LiteLLM Proxy (LLM Gateway)

Property	Details
Description	LiteLLM Proxy is an OpenAI-compatible gateway that allows you to interact with multiple LLM providers through a unified API. Simply use the litellm_proxy/ prefix before the model name to route your requests through the proxy.
Provider Route on LiteLLM	litellm_proxy/ (add this prefix to the model name, to route any requests to litellm_proxy - e.g. litellm_proxy/your-model-name)
Setup LiteLLM Gateway	LiteLLM Gateway ↗
Supported Endpoints	/chat/completions, /completions, /embeddings, /audio/speech, /audio/transcriptions, /images, /images/edits, /rerank

Required Variables

os.environ["LITELLM_PROXY_API_KEY"] = "" # "sk-1234" your litellm proxy api key 
os.environ["LITELLM_PROXY_API_BASE"] = "" # "http://localhost:4000" your litellm proxy api base

Usage (Non Streaming)

import os 
import litellm
from litellm import completion

os.environ["LITELLM_PROXY_API_KEY"] = ""

# set custom api base to your proxy
# either set .env or litellm.api_base
# os.environ["LITELLM_PROXY_API_BASE"] = ""
litellm.api_base = "your-openai-proxy-url"


messages = [{ "content": "Hello, how are you?","role": "user"}]

# litellm proxy call
response = completion(model="litellm_proxy/your-model-name", messages)

Usage - passing api_base, api_key per request

If you need to set api_base dynamically, just pass it in completions instead - completions(...,api_base="your-proxy-api-base")

import os 
import litellm
from litellm import completion

os.environ["LITELLM_PROXY_API_KEY"] = ""

messages = [{ "content": "Hello, how are you?","role": "user"}]

# litellm proxy call
response = completion(
    model="litellm_proxy/your-model-name", 
    messages=messages, 
    api_base = "your-litellm-proxy-url",
    api_key = "your-litellm-proxy-api-key"
)

Usage - Streaming

import os 
import litellm
from litellm import completion

os.environ["LITELLM_PROXY_API_KEY"] = ""

messages = [{ "content": "Hello, how are you?","role": "user"}]

# openai call
response = completion(
    model="litellm_proxy/your-model-name", 
    messages=messages,
    api_base = "your-litellm-proxy-url", 
    stream=True
)

for chunk in response:
    print(chunk)

Embeddings

import litellm

response = litellm.embedding(
    model="litellm_proxy/your-embedding-model",
    input="Hello world",
    api_base="your-litellm-proxy-url",
    api_key="your-litellm-proxy-api-key"
)

Image Generation

import litellm

response = litellm.image_generation(
    model="litellm_proxy/dall-e-3",
    prompt="A beautiful sunset over mountains",
    api_base="your-litellm-proxy-url",
    api_key="your-litellm-proxy-api-key"
)

Image Edit

import litellm

with open("your-image.png", "rb") as f:
    response = litellm.image_edit(
        model="litellm_proxy/gpt-image-1",
        prompt="Make this image a watercolor painting",
        image=[f],
        api_base="your-litellm-proxy-url",
        api_key="your-litellm-proxy-api-key",
    )

Audio Transcription

import litellm

response = litellm.transcription(
    model="litellm_proxy/whisper-1",
    file="your-audio-file",
    api_base="your-litellm-proxy-url",
    api_key="your-litellm-proxy-api-key"
)

Text to Speech

import litellm

response = litellm.speech(
    model="litellm_proxy/tts-1",
    input="Hello world",
    api_base="your-litellm-proxy-url",
    api_key="your-litellm-proxy-api-key"
)

Rerank

import litellm

import litellm

response = litellm.rerank(
    model="litellm_proxy/rerank-english-v2.0",
    query="What is machine learning?",
    documents=[
        "Machine learning is a field of study in artificial intelligence",
        "Biology is the study of living organisms"
    ],
    api_base="your-litellm-proxy-url",
    api_key="your-litellm-proxy-api-key"
)

Integration with Other Libraries

LiteLLM Proxy works seamlessly with Langchain, LlamaIndex, OpenAI JS, Anthropic SDK, Instructor, and more.

Learn how to use LiteLLM proxy with these libraries →

Send all SDK requests to LiteLLM Proxy

info

Requires v1.72.1 or higher.

Use this when calling LiteLLM Proxy from any library / codebase already using the LiteLLM SDK.

These flags will route all requests through your LiteLLM proxy, regardless of the model specified.

When enabled, requests will use LITELLM_PROXY_API_BASE with LITELLM_PROXY_API_KEY as the authentication.

Option 1: Set Globally in Code

# Set the flag globally for all requests
litellm.use_litellm_proxy = True

response = litellm.completion(
    model="vertex_ai/gemini-2.0-flash-001",
    messages=[{"role": "user", "content": "Hello, how are you?"}]
)

Option 2: Control via Environment Variable

# Control proxy usage through environment variable
os.environ["USE_LITELLM_PROXY"] = "True"

response = litellm.completion(
    model="vertex_ai/gemini-2.0-flash-001",
    messages=[{"role": "user", "content": "Hello, how are you?"}]
)

Option 3: Set Per Request

# Enable proxy for specific requests only
response = litellm.completion(
    model="vertex_ai/gemini-2.0-flash-001",
    messages=[{"role": "user", "content": "Hello, how are you?"}],
    use_litellm_proxy=True
)

OAuth2/JWT Authentication

If your LiteLLM Proxy requires OAuth2/JWT authentication (e.g., Azure AD, Keycloak, Okta), the SDK can automatically obtain and refresh tokens for you.

import litellm
from litellm.proxy_auth import AzureADCredential, ProxyAuthHandler

litellm.proxy_auth = ProxyAuthHandler(
    credential=AzureADCredential(),
    scope="api://my-litellm-proxy/.default"
)
litellm.api_base = "https://my-proxy.example.com"

response = litellm.completion(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)

Learn more about SDK Proxy Authentication (OAuth2/JWT Auto-Refresh) →

Sending tags to LiteLLM Proxy

Tags allow you to categorize and track your API requests for monitoring, debugging, and analytics purposes. You can send tags as a list of strings to the LiteLLM Proxy using the extra_body parameter.

Usage

Send tags by including them in the extra_body parameter of your completion request:

Usage

import litellm

response = litellm.completion(
    model="gpt-4",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
    api_base="http://localhost:4000",
    api_key="sk-1234",
    extra_body={"tags": ["user:ishaan", "department:engineering", "priority:high"]}
)

Async Usage

Async Usage

import litellm

response = await litellm.acompletion(
    model="gpt-4",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
    api_base="http://localhost:4000", 
    api_key="sk-1234",
    extra_body={"tags": ["user:ishaan", "department:engineering"]}
)