Framework Profiles
Each framework profile follows the same structure so you can compare like-for-like.
How to Read These Profiles
At a Glance table — Five-dot ratings (● = strong, ○ = weak) across 7 dimensions. Use for quick scanning.
Best For / Not Great For — Where the framework genuinely shines vs. where you’ll hit friction. Written from usage experience, not the vendor’s marketing page.
Tradeoffs vs. Alternatives — When to pick this over the closest competitor, and when not to.
Integration Effort — Realistic time-to-first-eval for a small team with an existing LLM application.
Cost at Scale — Open source is rarely free at scale. Includes LLM API costs when the framework uses LLM-as-judge by default.
EU AI Act Relevance — Which Article requirements this framework helps satisfy. Relevant for teams building high-risk AI systems under Annex III, or preparing for August 2026 enforcement.
Version Tracked — Framework versions move fast. Each profile notes when it was last verified.
The Six Frameworks
| Framework | Primary strength | Stars | License |
|---|---|---|---|
| RAGAS | RAG pipeline evaluation | ~14k | Apache 2.0 |
| DeepEval | LLM unit testing in CI/CD | ~16k | Apache 2.0 |
| promptfoo | Red teaming and adversarial testing | ~21k | MIT |
| Langfuse | Production observability and tracing | ~28k | MIT / Commercial |
| inspect_ai | Safety evaluation, government-grade | ~2k | MIT |
| OpenAI Evals | Benchmark registry, YAML-based | ~19k | MIT |
What’s Not Here
This guide covers the six frameworks above. Notable omissions and why:
- LangSmith — Excellent product, but commercial-first (LangChain Inc.). Langfuse is the open-source alternative with equivalent capabilities. Covered in comparisons where relevant.
- Braintrust — Strong step efficiency metrics and CI/CD integration. Excluded from v1 to keep scope manageable. Will be added in v2.
- Arize Phoenix — Good for ML observability teams. Overlap with Langfuse is high for LLM-specific use cases.
- TruLens — Predecessor to many patterns now in RAGAS and DeepEval. Less actively maintained.
- Giskard — Strong for ML model testing (bias, drift). Less LLM-native than others covered here.