Opik (Comet)
Free, open-source AI observability and evals for debugging agents
Opik is the strongest open-source choice for teams building complex agents. The auto-fix Ollie, Agent Playground, and Cost Intelligence address the real pains of agent production: debugging, testing, and spend. If you're debugging multi-step agents and want a free, self-hostable tool with depth, pick Opik over LangSmith or Langfuse.
Verified 8d ago · liveness 78/100 · cite: rightaichoice.com/tools/opik-comet
- Developers debugging complex multi-step AI agents in production
- Teams needing automated regression testing for agent behaviors
- Enterprises tracking and optimizing LLM spend across engineering teams
- ML engineers evaluating agent outcomes with LLM-as-a-judge metrics
- Simple single-turn chatbot monitoring
- Teams wanting a fully managed, no-code observability solution
- Projects that require extensive pre-built evaluation templates out of the box
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Skip Opik if you need a turnkey, no-code observability solution or if you're monitoring simple single-turn chatbots—you'll find the setup steep and the features overkill.
While Opik is open-source, self-hosting requires your own infrastructure and maintenance, which can incur significant engineering time and cloud costs.
Opik's open-source core is free, making it the most cost-effective option for developers and small teams. Comet's managed cloud has a generous free tier, while LangSmith and Langfuse charge per seat or usage. For enterprises needing advanced compliance and support, the Enterprise tier is custom-priced, likely comparable to or cheaper than alternatives like Arize or Braintrust.
In short
Opik (Comet) — Free, open-source AI observability and evals for debugging agents. Best for Developers debugging complex multi-step AI agents in production, Teams needing automated regression testing for agent behaviors, Enterprises tracking and optimizing LLM spend across engineering teams. Free to use.
What's new in Opik (Comet)
Checked 8 days agoAcross the latest 5 updates: 4 feature updates and 1 news mention.
I Built a RAG Pipeline for F1 Team Radio, Then Made It Grade Itself
A tutorial on building a self-grading RAG pipeline for F1 team radio, using Opik for evaluation.
Beyond the Single Trace: How We Built Agent Diagnostics for Opik
Opik adds agent diagnostics to trace and debug production AI agents beyond single traces.
How We Optimized Opik’s MCP Server for Cost & Performance
Engineering team reduces token spend and latency by optimizing Opik's MCP server.
Engineering Insights: How Internal Optimizations Led to Comet Cost Intelligence
Comet's Cost Intelligence product emerges from internal token-spend optimizations, now shared with users.
Opik + Oracle Agent Specification: Build Once, Run Anywhere
Opik integrates with Oracle's Open Agent Specification, enabling portable AI agents.
Viability Score
How well maintained and how widely used is Opik (Comet)? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- End-to-end agent tracing
- LLM-as-a-judge evaluation with 30+ metrics
- Test Suites with global and item-level assertions
- Ollie auto-fix coding assistant
- Agent Playground for end-to-end testing
- Prompt Optimizer with six algorithms
- Real-time production monitoring with alerts
- Guardrails for content and PII protection
- Cost Intelligence for Claude Code and Codex
- Token usage and model cost tracking
- Agent diagnostics for production debugging
- MCP server optimizations
- Integration with Oracle Open Agent Specification
- Self-hosted via GitHub
- Audit logs for compliance
About Opik (Comet)
Opik is Comet's open-source AI observability and evaluation platform, purpose-built for the agentic era. It captures every step your agent takes—user interactions, context retrieval, tool calls—and pairs that detailed tracing with automated eval workflows to surface and fix errors across development, testing, and production. Opik provides end-to-end logs, repeatable test cycles, and clear evaluation scores so you can scale agents from prototype to production with confidence. For evaluation, Opik uses LLM-as-a-judge metrics to score outcomes across 30+ dimensions like answer relevance, context precision, task completion, and hallucination. You define what 'good' looks like via reference datasets or plain-text assertions, and Opik flags failures out of thousands of traces. In production, it monitors traces in real time, alerts on failed test criteria, and applies guardrails to block content violations, prevent PII exposure, and manage compliance risks. It also tracks token usage and model cost per developer and team, with a dedicated Cost Intelligence feature that monitors Claude Code and Codex spend. The platform goes beyond passive logging: Test Suites let you define unit-test-style rules with global and item-level assertions. Ollie, a coding assistant, analyzes traces and writes fixes directly to your codebase, complete with version control and regression testing. Agent Playground lets you run your entire agent configuration end-to-end, version prompts and parameters, and share experimentation with stakeholders. A Prompt Optimizer offers six algorithms for tuning orchestration, tool calls, and model interactions. Opik is a true open-source project—core observability and eval features are free in the source code, and you can self-host via GitHub or use Comet's managed cloud with a generous free tier. Recent updates include agent diagnostics for production debugging, MCP server optimizations, and a cost intelligence product, plus an integration with Oracle's Open Agent Specification.
Behind the Verdict
Opik stands out in the crowded LLM observability space by combining deep tracing with an active development cycle. The platform's biggest strength is its open-source core—you get real observability and evaluation capabilities without a paywall, and you can self-host if you want full control. This makes it an attractive option for startups and individual developers who are tired of metered pricing from commercial alternatives. Ollie, the auto-fix coding assistant, is a differentiator. Instead of just surfacing errors, it examines traces, suggests fixes, and writes them directly to your codebase, with regression tests to prevent regressions. This moves you from passive monitoring to active debugging. Agent Playground extends that by letting you run your entire agent configuration end-to-end, versioning prompts and parameters, and sharing experiments with stakeholders. These features are particularly valuable for teams iterating on agent behavior. Cost Intelligence is another practical addition, especially for teams using Claude Code or Codex. It gives you a real-time view of token spend across developers and teams, helping you identify waste and optimize. The recent MCP server optimizations also reduce token spend and latency, directly addressing operational costs. However, Opik is not a no-code solution. Setting it up requires technical expertise—you'll need to integrate SDKs and understand tracing concepts. The full enterprise features, like advanced compliance and scalability, are gated behind the paid version, so large organizations may need to upgrade for those. Also, while the free tier is generous, heavy production use may eventually require a paid plan. Compared to LangSmith and Langfuse, Opik offers a more comprehensive suite for agent development, especially with Ollie and the Playground. But if you need a fully managed, no-code solution or pre-built evaluation templates, you might prefer LangSmith. For teams that want open-source flexibility and are willing to invest in setup, Opik is a compelling choice.
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Real-world workflow fit
Concrete scenarios for the personas Opik (Comet) actually fits — and what changes day-one when you adopt it.
You're building a RAG pipeline and want to see why your agent returns wrong answers. You use Opik to trace each step, add an LLM-as-a-judge metric for answer relevance, and run it on a few test traces. You spot the issue in context retrieval and tweak your prompt.
Outcome: Within an afternoon, you identify and fix a retrieval bug, improving answer quality without manual inspection of every trace.
Your team is deploying a coding agent. You set up Opik Test Suites with assertions for tool usage and output quality. Ollie analyzes failing traces and auto-fixes issues in your codebase, adding regression tests.
Outcome: You reduce regression debugging time significantly, as Ollie automates fixes and ensures the same issues don't recur.
Your team uses Claude Code and Codex. You configure Opik Cost Intelligence to track token spend per developer and team. You identify high usage and adjust configurations to cut costs.
Outcome: You gain real-time visibility into spend, leading to a 20% reduction in token costs within the first month.
Use Cases
- Trace and debug a multi-step AI agent from user query to tool call to final response
- Automatically evaluate thousands of traces with predefined LLM-as-a-judge metrics
- Define unit tests for agent behavior using plain-text assertions and auto-fix failures with Ollie
- Monitor production agent performance and get alerted on policy violations or PII exposure
- Experiment with prompt optimization algorithms to improve agent accuracy and consistency
- Generate audit logs for compliance by capturing every action an agent takes
Models Under the Hood
as of 2026-08-31
Limitations
- Initial setup and configuration require technical expertise.
- Full enterprise features (scalability, compliance) are gated behind Comet's paid offering.
- Context window and rate limits depend on the underlying LLMs used, not Opik itself.
as of 2026-08-29
Verification history
We have re-verified Opik (Comet) 18 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 18 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Opik (Comet) tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0/mo
Ideal for
Developers and teams who want full control and self-hosting, willing to set up and maintain their own infrastructure.
What this tier adds
Free, self-hosted version with all core observability and eval features; you manage your own instance.
Comet Cloud Free Tier
$0/mo
Ideal for
Individual developers and small teams wanting managed hosting without the hassle, with enough usage for experiments.
What this tier adds
Generous free tier on Comet's cloud, no credit card required, includes all core features with limited usage.
Enterprise
Custom
Ideal for
Large organizations needing scalable, industry-compliant observability with advanced security and support.
What this tier adds
Custom pricing with advanced features like audit logs, dedicated support, and enhanced scalability.
Where the pricing makes sense
The company stage and team size where Opik (Comet)'s pricing actually pencils out — and where peers do it cheaper.
Opik's open-source core is free, making it the most cost-effective option for developers and small teams. Comet's managed cloud has a generous free tier, while LangSmith and Langfuse charge per seat or usage. For enterprises needing advanced compliance and support, the Enterprise tier is custom-priced, likely comparable to or cheaper than alternatives like Arize or Braintrust.
Setup time & first value
How long it actually takes to get something useful out of Opik (Comet) — broken out by persona, not the marketing-page minute.
Individual developers can get Opik running in under an hour by following the quickstart. Teams integrating with existing CI/CD might take a few days to set up test suites and integrate Ollie. Enterprise deployment with custom compliance may take weeks.
Switching to or from Opik (Comet)
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangSmith: Use Opik's Python SDK to log traces and evals; you can export LangSmith runs and re-import them via API.
- →From Langfuse: Opik supports LangChain and LlamaIndex, so your existing tracing calls can be adapted with minimal changes.
- ↗To LangSmith: If you find Opik's setup too complex or need more managed features, you can export traces and metrics to LangSmith.
- ↗To Langfuse: Opik's open-source nature means you can self-host and switch if needed, but there's no one-click migration.
Integrations
Resources & Guides
- Documentationcomet.com
Docs Home - Comet Docs
Full product docs from comet.com
- Documentationcomet.com
Opik - Open-Source LLM Observability & Agent Optimization
Build, test, and optimize GenAI apps from prototype to production. Comprehensive tracing, evaluation, and prompt optimization for RAG, agents, and more.
- Resourcecomet.com
Opik
Opik is an end-to-end AI observability platform designed to help developers test, ship, and continuously improve agentic systems.
- Resourcegithub.com
GitHub - comet-ml/opik: Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. - comet-ml/opik
Tutorials & Learning
Official links
Tools that pair well with Opik (Comet)
Common stack mates teams adopt alongside Opik (Comet), with the specific reason each pairing earns its keep.
Alternatives to Opik (Comet)
View all
Arize Phoenix
Open-source LLM agent observability with tracing, evals, and experiments
Langfuse
Open-source LLM observability for tracing, evaluating, and optimizing AI agents end-to-end.
Evidently AI
Open-source AI evaluation and observability for LLMs, RAG, agents, and predictive ML.
Frequently Asked Questions
Best-of guides
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