# Coolhand vs Braintrust

> Braintrust is an eval-first platform for testing and scoring AI output. Coolhand diagnoses production issues
> and opens the fix as a pull request. Here's how the two fit together.

## What is Braintrust?

Braintrust markets itself as an "eval-first" observability platform — where LangSmith and Datadog bill and design
around traces, Braintrust bills and designs around scores. It runs experiments comparing prompts and models, logs
traces via its own "Brainstore" database, promotes production traces straight into eval datasets, and gates CI on
regressions. Its "Loop" agent generates prompts, scorers, and datasets from a plain-English description and helps
spot failure patterns in production traffic; "Discover" adds automatic pattern detection and continuous online
scoring.

Loop and Discover are authoring and detection tools — they write prompts, scorers, and datasets, and they flag
patterns. Neither writes a code fix or opens a pull request against your application.

## What Coolhand actually does

Coolhand watches your production AI agents continuously. When something breaks — a hard error, a quality
regression, a spike in cost — it diagnoses the root cause against your actual code, drafts the fix, and opens it
as a pull request in your repo. Nothing merges without a human reviewing it first. Alongside that, an open-source
skill audits your codebase for places to capture feedback that's already happening — edits, approvals,
corrections — instead of asking you to build a new annotation queue. Cost and quality dashboards then show whether
all of this is actually working, in dollars and quality-trend terms, not just "traces logged."

Where Coolhand excels: it's the only thing in this loop that turns a diagnosed problem into a reviewable code
change on its own, continuously and without per-issue manual triggering. It doesn't need an annotation team, a
dedicated eval engineer, or someone babysitting a dashboard — the loop runs in the background and only asks for
your attention when there's a PR to review.

That's the gap Braintrust leaves open: Loop and Discover can author a better scorer or flag a pattern, but neither
writes the fix to the thing being scored. Coolhand closes that gap and ships the fix as a PR.

## Feature comparison

| Capability | Braintrust | Coolhand |
|---|---|---|
| Primary purpose | Eval-first experimentation, scoring, and CI quality gates | Diagnose production issues and ship the fix as a PR |
| Tracing depth | Deep — proprietary Brainstore trace database, trace-to-dataset promotion | Request-level logs built for diagnosis, not a dedicated trace explorer |
| Evaluation | Experiments, LLM-graders, code/human scoring, CI regression gating | Correctness and sentiment evaluators feeding the diagnosis loop |
| Human feedback | Structured human review queues | Passive capture from your app's existing UI — no queue to build or staff |
| Opens a PR with a fix | No — Loop and Discover author prompts/scorers and detect patterns; neither writes code or opens PRs | Yes — opens a real PR in your repo; you review and merge |
| Ingestion | Python/TS/Go/Ruby/Java/C# SDKs, OpenTelemetry support | Ruby/Python/Node SDKs and provider proxies; no OpenTelemetry endpoint yet |
| Self-hostable server | Enterprise tier only (on-prem or hosted deployment) | No — managed service only (SDKs, CLI, and widget are open source) |
| ROI reporting | Not a core focus; score and cost dashboards instead | Cost-per-outcome and quality-trend dashboards built in |
| Pricing entry point | Free up to 10k scores/month, then $249/month Pro | Free up to 10M tokens/week |

## When you need both

If your team has real eval discipline — regression testing before every prompt or model change, CI gates on
quality scores — Braintrust is built for exactly that, and it does it better than Coolhand ever will; that's not
the problem Coolhand is solving. You need Coolhand once evals surface a problem and someone still has to translate
a low score into an actual prompt or tool-call change, then get it reviewed and merged. Braintrust's Loop can
draft a better scorer or dataset; it stops short of drafting the fix to the thing being scored.

## How to use them together

Keep Braintrust as your pre-deploy quality gate: experiments, scorers, and CI regression checks before a prompt or
model change ships. Run Coolhand against the same production traffic to catch what evals alone won't — real user
feedback, cost spikes, and failures that only show up live. When Coolhand proposes a fix, the dataset and scorers
you've already built in Braintrust are exactly what you'd use to confirm the fix doesn't regress before merging.
Braintrust proves a change is safe before it ships; Coolhand is what tells you a change is needed in the first
place, and drafts it.

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Source: [coolhandlabs.com/beyond-observability/braintrust](https://coolhandlabs.com/beyond-observability/braintrust)