Comet Opik¶

What it is¶

Opik is an open-source platform by Comet for evaluating, testing, and monitoring LLM applications. It provides tools for tracing execution, running automated evaluations, and managing production logs.

What problem it solves¶

It helps developers transition from experimentation to production-ready AI by providing the tools needed to detect errors, measure quality, and optimize cost and performance.

Where it fits in the stack¶

Category: Process & Understanding / Observability

Typical use cases¶

Production Monitoring: Real-time tracking of LLM application performance.
Root Cause Analysis: Drilling down into traces to understand why an agent failed.
Offline Evaluation: Running quality checks on historical logs.

Strengths¶

Open Source: Can be self-hosted, providing full control over your data.
Native OpenRouter Integration: Directly supports log ingestion from OpenRouter.
Comet Ecosystem: Integrates with the broader Comet ML platform for end-to-end model management.

Limitations¶

Newer Project: Compared to some competitors, the ecosystem around Opik is still maturing.

When to use it¶

When you want an open-source, self-hostable alternative to LangSmith or Braintrust.
When you are already using Comet for other machine learning tasks.

Getting started¶

Install the Opik library:

pip install opik

Configure the Opik client:

opik configure

CLI examples¶

opik configure¶

Sets up your API key and project settings:

opik configure

opik harbor run¶

Runs a benchmark with Opik tracking:

opik harbor run -d terminal-bench@head -a my_agent

Login to the broader Comet platform (required for cloud sync):

comet login

API examples¶

Python (Tracing a function)¶

from opik import track

@track
def call_llm(prompt):
    # Your LLM call here
    return "Response"

call_llm("Analyze this code")

Sources / references¶

Contribution Metadata¶

Last reviewed: 2026-05-26
Confidence: high