CAFE: A Systematic Framework for Design, Run, and Attribute LLM Experiments

Overview

CAFE is an open-source tool built for developers who work with Large Language Models. It helps teams test their AI systems in a way that follows scientific rules. Instead of guessing which part of an AI pipeline works best, CAFE runs structured experiments. It treats the AI system as a black box and automatically figures out which settings actually improve quality and which ones just add extra cost. The tool focuses on making results repeatable and statistically valid so engineers can trust their data.

Benefits

CAFE offers several key advantages for anyone building AI applications. First, it simplifies complex testing. Users can declare different factors like retrieval methods or model sizes, and the system handles the rest. It automatically creates full or partial experimental plans to save time and money. Second, it ensures reliable results. Because AI outputs can vary randomly, CAFE runs every test multiple times to separate real improvements from random noise. It also saves progress continuously, so if a test stops unexpectedly, it can pick up right where it left off. Finally, it provides deep insights. The tool uses advanced statistics to show exactly which factors drive performance and how they interact with each other. This helps teams make data-driven decisions instead of relying on gut feelings.

Use Cases

CAFE is designed for researchers and engineers who need to optimize AI pipelines. A common use case is evaluating a Retrieval Augmented Generation system. For example, a team might want to know if adding a specific reranker improves answer accuracy. They can set up a study to compare different retrieval methods and model sizes. The tool then runs the tests and generates a clear report showing which combination works best. Another use case is cost optimization. Teams can use CAFE to find the cheapest setup that still meets quality standards. By analyzing the data, they can see if a smaller, cheaper model performs almost as well as a larger, more expensive one. The framework also works well for any LLM provider since it connects through a standard interface.

Pricing

CAFE is completely free to use. It is an open-source project, which means anyone can download and install it without paying a fee. Users can host the software on their own servers using Docker Compose. This approach eliminates subscription costs and gives teams full control over their data and infrastructure.

Vibes

The community sees CAFE as a valuable bridge between fast engineering and careful science. Developers appreciate that it turns messy AI testing into a clean, repeatable process. Early users have praised its ability to cut through the noise of random AI outputs. One notable application involved testing a complex question-answering system with eighteen different configurations. The tool produced clear reports that helped the team identify the best setup quickly. Users value the transparency of the system because it stores every prompt and response for auditing. This level of detail builds trust in the final results.

Additional Information

CAFE is actively maintained by the community and is built on modern web technologies. The backend uses FastAPI and React, while the database is PostgreSQL. It is written in Python and requires no special dependencies for its core functions. The project supports self-hosting, which is important for teams that need to keep their data private. It integrates with LiteLLM to work with various AI providers. The framework is designed to be flexible enough to adapt to different types of evaluation rubrics, from simple pass-fail checks to detailed numeric scores.