Opik

Open-source AI Observability, Evaluation, and Optimization

Opik helps you build, test, and optimize generative AI applications that run better, from prototype to production. From RAG chatbots to code assistants to complex agentic systems, Opik provides comprehensive tracing, evaluation, and automatic prompt and tool optimization to take the guesswork out of AI development.

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🚀 What is Opik?

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:

• Comprehensive Observability: Deep tracing of LLM calls, conversation logging, and agent activity.
• Advanced Evaluation: Robust prompt evaluation, LLM-as-a-judge, and experiment management.
• Production-Ready: Scalable monitoring dashboards and online evaluation rules for production.
• Opik Agent Optimizer: Dedicated SDK and set of optimizers to enhance prompts and agents.
• Opik Guardrails: Features to help you implement safe and responsible AI practices.

Key capabilities include:

• Development & Tracing:

  • Track all LLM calls and traces with detailed context during development and in production (Quickstart).
  • Extensive 3rd-party integrations for easy observability: Seamlessly integrate with a growing list of frameworks, supporting many of the largest and most popular ones natively (including recent additions like Google ADK, Autogen, and Flowise AI). (Integrations)
  • Annotate traces and spans with feedback scores via the Python SDK or the UI.
  • Experiment with prompts and models in the Prompt Playground.

• Evaluation & Testing:

  • Automate your LLM application evaluation with Datasets and Experiments.
  • Leverage powerful LLM-as-a-judge metrics for complex tasks like hallucination detection, moderation, and RAG assessment (Answer Relevance, Context Precision).
  • Integrate evaluations into your CI/CD pipeline with our PyTest integration.

• Production Monitoring & Optimization:

  • Log high volumes of production traces: Opik is designed for scale (40M+ traces/day).
  • Monitor feedback scores, trace counts, and token usage over time in the Opik Dashboard.
  • Utilize Online Evaluation Rules with LLM-as-a-Judge metrics to identify production issues.
  • Leverage Opik Agent Optimizer and Opik Guardrails to continuously improve and secure your LLM applications in production.

[!TIP] If you are looking for features that Opik doesn't have today, please raise a new Feature request 🚀

🛠️ Opik Server Installation

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 Control

Deploy 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 Environment:

# 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 Environment:

# 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"

Service Profiles for Development

The Opik installation scripts now support service profiles for different development scenarios:

# Start full Opik suite (default behavior)
./opik.sh

# Start only infrastructure services (databases, caches etc.)
./opik.sh --infra

# Start infrastructure + backend services
./opik.sh --backend

# Enable guardrails with any profile
./opik.sh --guardrails # Guardrails with full Opik suite
./opik.sh --backend --guardrails # Guardrails with infrastructure + backend

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.

Self-Hosting with Kubernetes & Helm (for Scalable Deployments)

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 Client SDK

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.

Python SDK Quick Start

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):

opik configure

[!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 Integrations

The 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, AG2, and Flowise AI:

Integration	Description	Documentation
ADK	Log traces for Google Agent Development Kit (ADK)	Documentation
AG2	Log traces for AG2 LLM calls	Documentation
Agent Spec	Log traces for Agent Spec calls	Documentation
AIsuite	Log traces for aisuite LLM calls	Documentation
Agno	Log traces for Agno agent orchestration framework calls	Documentation
Anthropic	Log traces for Anthropic LLM calls	Documentation
Autogen	Log traces for Autogen agentic workflows	Documentation
Bedrock	Log traces for Amazon Bedrock LLM calls	Documentation
BeeAI (Python)	Log traces for BeeAI Python agent framework calls	Documentation
BeeAI (TypeScript)	Log traces for BeeAI TypeScript agent framework calls	Documentation
BytePlus	Log traces for BytePlus LLM calls	Documentation
Cloudflare Workers AI	Log traces for Cloudflare Workers AI calls	Documentation
Cohere	Log traces for Cohere LLM calls	Documentation
CrewAI	Log traces for CrewAI calls	Documentation
Cursor	Log traces for Cursor conversations	Documentation
DeepSeek	Log traces for DeepSeek LLM calls	Documentation
Dify	Log traces for Dify agent runs	Documentation
DSPY	Log traces for DSPy runs	Documentation
Fireworks AI	Log traces for Fireworks AI LLM calls	Documentation
Flowise AI	Log traces for Flowise AI visual LLM builder	Documentation
Gemini (Python)	Log traces for Google Gemini LLM calls	Documentation
Gemini (TypeScript)	Log traces for Google Gemini TypeScript SDK calls	Documentation
Groq	Log traces for Groq LLM calls	Documentation
Guardrails	Log traces for Guardrails AI validations	Documentation
Haystack	Log traces for Haystack calls	Documentation
Harbor	Log traces for Harbor benchmark evaluation trials	Documentation
Instructor	Log traces for LLM calls made with Instructor	Documentation
LangChain (Python)	Log traces for LangChain LLM calls	Documentation
LangChain (JS/TS)	Log traces for LangChain JavaScript/TypeScript calls	Documentation
LangGraph	Log traces for LangGraph executions	Documentation
Langflow	Log traces for Langflow visual AI builder	Documentation
LiteLLM	Log traces for LiteLLM model calls	Documentation
LiveKit Agents	Log traces for LiveKit Agents AI agent framework calls	Documentation
LlamaIndex	Log traces for LlamaIndex LLM calls	Documentation
Mastra	Log traces for Mastra AI workflow framework calls	Documentation
Microsoft Agent Framework (Python)	Log traces for Microsoft Agent Framework calls	Documentation
Microsoft Agent Framework (.NET)	Log traces for Microsoft Agent Framework .NET calls	Documentation
Mistral AI	Log traces for Mistral AI LLM calls	Documentation
n8n	Log traces for n8n workflow executions	Documentation
Novita AI	Log traces for Novita AI LLM calls	Documentation
Ollama	Log traces for Ollama LLM calls	Documentation
OpenAI (Python)	Log traces for OpenAI LLM calls	Documentation
OpenAI (JS/TS)	Log traces for OpenAI JavaScript/TypeScript calls	Documentation
OpenAI Agents	Log traces for OpenAI Agents SDK calls	Documentation
OpenClaw	Log traces for OpenClaw agent runs	Documentation
OpenRouter	Log traces for OpenRouter LLM calls	Documentation
OpenTelemetry	Log traces for OpenTelemetry supported calls	Documentation
OpenWebUI	Log traces for OpenWebUI conversations	Documentation
Pipecat	Log traces for Pipecat real-time voice agent calls	Documentation
Predibase	Log traces for Predibase LLM calls	Documentation
Pydantic AI	Log traces for PydanticAI agent calls	Documentation
Ragas	Log traces for Ragas evaluations	Documentation
Semantic Kernel	Log traces for Microsoft Semantic Kernel calls	Documentation
Smolagents	Log traces for Smolagents agents	Documentation
Spring AI	Log traces for Spring AI framework calls	Documentation
Strands Agents	Log traces for Strands agents calls	Documentation
Together AI	Log traces for Together AI LLM calls	Documentation
Vercel AI SDK	Log traces for Vercel AI SDK calls	Documentation
VoltAgent	Log traces for VoltAgent agent framework calls	Documentation
WatsonX	Log traces for IBM watsonx LLM calls	Documentation
xAI Grok	Log traces for xAI Grok 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 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 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 Applications

Opik 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.

⭐ 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.

🤝 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.