omk vs alternatives
A factual comparison with seven other LLM evaluation tools, as of 2026-04. Corrections welcome via PR — if a competitor adds a feature we mark ✗, we'll happily update.
TL;DR
omk's moat is statistical rigor: every release conclusion is traceable to a sealed design, Bootstrap uncertainty, explicit Gold calibration, frozen judge prompts, and fail-closed evidence coverage.
If you need a hosted SaaS dashboard, choose LangSmith or Confident AI. If you want quick local prompt iteration without statistics, choose promptfoo. If you need academic-grade benchmark coverage, choose lm-evaluation-harness. If you need agent sandbox isolation for safety evaluations, choose inspect-ai. If you ship to production and someone will ask "why should I trust this number?", choose omk.
Tools compared
| Tool | Language | Position | License |
|---|---|---|---|
| omk | TS / Node | Statistical knowledge-artifact eval + Codex / Claude native workflows | MIT |
| promptfoo | TS / Node | Local CLI, red-team focus, OpenAI acquired | MIT |
| DeepEval | Python | Pytest-style metrics, paid SaaS upsell | Apache 2.0 |
| RAGAS | Python | RAG-specific metrics, statement decomposition | Apache 2.0 |
| OpenAI Evals | Python | Benchmark registry, official OpenAI | MIT |
| LangSmith | Python (LangChain) | Hosted SaaS, tracing + eval | Commercial |
| lm-evaluation-harness | Python | Academic standard, HuggingFace Open LLM Leaderboard backend | MIT |
| inspect-ai | Python | UK AISI safety evaluations | MIT |
Statistical rigor
| omk | promptfoo | DeepEval | RAGAS | OpenAI Evals | LangSmith | lm-eval-harness | inspect-ai | |
|---|---|---|---|---|---|---|---|---|
| Bootstrap CI on variant means + diff | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Krippendorff α (judge ↔ human gold) | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Length-debias judge prompt (default) | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Missing/failed evidence preserved + coverage gate | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Paired-sample significance testing | ✓ (bootstrap) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
The omk column is anchored to its current Evaluation Core contracts and implementations. Competitor columns remain the dated comparison snapshot described at the top of this page.
→ These aren't marketing claims — each is documented and code-anchored: statistical rigor, scoring pipeline.
Scoring architecture
| omk | promptfoo | DeepEval | RAGAS | OpenAI Evals | LangSmith | lm-eval-harness | inspect-ai | |
|---|---|---|---|---|---|---|---|---|
| Three-layer scoring (Fact / Behavior / Judge) isolation | ✓ | ✗ | partial | ✗ | ✗ | ✗ | ✗ | ✗ |
| Layer-aware release gate + explicit evidence coverage | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Per-variant skill-discovery isolation (construct validity) | ✓ default | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | partial |
Sample design metadata + structure anchors (covers) | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Registered Decision (PROGRESS / REGRESSION / NOISE / ...) | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Knowledge gap signals (severity-weighted) | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Layer-aware release gates and explicit coverage prevent a positive composite point estimate from overriding a missing-evidence state or a treatment layer below its registered threshold. |
Per-variant skill-discovery isolation closes a subtle construct-validity hole: a native coding-agent baseline can discover undeclared local knowledge through project files, skill registries, subagents, or ordinary cwd reads. omk defaults to --strict-baseline, gives every implicit baseline execution a fresh empty cwd, and adds provider controls: Codex CLI ignores user config and rules and runs ephemerally; Codex SDK gets an isolated CODEX_HOME; Claude blocks skill discovery and the subagent Skill tool. Runtime and isolation fingerprints are persisted so incompatible reports cannot masquerade as artifact-only comparisons. --no-strict-baseline remains an explicit escape hatch. inspect-ai can achieve comparable isolation through per-sample solver wiring; promptfoo / DeepEval / OpenAI Evals do not address this dimension directly.
Judges
| omk | promptfoo | DeepEval | RAGAS | OpenAI Evals | LangSmith | lm-eval-harness | inspect-ai | |
|---|---|---|---|---|---|---|---|---|
| Multi-judge ensemble (cross-vendor) | ✓ Pearson + MAD | ✗ | ✗ | ✗ | ✗ | partial | ✗ | ✗ |
| Judge-repeat for stability | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Judge prompt hash traceability | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Auto contamination detection (gold annotator vs judge) | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
Specialized metrics
| omk | promptfoo | DeepEval | RAGAS | OpenAI Evals | LangSmith | lm-eval-harness | inspect-ai | |
|---|---|---|---|---|---|---|---|---|
| RAG: faithfulness / answer_relevancy / context_recall | ✓ (length-debias default on; mode fingerprinted) | partial | ✓ | ✓ (multi-step) | ✗ | partial | ✗ | ✗ |
| ROUGE-N / Levenshtein / BLEU | ✓ self-impl, zero dep | ✓ | partial | ✗ | ✓ | ✗ | ✓ | ✗ |
| Semantic similarity (LLM-graded) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ |
| Tool-call / agent assertions | ✓ 9 types | ✗ | partial | ✗ | ✗ | partial | ✗ | ✓ strong |
| Custom JS/Python assertion | ✓ JS | ✓ JS | ✓ Python | partial | ✓ Python | ✓ Python | ✓ Python | ✓ Python |
Workflow
| omk | promptfoo | DeepEval | RAGAS | OpenAI Evals | LangSmith | lm-eval-harness | inspect-ai | |
|---|---|---|---|---|---|---|---|---|
| Native agent skill evaluation | ✓ Codex / Claude Code | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Production session trace parsing (omk observe) | ✓ Codex / Claude Code / OpenClaw / markdown | ✗ | ✗ | ✗ | ✗ | ✓ LangChain only | ✗ | ✗ |
Auto self-iteration (omk evolve) | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| eval.yaml (evaluation-as-code) | ✓ | ✓ | ✗ | ✗ | partial | ✗ | partial | ✓ |
CI/CD omk eval exit-code routing | ✓ Core Decision | ✓ basic | ✓ | ✗ | ✗ | partial | ✗ | ✓ |
| Hard budget caps (workflow abort) | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Resume from interruption | ✓ --resume | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Independent-run Series variance | ✓ | ✗ | ✗ | ✗ | ✗ | partial | ✗ | ✗ |
Documentation & community
| omk | promptfoo | DeepEval | RAGAS | OpenAI Evals | LangSmith | lm-eval-harness | inspect-ai | |
|---|---|---|---|---|---|---|---|---|
| Full Chinese documentation | ✓ | partial (community) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Local Studio report views | ✓ EN/ZH | partial | ✗ | ✗ | ✗ | partial | ✗ | ✗ |
| GitHub stars (Apr 2026) | new | 9k+ | 12k+ | 9k+ | 16k+ | (commercial) | 7.5k+ | 2k+ |
| Cloud SaaS dashboard | ✗ | ✗ | ✓ Confident AI | ✗ | ✗ | ✓ | ✗ | ✗ |
When to choose omk
Researchers / academia / NIST AI 800-3 alignment. The statistical architecture is built to answer whether a conclusion survives resampling uncertainty, judge calibration, prompt identity, and incomplete evidence. Core artifacts preserve the design and lineage needed for audit.
ML platform teams at large companies. When you ship a skill / prompt to production and someone in the org will ask "why should I trust this number?", omk's audit trail (judge prompt hash, three-layer scores, bootstrap CI, gold α) gives you a defensible answer that survives a postmortem.
Chinese-speaking AI engineering teams. omk maintains native Chinese README, CLI help, Studio views, terminology, gap-signal, and RAG-metric documentation.
Codex and Claude Code users. omk ships one agent-neutral skill and native executors for both families. Codex CLI is the strongest isolated measurement path; Codex SDK and Claude runtimes remain available when their project context or event streams are intentional inputs. promptfoo / DeepEval / others usually need a custom-executor shim to reach the same artifact-oriented workflow.
When NOT to choose omk
You need a hosted SaaS dashboard with team accounts and shared dataset hubs. Choose LangSmith or Confident AI. omk is intentionally CLI + local Studio; we have no plan to ship a SaaS.
You're red-teaming and need a library of attack prompts. Choose promptfoo. It has 67+ red-team plugins; omk is general-purpose and doesn't focus on attack libraries.
You're benchmarking foundation models against academic standards (HumanEval / MMLU / etc.). Choose lm-evaluation-harness. It is the de-facto leaderboard backend; omk is not optimized for benchmark registry use.
You need to run agent evaluations in tightly sandboxed Docker / Kubernetes / Modal environments for safety reasons. Choose inspect-ai. UK AISI built it for that exact use case.
You only have 5 prompts to test once. Use a one-off Python script. omk's value compounds when you have repeated runs over time and need statistical comparability.
Coexistence patterns
omk is happy to live alongside other tools. Common combinations:
- omk + LangSmith — omk for offline evaluation rigor + LangSmith for production tracing
- omk + RAGAS — RAGAS for fine-grained statement-decomposition faithfulness, then omk for cross-version regression with statistical CI
- omk + lm-eval-harness — lm-eval for foundation model leaderboard scores, omk for prompt / skill / RAG layer above it
Updates and corrections
This page is maintained on a best-effort basis. Competitor capabilities change rapidly (e.g. promptfoo gained assert-set and DeepEval added the agentic eval suite during 2025). If you find a stale or wrong cell, please open a PR — we'll merge it.
Last verified: 2026-04-25.