Opik
Open-source end-to-end LLM Development Platform

Confidently evaluate, test and monitor LLM applications.

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🚀 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.
Evaluation: Automate the evaluation process of your LLM application:
- Datasets and Experiments: Store test cases and run experiments (Datasets, Evaluate your LLM Application)
- LLM as a judge metrics: Use Opik's LLM as a judge metric for complex issues like hallucination detection, moderation and RAG evaluation (Answer Relevance, Context Precision
- CI/CD integration: Run evaluations as part of your CI/CD pipeline using our PyTest integration
Production Monitoring: Monitor your LLM application in production and easily close the feedback loop by adding error traces to your evaluation datasets.

Tip

If you are looking for features that Opik doesn't have today, please raise a new Github discussion topic 🚀

🛠️ 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!

Opik reports anonymous usage reports to the Opik team to help with the development of new features. No private information is collected, there reports are fully anonymous.

You can opt-out of usage reporting by setting the OPIK_USAGE_REPORT_ENABLED environment variable to false before running Opik locally:

# Start the Opik platform
export OPIK_USAGE_REPORT_ENABLED=false

docker compose up --detach

All usage reports can be found here: Usage Reports

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

Installation methods	Docs link
Local instance
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.

📝 Logging Traces

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

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

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 using 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.

🧑‍⚖️ 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.

🔍 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:

Submit bug reports and feature requests
Review the documentation and submit Pull Requests to improve it
Speaking or writing about Opik and letting us know
Upvoting popular feature requests to show your support

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

comet-ml/opik

Opik Open-source end-to-end LLM Development Platform