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Install the Lumina SDK for Python applications.

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Prerequisites

Python 3.9+ Verify your installation: 
python --version  # Should show 3.9 or higher

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Install SDK

pip install lumina-sdk

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Install LLM Provider SDK

Install your LLM provider’s official SDK: 
pip install openai

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Quick Start

Create a simple trace: 
import os
from lumina import init_lumina
import anthropic

# Initialize Lumina
lumina = init_lumina({
    "endpoint": "http://localhost:9411/v1/traces",
    "service_name": "my-app",
})

# Initialize LLM provider
client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

# Trace an LLM call
response = lumina.trace_llm(
    lambda: client.messages.create(
        model="claude-sonnet-4-5",
        max_tokens=1024,
        messages=[{"role": "user", "content": "Hello!"}],
    ),
    name="chat-completion",
    system="anthropic",
    prompt="Hello!",
)

print("Trace sent! View at http://localhost:3000/traces")
Run it: 
python app.py
Check your dashboard at http://localhost:3000/traces to see the trace.

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Framework Integration

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FastAPI

# main.py
import os
from fastapi import FastAPI
from lumina import init_lumina
import openai

app = FastAPI()
client = openai.OpenAI(api_key=os.environ["OPENAI_API_KEY"])
lumina = init_lumina({
    "endpoint": os.environ.get("LUMINA_ENDPOINT", "http://localhost:9411/v1/traces"),
    "service_name": "fastapi-app",
})

@app.post("/api/chat")
async def chat(message: str):
    response = await lumina.trace_llm(
        lambda: client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": message}],
        ),
        name="chat-completion",
        system="openai",
        prompt=message,
    )
    return {"content": response.choices[0].message.content}

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Flask

# app.py
import os
from flask import Flask, request, jsonify
from lumina import init_lumina
import openai

app = Flask(__name__)
client = openai.OpenAI(api_key=os.environ["OPENAI_API_KEY"])
lumina = init_lumina({
    "endpoint": os.environ.get("LUMINA_ENDPOINT", "http://localhost:9411/v1/traces"),
    "service_name": "flask-app",
})

@app.route("/api/chat", methods=["POST"])
def chat():
    message = request.json["message"]

    response = lumina.trace_llm(
        lambda: client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": message}],
        ),
        name="chat-completion",
        system="openai",
        prompt=message,
    )

    return jsonify({"content": response.choices[0].message.content})

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Django

# views.py
import os
import json
from django.http import JsonResponse
from django.views.decorators.http import require_POST
from lumina import init_lumina
import openai

client = openai.OpenAI(api_key=os.environ["OPENAI_API_KEY"])
lumina = init_lumina({
    "endpoint": os.environ.get("LUMINA_ENDPOINT", "http://localhost:9411/v1/traces"),
    "service_name": "django-app",
})

@require_POST
def chat(request):
    message = json.loads(request.body)["message"]

    response = lumina.trace_llm(
        lambda: client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": message}],
        ),
        name="chat-completion",
        system="openai",
        prompt=message,
    )

    return JsonResponse({"content": response.choices[0].message.content})

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Environment Variables

Set up environment variables for configuration: 
# .env
LUMINA_ENDPOINT=http://localhost:9411/v1/traces
LUMINA_SERVICE_NAME=my-app
ANTHROPIC_API_KEY=sk-ant-...
OPENAI_API_KEY=sk-...
Load in your application: 
from dotenv import load_dotenv
import os

load_dotenv()

lumina = init_lumina({
    "endpoint": os.environ["LUMINA_ENDPOINT"],
    "service_name": os.environ["LUMINA_SERVICE_NAME"],
})
Install python-dotenv if needed: pip install python-dotenv

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Verification

Verify the SDK is working: 1. Send a test trace: 
# test_lumina.py
import asyncio
from lumina import init_lumina

lumina = init_lumina({
    "endpoint": "http://localhost:9411/v1/traces",
    "service_name": "test",
})

def run_test(span):
    span.set_attribute("test", "value")

lumina.trace("test-trace", run_test)
asyncio.run(lumina.flush())
print("✓ Test trace sent")

python test_lumina.py
2. Check dashboard: Open http://localhost:3000/traces and verify the trace appears.

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Troubleshooting

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Module not found

Error: ModuleNotFoundError: No module named 'lumina' Solution: Verify the SDK is installed in your active environment: 
pip show lumina-sdk
pip install lumina-sdk

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Connection refused

Error: Failed to export spans ... Connection refused Solution: Verify Lumina is running: 
curl http://localhost:9411/health
Start Lumina if not running: 
cd Lumina/infra/docker
docker compose up -d

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Traces not appearing

Traces are batched and exported in the background. Call flush() before your process exits to ensure all spans are sent: 
import asyncio
asyncio.run(lumina.flush())

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Next Steps

Configuration

Configure the SDK for your environment

Basic Usage

Learn basic tracing patterns

Advanced Usage

Multi-span tracing and async patterns

API Reference

Complete SDK API documentation