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<div align="center"><b><a href="README.md">English</a> | <a href="readme_CN.md">简体中文</a> | <a href="readme_JP.md">日本語</a> | <a href="readme_KO.md">한국어</a></b></div>
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<br>
Opik
</div>
Open source LLM evaluation framework<br>
</h1>
<p align="center">
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.
</p>
<div align="center">
[](https://pypi.org/project/opik/)
[](https://github.com/comet-ml/opik/blob/main/LICENSE)
[](https://github.com/comet-ml/opik/actions/workflows/build_apps.yml)
<a target="_blank" href="https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/opik_quickstart.ipynb">
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<a href="https://www.comet.com/site/products/opik/?from=llm&utm_source=opik&utm_medium=github&utm_content=website_button&utm_campaign=opik"><b>Website</b></a> •
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<a href="https://www.comet.com/docs/opik/?from=llm&utm_source=opik&utm_medium=github&utm_content=docs_button&utm_campaign=opik"><b>Documentation</b></a>
</p>
## 🚀 What is Opik?
Opik is an open-source platform for evaluating, testing and monitoring LLM applications. Built by [Comet](https://www.comet.com?from=llm&utm_source=opik&utm_medium=github&utm_content=what_is_opik_link&utm_campaign=opik).
<br>
You can use Opik for:
* **Development:**
* **Tracing:** Track all LLM calls and traces during development and production ([Quickstart](https://www.comet.com/docs/opik/quickstart/?from=llm&utm_source=opik&utm_medium=github&utm_content=quickstart_link&utm_campaign=opik), [Integrations](https://www.comet.com/docs/opik/tracing/integrations/overview/?from=llm&utm_source=opik&utm_medium=github&utm_content=integrations_link&utm_campaign=opik))
* **Annotations:** Annotate your LLM calls by logging feedback scores using the [Python SDK](https://www.comet.com/docs/opik/tracing/annotate_traces/#annotating-traces-and-spans-using-the-sdk?from=llm&utm_source=opik&utm_medium=github&utm_content=sdk_link&utm_campaign=opik) or the [UI](https://www.comet.com/docs/opik/tracing/annotate_traces/#annotating-traces-through-the-ui?from=llm&utm_source=opik&utm_medium=github&utm_content=ui_link&utm_campaign=opik).
* **Playground:** Try out different prompts and models in the [prompt playground](https://www.comet.com/docs/opik/prompt_engineering/playground).
* **Evaluation**: Automate the evaluation process of your LLM application:
* **Datasets and Experiments**: Store test cases and run experiments ([Datasets](https://www.comet.com/docs/opik/evaluation/manage_datasets/?from=llm&utm_source=opik&utm_medium=github&utm_content=datasets_link&utm_campaign=opik), [Evaluate your LLM Application](https://www.comet.com/docs/opik/evaluation/evaluate_your_llm/?from=llm&utm_source=opik&utm_medium=github&utm_content=eval_link&utm_campaign=opik))
* **LLM as a judge metrics**: Use Opik's LLM as a judge metric for complex issues like [hallucination detection](https://www.comet.com/docs/opik/evaluation/metrics/hallucination/?from=llm&utm_source=opik&utm_medium=github&utm_content=hallucination_link&utm_campaign=opik), [moderation](https://www.comet.com/docs/opik/evaluation/metrics/moderation/?from=llm&utm_source=opik&utm_medium=github&utm_content=moderation_link&utm_campaign=opik) and RAG evaluation ([Answer Relevance](https://www.comet.com/docs/opik/evaluation/metrics/answer_relevance/?from=llm&utm_source=opik&utm_medium=github&utm_content=alex_link&utm_campaign=opik), [Context Precision](https://www.comet.com/docs/opik/evaluation/metrics/context_precision/?from=llm&utm_source=opik&utm_medium=github&utm_content=context_link&utm_campaign=opik)
* **CI/CD integration**: Run evaluations as part of your CI/CD pipeline using our [PyTest integration](https://www.comet.com/docs/opik/testing/pytest_integration/?from=llm&utm_source=opik&utm_medium=github&utm_content=pytest_link&utm_campaign=opik)
* **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. Even small deployments can ingest more than 40 million traces per day!
* **Monitoring dashboards**: Review your feedback scores, trace count and tokens over time in the [Opik Dashboard](https://www.comet.com/docs/opik/production/production_monitoring/?from=llm&utm_source=opik&utm_medium=github&utm_content=dashboard_link&utm_campaign=opik).
* **Online evaluation metrics**: Easily score all your production traces using LLM as a Judge metrics and identify any issues with your production LLM application thanks to [Opik's online evaluation metrics](https://www.comet.com/docs/opik/production/rules/?from=llm&utm_source=opik&utm_medium=github&utm_content=dashboard_link&utm_campaign=opik)
> [!TIP]
> If you are looking for features that Opik doesn't have today, please raise a new [Feature request](https://github.com/comet-ml/opik/issues/new/choose) 🚀
<br>
## 🛠️ 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](https://www.comet.com/signup?from=llm&utm_source=opik&utm_medium=github&utm_content=install&utm_campaign=opik).
If you'd like to self-host Opik, you can do so by cloning the repository and starting the platform using Docker Compose:
On Linux or Mac do:
```bash
# 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 do:
```powershell
# 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.
Once all is up and running, you can now visit [localhost:5173](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 | [](https://www.comet.com/docs/opik/self-host/local_deployment?from=llm&utm_source=opik&utm_medium=github&utm_content=self_host_link&utm_campaign=opik)
| Kubernetes | [](https://www.comet.com/docs/opik/self-host/kubernetes/#kubernetes-installation?from=llm&utm_source=opik&utm_medium=github&utm_content=kubernetes_link&utm_campaign=opik)
## 🏁 Get Started
To get started, you will need to first install the Python SDK:
```bash
pip install opik
```
Once the SDK is installed, you can configure it by running the `opik configure` command:
```bash
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](https://www.comet.com/docs/opik/python-sdk-reference/?from=llm&utm_source=opik&utm_medium=github&utm_content=sdk_link2&utm_campaign=opik).
### 📝 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](https://www.comet.com/docs/opik/tracing/integrations/openai/?utm_source=opik&utm_medium=github&utm_content=openai_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/openai.ipynb) |
| LiteLLM | Call any LLM model using the OpenAI format | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/litellm/?utm_source=opik&utm_medium=github&utm_content=openai_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/litellm.ipynb) |
| LangChain | Log traces for all LangChain LLM calls | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/langchain/?utm_source=opik&utm_medium=github&utm_content=langchain_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/langchain.ipynb) |
| Haystack | Log traces for all Haystack calls | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/haystack/?utm_source=opik&utm_medium=github&utm_content=haystack_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/haystack.ipynb) |
| Anthropic | Log traces for all Anthropic LLM calls | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/anthropic?utm_source=opik&utm_medium=github&utm_content=anthropic_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/anthropic.ipynb) |
| Bedrock | Log traces for all Bedrock LLM calls | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/bedrock?utm_source=opik&utm_medium=github&utm_content=bedrock_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/bedrock.ipynb) |
| CrewAI | Log traces for all CrewAI calls | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/crewai?utm_source=opik&utm_medium=github&utm_content=crewai_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/crewai.ipynb) |
| DeepSeek | Log traces for all DeepSeek LLM calls | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/deepseek?utm_source=opik&utm_medium=github&utm_content=deepseek_link&utm_campaign=opik) | |
| DSPy | Log traces for all DSPy runs | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/dspy?utm_source=opik&utm_medium=github&utm_content=dspy_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/dspy.ipynb) |
| Gemini | Log traces for all Gemini LLM calls | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/gemini?utm_source=opik&utm_medium=github&utm_content=gemini_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/gemini.ipynb) |
| Groq | Log traces for all Groq LLM calls | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/groq?utm_source=opik&utm_medium=github&utm_content=groq_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/groq.ipynb) |
| Guardrails | Log traces for all Guardrails validations | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/guardrails/?utm_source=opik&utm_medium=github&utm_content=guardrails_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/guardrails-ai.ipynb) |
| Instructor | Log traces for all LLM calls made with Instructor | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/instructor/?utm_source=opik&utm_medium=github&utm_content=instructor_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/instructor.ipynb) |
| LangGraph | Log traces for all LangGraph executions | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/langgraph/?utm_source=opik&utm_medium=github&utm_content=langchain_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/langgraph.ipynb) |
| LlamaIndex | Log traces for all LlamaIndex LLM calls | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/llama_index?utm_source=opik&utm_medium=github&utm_content=llama_index_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/llama-index.ipynb) |
| Ollama | Log traces for all Ollama LLM calls | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/ollama?utm_source=opik&utm_medium=github&utm_content=ollama_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/ollama.ipynb) |
| Predibase | Fine-tune and serve open-source Large Language Models | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/predibase?utm_source=opik&utm_medium=github&utm_content=predibase_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/predibase.ipynb) |
| Pydantic AI | Fine-tune and serve open-source Large Language Models | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/predibase?utm_source=opik&utm_medium=github&utm_content=predibase_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/predibase.ipynb) |
| Ragas | PydanticAI is a Python agent framework designed to build production apps | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/pydantic-ai?utm_source=opik&utm_medium=github&utm_content=pydantic_ai_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/pydantic-ai.ipynb) |
| watsonx | Log traces for all watsonx LLM calls | [Documentation](https://www.comet.com/docs/opik/tracing/integrations/watsonx?utm_source=opik&utm_medium=github&utm_content=watsonx_link&utm_campaign=opik) | [](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/watsonx.ipynb) |
> [!TIP]
> If the framework you are using is not listed above, feel free to [open an issue](https://github.com/comet-ml/opik/issues) 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](https://www.comet.com/docs/opik/tracing/log_traces/?from=llm&utm_source=opik&utm_medium=github&utm_content=traces_link&utm_campaign=opik):
```python
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](https://www.comet.com/docs/opik/evaluation/metrics/overview/?from=llm&utm_source=opik&utm_medium=github&utm_content=metrics_2_link&utm_campaign=opik).
To use them, simply import the relevant metric and use the `score` function:
```python
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](https://www.comet.com/docs/opik/evaluation/metrics/overview?from=llm&utm_source=opik&utm_medium=github&utm_content=metrics_3_link&utm_campaign=opik).
### 🔍 Evaluating your LLM Application
Opik allows you to evaluate your LLM application during development through [Datasets](https://www.comet.com/docs/opik/evaluation/manage_datasets/?from=llm&utm_source=opik&utm_medium=github&utm_content=datasets_2_link&utm_campaign=opik) and [Experiments](https://www.comet.com/docs/opik/evaluation/evaluate_your_llm/?from=llm&utm_source=opik&utm_medium=github&utm_content=experiments_link&utm_campaign=opik).
You can also run evaluations as part of your CI/CD pipeline using our [PyTest integration](https://www.comet.com/docs/opik/testing/pytest_integration/?from=llm&utm_source=opik&utm_medium=github&utm_content=pytest_2_link&utm_campaign=opik).
## ⭐ Star Us on GitHub
If you find Opik useful, please consider giving us a star! Your support helps us grow our community and continue improving the product.
<img src="https://github.com/user-attachments/assets/ffc208bb-3dc0-40d8-9a20-8513b5e4a59d" alt="Opik GitHub Star History" width="600"/>
## 🤝 Contributing
There are many ways to contribute to Opik:
* Submit [bug reports](https://github.com/comet-ml/opik/issues) and [feature requests](https://github.com/comet-ml/opik/issues)
* Review the documentation and submit [Pull Requests](https://github.com/comet-ml/opik/pulls) to improve it
* Speaking or writing about Opik and [letting us know](https://chat.comet.com)
* Upvoting [popular feature requests](https://github.com/comet-ml/opik/issues?q=is%3Aissue+is%3Aopen+label%3A%22enhancement%22) to show your support
To learn more about how to contribute to Opik, please see our [contributing guidelines](CONTRIBUTING.md).
