Braintrust is an evaluation and observability platform for AI applications, helping teams test, debug, monitor, and improve LLM systems.

Who should use Braintrust?

Braintrust is best for engineering and product teams building production AI features where quality needs to be measured consistently.

AI evals are structured tests that measure whether model outputs meet quality, safety, accuracy, or business requirements.

Can Braintrust help with prompt testing?

Yes. Teams can compare prompts, models, datasets, and outputs to understand what performs better.

Is Braintrust only for developers?

It is developer oriented, but product, QA, and operations teams can also use its evaluation results to guide improvements.

Does Braintrust replace observability tools?

It focuses on AI quality and LLM traces, so teams may still use general observability tools for infrastructure metrics.

Braintrust

Name: Braintrust
Brand: Braintrust

AI evaluation and observability platform for shipping reliable LLM products.

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What is Braintrust?

TL;DR

Braintrust is an AI evaluation and observability platform for testing, debugging, comparing, and monitoring LLM applications, agents, prompts, datasets, and production quality across development and launch.

Trace and debug AI application behavior
Run evaluations and quality checks
Create datasets and compare model outputs

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About

Braintrust is an AI evaluation and observability platform for teams building production LLM applications. It helps developers trace AI workflows, run evaluations, compare prompts and models, manage datasets, and create quality gates before releasing AI features to users. The platform is designed for the hard part of AI product development: knowing whether an AI system is actually getting better. Teams can log traces, review outputs, test prompts, automate evals, compare versions, and monitor quality over time. This is useful for chatbots, agents, RAG systems, support tools, coding assistants, internal copilots, and any product where model output quality matters. For founders and SaaS builders, Braintrust can reduce the risk of shipping unreliable AI features. Instead of relying only on manual testing or gut feeling, teams can build repeatable evaluation workflows and catch regressions when prompts, models, retrieval settings, or agent logic change. Braintrust is especially relevant for technical teams moving beyond prototypes. Once real users depend on an AI workflow, debugging, evaluation, and production monitoring become essential. On FutureStack, Braintrust belongs in Developer Tools as a serious AI infrastructure platform for teams that care about LLM quality, reliability, and measurable improvement.

Use Cases

Model Quality & Safety Monitoring

Monitor execution logs, latency bottlenecks, and output accuracy across agent runs.

Automated Testing & Bug Resolution

DiscoveryTurn production signals into improvements automatically. Topics surfaces patterns in real time across task, issues, and sentiment, online scoring catches regressions, and quality gates.

Access Control & Audit Governance

Integrate with your identity provider for seamless authentication.

Multi-file Codebase Refactoring

How Graphite builds reliable AI code review at scale5%Reduction in negative rulesMalte Ubl, CTO.

Key Features

Trace and debug AI application behavior
Run evaluations and quality checks
Create datasets and compare model outputs
Monitor AI product quality before and after launch

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