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They watch the run. You own the record.

Cloud LLM-observability and eval tools instrument the agent and score its traces in their cloud. RepoOps attributes the shipped defect and the dollar to your git history, on your machine. Two axes tell the whole story: where your data lives, and what actually gets measured.

The wedge

Two axes, not a feature grid

We do not out-shout the observability category on eval count or framework count. We stand on the two axes it is built the other way around from.

Axis 1

Where your data lives

Cloud trace storeLocal-first

The observability model sends the agent's traces to a hosted store to be kept and scored. A real self-host option exists, but the product's center of gravity, and its cost tracking, involve uploading traces. RepoOps keeps the loop on your machine: your source and prompts stay local, and anything you sync is opt-in and redacted.

Axis 2

What gets measured

Observe the runAttribute the outcome

Observability measures the run: spans, latency, a LLM-as-a-judge score. RepoOps measures the outcome: a shipped defect walked back through your commit history to the prompt that wrote it, and a dollar fused to the merged PR that spent it. A score versus a defect and a dollar.

The map

The Accountability Score

Every tool plotted on the two axes, then scored 0 to 100 on each and averaged into one number. The Accountability Score rates a tool on the two axes of this comparison: can it attribute the shipped outcome (walk a defect and a dollar back to the AI session and prompt that produced them), and does it keep that record local-first on your machine. Each axis is scored 0 to 100 and the headline is their average. It is RepoOps's own framing of the wedge, not a neutral third-party benchmark.

A two-axis positioning map scoring 27 tools. The horizontal axis runs from cloud trace store on the left to local-first on the right. The vertical axis runs from observe the run at the bottom to attribute the outcome at the top. Cloud observability, eval, and analytics tools cluster in the lower-left region: cloud, run-level. The RepoOps local app sits alone in the upper-right corner: local-first, outcome-level. The exact score for every tool is in the ranked list below the map, which is a table with a column for each axis.

Attribute the outcomeObserve the run
Cloud trace storeLocal-first
ToolScoreAttributeLocal
1RepoOps local appAccountability score 98Attribute the outcome 96Local-first 1002Git AIAccountability score 72Attribute the outcome 60Local-first 843Entire.ioAccountability score 67Attribute the outcome 58Local-first 764OpenWiki 0.3.3Accountability score 43Attribute the outcome 16Local-first 705HALAccountability score 43Attribute the outcome 24Local-first 626LangfuseAccountability score 38Attribute the outcome 30Local-first 467Faros AIAccountability score 36Attribute the outcome 46Local-first 268Eval-engineering toolsAccountability score 36Attribute the outcome 30Local-first 429Comet / OpikAccountability score 35Attribute the outcome 34Local-first 3610Mem0Accountability score 35Attribute the outcome 16Local-first 5411Weave (workweave)Accountability score 32Attribute the outcome 52Local-first 1212AgentOpsAccountability score 32Attribute the outcome 22Local-first 4213HeliconeAccountability score 31Attribute the outcome 20Local-first 4214W&B HivemindAccountability score 30Attribute the outcome 46Local-first 1415SazabiAccountability score 29Attribute the outcome 42Local-first 1616LangChain / LangSmithAccountability score 29Attribute the outcome 36Local-first 2217TraversalAccountability score 29Attribute the outcome 42Local-first 1618GreptileAccountability score 29Attribute the outcome 28Local-first 3019OriginAccountability score 29Attribute the outcome 38Local-first 2020Vals AIAccountability score 28Attribute the outcome 26Local-first 3021Memory vendorsAccountability score 27Attribute the outcome 20Local-first 3422BraintrustAccountability score 26Attribute the outcome 30Local-first 2223DX (Atlassian)Accountability score 24Attribute the outcome 26Local-first 2224JellyfishAccountability score 23Attribute the outcome 34Local-first 1225LinearBAccountability score 21Attribute the outcome 30Local-first 1226SwarmiaAccountability score 21Attribute the outcome 30Local-first 1227Ramp / JuneOpsAccountability score 17Attribute the outcome 22Local-first 12
Observability

A trace scored badly. Observability tells you a run went wrong, on the runtime span.

RepoOps

Which prompt wrote the line. git blame finds the introducing commit, matched to the session by its Session-Id trailer and banded exact, strong, or weak.

Observability

Tokens observed and optimized. The monthly bill, and where waste came from across the team.

RepoOps

The dollar fused to the merged PR that spent it, reconciled against real billing, then to the defect it caused. Only attributable dollars are counted.

Observability

Score the run, then fix it. A closed observe-to-fix loop framed as autonomous repair.

RepoOps

Every fix becomes a lesson your AI reads next run, enforced as a pre-merge git gate that blocks the recurrence, graded by whether it actually held.

Where they win

What RepoOps does not claim

Concede these plainly. It is what keeps the wedge honest, and it is why the two axes above are the real comparison.

Evaluation breadth

The observability category leads here: many built-in LLM-as-a-judge metrics, datasets, experiments, and test suites you point at your own app. RepoOps grades a run as part of the accountability loop; it is not a general dataset-driven eval platform, and does not pretend to be.

Runtime trace depth

Deep span-level capture across many agent frameworks is their home turf. RepoOps is git-centric: the accountability lives in your commits, and it reads runtime data over OpenTelemetry where that helps, rather than chasing a per-framework adapter for each tool.

Not plotted: Microsoft Defender for Endpoint

Not plotted on purpose. Defender is an endpoint security product bought by security operations, and these two axes measure whether a tool can walk a shipped defect and a dollar back to the AI session that caused them. Defender does not attempt that, so a score here would grade it on someone else's exam and read as a verdict on its quality. It is a real competitor on local agent discovery and on runtime enforcement, and the head-to-head comparison is on its own tab.

Certifications and reach

A funded, widely distributed cloud carries its own compliance attestations today. Local-first removes the reason most of those exist for a solo developer: with your source and prompts on your machine, there is no vendor cloud to certify. Hosted RepoOps tiers carry their own attestations as they land.

Check our math

The wedge, on the record

Neither axis is a slogan. Both are backed by a reproducible, checked-in benchmark and settings you can inspect.

75%
Defects attributed

9 of 12 seeded defects traced to the exact session that caused them, at the proof-counting confidence bands. A reproducible synthetic fixture, not a real-world rate.

100%
Recurrences prevented

12 of 12 known bugs blocked at the pre-merge gate, out of 20 review findings, on the same fixture.

$24.55
Measured spend saved

The tracked session cost the prevented recurrences would have re-run: 6 priced, 2 unpriced, across 12 prevention events. Sourced from the committed results file, never estimated.

Reproducible, and yours next

The fixture, the code paths, and the results file are all committed, so the three numbers are fixed and auditable, not asserted. Coming soon: run the same metrics against your own repo from the installed app.

repoops benchSee the benchmark

See what leaves your machine

Local-first is a setting you can inspect, not a promise. Your source, prompts, and file contents never leave. Cloud sync, OTLP export, and team upload are off by default and redacted when you turn one on. The full head to head lists every dimension.

Full head to head

Fairness note: competitor statements describe the cloud observability and eval category as of 2026-06-24 (comet.com / opik, github.com/comet-ml/opik), framed on posture and unit of work, never a fabricated limitation of a named product. Privacy is scoped, not absolute: an anonymous, opt-out reliability beacon sends a per-install id, a one-way hashed repo id, and aggregate counts, never your source, prompts, file contents, or a readable repo name.

See it on your own repo

Free and local for one developer. Your source and prompts stay on your machine.