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comet-ml
opik
Python2024–2026
comet-ml / opik
opik
Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
4Distinct people
10Mentions
0Explicit recommenders
23.4KGitHub stars
What to know before exploring
What it is
Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
GitHub
Where it is used
No documented product use was verified in the last source check.
What people are saying
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Also in these discussions
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.
Mentions over time
HN mentions by month10 dated mentions
Observed mentions: 2024-09-17 13:01:44 UTC to 2026-02-11 21:53:25 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Sep 2024 | 2024-09-01 00:00:00 UTC | 2024-10-01 00:00:00 UTC | 1 | 1 | 0 |
| Oct 2024 | 2024-10-01 00:00:00 UTC | 2024-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2024 | 2024-11-01 00:00:00 UTC | 2024-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2024 | 2024-12-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 2 | 0 | 2 |
| Jan 2025 | 2025-01-01 00:00:00 UTC | 2025-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2025 | 2025-02-01 00:00:00 UTC | 2025-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2025 | 2025-03-01 00:00:00 UTC | 2025-04-01 00:00:00 UTC | 1 | 0 | 1 |
| Apr 2025 | 2025-04-01 00:00:00 UTC | 2025-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2025 | 2025-05-01 00:00:00 UTC | 2025-06-01 00:00:00 UTC | 1 | 0 | 1 |
| Jun 2025 | 2025-06-01 00:00:00 UTC | 2025-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2025 | 2025-07-01 00:00:00 UTC | 2025-08-01 00:00:00 UTC | 1 | 0 | 1 |
| Aug 2025 | 2025-08-01 00:00:00 UTC | 2025-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2025 | 2025-09-01 00:00:00 UTC | 2025-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2025 | 2025-10-01 00:00:00 UTC | 2025-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2025 | 2025-11-01 00:00:00 UTC | 2025-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2025 | 2025-12-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 1 | 1 | 0 |
| Jan 2026 | 2026-01-01 00:00:00 UTC | 2026-02-01 00:00:00 UTC | 2 | 2 | 0 |
| Feb 2026 | 2026-02-01 00:00:00 UTC | 2026-03-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
10 sourcesCAcalebkaiser·Feb 11, 2026Submission
Agent Optimizer: Self-improving prompts from production data
1 points · Open discussionOpen source agents to evaluate, debug, and optimize your prompts
1 points · Open discussionShow HN: Opik Optimizer – open-source agents for self-improving LLM applications
2 points · Open discussionOpik Agent Optimizer – Open-Source Prompt Optimization Framework
6 points · Open discussionI'm biased in that I work on an open source project in this space, but I would strongly recommend starting with a free/open source platform for debugging/tracing, annotating, and building custom evals.
This niche of the field has come a very long way just over the last 12 months, and the tooling is so much better than it used to be. Trying to do this from scratch, beyond a "kinda sorta good enough for now" project, is a full-time engineering project in and of itself.
I'm a maintainer of Opik, but you have plenty of options in the space these days for whatever your particular needs are: https://github.com/comet-ml/opik
In About AI EvalsI'm a maintainer of Opik, an open source LLM eval/observability framework. If you use something like LiteLLM or OpenRouter to handle the proxying of requests, Opik basically provides an out-of-the-box recording layer via its integrations with both:
https://github.com/comet-ml/opik
In How to vibe code for free: Running Qwen3 on your Mac, using MLXHow do you compare with https://github.com/comet-ml/opik in observability?
In Show HN: ArchGW – An open-source intelligent proxy server for promptsReally awesome seeing more people work on this! I’m one of the founders of Opik https://github.com/comet-ml/opik which does similar things but also has a UI and supports massive scale. Curious to hear if you have any feedback!
In RAG Logger: An Open-Source Alternative to LangSmithI'm a maintainer of Opik, an open source LLM evaluation and observability platform. We only launched a few months ago, but we're growing rapidly: https://github.com/comet-ml/opik
In Launch HN: Langfuse (YC W23) – OSS Tracing and Workflows to Improve LLM AppsShow HN: Opik, an open source LLM evaluation framework
Hey HN! I'm Caleb, one of the contributors to Opik, a new open source framework for LLM evaluations.
Over the last few months, my colleagues and I have been working on a project to solve what we see as the most painful parts of writing evals for an LLM application. For this initial release, we've focused on a few core features that we think are the most essential:
- Simplifying the implementation of more complex LLM-based evaluation metrics, like Hallucination and Moderation.
- Enabling step-by-step tracking, such that you can test and debug each individual component of your LLM application, even in more complex multi-agent architectures.
- Exposing an API for "model unit tests" (built on Pytest), to allow you to run evals as part of your CI/CD pipelines
- Providing an easy UI for scoring, annotating, and versioning your logged LLM data,
Read the rest
for further evaluation or training.
It's often hard to feel like you can trust an LLM application in production, not just because of the stochastic nature of the model, but because of the opaqueness of the application itself. Our belief is that with better tooling for evaluations, we can meaningfully improve this situation, and unlock a new wave of LLM applications.
You can run Opik locally, or with a free API key via our cloud platform. You can use it with any model server or hosted model, but we currently have a built-in integration with the OpenAI Python library, which means it automatically works not just with OpenAI models, but with any model served via a compatible model server (ollama, vLLM, etc). Opik also currently has out-of-the-box integrations with LangChain, LlamaIndex, Ragas, and a few other popular tools.
This is our initial release of Opik, so if you have any feedback or questions, I'd love to hear them!