judgment labs vs braintrust

What Each Product Actually Is

Judgment Labs has three parts: the Apache-2.0 judgeval SDK for OpenTelemetry tracing, judges that score traces (Agent Judge follows a natural-language rubric, Code Judge runs your Python), and Agent Behavior Monitoring, which runs judges on a sample of production traces and fires alerts, webhooks and dataset actions. Newer features - Behaviors, Behavior Discovery, AutoRubrics - aim at deciding what to judge in the first place. Offline tests run production-derived cases against a candidate agent.

Braintrust is built around the eval. An Eval() has three parts: data (test cases with inputs, optional expected outputs and metadata), a task (the function under test, from one LLM call to a multi-step agent) and scorers, which return numbers, or classifiers, which return labels. Each run is an experiment - in Braintrust's words, "the immutable, comparable record of your eval runs". Around that sit datasets, playgrounds, prompt versioning, tracing, online scoring of production logs, and Loop, an AI assistant that investigates your data and builds scorers, datasets and dashboards. SDKs cover TypeScript, Python, Go, Java, Ruby and C#.

The split is where each product starts. Judgment Labs starts in production and works back: watch what the agent does, judge it, find the recurring behaviors, turn them into tests. Braintrust starts before deploy and works forward: define the test set, score every change, then keep scoring in production. Each now reaches the other end, but the starting point still shapes the product.

Side by Side

Neither blocks, for the same reason. Judgment Labs' monitoring judges "completed traces", and the judgeval README says it scores "live production traffic server-side with no latency impact"; Braintrust's online scoring "evaluates production traces automatically as they're logged, running asynchronously with no impact on latency", and its docs note it runs on a schedule, so an alert can fire before a score lands on the span. Both keep the judge off the hot path, which is the right call for a scorer and the reason a scorer cannot stop anything.

Where Judgment Labs Is Stronger

Judgment Labs is the more specialised tool for agent behavior once it is live.

• Judges built for trajectories. Agent Judge reasons over a whole agent trace against a rubric; the company published its approach to long-context evaluation in May 2026. Braintrust scorers can do this too, but you assemble the prompt.
• Discovery before definition. Behavior Discovery and AutoRubrics go after failures nobody has written a scorer for. Braintrust's Loop will investigate data when you ask; it is not positioned as continuous discovery.
• Monitoring as a workflow. Detection rates, alerts, webhooks, automations and dataset actions in one place, with Slack alerting.
• Coding agents. A judgment-plugin sends Claude Code and Codex sessions for judging, and an MCP server gives coding agents access to Judgment project data.

It also has the better story for a team whose agents are already live and whose failures are not yet named. A dataset-first workflow needs someone to write the cases before anything gets scored; behavior discovery starts from the traces you already have. If you do not yet know what "wrong" looks like for your agent, that ordering matters more than any row in the table.

Where Braintrust Is Stronger

Braintrust is stronger at the discipline of change: every prompt, model or code change scored against the same data, with the result where the reviewer will see it.

• Experiments as records. Each eval run is immutable and comparable, so "did this PR make the agent worse" has an answer with a diff behind it.
• CI out of the box. The eval-action runs evals in GitHub Actions for node, python or go and posts a live summary comment on the pull request.
• A scorer library. autoevals, MIT-licensed, in Python and TypeScript: LLM-based scorers such as Factuality, ClosedQA and Summarization, RAG scorers such as Faithfulness and Context precision, heuristics such as Levenshtein, exact match and JSON diff, and embedding similarity.
• Breadth of SDKs. TypeScript, Python, Go, Java, Ruby and C#; judgeval covers Python with smaller JavaScript, Go and Java siblings.
• A price you can read, and self-hosting now. Starter is $0 and Pro $249 a month, both with unlimited users; Enterprise runs the data plane in your cloud today, where Judgment Labs lists self-hosting as coming soon.

name: Braintrust evals

on:
  pull_request:
  push:

permissions:
  contents: read
  pull-requests: write

jobs:
  eval:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0

      - uses: actions/setup-node@v4
        with:
          node-version: 22

      - name: Run evals
        uses: braintrustdata/eval-action@v2
        with:
          api_key: ${{ secrets.BRAINTRUST_API_KEY }}
          runtime: node

One Prompt Change, Both Tools

A support agent's system prompt is being rewritten for a new refund policy. The risk is an agent that refunds outside the new rules, or refuses refunds it should grant.

1. Braintrust: experiment before merge

   A dataset of a few hundred past conversations is the data, the agent is the task, and an LLM-as-a-judge scorer for policy compliance plus a code check on the refund amount are the scorers. The eval action runs on the pull request and posts the scores beside the diff. After merge, online scoring runs the same judge on a sample of production logs.

2. Judgment Labs: judge after deploy, test before the next one

   An Agent Judge rubric for refund compliance runs in monitoring on sampled production traces; the detection rate shows whether the new prompt made violations more or less common, and an alert fires past your threshold. Flagged traces go into a dataset, and an offline test runs those cases against the next candidate agent.

3. What each catches first

   Braintrust catches the regression you anticipated, before it ships, if the dataset contains it. Judgment Labs is better placed to catch the one you did not anticipate, after it ships, if the trace is in the sample.

Cost behaves differently too. The pre-merge gate runs a judge over the whole dataset on every pull request, so its cost grows with commits times dataset size. Production judging grows with traffic times sample rate. Size the dataset and the sample before either bill surprises you.

Running Both, or Choosing One First

The two overlap less than the feature table suggests. One workable split is Braintrust as the pre-merge gate - datasets, experiments, the CI comment - and Judgment Labs as the production judge that finds the behaviors worth adding to those datasets. The cost is two sets of evaluation logic: a Braintrust scorer and an Agent Judge rubric for the same failure are separate assets, calibrated separately.

If you have to pick one first, look at where last month's worst failure was caught. If it was a regression a test set could have held - a prompt change that broke a case you already knew about - start with experiments in CI. If it was a behavior nobody had thought to test for, start with production judging. And if the worst failure was an action rather than an answer, neither tool is the whole fix: a score cannot stop a tool call, so you also need a check that runs before the tool does.

Pricing and Buying

A worked Braintrust estimate, assumptions stated: 50,000 agent traces a month averaging 40 KB of processed data each (2 GB), scored online at a 20% sample by two scorers (20,000 scores). On Starter that is 1 GB over at $4 plus 10,000 scores over at $2.50 per 1,000, so $29 a month, with 14-day retention. On Pro it all fits inside the allowance: $249 a month, 30-day retention and RBAC. Trace size does the most work in that sum; an agent that pushes long documents through its context can be ten times larger. Model credits beyond the included amount are extra.

Braintrust's self-hosting is a hybrid: you run the data plane - the Braintrust API, PostgreSQL, Redis, object storage and Brainstore - in AWS, GCP or Azure with Terraform, while Braintrust hosts the web UI, authentication and metadata such as project and experiment names. Judgment Labs publishes no prices; ask on the demo about trace volume, judge runs, sampling, retention, SSO and the self-hosting timeline.