Braintrust

Braintrust is an AI observability and evaluation platform built for teams shipping large language model applications and AI agents to production. It lets engineers trace every prompt, tool call, and response their system generates, then score those outputs against real datasets using LLM judges, code-based checks, or human review. Unlike traditional software, LLM behavior can shift between runs, degrade silently after a model upgrade, or break in ways unit tests never catch. Braintrust turns that uncertainty into a measurable process: define what good output looks like, run evals against it automatically, and block a release the moment quality regresses instead of finding out from an angry customer.

How Braintrust works

Braintrust sits between your application code and the language models you call. You send it traces, which are structured records of what happened during a request: the prompt, the model’s response, any tool calls, and metadata like latency and cost. From there, the platform gives you three connected capabilities.

Tracing: Braintrust logs every request as a trace, so you can inspect exactly what a prompt, retrieval step, or tool call returned, and search across millions of logs when something breaks in production.

Evals: You define a task, the function you want to test, and pair it with a dataset and a scorer. Scorers can use built-in autoevals, LLM-as-a-judge for qualities like tone or accuracy, or plain code for deterministic checks like schema validation.

Automation: Braintrust’s GitHub Action runs your eval suite on every pull request and posts the score changes as a comment, so your team catches a regression before it merges instead of after a user reports it. Loop, Braintrust’s built-in AI assistant, can generate new scorers and datasets from a plain-language description of what you want to measure.

What you can build with Braintrust

Regression test suite for a production chatbot: Turn real customer conversations into an eval dataset, then run Braintrust’s GitHub Action on every pull request so a prompt change cannot ship without proving it does not break existing behavior.

Multi-turn support agent evaluator: Combine per-turn scorers that catch individual bad responses with trace-level scorers that catch conversation-wide failures like lost context or an unresolved ticket, so you can grade a support agent the way a QA reviewer would.

Model migration safety net: Before switching to a new frontier model, run the existing eval suite against both models side by side and compare cost, latency, and quality scores to know exactly what changes before rolling it out to real users.

Custom scorer built from a production failure: Take a trace where the agent failed, add it to a dataset with one click, then use Loop to generate a scorer from a plain-language description of what went wrong, turning every bug into a permanent regression test.

CI/CD quality gate for an AI feature: Wire Braintrust into the pull request pipeline so any change to a prompt, model, or tool definition has to pass a defined quality bar before merging, the same way a broken unit test blocks a deploy.