Braintrust
by Braintrust
The evaluation and observability platform for AI applications — helping cross-functional teams measure, improve, and govern agent quality in production.
See https://www.braintrustdata.com
Features
- Offline evaluation sets built from production traces and outcomes
- LLM-as-judge workflows for automated evaluation at scale
- Human annotation tooling for grounding automated evaluators
- Continuous comparison between automated evaluator outputs and human judgment
- Support for prompt and context iteration as the primary improvement loop
- Fine-tuning integration for cases where the use case clearly requires it
- Designed for cross-functional teams: product engineers, systems engineers, data scientists, and domain experts
Superpowers
Braintrust is built on the insight that GenAI evaluation is fundamentally different from classical ML evaluation. Where traditional ML teams optimize precision/recall/F1 on static benchmarks, agents must be evaluated on whether they actually solve the user problem — a much harder and more functional standard. Braintrust provides the infrastructure for this: gather production traces, add grounded examples to offline eval sets, compare automated evaluators with human judgment, and iterate on prompts and context. The platform is particularly valuable for organizations trying to avoid the trap of treating agents like predictive models and instead building rigorous, product-aligned evaluation loops. Phil Hetzel (Braintrust) argues that the ideal team mix for AI products is cross-functional — product engineers, systems engineers, data scientists, and domain experts — and Braintrust supports that entire workflow.