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From RAG chatbots to code assistants to complex agentic pipelines and beyond, build LLM systems that run better, faster, and cheaper with tracing, evaluations, and dashboards.

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Opik
Open source LLM evaluation framework

From RAG chatbots to code assistants to complex agentic pipelines and beyond, build LLM systems that run better, faster, and cheaper with tracing, evaluations, and dashboards.

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πŸš€ What is Opik?

Opik is an open-source platform for evaluating, testing and monitoring LLM applications. Built by Comet.


You can use Opik for:

  • Development:

    • Tracing: Track all LLM calls and traces during development and production (Quickstart, Integrations

    • Annotations: Annotate your LLM calls by logging feedback scores using the Python SDK or the UI.

    • Playground:: Try out different prompts and models in the prompt playground

  • Evaluation: Automate the evaluation process of your LLM application:

  • Production Monitoring:

    • Log all your production traces: Opik has been designed to support high volumes of traces, making it easy to monitor your production applications.

    • Monitoring dashboards: Review your feedback scores, trace count and tokens over time in the Opik Dashboard.

Tip

If you are looking for features that Opik doesn't have today, please raise a new Feature request πŸš€


πŸ› οΈ Installation

Opik is available as a fully open source local installation or using Comet.com as a hosted solution. The easiest way to get started with Opik is by creating a free Comet account at comet.com.

If you'd like to self-host Opik, you can do so by cloning the repository and starting the platform using Docker Compose:

# Clone the Opik repository
git clone https://github.com/comet-ml/opik.git

# Navigate to the opik/deployment/docker-compose directory
cd opik/deployment/docker-compose

# Start the Opik platform
docker compose up --detach

# You can now visit http://localhost:5173 on your browser!

For more information about the different deployment options, please see our deployment guides:

Installation methods Docs link
Local instance Local Deployment
Kubernetes Kubernetes

🏁 Get Started

To get started, you will need to first install the Python SDK:

pip install opik

Once the SDK is installed, you can configure it by running the opik configure command:

opik configure

This will allow you to configure Opik locally by setting the correct local server address or if you're using the Cloud platform by setting the API Key

Tip

You can also call the opik.configure(use_local=True) method from your Python code to configure the SDK to run on the local installation.

You are now ready to start logging traces using the Python SDK.

πŸ“ Logging Traces

The easiest way to get started is to use one of our integrations. Opik supports:

Integration Description Documentation Try in Colab
OpenAI Log traces for all OpenAI LLM calls Documentation Open Quickstart In Colab
LiteLLM Call any LLM model using the OpenAI format Documentation Open Quickstart In Colab
LangChain Log traces for all LangChain LLM calls Documentation Open Quickstart In Colab
Haystack Log traces for all Haystack calls Documentation Open Quickstart In Colab
Bedrock Log traces for all Bedrock LLM calls Documentation Open Quickstart In Colab
Anthropic Log traces for all Anthropic LLM calls Documentation Open Quickstart In Colab
Gemini Log traces for all Gemini LLM calls Documentation Open Quickstart In Colab
Groq Log traces for all Groq LLM calls Documentation Open Quickstart In Colab
LangGraph Log traces for all LangGraph executions Documentation Open Quickstart In Colab
LlamaIndex Log traces for all LlamaIndex LLM calls Documentation Open Quickstart In Colab
Ollama Log traces for all Ollama LLM calls Documentation Open Quickstart In Colab
Predibase Fine-tune and serve open-source Large Language Models Documentation Open Quickstart In Colab
Ragas Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines Documentation Open Quickstart In Colab
watsonx Log traces for all watsonx LLM calls Documentation Open Quickstart In Colab

Tip

If the framework you are using is not listed above, feel free to open an issue or submit a PR with the integration.

If you are not using any of the frameworks above, you can also use the track function decorator to log traces:

import opik

opik.configure(use_local=True) # Run locally

@opik.track
def my_llm_function(user_question: str) -> str:
    # Your LLM code here

    return "Hello"

Tip

The track decorator can be used in conjunction with any of our integrations and can also be used to track nested function calls.

πŸ§‘β€βš–οΈ LLM as a Judge metrics

The Python Opik SDK includes a number of LLM as a judge metrics to help you evaluate your LLM application. Learn more about it in the metrics documentation.

To use them, simply import the relevant metric and use the score function:

from opik.evaluation.metrics import Hallucination

metric = Hallucination()
score = metric.score(
    input="What is the capital of France?",
    output="Paris",
    context=["France is a country in Europe."]
)
print(score)

Opik also includes a number of pre-built heuristic metrics as well as the ability to create your own. Learn more about it in the metrics documentation.

πŸ” Evaluating your LLM Application

Opik allows you to evaluate your LLM application during development through Datasets and Experiments.

You can also run evaluations as part of your CI/CD pipeline using our PyTest integration.

🀝 Contributing

There are many ways to contribute to Opik:

To learn more about how to contribute to Opik, please see our contributing guidelines.