---
title: "Comet Opik - Logging + Evals"
description: "import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; import Image from '@theme/IdealImage';"
type: skill
canonical_url: https://claudary.paisolsolutions.com/skills/opik-integration
source: "Claudary"
difficulty: intermediate
author: "Claude Code Knowledge Pack"
date: 2026-07-10T11:31:31.999Z
license: CC-BY-4.0
attribution: "Comet Opik - Logging + Evals — Claudary (https://claudary.paisolsolutions.com/skills/opik-integration)"
---

# Comet Opik - Logging + Evals
import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; import Image from '@theme/IdealImage';

## Overview

import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import Image from '@theme/IdealImage';

# Comet Opik - Logging + Evals
Opik is an open source end-to-end [LLM Evaluation Platform](https://www.comet.com/site/products/opik/?utm_source=litelllm&utm_medium=docs&utm_content=intro_paragraph) that helps developers track their LLM prompts and responses during both development and production. Users can define and run evaluations to test their LLMs apps before deployment to check for hallucinations, accuracy, context retrevial, and more!


<Image img={require('../../img/opik.png')} />

:::info
We want to learn how we can make the callbacks better! Meet the LiteLLM [founders](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) or
join our [discord](https://discord.gg/wuPM9dRgDw)
:::

## Pre-Requisites

You can learn more about setting up Opik in the [Opik quickstart guide](https://www.comet.com/docs/opik/quickstart/). You can also learn more about self-hosting Opik in our [self-hosting guide](https://www.comet.com/docs/opik/self-host/local_deployment).

## Quick Start
Use just 4 lines of code, to instantly log your responses **across all providers** with Opik

Get your Opik API Key by signing up [here](https://www.comet.com/signup?utm_source=litelllm&utm_medium=docs&utm_content=api_key_cell)!

```python
import litellm
litellm.callbacks = ["opik"]
```

Full examples:

<Tabs>
<TabItem value="sdk" label="SDK">

```python
import litellm
import os

# Configure the Opik API key or call opik.configure()
os.environ["OPIK_API_KEY"] = ""
os.environ["OPIK_WORKSPACE"] = ""

# LLM provider API Keys:
os.environ["OPENAI_API_KEY"] = ""

# set "opik" as a callback, litellm will send the data to an Opik server (such as comet.com)
litellm.callbacks = ["opik"]

# openai call
response = litellm.completion(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "user", "content": "Why is tracking and evaluation of LLMs important?"}
    ]
)
```

If you are using liteLLM within a function tracked using Opik's `@track` decorator,
you will need provide the `current_span_data` field in the metadata attribute
so that the LLM call is assigned to the correct trace:

```python
from opik import track
from opik.opik_context import get_current_span_data
import litellm

litellm.callbacks = ["opik"]

@track()
def streaming_function(input):
    messages = [{"role": "user", "content": input}]
    response = litellm.completion(
        model="gpt-3.5-turbo",
        messages=messages,
        metadata = {
            "opik": {
                "current_span_data": get_current_span_data(),
                "tags": ["streaming-test"],
            },
        }
    )
    return response

response = streaming_function("Why is tracking and evaluation of LLMs important?")
chunks = list(response)
```

</TabItem>
<TabItem value="proxy" label="Proxy">

1. Setup config.yaml

```yaml
model_list:
  - model_name: gpt-3.5-turbo-testing
    litellm_params:
      model: gpt-3.5-turbo
      api_key: os.environ/OPENAI_API_KEY

litellm_settings:
  callbacks: ["opik"]

environment_variables:
  OPIK_API_KEY: ""
  OPIK_WORKSPACE: ""
```

2. Run proxy

```bash
litellm --config config.yaml
```

3. Test it! 

```bash
curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \\
-H 'Content-Type: application/json' \\
-H 'Authorization: Bearer sk-1234' \\
-d '{
  "model": "gpt-3.5-turbo-testing",
  "messages": [
    {
      "role": "user",
      "content": "What's the weather like in Boston today?"
    }
  ]
}'
```

</TabItem>
</Tabs>

## Opik-Specific Parameters

These can be passed inside metadata with the `opik` key.

### Fields 

- `project_name` - Name of the Opik project to send data to.
- `current_span_data` - The current span data to be used for tracing.
- `tags` - Tags to be used for tracing.
- `thread_id` - The thread id to group together multiple related traces.

### Usage

<Tabs>
<TabItem value="sdk" label="SDK">

```python
from opik import track
from opik.opik_context import get_current_span_data
import litellm

litellm.callbacks = ["opik"]

messages = [{"role": "user", "content": input}]
response = litellm.completion(
    model="gpt-3.5-turbo",
    messages=messages,
    metadata = {
        "opik": {
            "project_name": "your-opik-project-name",
            "current_span_data": get_current_span_data(),
            "tags": ["streaming-test"],
            "thread_id": "your-thread-id"
        },
    }
)
return response
```
</TabItem>
<TabItem value="proxy" label="Proxy">

```bash
curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \\
-H 'Content-Type: application/json' \\
-H 'Authorization: Bearer sk-1234' \\
-d '{
  "model": "gpt-3.5-turbo",
  "messages": [
    {
      "role": "user",
      "content": "What's the weather like in Boston today?"
    }
  ],
  "metadata": {
    "opik": {
      "project_name": "your-opik-project-name",
      "current_span_data": "...",
      "tags": ["streaming-test"],
      "thread_id": "your-thread-id"
    },
  }
}'
``` 

</TabItem>
</Tabs>



You can also pass the fields as part of the request header with a `opik_*` prefix:

```shell
curl --location --request POST 'http://0.0.0.0:4000/chat/completions' \\
    --header 'Content-Type: application/json' \\
    --header 'Authorization: Bearer sk-1234' \\
    --header 'opik_project_name: your-opik-project-name' \\
    --header 'opik_thread_id: your-thread-id' \\
    --header 'opik_tags: ["streaming-test"]' \\
    --data '{
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "What's the weather like in Boston today?"
        }
    ]
}'
```

## Automatic Metadata from API Keys

In some cases, the requester may be unable or unaware of how to add Opik metadata to their requests. To ensure all Opik-related actions are properly tracked, LiteLLM Proxy can automatically associate metadata from a user-specific API key when none is provided in the request.

### How It Works

When you create an API key in LiteLLM Proxy, you can attach Opik-specific metadata to the key itself. This metadata will be automatically applied to all requests made with that key, unless the request explicitly provides its own Opik metadata (which takes precedence).


### Usage

**Step 1: Save Opik Metadata to the corresponding Api Key**
Go to 'Virtual Keys', click on your choosen api key and edit 'Settings'.
Now save the opik metadata as user api key metdata.

<Image img={require('../../img/opik_key_metadata.png')} />

**Step 2: Use the key - Opik metadata is automatically applied**

```bash
curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \\
-H 'Content-Type: application/json' \\
-H 'Authorization: Bearer sk-key-from-step-1' \\
-d '{
  "model": "gpt-3.5-turbo",
  "messages": [
    {
      "role": "user",
      "content": "What's the weather like in Boston today?"
    }
  ]
}'
```

All requests made with this key will automatically be tracked in the "TestProject" Opik project with the specified tags, without requiring the user to pass metadata in each request.


## Support & Talk to Founders

- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
- [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai

---

Source: [Claudary](https://claudary.paisolsolutions.com/skills/opik-integration) · https://claudary.paisolsolutions.com