litellm

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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

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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 litellm/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)

This is the beginning of the README. Read the rest at the original. Read the full README ↗