litellm

name	litellm
description	Call 100+ LLMs through a single OpenAI-compatible interface with LiteLLM — use completion/acompletion/embedding with any provider (Anthropic, OpenAI, Google, Groq, Ollama, etc.), run a proxy server for team rate-limiting and cost tracking, load-balance across providers.
triggers	["litellm","lite llm","unified llm api","llm proxy server","multi provider llm","litellm completion","litellm proxy","llm fallback","llm load balancing","litellm budget","litellm router","openai compatible proxy","switch llm provider"]
do_not_use_for	["Fine-tuning — use llamafactory instead","Agent orchestration — use crewai or langgraph instead","Structured output validation — use pydantic-ai or instructor-structured-output instead"]
see_also	["instructor-structured-output","pydantic-ai","vercel-ai-sdk"]

LiteLLM — Unified LLM API

Source: BerriAI/litellm (MIT) — call 100+ LLMs with a single OpenAI-compatible interface

Why LiteLLM

• One API for OpenAI, Anthropic, Google, Groq, Mistral, Ollama, AWS Bedrock, Azure, etc.
• OpenAI SDK compatible — drop-in replacement, just change base_url
• Proxy server — centralize API keys, add rate limits, track costs, log requests
• Router — load balance + fallback across multiple models/providers

Install

pip install litellm
# Optional: proxy server
pip install 'litellm[proxy]'

Basic Completion

from litellm import completion

# Anthropic
response = completion(
    model="claude-sonnet-4-5",
    messages=[{"role": "user", "content": "Hello"}],
    api_key="your-anthropic-key",
)

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

# Google
response = completion(
    model="gemini/gemini-2.0-flash",
    messages=[{"role": "user", "content": "Hello"}],
)

# Groq
response = completion(
    model="groq/llama-3.1-70b-versatile",
    messages=[{"role": "user", "content": "Hello"}],
)

# Local Ollama
response = completion(
    model="ollama/llama3",
    messages=[{"role": "user", "content": "Hello"}],
    api_base="http://localhost:11434",
)

# Access response (OpenAI format)
print(response.choices[0].message.content)
print(response.usage.total_tokens)

Async Completion

import asyncio
from litellm import acompletion

async def main():
    response = await acompletion(
        model="claude-sonnet-4-5",
        messages=[{"role": "user", "content": "Explain quantum computing"}],
    )
    print(response.choices[0].message.content)

asyncio.run(main())

Streaming

from litellm import completion

response = completion(
    model="claude-sonnet-4-5",
    messages=[{"role": "user", "content": "Write a poem about AI"}],
    stream=True,
)

for chunk in response:
    content = chunk.choices[0].delta.content or ""
    print(content, end="", flush=True)

Function Calling / Tools

from litellm import completion

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a city",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string"},
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["city"],
            },
        },
    }
]

response = completion(
    model="claude-sonnet-4-5",
    messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
    tools=tools,
    tool_choice="auto",
)

# Check if tool was called
if response.choices[0].finish_reason == "tool_calls":
    tool_call = response.choices[0].message.tool_calls[0]
    print(tool_call.function.name)    # "get_weather"
    print(tool_call.function.arguments)  # '{"city": "Tokyo"}'

Embeddings

from litellm import embedding

# OpenAI
resp = embedding(model="text-embedding-3-small", input=["Hello world"])

# Cohere
resp = embedding(model="cohere/embed-english-v3.0", input=["Hello world"])

# Bedrock
resp = embedding(model="bedrock/amazon.titan-embed-text-v1", input=["Hello world"])

vectors = resp.data[0]["embedding"]  # list of floats

Router — Load Balancing & Fallbacks

from litellm import Router

router = Router(
    model_list=[
        {
            "model_name": "claude-fast",
            "litellm_params": {
                "model": "claude-haiku-4-5",
                "api_key": "your-key",
            },
            "tpm": 100_000,  # tokens per minute limit
        },
        {
            "model_name": "claude-fast",  # same group name = load balance
            "litellm_params": {
                "model": "claude-sonnet-4-5",
                "api_key": "your-key",
            },
        },
        {
            "model_name": "fallback-model",
            "litellm_params": {"model": "gpt-4o-mini"},
        },
    ],
    fallbacks=[{"claude-fast": ["fallback-model"]}],  # fallback on error
    routing_strategy="least-busy",  # or "latency-based", "simple-shuffle"
    retry_after=5,
    num_retries=3,
)

response = router.completion(
    model="claude-fast",
    messages=[{"role": "user", "content": "Hello"}],
)

Proxy Server

# Start proxy (routes all requests through LiteLLM)
litellm --model claude-sonnet-4-5 --port 8000

# Or with config file
litellm --config config.yaml

# config.yaml
model_list:
  - model_name: claude-fast
    litellm_params:
      model: claude-haiku-4-5
      api_key: os.environ/ANTHROPIC_API_KEY

  - model_name: gpt4
    litellm_params:
      model: gpt-4o
      api_key: os.environ/OPENAI_API_KEY

litellm_settings:
  drop_params: true          # ignore unsupported params per model
  request_timeout: 30
  max_budget: 100            # $100 budget limit

router_settings:
  routing_strategy: least-busy
  num_retries: 3
  fallbacks: [{"claude-fast": ["gpt4"]}]

# Use proxy with OpenAI SDK (drop-in replacement)
from openai import OpenAI

client = OpenAI(
    api_key="anything",       # proxy handles auth
    base_url="http://localhost:8000",
)

response = client.chat.completions.create(
    model="claude-fast",      # proxy routes to correct model
    messages=[{"role": "user", "content": "Hello"}],
)

Cost Tracking

import litellm

litellm.success_callback = ["langfuse"]  # or "langsmith", "helicone"

# Or manual cost calculation
from litellm import completion_cost, model_cost

response = completion(model="claude-sonnet-4-5", messages=[...])
cost = completion_cost(completion_response=response)
print(f"Cost: ${cost:.6f}")

# Get model pricing
pricing = model_cost["claude-sonnet-4-5"]
print(pricing["input_cost_per_token"], pricing["output_cost_per_token"])

Environment Variable Config

# Set API keys as env vars — LiteLLM picks them up automatically
export ANTHROPIC_API_KEY="..."
export OPENAI_API_KEY="..."
export GOOGLE_API_KEY="..."
export GROQ_API_KEY="..."

# LiteLLM debug logging
export LITELLM_LOG="DEBUG"

Drop-In for OpenAI SDK

# Before (OpenAI only)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(model="gpt-4o", messages=[...])

# After (any provider via LiteLLM proxy)
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000", api_key="fake")
response = client.chat.completions.create(model="claude-sonnet-4-5", messages=[...])

Anti-Fake-Pass Checks

• Response format is always OpenAI-compatible: response.choices[0].message.content
• Model string format: provider/model for non-OpenAI (e.g., groq/llama3, ollama/mistral)
• anthropic/ prefix NOT needed for Anthropic — just "claude-sonnet-4-5" works
• Router model_name is the alias you call — litellm_params.model is the actual model
• Fallbacks need same model_name group — different names require fallbacks config
• drop_params=True in config prevents errors when sending unsupported params cross-provider