Opik is open-source LLM observability, evaluation, and AI agent tracing platform for teams building LLM apps, RAG systems, and agentic workflows, built by Comet, Apache-2.0 licensed, free to self-host full platform.

What it is

Opik covers full LLM application lifecycle, from first trace in development to production monitoring. Platform handles AI agent tracing and observability, LLM evaluation, prompt management, and prompt and agent optimization. Deep tracing covers LLM calls, conversation logging, and agent activity, with full trace trees for multi-step agents and tool calls. Package ships as Python SDK on PyPI under name opik, with separate server component for self-hosting.

Project lives in LLM observability and evaluation ecosystem, commonly shortened to LLMOps. It replaces ad-hoc print logging, scattered notebook experiments, and homegrown trace viewers that teams otherwise assemble to watch agent steps and judge output quality. Datasets, Experiments, and LLM-as-a-judge metrics give one place to store test cases, run evaluations, and score results, instead of stitching separate logging, dataset, and scoring tools together.

Key capabilities

• AI agent tracing and observability: deep tracing of LLM calls, conversation logging, and full trace trees for multi-step agents and tool calls.
• LLM evaluation with Datasets and Experiments, plus LLM-as-a-judge metrics for hallucination detection, moderation, and RAG assessment.
• Opik Agent Optimizer SDK to improve prompts and agents.
• Production monitoring with scalable dashboards and online evaluation rules.
• Opik Guardrails for safe and responsible AI practices.
• CI/CD evaluation through PyTest integration, so LLM pipelines get tested on every commit.
• Prompt Playground for experimenting with prompts and models, plus third-party integrations including Google ADK, Autogen, Flowise AI, LangChain, and LlamaIndex.

Who uses it and how

• Teams building multi-step agents and RAG systems use trace trees to follow tool calls and conversation flow across development and production.
• Evaluation engineers run Datasets and Experiments with LLM-as-a-judge metrics to catch hallucination and moderation problems before release.
• Platform teams wire the PyTest integration into CI so LLM pipeline tests run on every commit.
• Developers annotate traces and spans with feedback scores through the Python SDK or the UI, then feed that signal back into optimization.
• Operators deploy self-hosted Opik Server and apply online evaluation rules to score live traffic.

Getting started

Install the Python SDK package opik from PyPI for client-side tracing, then follow the quickstart to log first trace. Self-hosters instead run the Opik Server installation, and Comet offers a hosted option linked from the README.

How it compares

No paid products are listed as replacements in the supplied facts, and no directly comparable tools are named there either, so on the evidence given Opik stands alone in this registry. Its integrations with LangChain, LlamaIndex, and OpenAI describe interoperability, not competition.

When to use it — and when not to

Pick Opik if team wants Apache-2.0 tracing, evaluation, and monitoring in one stack, and can operate the Opik Server component when self-hosting rather than using the hosted Comet option. Avoid it if team wants a zero-infrastructure, SDK-only logging library, because full platform means running and maintaining a server alongside the opik client. Note the honest weaknesses: README excerpt supplied here is truncated mid-sentence, so public documentation summary is incomplete, and no database, storage, or SMTP requirements appear in the facts — confirm those operational details from the docs before committing to a self-hosted deployment.