Coolhand vs Braintrust
Braintrust is built around evals as the primary unit of work: experiments, scores, and quality gates. Coolhand watches production and turns a diagnosed issue into a pull request. Both matter; they answer different questions.
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.
Coolhand vs Braintrust, feature by feature
| 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. That gap is where Coolhand operates.
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.
Frequently asked questions
What is Coolhand?
Coolhand is agents that improve your agents. It observes your production AI agents, proposes fixes as pull requests, helps you collect human feedback, and reports the ROI of your agent work — so your team stays in control without babysitting what's already live.
How is Coolhand different from observability tools?
Observability platforms like Langfuse or Datadog show you what happened. Coolhand goes further: it diagnoses why, proposes a fix as a reviewable pull request, and closes the loop with feedback and ROI reporting. Coolhand isn't a replacement for observability — many teams run it alongside their existing dashboard — and it doesn't compete on observability's own feature set.
How do Coolhand fixes work?
When Coolhand finds a problem in a production agent, it proposes a fix at the agentic layer — prompt or tool-call changes, not model retraining or infrastructure changes — as a pull request in your repository. Hard errors typically get a proposed fix within minutes; more nuanced, feedback-derived issues can take hours or longer to diagnose properly. Nothing ships to production until your team reviews and approves it.
How does Coolhand collect feedback without an annotation team?
Coolhand's open-source feedback-collection skill reviews your codebase and shows you where to capture high-signal feedback from work you're already doing — edits users make to AI output, the actions they take instead of accepting a suggestion — rather than asking you to build a new annotation queue. You can start with passive capture and graduate to richer prompts over time.
Does Coolhand replace engineers?
No. Coolhand takes on the drudge work — watching for silent failures, sifting through feedback, building ROI reports — so the engineers you already have spend their time building what's next instead of babysitting what's already live.
Is Coolhand open source?
Coolhand's SDKs, JS widget, CLI, and feedback-collection skill are open source and Apache-licensed, and our API definitions are open. You can point every tool at your own backend if you choose — nothing is locked behind Coolhand's managed service.