LiteLLM
Expert guidance for unified LLM API access across providers.
Triggers
Use this skill when:
- Calling multiple LLM providers with a unified interface
- Building multi-provider AI applications
- Implementing fallbacks and retries across providers
- Deploying an LLM proxy or gateway
- Managing costs across different LLM providers
- Keywords: litellm, unified api, multi-provider, llm proxy, gateway, fallback
Installation
pip install litellm
Quick Start
from litellm import completion
# OpenAI
response = completion(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello!"}]
)
# Anthropic
response = completion(
model="claude-3-5-sonnet-20241022",
messages=[{"role": "user", "content": "Hello!"}]
)
# Azure OpenAI
response = completion(
model="azure/gpt-4o",
messages=[{"role": "user", "content": "Hello!"}],
api_base="https://my-resource.openai.azure.com",
api_key="your-key",
api_version="2024-02-01"
)
# Ollama
response = completion(
model="ollama/llama3.1",
messages=[{"role": "user", "content": "Hello!"}],
api_base="http://localhost:11434"
)
Streaming
from litellm import completion
response = completion(
model="gpt-4o",
messages=[{"role": "user", "content": "Tell me a story"}],
stream=True
)
for chunk in response:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")
Async
import asyncio
from litellm import acompletion
async def main():
response = await acompletion(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)
asyncio.run(main())
Embeddings
from litellm import embedding
# OpenAI
response = embedding(
model="text-embedding-3-small",
input=["Hello world"]
)
# Cohere
response = embedding(
model="cohere/embed-english-v3.0",
input=["Hello world"]
)
embeddings = response.data[0].embedding
Function Calling
from litellm import completion
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get weather for a city",
"parameters": {
"type": "object",
"properties": {
"city": {"type": "string"}
},
"required": ["city"]
}
}
}]
response = completion(
model="gpt-4o",
messages=[{"role": "user", "content": "What's the weather in Paris?"}],
tools=tools,
tool_choice="auto"
)
if response.choices[0].message.tool_calls:
for tool_call in response.choices[0].message.tool_calls:
print(f"Function: {tool_call.function.name}")
print(f"Arguments: {tool_call.function.arguments}")
Fallbacks & Retries
from litellm import completion
import litellm
# Enable fallbacks
litellm.set_verbose = True
response = completion(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello"}],
fallbacks=["claude-3-5-sonnet-20241022", "gpt-3.5-turbo"],
num_retries=3
)
Router (Load Balancing)
from litellm import Router
router = Router(
model_list=[
{
"model_name": "gpt-4",
"litellm_params": {
"model": "azure/gpt-4-deployment",
"api_base": "https://us-east.openai.azure.com",
"api_key": "key1"
}
},
{
"model_name": "gpt-4",
"litellm_params": {
"model": "azure/gpt-4-deployment",
"api_base": "https://us-west.openai.azure.com",
"api_key": "key2"
}
}
],
routing_strategy="least-busy" # or "simple-shuffle", "latency-based-routing"
)
response = router.completion(
model="gpt-4",
messages=[{"role": "user", "content": "Hello"}]
)
Proxy Server
Configuration (config.yaml)
model_list:
- model_name: gpt-4
litellm_params:
model: azure/gpt-4
api_base: https://my-resource.openai.azure.com
api_key: os.environ/AZURE_API_KEY
- model_name: claude
litellm_params:
model: claude-3-5-sonnet-20241022
api_key: os.environ/ANTHROPIC_API_KEY
litellm_settings:
drop_params: true
set_verbose: false
general_settings:
master_key: sk-1234
database_url: postgresql://user:pass@localhost/litellm
Run Proxy
# Start server
litellm --config config.yaml --port 4000
# Or with Docker
docker run -p 4000:4000 \
-v $(pwd)/config.yaml:/app/config.yaml \
ghcr.io/berriai/litellm:main-latest \
--config /app/config.yaml
Use Proxy
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:4000",
api_key="sk-1234"
)
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Hello"}]
)
Cost Tracking
from litellm import completion
import litellm
litellm.success_callback = ["langfuse"] # or "langsmith", "helicone"
response = completion(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello"}]
)
# Access cost
print(f"Cost: ${response._hidden_params['response_cost']}")
Budget Management
from litellm import BudgetManager
budget = BudgetManager(project_name="my-project")
# Set budget
budget.create_budget(
total_budget=100, # $100
user="user-123",
duration="monthly"
)
# Check before request
if budget.get_current_cost("user-123") < budget.get_total_budget("user-123"):
response = completion(model="gpt-4o", messages=[...])
budget.update_cost(response._hidden_params['response_cost'], "user-123")
Model Aliases
import litellm
litellm.model_alias_map = {
"fast": "gpt-3.5-turbo",
"smart": "gpt-4o",
"cheap": "claude-3-haiku-20240307"
}
response = completion(
model="smart", # Uses gpt-4o
messages=[{"role": "user", "content": "Hello"}]
)
Resources
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