Opik

Opik is an open-source, framework-agnostic LLM evaluation and observability platform launched in 2017 by Comet, based in New York, United States. It is designed for AI engineering teams — including product, engineering, and AI specialists — who need to log, score, annotate, and version LLM interactions across development and production. The platform targets teams that want transparent debugging, cost tracking, and collaborative annotation workflows without being locked into a specific LLM framework. Opik is especially suited for organizations that require audit-ready logs for governance, as it automatically produces audit trails of all model interactions. It also appeals to teams building multi-turn conversational agents or RAG pipelines, offering thread-level evaluation and human feedback loops for conversation threads. By providing a unified environment for code, data, and experiment management, Opik aims to align cross-functional teams around model performance and regression prevention.

Opik captures end-to-end traces of every action taken by an agent, including LLM calls, tool invocations, retrieval steps, and multi-turn conversations. Users can visualize these traces in an easy UI for scoring, annotating, and versioning logged data. The platform supports plain-text assertions for defining desired outcomes, and it can auto-implement fixes using the Ollie coding harness. For testing, Opik offers an Agent Playground where entire agents can be evaluated. It integrates with popular agent frameworks like LangGraph and LlamaIndex, and provides built-in metrics for auto-scoring LLM output datasets. Opik tracks token usage and model cost per request, and includes guardrails to detect and block unwanted content. The platform also supports automated dataset expansion for robust LLM evaluation, and offers a comprehensive dashboard for monitoring production traces and applying guardrails.

In the LLM observability market, Opik competes directly with Braintrust, LangSmith, Langfuse, and Weights & Biases. Compared to LangSmith, which is tightly coupled with the LangChain ecosystem, Opik is framework-agnostic and open-source, appealing to teams that want flexibility and self-hosting. Unlike Braintrust, which focuses on CI/CD quality gates and release enforcement, Opik emphasizes transparent feedback and debugging to align product, engineering, and AI teams. Langfuse offers a similar open-source model but with a stronger focus on prompt management and A/B testing, while Weights & Biases is built primarily for ML experiment tracking and lacks native CI/CD quality gates. Opik differentiates itself by combining open-source LLM evaluation with ML experiment tracking in a single platform, making it a strong choice for teams already using Comet's ecosystem.

Opik's primary trade-off is its learning curve for teams not already familiar with Comet's platform, as it integrates deeply with Comet's ML experiment tracking. While it offers robust tracing and evaluation, its CI/CD integration is less mature than Braintrust's, which provides automatic quality gates that block merges on score regression. The open-source version may require significant self-hosting effort, and the tiered pricing for cloud-hosted plans can become expensive at scale for large teams with high storage needs. Additionally, Opik's guardrails and auto-fix features are still evolving, and teams may need to supplement with dedicated guardrail tools like Galileo for real-time content filtering. Despite these limitations, Opik's transparent feedback and collaborative annotation capabilities make it a compelling choice for teams prioritizing visibility and cross-team alignment over release enforcement.

Tiered pricing based on usage and features, including number of seats, storage, and advanced functionalities. Custom enterprise options available for scale or additional integrations. Monthly or annual subscription. Specific dollar figures not disclosed in sources.