🌙 Lunary - GenAI Observability

Lunary is an open-source platform providing observability, prompt management, and analytics to help team manage and improve LLM chatbots.

You can reach out to us anytime by email or directly schedule a Demo.

Usage with LiteLLM Python SDK

Pre-Requisites

uv add litellm lunary

Quick Start

First, get your Lunary public key on the Lunary dashboard.

Use just 2 lines of code, to instantly log your responses across all providers with Lunary:

litellm.success_callback = ["lunary"]
litellm.failure_callback = ["lunary"]

Complete code:

from litellm import completion

os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key" # from https://app.lunary.ai/)
os.environ["OPENAI_API_KEY"] = ""

litellm.success_callback = ["lunary"]
litellm.failure_callback = ["lunary"]

response = completion(
  model="gpt-4o",
  messages=[{"role": "user", "content": "Hi there 👋"}],
  user="ishaan_litellm"
)

Usage with LangChain ChatLiteLLM

import os
from langchain.chat_models import ChatLiteLLM
from langchain.schema import HumanMessage
import litellm

os.environ["LUNARY_PUBLIC_KEY"] = "" # from https://app.lunary.ai/settings
os.environ['OPENAI_API_KEY']="sk-..."

litellm.success_callback = ["lunary"] 
litellm.failure_callback = ["lunary"] 

chat = ChatLiteLLM(
  model="gpt-4o"
  messages = [
    HumanMessage(
        content="what model are you"
    )
]
chat(messages)

Usage with Prompt Templates

You can use Lunary to manage prompt templates and use them across all your LLM providers with LiteLLM.

from litellm import completion
from lunary

template = lunary.render_template("template-slug", {
  "name": "John", # Inject variables
})

litellm.success_callback = ["lunary"]

result = completion(**template)

Usage with custom chains

You can wrap your LLM calls inside custom chains, so that you can visualize them as traces.

import litellm
from litellm import completion
import lunary

litellm.success_callback = ["lunary"]
litellm.failure_callback = ["lunary"]

@lunary.chain("My custom chain name")
def my_chain(chain_input):
  chain_run_id = lunary.run_manager.current_run_id
  response = completion(
    model="gpt-4o", 
    messages=[{"role": "user", "content": "Say 1"}],
    metadata={"parent_run_id": chain_run_id},
  )

  response = completion(
    model="gpt-4o", 
    messages=[{"role": "user", "content": "Say 2"}],
    metadata={"parent_run_id": chain_run_id},
  )
  chain_output = response.choices[0].message
  return chain_output

my_chain("Chain input")

Usage with LiteLLM Proxy Server

Step1: Install dependencies and set your environment variables

Install the dependencies

uv add litellm lunary

Get you Lunary public key from from https://app.lunary.ai/settings

export LUNARY_PUBLIC_KEY="<your-public-key>"

Step 2: Create a config.yaml and set lunary callbacks

model_list:
  - model_name: "*"
    litellm_params:
      model: "*"
litellm_settings:
  success_callback: ["lunary"]
  failure_callback: ["lunary"]

Step 3: Start the LiteLLM proxy

litellm --config config.yaml

Step 4: Make a request

curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-d '{
    "model": "gpt-4o",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful math tutor. Guide the user through the solution step by step."
      },
      {
        "role": "user",
        "content": "how can I solve 8x + 7 = -23"
      }
    ]
}'

You can find more details about the different ways of making requests to the LiteLLM proxy on this page

Support & Talk to Founders

• Schedule Demo 👋
• Community Discord 💭
• Our emails ✉️ ishaan@berri.ai / krrish@berri.ai