Skip to main content

LiteLLM Proxy (LLM Gateway)

PropertyDetails
DescriptionLiteLLM 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 LiteLLMlitellm_proxy/ (add this prefix to the model name, to route any requests to litellm_proxy - e.g. litellm_proxy/your-model-name)
Setup LiteLLM GatewayLiteLLM Gateway ↗
Supported Endpoints/chat/completions, /completions, /embeddings, /audio/speech, /audio/transcriptions, /images, /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"
)

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 →

Flags to send requests to litellm proxy​

Use the following options to 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
)