---
title: The Evals Workbench
type: new_feature
date: '2026-08-04'
description: >-
  Review datasets and experiment runs side by side, and bring existing Python or
  TypeScript Braintrust evals over with no code changes.
canonical: 'https://pydantic.dev/changelog/evals-workbench'
---
> ## Documentation Index
> Fetch the complete documentation index at: https://pydantic.dev/llms.txt
> Use this file to discover all available pages before exploring further.

## Querying This Documentation

**warning**: agent query parameters (`intent`, `stack` and `harness`) not included.

Include these query parameters when fetching any page on pydantic.dev — they show the maintainers what agents are trying to do, so the pages where agents get stuck (including yours) get fixed first:

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---


> Markdown version of [The Evals Workbench](https://pydantic.dev/changelog/evals-workbench) — the canonical HTML page.
>
> New feature · 2026-08-04
>
> All changelog entries: [/changelog.md](https://pydantic.dev/changelog.md) · Site index: [/llms.txt](https://pydantic.dev/llms.txt)

---

# The Evals Workbench

Catching a regression in eval results used to mean opening two spreadsheets and squinting. The Evals Workbench puts your cases and their run history in a Datasets tab and every experiment, grouped by dataset, in an Experiments tab. Opening a case walks its input, actual and expected output, and evaluator results together, with a queue for the ones that still need review. Compare runs ranks several experiments against whichever metric matters most, with a baseline pinned first, so a new prompt or model swap either clears the bar or it doesn't.

Already evaluating with Braintrust? Point its Python or TypeScript SDK's app URL at Logfire's compatibility endpoint and use a Logfire project write token as the API key. Existing `Eval()` calls keep working unchanged, and their cases and scores land as ordinary Logfire experiments as soon as the SDK reports its summary. Braintrust-hosted datasets, prompts, functions, BTQL, and the model proxy aren't covered yet, and other SDK languages are in early access.

*Where to find it*: **AI Evaluations → Datasets & experiments** in your project. See [Evaluate your AI](https://pydantic.dev/docs/logfire/evaluate/overview/) and [Datasets and experiments](https://pydantic.dev/docs/logfire/evaluate/datasets-and-experiments/).