automation

opik: Simplify LLM Monitoring

Name: opik: Simplify LLM Monitoring
Author: comet-ml

Get comprehensive tracing and automated evaluations for LLM applications with opik, a tool for startup founders, with 20k+ GitHub stars.

intermediate⏱ 1-2 hours💵 Free (self-hosted)

21,645 stars1,734 forksPythonHealth Score 8/10Updated 8/28/2026100% free · open source

What it is

Use opik to debug and test your AI-powered applications.

What you can make with it

Automations like: when your LLM model misclassifies a customer, send a notification to your Slack channel.

How it helps

Opik helps you catch bugs and issues early in your AI project, saving time and effort down the line. It also gives you valuable insights to refine your models.

Real use case example

"A founder named Alex uses opik to debug a LLM-powered chatbot. They set up opik to automatically track errors and discrepancies in responses. After a few hours, opik sends them an alert when it detects a pattern of misclassifications. Alex investigates and refines the model, greatly improving its accuracy."

If you're new

Pick up opik if you want to learn about AI debugging and evaluation without any prior experience.

If you're senior

Senior engineers will reach for opik when needing a robust and customizable solution for monitoring and evaluating their LLM applications.

Common confusion cleared up

Some users might confuse opik with generic logging tools, but opik is specifically designed for AI application debugging.

Best inside these AI tools

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Pairs with

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Why we list it on WorkflowStacks: Opik is included because it's a comprehensive and open-source option for monitoring and evaluating LLM applications.

What it does

Opik provides comprehensive tracing and automated evaluations for Large Language Model (LLM) applications, helping startup founders debug, evaluate, and monitor their models with production-ready dashboards.

Install / run

pip install opik

When to use it

•When you need to identify and fix issues in your LLM application's workflow
•When you want to evaluate and compare the performance of different LLM models
•When you need to monitor and analyze the performance of your RAG systems and agentic workflows

Quick start

1Import opik in your Python script with `import opik`
2Initialize opik with `opik.init()`, which sets up the tracing and evaluation framework
3Use `opik.trace()` to capture traces of your LLM application's execution
4Visualize the traces and evaluations with `opik.dashboard()`
5Configure opik with a YAML file, as shown in the example `opik_config.yaml` file in the README

Ready-to-paste prompt

opik.trace(my_llm_model, input_data='example_input', output_data='example_output')

Heads up: Make sure to set the `OPIK_API_KEY` environment variable with a valid Comet ML API key to use opik's production-ready dashboards and automated evaluations

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