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Open-source platform for tracing, evaluating, and optimizing LLM applications
Opik provides end‑to‑end observability, prompt evaluation, and production‑grade monitoring for LLM‑powered systems, with integrations, an Agent Optimizer, and Guardrails to improve performance and safety.

Opik is a platform that gives developers end‑to‑end observability, testing, and optimization for LLM‑powered applications. It captures every LLM call, conversation, and agent activity, letting you annotate traces with feedback scores through the Python SDK or UI.
Opik can be accessed instantly on Comet.com’s cloud service, or self‑hosted with Docker Compose for local development or Helm on Kubernetes for scalable production. The opik.sh script simplifies service profile selection, and all containers run as non‑root users for added security.
When teams consider Opik, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
RAG chatbot performance tuning
Iteratively refine prompts and retrieval strategies, reducing hallucinations as measured by LLM‑as‑a‑judge metrics.
CI/CD integration for LLM code assistant
Automated tests validate new model releases, catching regressions before deployment.
Production monitoring of a multi‑agent workflow
Dashboard alerts trigger guardrail actions when token usage spikes or unsafe responses are detected.
Self‑hosted compliance‑driven deployment
Run Opik within a private VPC, keeping all trace data on‑premise while leveraging full observability features.
Use the hosted version on Comet.com by creating a free account, or run the Docker Compose script (`./opik.sh`) for a local instance.
Opik provides official SDKs for Python, TypeScript, and Ruby (via OpenTelemetry), plus a REST API.
Yes, Opik offers a PyTest integration that lets you run evaluations as part of automated tests.
The server is designed for high volume, supporting over 40 million traces per day.
Guardrails let you define safety rules evaluated by LLM‑as‑a‑judge, while the Agent Optimizer provides SDK tools to automatically improve prompts and agent behavior.
Project at a glance
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