Open-source evaluation and security testing for prompts, models, RAG systems, and agents
Advisor score
Documented editorial evaluation
The decision
Promptfoo earns a shortlist for engineering and security teams that want evals in source control and CI. Begin with a small human-labeled suite, favor deterministic assertions where possible, and treat red-team results as a triage input rather than a compliance certificate.
Best for
Engineering teams adding AI tests to CI; Security teams red-teaming LLM applications.
Choose something else if
Teams without labeled acceptance criteria; Buyers seeking one universal quality score
Evidence
Editorial research pending · 8.0/10
Pricing checked
See current vendor pricing · 2026-09-01
Free access is available, with limits.
Yes. The core open-source CLI supports local evaluation and red teaming; external model and target usage is billed by those providers.
Community choice
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Promptfoo brings repeatable evals, model comparisons, assertions, red teaming, and CI gates to AI development, but test-set quality, judge calibration, sensitive traces, and remediation ownership determine its value.
Promptfoo earns a shortlist for engineering and security teams that want evals in source control and CI. Begin with a small human-labeled suite, favor deterministic assertions where possible, and treat red-team results as a triage input rather than a compliance certificate.
Take 100 production-like cases and 30 human-labeled failures. Compare deterministic checks, two model judges, and blinded human review. Add prompt injection, data exposure, harmful action, tool abuse, and policy cases. Measure judge agreement, false passes, false blocks, reproducibility, runtime, cost, secret handling, and remediation time.
Personal Recommendation
Promptfoo earns a shortlist for engineering and security teams that want evals in source control and CI. Begin with a small human-labeled suite, favor deterministic assertions where possible, and treat red-team results as a triage input rather than a compliance certificate.
Try the recommendation
Open-source local workflow
Overall Score
Editorial Review Framework
Overall Score
8.0
Ease of Use
7.8
AI Quality
8.0
Features
8.4
Speed
8.0
Integrations
8.4
Value for Money
8.0
Customer Support
7.6
Learning Curve
7.4
Recommended Because…
Open-source local workflow
Scores use a 0-10 editorial scale: 9–10 exceptional, 8–8.9 strong, 7–7.9 capable with material tradeoffs, 6–6.9 limited fit, and below 6 not recommended. Scores publish only when a documented evaluation supports them.
Reusable trial worksheet
Turn this review’s buyer test into evidence. Your entries autosave only in this browser and are never added to shared shortlist links.
Confirm the tool meets every must-have workflow and stakeholder requirement.
Review starting point: Engineering teams adding AI tests to CI; Security teams red-teaming LLM applications; Builders comparing prompts and providers
Run the same representative work you would use in production; do not score a polished demo.
Review starting point: Complete three to five representative tasks with known acceptable outcomes and compare them with your current process.
Calculate the effective cost per accepted result, including usage, review, corrections, and required add-ons.
Review starting point: Promptfoo's open-source CLI can run without a software subscription, while hosted collaboration and enterprise security offerings use current plan limits or sales-led pricing that should be confirmed directly. Evaluation model calls, target systems, CI compute, storage, and red-team generation create separate costs. Reviewed September 1, 2026.
Define an acceptance threshold, test known answers and edge cases, and record every correction.
Review starting point: Editorial quality signals: features 4.2/5; AI quality 4.0/5. Validate these signals in your own work.
Verify what data enters the product, who can access it, how long it is retained, and whether it trains models.
Review starting point: Use approved low-risk data first. Check roles, consent, deletion, subprocessors, model-training settings, and the contract—not only the marketing page.
Test the real handoffs, permissions, failure states, and export path your team depends on.
Review starting point: GitHub Actions, OpenAI, Anthropic, Ollama, REST API, JavaScript
Record training, governance, reliability, accessibility, ownership, and change-management risks before rollout.
Review starting point: Model judges require calibration; Large suites can be costly and noisy; Findings still need human triage
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Evaluation
Research-based
Price posture
freemium
Reviewed
2026-09-01
No authentic product screenshot is published for this review. DiscoverAI does not use generated interface images as product evidence.
Editorial freshness
Pricing and material product claims were checked September 1, 2026.
Community evidence
Structured, editor-moderated experience—not star ratings. This complements our independent review and never changes its score.
No approved community evidence yet. Be the first verified user to contribute.
Yes. The core CLI is open source; model calls, infrastructure, hosted collaboration, and enterprise services can add cost.
Yes. Its red-team framework includes prompt-injection and other vulnerability strategies, which should be supplemented with application-specific attacks.
Yes. Tests can run in CI and gate changes using configured assertions and thresholds.
No tool can provide that guarantee. Its results cover the configured targets, attacks, models, and test period and require human triage and remediation.
Keep Deciding
Build a Small AI Red-Team Test Set Before Your First Production Launch
Start with 50 to 100 cases drawn from the exact data, users, tools, and harms in your AI workflow. Cover prompt injection, secret extraction, cross-user leakage, forbidden actions, hallucinated authority, ambiguous requests, bias, tool failure, and unsafe escalation. Give every case an expected policy outcome, severity, owner, and reproducible evidence. Run the set before launch and on every material prompt, model, tool, or retrieval change.
DeepEval Review 2026: LLM Testing, Cost, and Limitations
DeepEval is a local-first open-source framework for end-to-end, component, and trajectory evaluations with Pytest-style assertions and configurable metrics.
Giskard Review 2026: AI Agent Red Teaming, Scans, and Fit
Giskard generates adversarial and quality scenarios for AI agents, but generated attacks and LLM judges need human review and cannot establish complete security.
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