Comet
Opik, Comet's open-source LLM observability and eval platform, turns agent traces into root-cause groupings and git-committed code fixes
If your agents fail silently and your team is small, Opik's attempt to write the fix — Diagnostics grouping root causes, Ollie proposing changes you pull through git, Test Suites scoring whether the fix held — is a meaningfully different loop from trace viewers like Langfuse and LangSmith, which stop at reporting. Combine that with a real open-source, self-hostable core on Comet's enterprise infrastructure and you have a platform worth piloting. The catch is unchanged: the auto-fix loop assumes git-based workflows and agents with enough moving parts that trace debugging pays off. If you only need prompt logging, you'll pay in setup complexity for capability you won't use.
Verified 9d ago · liveness 87/100 · cite: rightaichoice.com/tools/comet-ml
- AI platform teams whose agents fail silently and need root-cause grouping across thousands of traces
- Engineering managers wanting Claude Code and Codex spend broken down by model, MCP install, and context retrieval
- Teams practicing evaluation-driven development with test suites, golden datasets, and human annotation
- Enterprises needing self-hosted or custom-deployed LLM observability with governance and alerting
- Non-technical users who want a no-code prompt playground or drag-and-drop eval builder
- Projects with simple single-step agents where trace debugging and auto-fix add cost without payoff
- Teams unwilling to let a tool propose code changes through git-based automation
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Skip Comet's Opik if your LLM app is a single-step prompt with no tool calls or retrieval, because trace grouping, Diagnostics, and Ollie's fix proposals only earn their setup cost once there are multiple steps that can fail quietly.
The open-source path is free of license fees but not of cost — self-hosting means running and upgrading Opik yourself, which is engineering hours rather than a line item, and those hours are the real price of the free
Comet splits its pricing across two products rather than one ladder: the open-source Opik core is free to self-host, the hosted Opik platform sells as Teams at $179/mo for cloud workspaces plus Test Suites, 40+ LLM-as-a-judge metrics, Diagnostics, and Ollie, and Enterprise is custom for deployment options, governance, and Cost Intelligence. That $179/mo hosted tier is aimed at funded engineering teams running multi-step agents in production, not solo builders — a solo developer prototyping a
In short
Comet — Opik, Comet's open-source LLM observability and eval platform, turns agent traces into root-cause groupings and git-committed code fixes. Best for AI platform teams whose agents fail silently and need root-cause grouping across thousands of traces, Engineering managers wanting Claude Code and Codex spend broken down by model, MCP install, and context retrieval, Teams practicing evaluation-driven development with test suites, golden datasets, and human annotation. Free to start; paid plans from $179/mo.
What's new in Comet
Checked 9 days agoAcross the latest 5 updates: 4 feature updates and 1 news mention.
Capturing a 400-Turn Claude Code Session in a Single Image
Comet ran a small model over thousands of Claude Code sessions to map how developers actually use coding agents, turning a 400-turn session into a single visual representation.
Prompt Management for AI Agents: The Best Tools Compared (2026)
Comet compares prompt management tooling for AI agents and notes that most teams still lack a real prompt management process.
Export TrueFoundry AI Gateway Traces to Opik with OpenTelemetry
Opik gains TrueFoundry AI Gateway trace export via OpenTelemetry, bringing prompt, model, and latency data into Opik.
Notes from Researching Terminal UX for Opik MCP
Comet shares research on terminal-based UX for the Opik MCP server, moving interaction out of the dashboard.
I Built a RAG Pipeline for F1 Team Radio, Then Made It Grade Itself
A Comet tutorial builds a self-grading RAG pipeline over F1 team radio using Opik for evaluation.
What people actually say about Comet — is it worth it?
We scanned public community sources for Comet on Oct 8, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 0 of the posts we fetched could be positively tied to Comet. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
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: October 2026
How we score →Key Features
- Trace-level LLM observability across every agent step
- Diagnostics that group recurring silent errors and identify root causes
- Ollie coding agent that proposes fixes from traces and eval results
- Test Suites with pass/fail scoring on sets of traces
- 40+ LLM-as-a-judge eval metrics including hallucination detection
- Golden dataset creation and evaluation runs
- Human annotation of traces by expert reviewers
- Cost Intelligence for Claude Code and Codex token spend
- Opik MCP server so external coding agents can query trace data
- 60+ framework and model integrations for instrumentation
- OpenTelemetry trace ingestion, including TrueFoundry AI Gateway export
- Terminal-based UX for the Opik MCP server
- Self-hosted open-source deployment option
- Experiment management with model versioning and dataset management
- Automated prompt engineering for multi-step agents
About Comet
Comet is an AI developer platform whose LLM-side product, Opik, instruments your generative AI application with a few lines of code and logs every step of an agent run as a trace — context retrieval, tool selection, system prompts, tool calls and user feedback scores. Tracing works through 60+ framework and model integrations, and the Opik MCP server lets your own coding agent query that trace data directly. Opik's differentiator is that it acts on what it logs rather than only displaying it: Diagnostics scans thousands of traces, groups recurring issues and names root causes even when no explicit error was thrown, while Ollie, the in-platform coding agent, reads those traces plus eval results and proposes fixes you can pull and mark Resolved. Validation runs through Test Suites for blunt pass/fail scoring of trace sets, golden datasets, human annotation, and 40+ LLM-as-a-judge metrics including hallucination detection. On the cost side, Cost Intelligence tracks Claude Code and Codex token spend across an engineering team, breaking usage down by MCP install, skills, model selection and context retrieval. Comet's older MLOps platform — experiment management, model versioning, dataset/artifact management, production monitoring — sits alongside Opik, so teams already logging training runs keep both in one vendor. The vendor counts 150,000+ users, 10,000+ teams and 22,000+ GitHub stars, and Opik is genuinely open source and self-hostable, with a hosted cloud option and custom deployment for enterprises.
Behind the Verdict
Comet's history is in classical MLOps — experiment tracking, artifact and dataset versioning, model registry, production monitoring — and its docs still open with that framing. Opik is the newer bet, aimed squarely at teams shipping LLM applications and autonomous agents, and it is the part of the platform worth evaluating on its own. The logging layer is table stakes now, and Opik meets it: a few lines of code, 60+ integrations, traces that show context retrieval, tool calls, system prompts and user feedback, plus an MCP server so your coding agent can read the same data. Where it goes further is the loop. Diagnostics works on the failure mode that actually hurts in production — the run that returns a plausible answer without raising an exception — by grouping recurring issues across thousands of traces and naming root causes. Ollie then drafts fixes against that evidence, and Test Suites plus 40+ LLM-as-a-judge metrics let you confirm the change instead of shipping on vibes. Golden datasets and human annotation cover the cases where a judge model isn't enough. Cost Intelligence is a less crowded angle and, for many teams, the immediately useful one: Claude Code and Codex spend broken down by MCP install, skills, model selection and context retrieval is the kind of breakdown that actually leads to a decision — retire that MCP server, swap that model, tighten that retrieval step. The trade-offs are real. Auto-fixes need human review and a git-based workflow; self-hosting the open-source version is engineering effort, not a checkbox; and the platform is aimed at people who write code, not at drag-and-drop eval builders. Where it fits: AI platform and agent teams with multi-step systems, evaluation-driven development practices, and enough volume that silent failures are a recurring cost. Where it doesn't: single-step prompt apps, teams that won't let a tool propose code changes to their repo, and anyone who wants a no-code playground. If you already run Comet MLOps, consolidating is straightforward; if you're choosing fresh between Opik, Langfuse and LangSmith, the deciding question is whether you want a tool that proposes fixes or one that presents evidence.
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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.
A support agent starts giving confidently wrong answers with no exception raised. You filter the Opik traces for the affected intent, run Diagnostics, and get the recurring failure grouped with a named root cause — a stale retrieval step. Ollie proposes the fix, you pull it, and mark the issue Resolved.
Outcome: The recurring issue is identified and fixed from trace evidence rather than from user complaints, and the Resolved status closes the loop for the rest of the team.
Claude Code and Codex usage climbs but nobody can say where the tokens go. Cost Intelligence breaks the spend down by MCP install, skills, model selection, and context retrieval across the team.
Outcome: You can point at the specific MCP install or model choice generating waste and make a concrete decision to cut it instead of asking engineers to use agents less.
Training runs are already logged in Comet MLOps. You instrument the LLM application with Opik, build a golden dataset from production traces, and score releases against Test Suites and 40+ LLM-as-a-judge metrics before promoting.
Outcome: Model and LLM work stay with one vendor, and releases ship against measured pass/fail results instead of manual spot checks.
Use Cases
- Debug multi-step LLM agents with full trace visibility into context retrieval and tool selection
- Run Test Suites to score agent responses pass/fail before shipping a change
- Use Ollie's recommended fixes to resolve recurring production issues and mark them Resolved
- Sandbox and compare agent versions before promoting to production
- Monitor production agent behavior, costs, and governance requirements with dashboards and alerts
- Break down Claude Code and Codex token spend across an engineering team by MCP install and model
- Evaluate LLM applications against golden datasets with LLM-as-a-judge metrics and human annotation
- Ingest OpenTelemetry traces from an AI gateway such as TrueFoundry into Opik
Models Under the Hood
as of 2026-10-09
Limitations
- Opik is Comet's LLM observability and evaluation platform, and the evidence describes it as a distinct surface from Comet's broader experiment management, model registry, and production monitoring tooling.
- The Ollie agent recommends fixes from traces and eval results that the docs describe you grab and mark Resolved, implying human review before a change is trusted.
- Cost Intelligence is described specifically around tracking and optimizing Claude Code token spend.
as of 2026-09-29
Verification history
We have re-verified Comet 20 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 20 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
Developers and small teams who want to instrument an LLM app and self-host Opik while evaluating whether trace-level observability fits their workflow
What this tier adds
Starting tier: open-source Opik with self-hosting, trace-level observability, 60+ framework and model integrations, and community support
Teams
$179/mo
Ideal for
Funded engineering teams running multi-step agents in production who need Diagnostics, Ollie fix proposals, and shared evaluation practice
What this tier adds
Adds the hosted Opik cloud workspace, Test Suites, 40+ LLM-as-a-judge metrics, Diagnostics and Ollie fix recommendations, production dashboards and alerts, plus team collaboration and annotation
Enterprise
Custom
Ideal for
Large organizations that need custom deployment, governance and compliance support, and engineering-wide coding-agent spend visibility
What this tier adds
Adds custom deployment options, Comet enterprise reliability and security, governance and compliance support, Cost Intelligence across engineering teams, and vendor support and onboarding
Where the pricing makes sense
The company stage and team size where Comet's pricing actually pencils out — and where peers do it cheaper.
Comet splits its pricing across two products rather than one ladder: the open-source Opik core is free to self-host, the hosted Opik platform sells as Teams at $179/mo for cloud workspaces plus Test Suites, 40+ LLM-as-a-judge metrics, Diagnostics, and Ollie, and Enterprise is custom for deployment options, governance, and Cost Intelligence. That $179/mo hosted tier is aimed at funded engineering teams running multi-step agents in production, not solo builders — a solo developer prototyping a
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.
Instrumenting a Python LLM app takes a few lines of code — decorate your chain functions with Opik's @track decorator or set the LlamaIndex global handler — so first traces can land in under an hour for a simple pipeline. Expect a day or more for a multi-step agent with custom retrieval and tool calls, and longer if you're self-hosting the open-source version or wiring OpenTelemetry from an AI
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: re-instrument with Opik's SDK decorators or an existing integration, then rebuild evaluation sets as Test Suites and golden datasets in Opik
- →From LangSmith: point your existing framework integrations at Opik, replay representative traces to seed a golden dataset, and reproduce your eval metrics with Opik's LLM-as-a-judge set
- →From ad-hoc logging: add the @track decorator or a framework integration handler, let traces accumulate, then run Diagnostics to surface the repeating issues your logs never grouped
- →From Comet MLOps only: add Opik instrumentation alongside your existing experiment tracking so LLM calls and training runs report into the same Comet account
- ↗To Langfuse: swap Opik instrumentation for the Langfuse SDK, then recreate evaluation runs and dashboards by hand since Test Suites and Ollie have no direct export equivalent
- ↗To LangSmith: rewire integrations to LangSmith, export golden datasets, and rebuild judge metrics in LangSmith's evaluator format
- ↗To a self-hosted stack: keep Opik's open-source deployment and drop the hosted platform, accepting that you run upgrades and storage yourself
- ↗To general APM: export traces over OpenTelemetry to your existing monitoring backend, losing Diagnostics grouping, Test Suites, and Ollie fix proposals
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Comet”, and we withheld 6: 6 could not be judged, because “Comet” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Comet.
Official links
Tools that pair well with Comet
Common stack mates teams adopt alongside Comet, with the specific reason each pairing earns its keep.
Arize Phoenix
Arize Phoenix is open-source LLM observability: trace every agent step, run evals, and self-host your traces.
Phoenix
Trace, evaluate, and iterate AI agents with Phoenix — open-source LLM observability you can self-host.
Langfuse
Open-source LLM observability, prompt management, and evaluation for teams running AI agents in production.
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Arize Phoenix
Arize Phoenix is open-source LLM observability: trace every agent step, run evals, and self-host your traces.
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