Analysis updated 2026-05-18
Debug why your chatbot gives wrong or irrelevant answers by seeing every AI call it makes.
Catch hallucinations and quality issues in production before users notice them.
Automatically test and improve your AI prompts and agent configurations over time.
Monitor thousands of AI conversations at scale and spot patterns in failures.
| comet-ml/opik | anthropics/claude-plugins-official | nari-labs/dia | |
|---|---|---|---|
| Stars | 19,288 | 19,291 | 19,294 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | easy | hard |
| Complexity | 3/5 | 2/5 | 3/5 |
| Audience | developer | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires running a backend service and dashboard, likely needs API key or local LLM setup to see meaningful traces.
Opik, built by Comet, is an open-source platform for debugging, evaluating, and monitoring AI applications that use large language models (LLMs). When you build something like a chatbot, a code assistant, or an automated agent powered by an AI model, it can be hard to know whether it is actually working well, or why it sometimes gives bad answers. Opik solves that by giving you full visibility into what the AI is doing under the hood. It records every AI call and conversation in detail (called tracing), lets you run automated quality checks to catch problems like hallucinations (when an AI confidently states something false) or irrelevant responses, and displays everything in a production-ready dashboard. It also includes tools to automatically improve the prompts and tool configurations you use with your AI agent. Opik integrates with popular AI frameworks like LangChain and LlamaIndex, and is designed to handle over 40 million traces per day at scale. You would use Opik if you are building any AI-powered product and want to move beyond guesswork, testing it systematically, catching quality issues in production, and continuously improving its performance without manually reading through thousands of responses.
Open-source platform for debugging, evaluating, and monitoring AI applications. Records every AI call, runs quality checks, and shows results in a dashboard.
Mainly Python. The stack also includes Python, LangChain, LlamaIndex.
Use freely for any purpose including commercial. Keep the notice and disclose changes to the patent grant.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.