Braintrust

Braintrust is a cloud-based AI evaluation and observability platform that treats LLM quality measurement as a software engineering discipline. It provides experiment tracking, dataset management, LLM-as-judge scoring, production tracing, and CI/CD integration for running evals as part of deployment pipelines. Strong developer ergonomics and statistical tooling distinguish it from more operations-focused observability tools.

Most teams evaluate their LLM applications the same way: manually review a handful of outputs before shipping, maybe run a quick sanity check, then cross their fingers. Braintrust exists because that approach doesn't scale and because the quality problems it misses tend to surface in production at the worst possible time.

The platform is built on a specific philosophy: LLM evaluation should be treated like software testing. Write evaluators. Build datasets. Run them in CI. Compare results across versions. Track scores over time. This is common sense in software engineering and still genuinely rare in LLM development.

What Braintrust is

Braintrust is a cloud-based AI evaluation and observability platform. The company was founded in 2023 and launched publicly to significant interest from developers frustrated with informal quality assessment workflows. The platform is closed-source SaaS; an open-source proxy component exists at braintrustdata/braintrust-proxy but the full evaluation platform is cloud-only.

The core product covers:

1. Experiment tracking for comparing quality across prompt versions, model changes, and parameter tuning
2. Dataset management for storing and versioning evaluation inputs and expected outputs
3. Scoring via LLM-as-judge, code-based evaluators, or human annotation
4. Production tracing for logging real-world usage and running continuous evaluation
5. CI/CD integration for embedding evaluation in deployment pipelines

The developer ergonomics are unusually clean. Getting a first experiment running takes about ten minutes, and the SDK stays out of your way once the initial setup is done.

Getting started with experiments

Install the SDK:

pip install braintrust autoevals

A complete evaluation experiment in Braintrust looks like this:

import braintrust
from autoevals import LLMClassifier

# Your application function
def classify_intent(inputs):
    import openai
    client = openai.OpenAI()
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": "Classify the user's intent as: question, complaint, or request."},
            {"role": "user", "content": inputs["message"]}
        ]
    )
    return {"intent": response.choices[0].message.content.strip()}

# Run the experiment
braintrust.Eval(
    "intent-classifier",
    data=[
        {"input": {"message": "Where is my order?"}, "expected": {"intent": "question"}},
        {"input": {"message": "This product broke after one day."}, "expected": {"intent": "complaint"}},
        {"input": {"message": "Please cancel my subscription."}, "expected": {"intent": "request"}},
    ],
    task=classify_intent,
    scores=[
        LLMClassifier(
            name="correct-classification",
            prompt_template="Is the classification '{{output.intent}}' correct for the message '{{input.message}}'? The expected answer is '{{expected.intent}}'.",
            choice_scores={"Yes": 1, "No": 0},
            use_cot=True,
        )
    ],
)

Run it:

python eval.py

Braintrust logs the experiment, scores each example, and shows the results in the dashboard with a comparison to your previous baseline run. Each subsequent run is automatically compared to the last, so regressions are visible immediately.

Datasets as the evaluation foundation

The dataset is the most undervalued concept in LLM evaluation, and Braintrust puts it front and center. A dataset in Braintrust is a versioned collection of input/expected output pairs that you run your application against to measure quality.

You build datasets two ways. The first is manual: you write examples based on what your application should handle. The second, more valuable approach, is mining production data:

import braintrust

project = braintrust.init_logger(project="intent-classifier")

# Log production data with expected outputs when you know them
project.log(
    input={"message": "I never received my package"},
    output={"intent": "complaint"},
    expected={"intent": "complaint"},
    scores={"correct": 1},
    tags=["production", "shipping"],
)

Production logs with attached scores become your evaluation dataset. Over time, you accumulate a dataset that reflects the actual distribution of inputs your application sees, not just the examples you thought of in advance. When you upgrade models or change prompts, running against this dataset tells you whether quality held up on real-world inputs.

CI/CD integration

This is where Braintrust differentiates most clearly from observation-focused tools. You can run eval suites directly in your CI pipeline:

# .github/workflows/eval.yml
name: LLM Evaluation
on: [pull_request]

jobs:
  eval:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Install dependencies
        run: pip install braintrust autoevals openai
      - name: Run evaluations
        env:
          BRAINTRUST_API_KEY: ${{ secrets.BRAINTRUST_API_KEY }}
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
        run: python evals/run_all.py

When the eval run completes, Braintrust compares the scores to your baseline (typically the main branch or the last passing run) and shows the delta. If you configure thresholds, a score drop can fail the build. The PR gets a comment with a link to the experiment comparison showing exactly which examples regressed.

This workflow treats LLM quality the same way engineering teams treat code correctness: automated, measurable, part of the review process. For teams who ship LLM features frequently, this matters a lot.

Production tracing and logging

Beyond offline evaluation, Braintrust logs production usage:

import braintrust
from openai import OpenAI

client = braintrust.wrap_openai(OpenAI())

# Wrap any OpenAI call to log it automatically
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Explain quantum entanglement simply."}]
)

wrap_openai wraps the OpenAI client so every call is logged to Braintrust with the full input, output, token counts, and latency. For Anthropic:

import anthropic
client = braintrust.wrap_anthropic(anthropic.Anthropic())

Production logs can feed your evaluation datasets directly. When a user reports a bad output, you find it in the logs, add it to a dataset, and run it against future versions to catch regressions.

For complex agent workflows, Braintrust provides spans:

with braintrust.start_span(name="retrieval") as span:
    docs = retrieve_documents(query)
    span.log(input=query, output=docs)

with braintrust.start_span(name="generation") as span:
    answer = generate_answer(docs, query)
    span.log(input={"docs": docs, "query": query}, output=answer)

Span-level tracing gives you visibility into multi-step workflows. The depth here is somewhat less detailed than Langfuse's span model, particularly around automatic cost tracking and nested span aggregation, but it's functional for most agent debugging needs.

Scoring and statistical analysis

Braintrust's scoring system is flexible. The autoevals library ships with pre-built evaluators for common patterns:

from autoevals import (
    LLMClassifier,
    Factuality,
    Battle,
    ClosedQA,
    Humor,
    Summary,
)

# Factuality check against a reference answer
factuality_scorer = Factuality()

# Head-to-head comparison between two outputs
battle_scorer = Battle(instructions="Which response better answers the question?")

You can also write custom code-based scorers for deterministic evaluation:

def json_validity(output):
    try:
        import json
        json.loads(output)
        return {"name": "json-valid", "score": 1}
    except:
        return {"name": "json-valid", "score": 0}

The experiment dashboard shows score distributions, not just averages. You can see what percentage of examples scored above 0.8, where the failures cluster, and how the distribution shifted between runs. This statistical view is more honest than a single aggregate score and catches cases where the average looks fine but one subset of inputs regressed.

Braintrust vs the alternatives

Braintrust vs LangSmith

LangSmith is stronger on production observability: better monitoring dashboards, tighter LangChain integration, and more mature cost tracking. Braintrust is stronger on systematic evaluation: the CI/CD integration, the experiment comparison UI, and the statistical reporting are better suited to teams treating evals as a development practice rather than an occasional check. These tools address different parts of the problem, and teams with resources to run both often do.

Braintrust vs Humanloop

Humanloop is more complete on the prompt management and production deployment side, with stronger access controls and human annotation workflows designed for enterprise teams. Braintrust's evaluation tooling is more developer-centric and its CI/CD story is cleaner. Teams that want evals in their engineering process lean Braintrust. Teams that want structured prompt management with stakeholder review lean Humanloop.

Braintrust vs Langfuse

Langfuse is MIT-licensed, self-hostable, and stronger on production tracing depth. Braintrust is cloud SaaS with better experiment tracking and a cleaner CI/CD integration. Teams with data residency requirements or tight budgets favor Langfuse. Teams who want evaluation-first tooling with minimal infrastructure management favor Braintrust.

Pricing in practice

The free plan includes 1,000 experiment runs per month, which is enough to start and run a couple of eval suites against small datasets. It runs out quickly if you're running experiments in CI on every PR.

The Teams plan at $150/month removes most practical limits for growing engineering teams. Enterprise pricing is negotiated for larger organizations with additional requirements.

Where Braintrust is priced competitively: it's cheaper than enterprise LangSmith or Humanloop at similar feature levels, and the Teams plan is reasonable for a team that's serious about systematic evaluation. Where it's harder to justify: if you're already spending on Langfuse's self-hosted deployment and you want evaluation on top, building evals on Langfuse's dataset infrastructure is cheaper even if it's less polished.

Who should use Braintrust

Braintrust is a strong fit for specific teams:

Engineers who want LLM quality in their CI/CD pipeline. The GitHub Actions integration and the baseline comparison workflow are the cleanest implementation of this idea in the category. If "our PR checks include an LLM eval run" sounds like where you want to be, Braintrust gets you there faster than building it yourself.

Teams upgrading or switching LLM models frequently. When you're evaluating GPT-4o versus Claude 3.5 Sonnet for your use case, or considering a major model upgrade, running both against your production dataset in Braintrust gives you a quantitative answer rather than intuition.

Smaller technical teams that prioritize developer experience. The SDK is clean, the setup is fast, and the UI communicates experiment results clearly without requiring explanation. Teams that find LangSmith or Humanloop over-engineered for their current scale often find Braintrust's scope better matched.

Teams building classification, structured output, or Q&A systems. Braintrust's evaluators are especially well-suited to tasks with measurable correctness. Open-ended generation tasks are harder to evaluate systematically, though the LLM-as-judge approach handles them reasonably well.

The verdict

Braintrust is the most developer-friendly evaluation-first platform in this space. The experiment tracking is clear, the CI/CD story is real and not just a marketing claim, and the statistical reporting is more honest than the average-score dashboards most tools show.

The tradeoffs are the cloud-only constraint and narrower production monitoring depth. If self-hosting is a requirement or if you need deep span-level agent tracing as your primary use case, Langfuse is the better fit. If you want systematic evaluation that runs like software tests and fits into an engineering workflow, Braintrust is worth a serious look.

Key features

• Experiment tracking with automatic score comparison across runs
• Dataset management for storing and versioning evaluation inputs and expected outputs
• LLM-as-judge evaluators with configurable rubrics and multi-dimensional scoring
• Tracing for production logs with span-level visibility into agent runs
• Prompt versioning and playground for iterating on templates
• CI/CD integration for running eval suites as part of your deployment pipeline
• Real-time scoring on production traces with configurable sampling
• Statistical reporting with score distributions and significance testing