Braintrust
Braintrust is an evaluation and observability platform for LLM applications that helps teams systematically test, score, and compare model outputs, prompts, and application versions before and after deployment. It provides tooling for…
Definition
Braintrust is an evaluation and observability platform for LLM applications that helps teams systematically test, score, and compare model outputs, prompts, and application versions before and after deployment. It provides tooling for building evaluation datasets, running automated scoring, and tracing production LLM requests.
Overview
A recurring challenge in building LLM applications is knowing whether a change — a new prompt, a different model, an updated RAG pipeline — actually improves output quality, since LLM outputs are non-deterministic and hard to assess with traditional software testing methods. Braintrust was built to bring rigor to this process, providing an evaluation framework where teams define test cases, run their application against them, and score the outputs using automated scorers (including LLM-as-judge scoring), human review, or custom evaluation functions. The platform's core workflow centers on 'evals': a developer defines a dataset of inputs (and often expected outputs), runs their LLM application or a specific prompt/model configuration against that dataset, and Braintrust scores and visualizes the results, making it possible to compare two versions side by side and see exactly which test cases regressed or improved. This turns prompt and model iteration from a subjective, ad hoc process into something closer to a CI test suite for LLM behavior. Beyond evaluation, Braintrust provides production observability — logging live requests, tracing multi-step chains, and capturing user feedback — which can feed back into evaluation datasets, creating a loop where real production failures become new test cases. It also includes a prompt playground for iterating on prompts against the same scoring criteria used in evals. Braintrust is commonly adopted by teams building production LLM products who need confidence that changes won't silently degrade quality, and it competes with similar eval-focused platforms like Humanloop, LangSmith, and Arize AI's Phoenix, generally emphasizing developer-friendly, code-first evaluation workflows.
Key Features
- Structured 'eval' workflow for testing LLM outputs against datasets
- Automated scoring including LLM-as-judge and custom scoring functions
- Side-by-side comparison of application versions, prompts, or models
- Production request logging and multi-step trace visualization
- Feedback loop turning production failures into new evaluation cases
- Prompt playground for iterative prompt development
- SDKs for integrating evals directly into CI/CD pipelines
- Dataset management for building and versioning test suites