---
id: opik
version: "2.2.29"
license: Apache 2.0 License
license_treatment: permissive
maintenance: active
---

# opik — Comet tool for logging and evaluating LLM traces

License: permissive · Maintenance: active · Downloads: 3.7M/mo

## What it is and what it does

Opik is a Python SDK and full-stack platform for observing, evaluating, and optimizing LLM applications and AI agents. It wraps functions with a `@track` decorator to automatically log traces of LLM calls, nested function calls, and agent activity, capturing full execution trees for multi-step workflows. The platform includes evaluation tools—datasets, experiments, and LLM-as-a-judge metrics—for testing hallucinations, moderation, and RAG quality. It also provides prompt management, a playground for experimentation, CI/CD integration via pytest, and production dashboards for monitoring feedback scores and token usage.

The package is built by Comet and released under Apache 2.0, meaning you can self-host the entire backend in your own infrastructure. It supports modern Python versions (3.10–3.14) and integrates with popular LLM frameworks. The SDK depends on litellm, openai, pydantic, httpx, and other standard libraries for tracing, configuration, and API communication.

Use it for:

- Log and inspect full execution traces of multi-step agents and LLM pipelines during development and in production.
- Evaluate LLM outputs for hallucinations, factual accuracy, and moderation using LLM-as-a-judge metrics.
- Test RAG systems by measuring answer relevance and context precision across datasets and experiments.
- Integrate LLM evaluation into CI/CD pipelines using pytest to validate prompts and models on every commit.
- Monitor production LLM applications by tracking trace counts, token usage, and feedback scores over time.
- Manage and version prompts, then experiment with different models and parameters in a shared playground.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Opik is an open-source LLM observability and evaluation platform that logs traces of LLM calls, agents, and pipelines, then evaluates them with datasets, experiments, and LLM-as-a-judge metrics.

Yes. Opik is actively maintained with no known vulnerabilities and offers low install friction. It is genuinely open-source (Apache 2.0) with a self-hostable full platform, not just a client SDK. If you are building LLM agents or need observability and evaluation for LLM applications, this is a solid choice—especially if you want to avoid vendor lock-in or need to keep traces within your own infrastructure.

## Install

pip install opik
uv add opik
poetry add opik

## Installing opik

Before you install: Low install friction with a pure-Python wheel. Active maintenance with a recent release. Requires Python 3.10+. Depends on 19 runtime packages including litellm, openai, and pydantic, which are widely used and stable.

License in practice: Apache 2.0 licensed and permissive. The full platform—server backend, web application, tracing, evaluation, and prompt management—is free to self-host with no data leaving your environment, not just the client SDK.

Quickstart:

pip install opik
opik configure

from opik import track

@track
def my_function(input: str) -> str:
    return input

my_function("test")

Requires Python 3.10 or later. The `opik configure` step sets up connection to a backend (cloud or self-hosted); without it, tracing may not persist.

Verify before relying:

- Whether the self-hosted platform deployment requires additional system dependencies or infrastructure beyond the Python package.
- Performance characteristics and scalability limits when self-hosting at scale (the description mentions 40M+ traces/day capability).
- Whether all integrations mentioned (Google ADK, Autogen, Flowise AI) are included in this package version or require separate setup.

## Package facts

- License: Apache 2.0 License (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 3.7M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

llm observability tracing, ai agent evaluation platform, prompt management and testing, llm-as-a-judge evaluation, production llm monitoring, trace logging for agents, rag evaluation metrics, llm-observability, agent-tracing, evaluation-framework

[View on SkillFed](https://skillfed.io/packages/opik) · [View on PyPI](https://pypi.org/project/opik/)