Library of the Week — Braintrust

Braintrust — an eval and logging platform with a dead-simple Python SDK

GitHub · Language: Python/TypeScript · License: MIT

What it does

Braintrust is an evaluation, tracing, and dataset management library for LLM applications. It lets you log traces in production, run scored evals against datasets, and compare prompt or model changes — all from code, without a separate infra setup.

Why it stands out

• Eval-as-code philosophy: evals are just Python functions that return a score between 0 and 1, no DSL or config files required — this makes them easy to version-control and run in CI
• Unified logging and eval surface: the same traced() decorator you use in production also works in eval runs, so your offline evals reflect real call shapes rather than synthetic wrappers
• Dataset versioning built in: you can push curated examples directly to Braintrust datasets via SDK and pull them back in evals, closing the feedback loop from production bugs to regression tests
• Multi-model support: works with any OpenAI-compatible endpoint without special adapters, so you can swap between providers freely

Quick start

import braintrust
from braintrust import Eval

def exact_match(output, expected):
    return 1.0 if output.strip() == expected.strip() else 0.0

Eval(
    "my-summarizer",
    data=lambda: [
        {"input": "Summarize: The cat sat on the mat.", "expected": "A cat sat on a mat."},
    ],
    task=lambda input: my_llm_call(input),  # your function here
    scores=[exact_match],
)

Running braintrust eval eval_script.py executes the suite and pushes results to the dashboard.

When to use it

• You’re moving past vibe-checking outputs and want repeatable, scored evals that run in CI on every prompt change
• You need production tracing and offline evals to share the same instrumentation rather than maintaining two separate setups
• Your team wants a UI for comparing eval runs across model versions without building internal tooling

When to skip it

• If you need fully local, air-gapped eval infrastructure — Braintrust’s dashboard is a hosted service and the SDK talks to their API by default
• For pure unit-test-style evals with no dataset management needs, a lighter tool like pytest with a custom scorer may be sufficient overhead

The verdict

Braintrust fills a real gap between “print the output and eyeball it” and “build a full internal eval platform.” The SDK is genuinely minimal — you can be logging scored evals in under 20 lines — and the shared tracing model between prod and evals is a design decision that pays off fast. If you’re shipping LLM features in 2026 and still don’t have a systematic eval loop, this is the lowest-friction place to start.