

cosign verify \
  --key https://raw.githubusercontent.com/BerriAI/litellm/e82a3c646908ba0a6ee0beddc4310d7a6cac4065/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.

If you want to work closely with our team, check out LiteLLM OSS Champions, our program for trusted OSS contributors with their own Slack channel and a voice in the roadmap

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 ✉️ [email protected] / [email protected]

Contributors