Full Comparison Matrix
All 6 frameworks × 10 decision criteria. Use this to compare across dimensions, not to find one winner — the right answer is almost always a combination.
The Matrix
| Criterion | RAGAS | DeepEval | promptfoo | Langfuse | inspect_ai | OpenAI Evals |
|---|---|---|---|---|---|---|
| Setup effort | Medium | Medium | Low | Low | High | Low |
| Coding required | Python | Python | YAML (no code) | Python (decorator) | Python | YAML + JSONL |
| RAG evaluation | ● ● ● ● ● | ● ● ● ○ ○ | ● ● ○ ○ ○ | ● ● ○ ○ ○ | ● ○ ○ ○ ○ | ● ○ ○ ○ ○ |
| Red teaming / adversarial | ○ ○ ○ ○ ○ | ● ● ○ ○ ○ | ● ● ● ● ● | ○ ○ ○ ○ ○ | ● ● ● ● ○ | ● ● ○ ○ ○ |
| Production observability | ○ ○ ○ ○ ○ | ○ ○ ○ ○ ○ | ○ ○ ○ ○ ○ | ● ● ● ● ● | ○ ○ ○ ○ ○ | ○ ○ ○ ○ ○ |
| EU AI Act support | ● ○ ○ ○ ○ | ● ● ○ ○ ○ | ● ● ● ○ ○ | ● ● ● ● ○ | ● ● ● ● ● | ● ● ○ ○ ○ |
| LLM judge required | Yes (most) | Yes (most) | Optional | Optional | Yes (most) | Yes (model-graded) |
| Self-host option | Yes | Yes | Yes | Yes (Docker/Helm) | Yes | Yes |
| CI/CD integration | Manual | Native (pytest) | Native | N/A | Manual | Manual |
| Cost at scale | Low–Med | Low–Med | Low | Free–Usage | Low–Med | Low |
Reading the Matrix
● ● ● ● ● = best-in-class for this criterion
○ ○ ○ ○ ○ = not designed for this use case
Ratings are relative within this set of 6, not against the entire ecosystem.
“LLM judge required” means: the core use of the framework requires LLM API calls for evaluation (not just for the model under test). This is relevant for cost estimation and for offline/air-gapped environments.
Dimension Deep-Dives
RAG Evaluation
| Framework | What it measures | Key gap |
|---|---|---|
| RAGAS | Context precision, context recall, faithfulness, answer relevancy | No CI/CD runner; no non-RAG metrics |
| DeepEval | Contextual relevancy, faithfulness, answer relevancy | No context recall; weaker retrieval depth than RAGAS |
| promptfoo | Basic output matching, LLM-rubric | No retrieval-specific metrics |
| Langfuse | Scores you define and attach | Doesn’t compute retrieval metrics natively; use with RAGAS |
| inspect_ai | Task-completion scoring | No RAG-specific metrics |
| OpenAI Evals | Match-based, model-graded | No retrieval metrics |
→ For RAG: RAGAS is the only choice with purpose-built retrieval metrics. Use DeepEval as the CI harness around RAGAS scores.
Red Teaming / Adversarial
| Framework | Coverage | Audit value |
|---|---|---|
| promptfoo | OWASP LLM Top 10, auto-generated attacks, 12+ harm categories | Developer report |
| inspect_ai | AgentHarm (110 behaviors), HarmBench, CyberSecEval | Audit-grade .eval logs |
| DeepEval | Bias, toxicity, hallucination checks | Quality metrics, not security |
| OpenAI Evals | Model-graded safety checks | Benchmark registry |
| RAGAS | None | — |
| Langfuse | None (observation only) | — |
→ For red teaming: promptfoo for coverage and speed; inspect_ai for regulatory documentation.
Production Observability
| Framework | What it captures | Key feature |
|---|---|---|
| Langfuse | Every inference: input, output, latency, tokens, cost | Live trace collection + scoring |
| DeepEval | Batch eval results | No live trace |
| RAGAS | Batch eval scores | No live trace |
| promptfoo | Pre-production test results | No production capability |
| inspect_ai | Eval run logs | Batch only |
| OpenAI Evals | Benchmark run results | Batch only |
→ For production: Langfuse is the only choice. None of the others are designed for live traffic.
EU AI Act Support
| Framework | Articles addressed | Audit trail | Data residency |
|---|---|---|---|
| inspect_ai | Art. 9, 13, 15, 17, Annex IV, Art. 40 | ● ● ● ● ● .eval logs | Self-host |
| Langfuse | Art. 9, 10, 13, 17, Annex IV | ● ● ● ● ● trace exports | EU Cloud (Frankfurt) |
| promptfoo | Art. 9, 13, Annex IV | ● ● ○ ○ ○ HTML/JSON report | Self-host |
| DeepEval | Art. 13, 17 | ● ● ○ ○ ○ pytest output | Self-host |
| OpenAI Evals | Annex IV (citable benchmarks) | ● ○ ○ ○ ○ run logs | OpenAI infra |
| RAGAS | Art. 17 (indirect) | ● ○ ○ ○ ○ metric outputs | Self-host |
→ For EU AI Act: inspect_ai for safety evidence; Langfuse for production audit trail. Both are needed for Annex III high-risk AI systems.
Cost at Scale
All frameworks using LLM judges have costs that scale with: samples × metrics × judge model cost.
| Framework | Cost model | 1,000 samples estimate |
|---|---|---|
| RAGAS | LLM judge per metric | $5–30 (GPT-4o); $0.50–3 (GPT-4o-mini) |
| DeepEval | LLM judge per metric | $5–30 (GPT-4o); $0.50–3 (GPT-4o-mini) |
| promptfoo | LLM for attack generation (one-time) + deterministic assertions | $0.01–0.10 (regex/JSON); $10–50 (full red team) |
| Langfuse | Free self-host; usage-based cloud | Free–$29/mo + observations |
| inspect_ai | LLM judge per sample | $5–30 (GPT-4o); $0.50–3 (GPT-4o-mini) |
| OpenAI Evals | Model API cost only | $0.10–5 depending on eval type |
Cost reduction strategies:
- Use GPT-4o-mini or Claude Haiku as judge — 10–20× cheaper, acceptable quality for most metrics
- Sample production traffic (10–20%) for scoring in Langfuse; don’t judge every inference
- promptfoo: deterministic assertions (regex, JSON schema) cost nothing; reserve LLM rubric for complex cases
- RAGAS:
NonLLMContextPrecisionWithReferenceis free for binary precision cases
CI/CD Integration
| Framework | Integration | How |
|---|---|---|
| DeepEval | Native | pytest or deepeval test run; JUnit output |
| promptfoo | Native | npx promptfoo eval --ci; exit code on assertion failure |
| RAGAS | Manual | Wrap in script; assert on metric thresholds yourself |
| Langfuse | N/A | Production tool — not a CI tool |
| inspect_ai | Manual | Wrap inspect eval in CI step; parse .eval log |
| OpenAI Evals | Manual | oaieval CLI; parse output |
→ For CI gates: DeepEval is the only framework with native pytest integration. promptfoo works well for adversarial regression gates specifically.
Recommended Stacks
Rather than picking one, most production teams need a combination. Here are the most common patterns:
Minimum viable (RAG startup)
RAGAS → measure retrieval quality
Langfuse → trace production traffic
promptfoo → red team before first launch
Standard (growth-stage product team)
RAGAS + DeepEval → eval depth + CI/CD runner
Langfuse → production observability
promptfoo → adversarial regression in CI
Compliance-first (regulated EU company)
DeepEval → CI/CD quality regression
inspect_ai → safety eval + audit-grade logs
Langfuse (EU Cloud / self-hosted) → production audit trail
Research / frontier safety
inspect_ai → primary eval framework
inspect_evals → published benchmarks (AgentHarm, GAIA, etc.)
OpenAI Evals → contributing domain benchmarks to public registry
The Framework Nobody Mentions: What’s Missing from All of Them
No single framework in this guide provides:
- End-to-end coverage from development through production — you need at least two
- Business-level metrics (task deflection rate, user satisfaction, cost-per-query) — these live in your product analytics stack
- Fine-grained attribution (which retrieved chunk caused the hallucination?) — emerging in RAGAS but not mature anywhere
- Multilingual eval depth — most benchmarks and metrics assume English; non-English RAG products are under-served
These gaps are worth knowing before you design your eval strategy.
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