LiteLLM allows you to interact with 100+ LLMs seamlessly using a consistent OpenAI-compatible format, either use their python SDK or their proxy server.

Starting from LiteLLM v1.48.12, you can:

  • Log LLM calls to Literal AI and evaluate your LLM or prompt performance
  • Create multi-step traces with Literal AI decorators
  • Bind Prompt Templates directly to LiteLLM calls

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Pre-Requisites

Ensure you have the literalai package installed:

pip install literalai litellm

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Quick Start

import litellm
import os

os.environ["LITERAL_API_KEY"] = ""
os.environ['OPENAI_API_KEY']= ""
os.environ['LITERAL_BATCH_SIZE'] = "1" # You won't see logs appear until the batch is full and sent

litellm.success_callback = ["literalai"] # Log Input/Output to LiteralAI
litellm.failure_callback = ["literalai"] # Log Errors to LiteralAI

# openai call
response = litellm.completion(
  model="gpt-4o-mini",
  messages=[
    {"role": "user", "content": "Hi 👋 - i'm openai"}
  ]
)

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Multi Step Traces

This integration is compatible with the Literal AI SDK decorators, enabling conversation and agent tracing

import litellm
from literalai import LiteralClient
import os

os.environ["LITERAL_API_KEY"] = ""
os.environ['OPENAI_API_KEY']= ""
os.environ['LITERAL_BATCH_SIZE'] = "1" # You won't see logs appear until the batch is full and sent

litellm.input_callback = ["literalai"] # Support other Literal AI decorators and prompt templates
litellm.success_callback = ["literalai"] # Log Input/Output to LiteralAI
litellm.failure_callback = ["literalai"] # Log Errors to LiteralAI

literalai_client = LiteralClient()

@literalai_client.run
def my_agent(question: str):
    # agent logic here
    response = litellm.completion(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": question}],
        metadata={"literalai_parent_id": literalai_client.get_current_step().id}
    )
    return response

my_agent("Hello world")

# Waiting to send all logs before exiting, not needed in a production server
literalai_client.flush()

Learn more about Literal AI logging capabilities.

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Bind a Generation to its Prompt Template

This integration works out of the box with prompts managed on Literal AI. This means that a specific LLM generation will be bound to its template.

Learn more about Prompt Management on Literal AI.

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OpenAI Proxy Usage

If you are using the Lite LLM proxy, you can use the Literal AI OpenAI instrumentation to log your calls.

from literalai import LiteralClient
from openai import OpenAI

client = OpenAI(
    api_key="anything",            # litellm proxy virtual key
    base_url="http://0.0.0.0:4000" # litellm proxy base_url
)

literalai_client = LiteralClient(api_key="")

# Instrument the OpenAI client
literalai_client.instrument_openai()

settings = {
    "model": "gpt-4o-mini", # model you want to send litellm proxy
    "temperature": 0,
    # ... more settings
}

response = client.chat.completions.create(
    messages=[
        {
            "role": "system",
            "content": "You are a helpful bot, you always reply in Spanish"
        },
        {
            "role": "user",
            "content": message.content
        }
    ],
    **settings
)