DEV.co
AI Frameworks · comet-ml

opik

Opik is an open-source observability and evaluation platform for LLM applications, RAG systems, and agentic workflows. It provides tracing, automated evaluations, LLM-as-a-judge metrics, and production dashboards to help developers build and optimize AI applications from prototype to production.

Source: GitHub — github.com/comet-ml/opik
20.4k
GitHub stars
1.6k
Forks
Python
Primary language
Apache-2.0
License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

FieldValue
Repositorycomet-ml/opik
Ownercomet-ml
Primary languagePython
LicenseApache-2.0 — OSI-approved
Stars20.4k
Forks1.6k
Open issues149
Latest release2.1.19 (2026-07-07)
Last updated2026-07-08
Sourcehttps://github.com/comet-ml/opik

What opik is

Python-based platform offering comprehensive LLM tracing with integration into popular frameworks (LangChain, LLaMA Index, Autogen, Flowise AI), dataset and experiment management for evaluation, LLM-as-a-judge metrics (hallucination detection, moderation, RAG assessment), and scalable production monitoring designed for 40M+ traces/day with online evaluation rules and PyTest CI/CD integration.

Quickstart

Get the opik source

Clone the repository and explore it locally.

terminalbash
git clone https://github.com/comet-ml/opik.gitcd opik# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

LLM Application Development & Debugging

Trace all LLM calls, conversations, and agent activities with detailed context during development. Use the Prompt Playground to experiment with prompts and models iteratively.

Automated Evaluation & Testing

Build datasets, run experiments with LLM-as-a-judge metrics (hallucination, context precision, answer relevance), and integrate evaluations into CI/CD pipelines via PyTest integration for continuous validation.

Production Monitoring & Optimization

Monitor high-volume production traces (40M+/day), track feedback scores and token usage, apply online evaluation rules to detect issues, and use Agent Optimizer to continuously improve prompts and agents.

Implementation considerations

  • Opik provides both cloud-hosted (Comet.com) and self-hosted (Docker/Kubernetes) deployment paths; choose based on operational capacity and data residency needs.
  • Native integrations exist for LangChain, LLaMA Index, Autogen, Flowise AI, and Google ADK; custom integrations may require SDK instrumentation for non-standard frameworks.
  • LLM-as-a-judge metrics depend on external LLM APIs (OpenAI, etc.) for evaluation; token costs and latency should be factored into evaluation workflows.
  • Production monitoring at 40M+ traces/day requires proper infrastructure sizing; self-hosted deployments need database and storage capacity planning.
  • Feedback annotation and experiment management integrate Python SDK, UI, and API calls; teams must align on feedback workflows and metric definitions upfront.

When to avoid it — and what to weigh

  • Minimal LLM Integration Needed — If your application has minimal or no LLM components, Opik's observability and evaluation focus adds unnecessary overhead.
  • Strict Data Residency or Air-Gapped Environments — While self-hosting is available, the recommended Comet Cloud option involves sending data to external servers. Self-hosted Kubernetes deployments add operational complexity.
  • Budget Constraints with Enterprise Scale — Self-hosting and operating Opik at 40M+ traces/day requires infrastructure and DevOps investment. Commercial support and scaling may require Comet engagement.
  • Closed-Source or Proprietary Requirements — Opik is open-source under Apache-2.0. If closed-source derivative distributions are required, review licensing implications for your use case.

License & commercial use

Licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved open-source license allowing commercial use, modification, and distribution with minimal restrictions (attribution and license copy required).

Apache-2.0 permits commercial use without explicit restrictions. However, the platform is built and hosted by Comet; self-hosted deployments are free, but cloud-hosted (Comet.com) deployments may incur costs. Review Comet's commercial terms for hosted services separately. No restrictions on using Opik to build proprietary LLM applications.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

SignalAssessment
MaintenanceActive
DocumentationStrong
License clarityClear
Deployment complexityModerate
DEV.co fitStrong
Assessment confidenceHigh
Security considerations

Cloud-hosted deployments transmit trace and evaluation data to Comet servers; assess data sensitivity and compliance requirements (PII in prompts/responses, HIPAA, SOC 2, etc.). Self-hosted deployments maintain data control but require secure infrastructure setup (DB encryption, network isolation, credential management for LLM APIs). Apache-2.0 license does not provide security audit or liability guarantees; conduct security review before production use. No explicit security policy, vulnerability disclosure process, or security audit results provided in data.

Alternatives to consider

Langfuse

Open-source LLM observability and evaluation platform with similar tracing, dataset management, and LLM-as-a-judge capabilities; lighter footprint, also supports self-hosting.

LlamaIndex Observability

Native observability and evaluation built into LlamaIndex framework; tighter integration if already using LlamaIndex-based RAG, but narrower scope than standalone platform.

DataDog / New Relic (LLM Monitoring)

Enterprise APM platforms with LLM tracing and observability add-ons; better fit for existing enterprise monitoring stacks, but higher cost and less specialized for LLM evaluation workflows.

Software development agency

Build on opik with DEV.co software developers

Start with Comet Cloud (free signup) or deploy Opik on your infrastructure. Explore integrations, evaluate with LLM-as-a-judge, and monitor production in minutes.

Talk to DEV.co

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

Related on DEV.co

Explore the category and the services that help you build with it.

opik FAQ

Can I use Opik without Comet Cloud?
Yes. Opik supports self-hosting via Docker Compose (local development) or Kubernetes (production). Self-hosted deployments are free; infrastructure and operational costs are your responsibility.
What frameworks does Opik integrate with natively?
Native integrations include LangChain, LLaMA Index, Autogen, Flowise AI, Google ADK, and others. Custom integrations via Python SDK are available for unsupported frameworks.
How much does it cost to evaluate with LLM-as-a-judge metrics?
Opik itself is free (Apache-2.0). However, LLM-as-a-judge evaluations call external LLM APIs (OpenAI, etc.); you pay the LLM provider's token costs directly.
Is Opik suitable for production use?
Yes, it is designed for production: supports 40M+ traces/day, offers online evaluation rules for anomaly detection, and provides dashboards for monitoring. Self-hosted production deployments require proper infrastructure and DevOps support.

Custom software development services

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If opik is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.

Ready to Observe and Evaluate Your LLM Apps?

Start with Comet Cloud (free signup) or deploy Opik on your infrastructure. Explore integrations, evaluate with LLM-as-a-judge, and monitor production in minutes.