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CAFE — Compound-AI Factorial Evaluation
Stop guessing which AI config is better. Prove it.
CAFE treats every knob in your AI pipeline - retrieval, reranking, prompts, models, and tools - as an experimental factor. It runs factorial experiments, evaluates outputs using a configurable LLM (and optionally human reviewers), and applies mixed-effects models to determine: - Which techniques actually improve quality - How much each technique contributes - Whether the observed differences are statistically significant Open source and self-hostable.
Hi! As part of a research paper, we built CAFE to answer a question that kept coming up: when I tweaked my RAG or agent pipeline and the output improved, which change actually made the difference? Aggregate benchmarks and eyeballing a handful of outputs never really answered that - especially when LLMs are nondeterministic from run to run.
CAFE treats every knob in your pipeline (retrieval, reranking, context assembly, prompts, models, tools, etc.) as an experimental factor. It:
- Generates a full or fractional factorial design - every configuration combination worth testing
- Runs each configuration as a black box with replication (concurrent and resumable)
- Scores outputs using a configurable LLM judge and/or human raters
- Attributes performance differences using mixed-effects models matched to your rubric's scale
The result is a statistically grounded answer to questions like:
- Which techniques actually improve quality?
- How much does each technique contribute?
- What is the best-performing configuration?
- Are the observed differences real, or just noise?
CAFE also includes a cost–quality Pareto frontier and judge↔human agreement analysis using Krippendorff's α.
It's open source (Apache-2.0) and fully self-hostable. You can use it as a Python librar or a FastAPI + React web application. Nothing leaves your machine.
⭐ GitHub: https://github.com/fabian-lu/Cafe
🧪 Live demo: https://cafe-ai.de/demo
📚 Documentation: https://fabian-lu.github.io/Cafe
I'd love your feedback!!
About CAFE — Compound-AI Factorial Evaluation on Product Hunt
“Stop guessing which AI config is better. Prove it.”
CAFE — Compound-AI Factorial Evaluation was submitted on Product Hunt and earned 8 upvotes and 5 comments, placing #159 on the daily leaderboard. CAFE treats every knob in your AI pipeline - retrieval, reranking, prompts, models, and tools - as an experimental factor. It runs factorial experiments, evaluates outputs using a configurable LLM (and optionally human reviewers), and applies mixed-effects models to determine: - Which techniques actually improve quality - How much each technique contributes - Whether the observed differences are statistically significant Open source and self-hostable.
On the analytics side, CAFE — Compound-AI Factorial Evaluation competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how CAFE — Compound-AI Factorial Evaluation performed against the three products that launched closest to it on the same day.
Who hunted CAFE — Compound-AI Factorial Evaluation?
CAFE — Compound-AI Factorial Evaluation was hunted by Fabian Lukassen. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of CAFE — Compound-AI Factorial Evaluation including community comment highlights and product details, visit the product overview.
Fabian Lukassen