Opik
Open-source LLM evaluation platformOpik helps you build, evaluate, and optimize LLM systems that run better, faster, and cheaper. From RAG chatbots to code assistants to complex agentic pipelines, Opik provides comprehensive tracing, evaluations, dashboards, and powerful features like Opik Agent Optimizer and Opik Guardrails to improve and secure your LLM powered applications in production.
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Opik (built by Comet) is an open-source platform designed to streamline the entire lifecycle of LLM applications. It empowers developers to evaluate, test, monitor, and optimize their models and agentic systems. Key offerings include:
Key capabilities include:
Development & Tracing:
Evaluation & Testing:
Production Monitoring & Optimization:
Tip
If you are looking for features that Opik doesn't have today, please raise a new Feature request 🚀
Get your Opik server running in minutes. Choose the option that best suits your needs:
Option 1: Comet.com Cloud (Easiest & Recommended)Access Opik instantly without any setup. Ideal for quick starts and hassle-free maintenance.
👉 Create your free Comet account
Option 2: Self-Host Opik for Full ControlDeploy Opik in your own environment. Choose between Docker for local setups or Kubernetes for scalability.
Self-Hosting with Docker Compose (for Local Development & Testing)This is the simplest way to get a local Opik instance running. Note the new ./opik.sh installation script:
On Linux or Mac Enviroment:
# Clone the Opik repository git clone https://github.com/comet-ml/opik.git # Navigate to the repository cd opik # Start the Opik platform ./opik.sh
On Windows Enviroment:
# Clone the Opik repository git clone https://github.com/comet-ml/opik.git # Navigate to the repository cd opik # Start the Opik platform powershell -ExecutionPolicy ByPass -c ".\\opik.ps1"
Use the --help or --info options to troubleshoot issues. Dockerfiles now ensure containers run as non-root users for enhanced security. Once all is up and running, you can now visit localhost:5173 on your browser! For detailed instructions, see the Local Deployment Guide.
For production or larger-scale self-hosted deployments, Opik can be installed on a Kubernetes cluster using our Helm chart. Click the badge for the full Kubernetes Installation Guide using Helm.
Important
Version 1.7.0 Changes: Please check the changelog for important updates and breaking changes.
Opik provides a suite of client libraries and a REST API to interact with the Opik server. This includes SDKs for Python, TypeScript, and Ruby (via OpenTelemetry), allowing for seamless integration into your workflows. For detailed API and SDK references, see the Opik Client Reference Documentation.
To get started with the Python SDK:
Install the package:
# install using pip pip install opik # or install with uv uv pip install opik
Configure the python SDK by running the opik configure command, which will prompt you for your Opik server address (for self-hosted instances) or your API key and workspace (for Comet.com):
Tip
You can also call opik.configure(use_local=True) from your Python code to configure the SDK to run on a local self-hosted installation, or provide API key and workspace details directly for Comet.com. Refer to the Python SDK documentation for more configuration options.
You are now ready to start logging traces using the Python SDK.
📝 Logging Traces with IntegrationsThe easiest way to log traces is to use one of our direct integrations. Opik supports a wide array of frameworks, including recent additions like Google ADK, Autogen, and Flowise AI:
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.
🧑⚖️ LLM as a Judge metricsThe 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 ApplicationOpik allows you to evaluate your LLM application during development through Datasets and Experiments. The Opik Dashboard offers enhanced charts for experiments and better handling of large traces. You can also run evaluations as part of your CI/CD pipeline using our PyTest integration.
If you find Opik useful, please consider giving us a star! Your support helps us grow our community and continue improving the product.
There are many ways to contribute to Opik:
To learn more about how to contribute to Opik, please see our contributing guidelines.
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