Braintrust

Braintrust is an observability and evaluation platform for AI agents and LLM apps. It traces prompts, responses and tool calls in production, runs scored experiments against versioned datasets, and turns recurring failures into regression tests, with shared views for engineers, product and QA.

Braintrust is an observability and evaluation platform built specifically for AI agents and LLM applications. Instead of treating a model call like any other API request, it records full traces of what the agent did: the prompts sent, the responses returned, every tool call in between, plus latency and cost. Engineering, product and QA teams share one view of that data, which matters because agents degrade quietly rather than crashing outright. The platform is organised around three products. Observe handles trace ingestion and log search, including the Nitro query engine inside Brainstore, the company's own database for AI data. Evaluate covers experiments: versioned datasets, side-by-side comparison of prompts and models, and scoring with LLM judges, code, or human reviewers. Discover adds an investigation layer, using natural language queries and pattern automations to surface recurring failures, then turning those traces into regression datasets with a click. A built-in assistant called Loop can draft prompts, scorers and datasets from a plain description of what needs improving. In practice a team starts by instrumenting an agent with one of the native SDKs, reviews real traces, and marks or clusters the failures worth fixing. Those examples become a dataset, a scorer is written and calibrated against known good and bad traces, and the whole eval runs in CI to block releases that regress. For anyone already using generic application monitoring, the difference is that Braintrust understands prompts, tool calls and model outputs as first-class objects, and its MCP server lets coding agents in tools like Cursor or Claude Code query logs and update prompts directly. It suits teams shipping agents to real users: AI engineers, product managers who need to review output quality, and QA staff building annotation workflows. Smaller prototypes can get by with basic logging, but once an agent has many tools and non-deterministic paths, the eval and regression tooling is what makes releases defensible.