Open-source framework from OpenAI for evaluating LLM and agent behavior with reusable eval definitions, grading logic, datasets, and regression workflows.
Open the source and read safety notes before installing.
Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.
Decision playbook
Signals are present but mixed. Use the checklist below to confirm the source and operational safety for your environment.
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No baseline selected
No major trust-signal divergence detected in the current selection.
Confirm ownership and provenance before trusting install instructions.
Source link availableRequired
Open the canonical repository and verify ownership.
Source provenance statusRequired
Marked as source-backed.
Metadata reviewed
No reviewed flag detected in metadata.
Validate risk disclosures before installation or API wiring.
Safety notes presentRequired
Review the listed safety guidance before running commands.
Privacy notes presentRequired
Review data handling notes before connecting accounts or secrets.
Trust level risk gateRequired
Trust level does not block evaluation.
Check package metadata and artifact integrity signals.
Install payload available
Install or copy payload is available for review.
Package verification flag
No package verification flag provided.
Checksum metadata
No checksum provided for downloaded artifact.
Use compare context to validate trade-offs before adoption.
Compare tray has multiple entries
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Baseline comparison available
No baseline peer selected yet.
Diverging trust signals identified
No major trust-signal divergence found.
Setup at a glance
Copy-ready — paste the snippet to get started.
Adoption plan
Current risk score 24/100. Use staged verification before broader rollout.
Validate source and review signals before any execution.
Confirm source provenanceRequired
Source URL/provenance metadata is present.
Confirm metadata review state
No review metadata found; increase manual validation.
Verify install payload
Install/config payload exists and can be inspected.
Confirm safety, privacy, and package integrity signals.
Review safety notesRequired
Safety notes are present.
Review privacy notesRequired
Privacy notes are present.
Verify package integrity metadata
No package verification/checksum metadata.
Adopt in controlled steps based on the selected plan.
Run in isolated sandbox firstRequired
Use a constrained sandbox and observe behavior across multiple tasks.
Roll out graduallyRequired
Roll out to a small cohort before wider usage.
Set monitoring and fallback
Define rollback path and monitor errors after adoption.
Evidence readiness
Missing required evidence: Metadata review. Risk score 31.
Source repository/provenance is listed.
Required in this preset
Review metadata is missing.
Required in this preset
Safety notes are present.
Required in this preset
Privacy notes are present.
Optional in this preset
Package integrity metadata is missing.
Optional in this preset
Install payload is available.
Required in this preset
Required gaps: Metadata review
Decision timeline
Blocking gaps: Check metadata review status. Risk 28.
triage
Source/provenance metadata is available.
triage
Review metadata is missing.
verify
Safety notes are available.
verify
Privacy notes are available.
verify
Package integrity metadata is missing.
rollout
Install payload is available.
Blockers: Check metadata review status
Prerequisite readiness
3 prerequisites to line up before setup. Have accounts and credentials ready first.
Safety & privacy surface
3 safety and 3 privacy notes across 5 risk areas. Review closely: credentials & tokens, third-party handling.
Disclosure: editorial
oaieval <completion-fn> <eval-name>OpenAI Evals gives teams a code-first way to define and run repeatable language model evaluations. It is useful when a Claude or AI-agent workflow needs a regression suite instead of one-off prompt testing: expected outputs, graders, fixtures, completion functions, and run results can be kept close to the code that depends on them.
Treat eval results as evidence, not authority. A passing eval suite can still miss distribution shift, tool misuse, privacy leakage, security issues, or unfair behavior. Keep holdout cases, redaction rules, and human escalation paths separate from the framework itself.
Editorial listing. No paid placement or affiliate link is used.
Show that OpenAI Evals is listed on HeyClaude. Paste this Markdown into your README — it renders the badge and links back to this page.
[](https://heyclau.de/entry/tools/openai-evals)OpenAI Evals side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.
1 trust signal differ across this comparison (Submitter).
Next steps differ across entries — use the actions in the table below to copy install commands and source links per resource.
| Field | Open-source framework from OpenAI for evaluating LLM and agent behavior with reusable eval definitions, grading logic, datasets, and regression workflows. Open dossier | Open-source LLMOps platform for prompt management, prompt versioning, evaluation, and observability across LLM applications. Open dossier | Open-source evaluation framework for testing RAG systems, prompts, agents, workflows, and other LLM application behavior. Open dossier | Open-source Python framework for unit-testing LLM applications, agents, RAG pipelines, metrics, regression suites, and traces. Open dossier |
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| Next stepsDiffers | ||||
| Trust | ||||
| Review status | Not reviewed | Not reviewed | Not reviewed | Not reviewed |
| Package trust | Package not verified | Package not verified | Package not verified | Package not verified |
| Source provenance | Source-backed | Source-backed | Source-backed | Source-backed |
| SubmitterDiffers | JSONbored | oktofeesh1 | oktofeesh1 | oktofeesh1 |
| Install risk | Review first | Review first | Review first | Review first |
| Notes | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ |
| Brand | — | |||
| Category | tools | tools | tools | tools |
| Source | Source-backed | Source-backed | Source-backed | Source-backed |
| Author | OpenAI | Agenta | Vibrant Labs | Confident AI |
| Added | 2026-06-05 | 2026-06-03 | 2026-06-03 | 2026-06-03 |
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| Harness | ||||
| Source repo | — | — | — | — |
| Safety notes | ✓Eval scores are regression and quality signals, not proof that a model or agent is safe, fair, or production-ready. Run adversarial, prompt-injection, or tool-use evals against isolated environments and reviewed credentials. Large eval runs can issue many model calls; set budgets, rate limits, and stop conditions before running them. | ✓Agenta can manage and deploy prompt or configuration changes, so production updates should go through review and rollback controls. Webhooks and GitHub automations tied to prompt or deployment changes should be scoped to trusted repositories and guarded workflows. Evaluation and online monitoring results should support, not replace, domain review for high-risk application behavior. | ✓Ragas scores should be treated as decision support, not a substitute for domain review of critical outputs. LLM-based metrics can call configured model providers, so evaluation runs should be scoped and budgeted before use on large datasets. Generated test data and evaluator prompts should be reviewed before they influence release, ranking, or regression decisions. | ✓DeepEval metrics should be treated as regression and review signals, not proof that an LLM application is safe, correct, or production-ready. LLM-as-a-judge metrics can call configured model providers, consume quota, hit rate limits, and produce judge-model errors that need separate handling. Evaluation thresholds should be calibrated on real examples before they block deployments or trigger automated rollback, ranking, billing, or moderation decisions. Tracing instrumentation can wrap live application code, agents, retrievers, tools, and model calls; keep eval and production environments clearly separated. |
| Privacy notes | ✓Prompts, model outputs, labels, traces, retrieved documents, and grader notes can contain user, customer, or proprietary data. Completion functions may send eval payloads to the configured model provider unless a reviewed local model path is used. Store eval datasets and results according to the same retention and redaction rules used for production AI data. | ✓Prompt records, variants, test sets, traces, model inputs and outputs, feedback, annotations, and evaluation results may be stored in Agenta. Hosted Agenta use sends that data to Agenta Cloud; self-hosted deployments still require retention, access-control, and backup policies. Review Agenta's sensitive-data redaction and retention guidance before sending production, customer, or regulated data. | ✓Evaluation examples may include prompts, retrieved context, generated responses, references, and metadata from the application under test. LLM-based metrics can send evaluation payloads to the configured model provider unless a local model path is used. The upstream README says Ragas collects minimal, anonymized usage analytics; review or disable analytics where policy requires it. | ✓Test cases, traces, spans, prompts, actual outputs, expected outputs, retrieval context, tool arguments, metadata, and evaluation results may contain sensitive user or business data. LLM-based metrics can send evaluation payloads to the configured model provider unless a reviewed local model path is used. DeepEval documentation says evaluations run locally by default, while Confident AI login and cloud reporting are optional paths for centralized results. The official data privacy docs say DeepEval collects basic PostHog telemetry by default, including event names, metric names, notebook usage, an anonymous UUID, and public IP, with `DEEPEVAL_TELEMETRY_OPT_OUT=1` available for opt-out. |
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| Claim | Unclaimed | Unclaimed | Unclaimed | Unclaimed |
Open-source prompt management, evaluation, and observability for LLM apps.
Open-source evaluation framework for RAG and LLM application testing.
Source-backed guides for putting this to work.
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