Comet
AI observability and evals that auto-fix agent code via git
Pick Opik when your agents are complex enough that debugging by hand fails and you want automated fixes plus cost controls. The trace-to-fix loop with Ollie is genuinely useful, not a gimmick. Skip it if you need simple prompt logging or don't want git-based automation. Lighter tools like Langfuse are easier for basic needs, but they won't write the fix for you.
Verified 1d ago · liveness 87/100 · cite: rightaichoice.com/tools/comet-ml
- AI teams shipping complex agents that need trace-level observability and automated fixes via Ollie
- Engineering managers tracking and cutting Claude Code and Codex spend with cost intelligence
- ML teams wanting combined LLM evaluation, experiment management, and model versioning
- Enterprises requiring self-hosted, secure observability with diagnostics and test suites
- Non-technical users who need a no-code LLM evaluation dashboard or simple prompt playground
- Teams needing only basic logging for low-complexity projects without trace-level debugging
- Organizations that cannot integrate git-based code automation into their workflow
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Skip Opik if you just need simple prompt logging or you're not ready to integrate git-based code automation into your workflow, or if you're on a tight budget and can't afford the Teams tier for full diagnostics.
The Teams tier at $179/mo is a big jump from the free tier, and you need it for full diagnostics, Ollie, and cost intelligence.
Opik's Free tier is generous for small teams exploring LLM observability, but the jump to Teams at $179/mo is significant. Compared to Langfuse's open-source free tier and paid plans starting lower, Opik's paid tier is pricier but includes auto-fix and cost intelligence that others lack.
In short
Comet — AI observability and evals that auto-fix agent code via git. Best for AI teams shipping complex agents that need trace-level observability and automated fixes via Ollie, Engineering managers tracking and cutting Claude Code and Codex spend with cost intelligence, ML teams wanting combined LLM evaluation, experiment management, and model versioning. Free to start; paid plans from $179/mo.
What's new in Comet
Checked 8 days agoAcross the latest 5 updates: 3 feature updates and 2 news mentions.
One Prompt, 24 Versions: How Digibee Builds Prompts with Opik
Case study of Digibee using Opik to manage 24 prompt versions for their integration platform.
Beyond the Single Trace: How We Built Agent Diagnostics for Opik
Introduces agent diagnostics that analyze production behavior beyond single traces.
How We Optimized Opik's MCP Server for Cost & Performance
Details optimizations to the MCP server that cut token spend.
How Evaluation-Driven Development (EDD) Works
Explains evaluation-driven development: turning agent changes into experiments to detect regressions.
Opik + Oracle Agent Specification: Build Once, Run Anywhere
Opik integrates with Oracle's Open Agent Specification for build-once-run-anywhere agents.
Viability Score
How well maintained and how widely used is 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: August 2026
How we score →Key Features
- Trace-level observability for every agent step (context retrieval, tool calls)
- Diagnostics to detect silent errors and group recurring issues
- Ollie auto-fix coding agent that writes fixes to git
- 40+ LLM-as-a-judge metrics including hallucination detection
- Agent diagnostics for production behavior beyond single traces
- Cost intelligence tracking Claude Code and Codex spend
- Test Suites with pass/fail results on trace sets
- Evaluation-driven development (EDD) to measure agent changes
- Optimized MCP server for coding agent access and token cost control
- Human annotation and debugging of individual traces
- Experiment management with custom visualizations
- Model versioning and dataset management
- Production monitoring dashboards and alerts
- Open-source core with self-hosted option
- 60+ integrations including LangChain, LlamaIndex, and OpenAI
About Comet
Comet's Opik platform is an open-source AI observability and evaluation platform that goes beyond logging to actually fix your agents. Built for AI teams shipping complex generative AI applications — from autonomous agents to RAG pipelines — Opik gives you complete trace-level visibility into every step your agent takes: context retrieval, tool calls, system prompts, and user feedback. With 60+ integrations, it's easy to instrument, whether you're using LangChain, LlamaIndex, OpenAI, or a custom stack. The platform's Diagnostics automatically surface silent errors across thousands of traces, grouping similar issues, identifying root causes, and suggesting fixes — even when no explicit error message exists. Then Ollie, the built-in coding agent, reads those traces and eval results and writes fixes directly to your codebase via git. This trace-to-fix loop is what sets Opik apart from tools that only report problems. Beyond debugging, Opik provides 40+ LLM-as-a-judge metrics, including hallucination detection, and Test Suites that give simple pass/fail results on sets of traces. You can create golden datasets, run evaluations, and bring in human annotators to validate changes before shipping. The platform supports evaluation-driven development (EDD), a methodology for measuring agent changes as experiments, and agent diagnostics that analyze production behavior beyond single traces. Cost Intelligence is another central feature, letting you track Claude Code and Codex usage across your team to cut token waste — MCP installs, skills, model selection, context retrieval, and configurations. It also handles experiment management, model versioning, and dataset management for traditional ML training runs. Opik is open source with over 150,000 developers and 10,000+ teams using it. You can self-host, use the cloud, or custom deploy, backed by Comet's enterprise-grade infrastructure. Companies like Digibee manage 24 prompt versions for enterprise integrations. If you already
Behind the Verdict
If you're wrestling with agents that misbehave in production, Comet's Opik offers a rare combination: full observability plus a mechanism to actually act on what you see. The centerpiece is Ollie, a coding agent embedded in the platform that reads traces, identifies underperforming steps, and generates code fixes directly to your git repo. That's a step beyond observability tools that stop at dashboards and alerts. In practice, this is most useful for teams with complex, multi-step agents where silent failures are common and manual debugging is a time sink. The Diagnostics feature is a strong complement, automatically grouping recurring issues and pointing to root causes even when there's no error message. What also makes Opik stand out is its Cost Intelligence. If you're spending heavily on Claude Code or Codex, this gives you granular visibility into where tokens go — MCP installs, skills, model choices, context retrieval, and configs. For engineering managers, that's a direct lever on your AI spend. Combined with the platform's support for evaluation-driven development (EDD), you can treat every agent change as an experiment, measure regressions, and validate fixes before they ship. That's a workflow that scales with maturity. However, Opik is not for everyone. If you only need basic logging for a simple LLM call or a single-prompt app, the full feature set is overkill. The auto-fix capability relies on git integration, so teams that can't or won't allow automated commits may find Ollie less useful. And while the open-source version is free, the enterprise-grade features like custom deployment and advanced support likely require a paid plan — though specific pricing isn't published on the site. Compared to Langfuse, which is more focused on tracing and has a
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Real-world workflow fit
Concrete scenarios for the personas Comet actually fits — and what changes day-one when you adopt it.
You notice your agent fails on certain tasks but no error is logged. You use Opik's Diagnostics to surface silent errors, group recurring issues, and get a recommended fix.
Outcome: You apply Ollie's fix to your git branch, run Test Suites to validate, and see pass rates improve—debugging time cut significantly.
Your team's Claude Code usage is costing more than expected. You turn on Cost Intelligence in Opik to see where tokens go across MCP installs and model selection.
Outcome: You identify waste, adjust configurations, and reduce token spend by up to 30%.
You're planning to upgrade your LLM and want to ensure it doesn't regress on your test set. You build a golden dataset and run evals with Opik's 40+ metrics.
Outcome: You compare old vs. new model on the same traces, see pass/fail results, and confidently deploy the upgrade.
Use Cases
- Debugging complex LLM agents with full trace visibility
- Automated testing of agent responses with Test Suites
- Iterative development with Ollie AI-assisted code fixes
- Sandbox testing of agent versions before production
- Monitoring production agent behavior, costs, and governance
- Analyzing Claude Code and Codex spend across your engineering team
- Evaluating multimodal LLMs using product images and metadata
Models Under the Hood
as of 2026-08-14
Limitations
- Opik is designed for LLM observability and evaluation; it does not provide general ML experiment tracking outside of agent contexts.
- The Ollie auto-fix agent may not always generate correct fixes and requires human review.
- Self-hosting the open-source version may require engineering effort.
as of 2026-08-14
Verification history
We have re-verified 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-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-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
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 Comet tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Individual developers and small teams exploring LLM observability and evaluation with basic tracing and evals.
What this tier adds
Free entry point; includes cloud access with generous trace volume, basic evals, and limited Test Suites.
Teams
$179/mo
Ideal for
AI engineering teams shipping complex agents that need Diagnostics, Ollie auto-fix, and cost intelligence.
What this tier adds
Adds advanced diagnostics, Ollie, and cost intelligence—the core differentiators—plus higher trace volume.
Enterprise
Custom
Ideal for
Large organizations needing self-hosted deployment, SSO, compliance, and dedicated support.
What this tier adds
Adds self-hosting, SSO, SLAs, custom integrations, and governance features.
Where the pricing makes sense
The company stage and team size where Comet's pricing actually pencils out — and where peers do it cheaper.
Opik's Free tier is generous for small teams exploring LLM observability, but the jump to Teams at $179/mo is significant. Compared to Langfuse's open-source free tier and paid plans starting lower, Opik's paid tier is pricier but includes auto-fix and cost intelligence that others lack.
Setup time & first value
How long it actually takes to get something useful out of Comet — broken out by persona, not the marketing-page minute.
You can get started in minutes: install the SDK, add a few lines of code to track your LLM calls, and you'll see traces in the cloud dashboard immediately. Self-hosting takes a few hours of engineering setup, but cloud is instant.
Switching to or from Comet
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Langfuse: Use Opik's REST API to replay logged traces, or use the open-source SDK to instrument your app and start fresh—your existing Langfuse data won't automatically transfer, but you can re-run your evals.
- ↗To Langfuse: Opik's open-source core means you can export your trace data via the API and re-import into Langfuse, though you'll lose Opik-specific features like Ollie.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Comet
Common stack mates teams adopt alongside Comet, with the specific reason each pairing earns its keep.
Alternatives to Comet
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Arize Phoenix
Open-source LLM agent observability with tracing, evals, and experiments
Dash0
OpenTelemetry-native observability with autonomous AI SRE Agent0 and AI Coding Insights.
Opik (Comet)
Open-source AI observability and evals for the agentic era
Frequently Asked Questions
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