Fewer subscriptions.
More vibe-coding.

Open-source tools & agent skills to build the things you’re paying for.

Opik

Project22.4K

Self-hostable tracing and evaluation for LLM applications, RAG systems, and agents.

Added 2026-10-07
Replaces
GitHub

What you can build

  • Build a converter that reads your team's labeled CSV examples and creates an Opik dataset for repeatable application evaluations, leaving the source file unchanged.
  • Build a report generator that turns your CI's LLM test results into a concise artifact grouped by failing case and prompt revision, using Opik's documented PyTest integration.
  • Build a release utility that runs a fixed set of evaluation cases against prompt candidates and writes a score-difference report for reviewers.
  • Build a small feedback control in your application that records a user's rating against the relevant trace or span through the documented Python SDK annotation capability.
  • Build a report that takes RAG evaluation results, including answer-relevance and context-precision scores, and outputs low-scoring queries for targeted human review.

Try this prompt

Build a small Python workflow that reads a local CSV of RAG queries and expected outcomes, runs each query through an existing application function, and evaluates results with Opik's documented dataset, experiment, and RAG evaluation capabilities. Write a review file identifying low-scoring cases with their input and result. Preserve the original CSV and existing Opik data. Include a small fixture and setup documentation. Use documented interfaces only; if an operation lacks a documented interface, explain the gap rather than inventing an SDK method.

Get started

  1. Choose a deployment

    Use the README's server installation guidance to choose local Docker setup or Kubernetes deployment through the documented Helm installation guide.

  2. Configure the Python SDK

    Follow the Python SDK Quick Start to install and configure the SDK with your self-hosted server address or Comet workspace and API key.

  3. Instrument and evaluate

    Use the documented Python tracking decorator to log traces; consult the evaluation documentation for datasets, experiments, metrics, and the PyTest integration.

License & notes

Apache-2.0Apache-2.0 permits commercial use subject to its terms. When distributing modified files, mark changes and retain required notices. Self-hosting requires infrastructure that you provide and operate. LLM-as-a-judge evaluations may require a model or API with separate costs; cloud use may also have separate terms or charges. No prices are quoted.

  • Self-hosting requires infrastructure you provision and maintain; judge-based evaluations may require a separately billed model or API.
  • Evaluation findings depend on the cases and metrics selected; they do not guarantee application quality.
  • Check the documentation for current SDK details and support for your framework and deployment.

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Added 2026-10-07
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Immich

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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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