Braintrust

Braintrust is a platform for building and maintaining quality AI products, combining observability, evaluation, and experimentation in one workflow. Its premise is that AI systems behave unlike traditional software: the same input can produce different outputs, there is rarely one correct answer, and a change that improves one thing can quietly degrade another. Braintrust is built to measure that quality systematically, catch regressions before they reach users, and give teams confidence that their system is actually improving.

The heart of the platform is evaluation. Teams define what good looks like, run experiments against real datasets, compare prompts and models side by side, and score outputs using language-model judges, code-based scorers, or human review. Offline evals run against known datasets before deployment, while online scoring runs the same judges on live production traffic to surface regressions and edge cases as they happen, automatically turning real failures into new test cases. A browser-based playground supports fast iteration, and experiments capture immutable, comparable snapshots of each run.

Observability sits alongside the evals rather than separate from them. Braintrust captures full traces of multi-step LLM and agent workflows, recording every model call and tool invocation with its inputs, outputs, cost, and latency, so engineers can see exactly what an agent did and where it went wrong. Because AI traces are large and deeply nested, the company built its own data store, Brainstore, to query millions of them quickly. Eval scores are native to the trace view, so quality signals and execution detail live in one place.

The platform closes the loop into shipping. A GitHub integration runs evals on every pull request and can block merges that degrade quality, while an AI assistant, Loop, takes natural-language instructions to analyze traces, generate eval datasets, and suggest better prompts and scorers. Braintrust also offers an AI gateway for logging, caching, and provider fallbacks, and an MCP server that lets a coding agent query logs and run evals from the IDE. It is framework-agnostic with SDKs across several languages, and carries enterprise security including SOC 2, GDPR, SSO, and HIPAA compliance.