Understand the project
Practical overview
When it fits
Use Opik when an LLM or agent workflow needs trace-level visibility, repeatable evaluation, prompt experimentation, or ongoing production monitoring rather than review of final responses alone.
Who it is for
- Teams developing LLM applications
- Engineers building AI agents and tool-using workflows
- Teams evaluating RAG systems
- Developers who need prompt and model comparison
Documented uses
- Trace LLM calls, conversations, agent steps, and tool calls.
- Debug multi-step AI agent and LLM application behavior.
- Evaluate outputs with datasets, experiments, and LLM-as-a-judge metrics.
- Assess RAG quality and investigate hallucination-related results.
- Compare prompts and models in the Prompt Playground.
Before you choose
Requirements & limits
The project is identified as open source under the Apache-2.0 license. The documented deployment scope includes web, cloud, self-hosted, and server environments.
- Opik combines tracing, evaluation, prompt experimentation, and production monitoring in one project.
- Its Python SDK is relevant for Python-based LLM applications and agents.
- Repository topics identify OpenAI, LangChain, and LlamaIndex ecosystems, while exact integration configuration is not established here.
- Self-hosting is part of the documented platform scope, which may matter when deployment control is a requirement.
- The evaluation workflow includes datasets, experiments, LLM-as-a-judge metrics, and a documented PyTest integration.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Evidence and transparency
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 2 October 2026
- Original website title
- comet-ml/opik · Official homepage title · retained in its source language