Opik
Open-source LLM tracing and evaluation platform by Comet ML - logs every LLM call, runs automated quality metrics, and integrates with CI/CD to catch regressions before production.
Opik is an open-source LLM observability, evaluation, and annotation platform developed by Comet ML and released in 2024, providing a comprehensive toolkit for tracing, testing, and improving LLM-powered applications. The platform captures structured traces of every LLM call including prompts, completions, token usage, cost, and latency, then aggregates them into experiment-level dashboards for monitoring quality over time. Opik's automated evaluation suite runs hallucination detection, answer relevance, context recall, and custom metric checks against test datasets, enabling quality gates in CI/CD pipelines. Human annotation queues allow subject-matter experts to label and rate outputs at scale, feeding corrected data back into fine-tuning or prompt improvement workflows. The core library is Apache 2.0 licensed with a free cloud tier and a self-hosted Docker deployment option for data-sensitive environments.
Key Features
- Distributed tracing captures every LLM call with prompts, completions, token counts, latency, and cost in one structured view
- Automated evaluation runs hallucination detection, relevance scoring, and custom LLM-as-judge metrics against held-out test datasets
- LangChain, LlamaIndex, LiteLLM, and OpenAI SDK integrations add tracing via a single decorator or environment variable with no code refactor
- Prompt versioning links every prompt iteration to its measured performance metrics for instant regression identification across versions
- Human annotation queues route LLM outputs to domain experts for manual scoring and labeling to build gold-standard evaluation datasets
- CI/CD integration blocks deployment when evaluation quality metrics fall below team-configured thresholds on any code change
- Self-hosted Docker deployment provides full data control for air-gapped environments with no outbound data transmission required
Use Cases
- ML teams tracing production LLM calls to debug unexpected outputs and identify which prompt version caused a quality regression
- Developers integrating automated hallucination and relevance checks into GitHub Actions before deploying any prompt or model change
- AI teams managing human annotation workflows to produce labeled datasets for LLM fine-tuning and RLHF preference alignment pipelines
- Enterprises that need self-hosted LLM observability infrastructure with no third-party data transmission for compliance requirements
Pros
- Apache 2.0 open-source with full self-hosted support enables LLM observability without sending production prompts to external servers
- Comet ML parentage brings proven MLOps tracking expertise to LLM observability from a team with a decade of ML monitoring experience
- End-to-end coverage from tracing through human annotation and CI/CD evaluation gates reduces the number of separate monitoring tools needed
Cons
- Smaller community and fewer third-party integrations than Langfuse or LangSmith for teams selecting an observability platform today
- Self-hosted Docker deployment requires managing database and infrastructure separately from the application being monitored
- Automated evaluation quality depends on having an LLM judge configured - teams without evaluation API access cannot run model-based metrics
Opik Alternatives
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Langfuse
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AI evaluation and testing platform for LLM applications with experiment tracking, human annotation, automated scoring, and production tracing.
Helicone
LLM observability platform that logs every API call, tracks costs, and monitors latency via a one-line proxy integration - YC W23.
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