Confident AI
DeepEval-powered LLM evaluation platform to test, benchmark, and safeguard apps
Opik
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.
Overview
Overview
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.
Core capabilities
- Tracing & dashboards – Store and visualise millions of traces (40M+ per day) to monitor token usage, latency, and quality metrics.
- Evaluation – Run prompt experiments, use LLM‑as‑a‑judge metrics such as answer relevance or hallucination detection, and integrate evaluations into CI/CD via PyTest.
- Production safety – Apply online evaluation rules, Guardrails, and the Agent Optimizer to continuously improve and secure agents in real time.
Deployment options
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.
Highlights
Deep tracing of LLM calls and agent activity
LLM‑as‑a‑judge evaluation with custom metrics
Scalable production monitoring (40M+ traces/day)
Agent Optimizer and Guardrails for continuous improvement and safety
Pros
- Comprehensive observability across development and production
- Extensible integrations with major LLM frameworks
- Built‑in prompt playground and experiment management
- Flexible deployment: hosted SaaS or self‑hosted Docker/Kubernetes
Considerations
- Self‑hosting adds operational overhead and requires container expertise
- Advanced Guardrails and Optimizer need additional configuration
- Integration list may not cover every niche framework
- Focuses on LLM observability rather than general ML monitoring
Managed products teams compare with
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Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- ML engineers building RAG chatbots who need traceability
- Teams deploying LLM agents in production and want safety guardrails
- Developers seeking automated prompt evaluation within CI/CD pipelines
- Organizations that prefer self‑hosted observability for data privacy
Not ideal when
- Projects that only need simple API calls without monitoring
- Teams without Docker/Kubernetes expertise for self‑hosting
- Use cases unrelated to LLM or generative AI
- Environments requiring out‑of‑the‑box analytics for non‑LLM models
How teams use it
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.
Tech snapshot
Python70%
TypeScript28%
Jupyter Notebook1%
Shell1%
PowerShell1%
SCSS1%
Frequently asked questions
How can I start using Opik quickly?
Use the hosted version on Comet.com by creating a free account, or run the Docker Compose script (`./opik.sh`) for a local instance.
Which programming languages are supported by the client SDK?
Opik provides official SDKs for Python, TypeScript, and Ruby (via OpenTelemetry), plus a REST API.
Can Opik be integrated into CI/CD pipelines?
Yes, Opik offers a PyTest integration that lets you run evaluations as part of automated tests.
What scale can the platform handle?
The server is designed for high volume, supporting over 40 million traces per day.
How do Guardrails and the Agent Optimizer work?
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
Active- Stars
- 17,386
- Watchers
- 17,386
- Forks
- 1,307
LicenseApache-2.0
Repo age2 years old
Last commit3 hours ago
Primary languagePython
Last synced 3 hours ago