---
headline: Quickstart | Opik Documentation
og:description: Integrate Opik with your LLM application to log calls and chains efficiently.
Get started with our step-by-step guide.
og:site_name: Opik Documentation
og:title: Quickstart Guide - Opik Integration
title: Quickstart
---
This guide helps you integrate the Opik platform with your existing Agent. The goal of
this guide is to help you log your first traces and start tracking your prompts and agent
configuration in Opik.
## Prerequisites
Before you begin, you'll need to choose how you want to use Opik:
- **Opik Cloud**: Create a free account at [comet.com/opik](https://www.comet.com/signup?from=llm&utm_source=opik&utm_medium=colab&utm_content=quickstart&utm_campaign=opik)
- **Self-hosting**: Follow the [self-hosting guide](/self-host/overview) to deploy Opik locally or on Kubernetes
## Logging your first LLM calls
Opik makes it easy to integrate with your existing LLM application. Pick the tab that matches your
stack and follow the three steps to log your first trace:
If you are using the Python function decorator, you can integrate by:
Install the Opik Python SDK:
```bash
pip install opik
```
Configure the Opik Python SDK:
```bash
opik configure
```
Wrap your function with the `@track` decorator:
```python
from opik import track
@track
def my_function(input: str) -> str:
return input
```
All calls to the `my_function` will now be logged to Opik. This works well for any function
even nested ones and is also supported by most integrations (just wrap any parent function
with the `@track` decorator).
If you want to use the TypeScript SDK to log traces directly:
Install the Opik TypeScript SDK:
```bash
npm install opik
```
Configure the Opik TypeScript SDK by running the interactive CLI tool:
```bash
npx opik-ts configure
```
This will detect your project setup, install required dependencies, and help you configure environment variables.
Log a trace using the Opik client:
```typescript
import { Opik } from "opik";
const client = new Opik();
const trace = client.trace({
name: "My LLM Application",
input: { prompt: "What is the capital of France?" },
output: { response: "The capital of France is Paris." },
});
trace.end();
await client.flush();
```
All traces will now be logged to Opik. You can also log spans within traces for more detailed observability.
If you are using the OpenAI Python SDK, you can integrate by:
Install the Opik Python SDK:
```bash
pip install opik
```
Configure the Opik Python SDK, this will prompt you for your API key if you are using Opik
Cloud or your Opik server address if you are self-hosting:
```bash
opik configure
```
Wrap your OpenAI client with the `track_openai` function:
```python
from opik.integrations.openai import track_openai
from openai import OpenAI
# Wrap your OpenAI client
client = OpenAI()
client = track_openai(client)
# Use the client as normal
completion = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "user", "content": "Hello, how are you?",
},
],
)
print(completion.choices[0].message.content)
```
All OpenAI calls made using the `client` will now be logged to Opik. You can combine
this with the `@track` decorator to log the traces for each step of your agent.
If you are using the OpenAI TypeScript SDK, you can integrate by:
Install the Opik TypeScript SDK:
```bash
npm install opik-openai
```
Configure the Opik TypeScript SDK by running the interactive CLI tool:
```bash
npx opik-ts configure
```
This will detect your project setup, install required dependencies, and help you configure environment variables.
Wrap your OpenAI client with the `trackOpenAI` function:
```typescript
import OpenAI from "openai";
import { trackOpenAI } from "opik-openai";
// Initialize the original OpenAI client
const openai = new OpenAI({
apiKey: process.env.OPENAI_API_KEY,
});
// Wrap the client with Opik tracking
const trackedOpenAI = trackOpenAI(openai);
// Use the tracked client just like the original
const completion = await trackedOpenAI.chat.completions.create({
model: "gpt-4",
messages: [{ role: "user", content: "Hello, how can you help me today?" }],
});
console.log(completion.choices[0].message.content);
// Ensure all traces are sent before your app terminates
await trackedOpenAI.flush();
```
All OpenAI calls made using the `trackedOpenAI` will now be logged to Opik.
If you are using LangGraph, you can integrate by:
Install the Opik SDK:
```bash
pip install opik
```
Configure the Opik SDK by running the `opik configure` command in your terminal:
```bash
opik configure
```
Track your LangGraph graph with `track_langgraph`:
```python
from opik.integrations.langchain import OpikTracer, track_langgraph
# Create your LangGraph graph
graph = ...
app = graph.compile(...)
# Create OpikTracer and track the graph once
# The graph visualization is automatically extracted by track_langgraph
opik_tracer = OpikTracer()
app = track_langgraph(app, opik_tracer)
# Now all invocations are automatically tracked!
result = app.invoke({"messages": [HumanMessage(content = "How to use LangGraph ?")]})
```
All LangGraph calls will now be logged to Opik. No need to pass callbacks on every invocation!
If you already use a coding agent (Claude Code, Codex, Cursor, OpenCode, etc.), you can let it
instrument your app for you with the Opik Skill. Requires Node.js installed.
```bash
npx skills add comet-ml/opik-skills
```
Once the skill is installed, you can integrate with Opik using the following prompt:
```
Instrument my agent with Opik using the /instrument command.
```
Opik has **30+ integrations** with popular frameworks and model providers:
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**[View all 30+ integrations →](/integrations/overview)**
## Analyze your traces
After running your application, you will start seeing your traces in Opik and you can use Ollie to analyze them and improve your agent.
If you don't see traces appearing, reach out to us on [Slack](https://chat.comet.com) or raise an issue on [GitHub](https://github.com/comet-ml/opik/issues) and we'll help you troubleshoot.
## Next steps
Now that you have logged your first traces, here's what to explore next:
1. [In depth guide on agent observability](/tracing/advanced/log_traces): Learn how to customize the data
that is logged to Opik and how to log conversations.
2. [Opik Experiments](/evaluation/concepts): Opik allows you to automated the evaluation process of
your LLM application so that you no longer need to manually review every LLM response.
3. [Opik's evaluation metrics](/evaluation/metrics/overview): Opik provides a suite of evaluation
metrics (Hallucination, Answer Relevance, Context Recall, etc.) that you can use to score your
LLM responses.