Coolhand vs LangSmith
LangSmith now has a beta feature that can open a PR too. The real difference isn't whether a PR shows up — it's whether that happens continuously, on your infrastructure, without someone clicking "fix."
What is LangSmith?
LangSmith is LangChain's observability and evaluation platform — tracing, a Prompt Hub, LLM-as-judge and pairwise evaluators, human annotation queues, and alerting, all framework-agnostic (it works fine without LangChain or LangGraph). It's the best-known name in this space, backed by a $1.25B valuation as of late 2025.
In May 2026, LangChain shipped LangSmith Engine in public beta: it periodically re-scans production traces, clusters recurring failures into named issues, diagnoses them against your connected GitHub repo, and — when a person clicks "Fix" on a specific issue — drafts a code or prompt change and opens a PR for review. It's a genuine step toward what Coolhand already does by default, and we think that's worth being upfront about rather than pretending it doesn't exist.
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 LangSmith mostly still leaves open: Engine can draft a fix, but only per-issue, only when someone clicks "Fix," and only against LangChain-managed inference. Coolhand runs the same loop continuously and works with whatever model provider you're already using.
Coolhand vs LangSmith, feature by feature
| Capability | LangSmith | Coolhand |
|---|---|---|
| Primary purpose | Trace, evaluate, and manage prompts for LLM apps | Continuously diagnose production issues and ship the fix as a PR |
| Tracing depth | Deep — production dashboards, full-text/JSON search, trajectory clustering | Request-level logs built for diagnosis, not a dedicated trace explorer |
| Evaluation | LLM-as-judge, pairwise comparison, human annotation queues, datasets | Correctness and sentiment evaluators feeding the diagnosis loop |
| Human feedback | Manual annotation queues | Passive capture from your app's existing UI — no queue to build or staff |
| Opens a PR with a fix | Yes, via "Engine" (public beta) — but only when a person clicks Fix on a specific issue, and only on LangChain-managed inference | Yes — continuous, always on, works with whatever model provider you already use |
| Ingestion | Python/TS/Go/Java SDKs, OpenTelemetry support | Ruby/Python/Node SDKs and provider proxies; no OpenTelemetry endpoint yet |
| Self-hostable server | Enterprise tier only (BYOC or self-hosted on Kubernetes) | No — managed service only (SDKs, CLI, and widget are open source) |
| ROI reporting | Cost and token dashboards; ROI framing is on you | Cost-per-outcome and quality-trend dashboards built in |
| Pricing entry point | Free up to 5k traces/month, then $39/seat/month Plus | Free up to 10M tokens/week |
When you need both
If your team already lives in LangSmith for prompt versioning and trace search — especially if you're deep in the LangChain/LangGraph ecosystem — there's no reason to give that up. LangSmith Engine is a real capability, but it's opt-in and per-issue: someone still has to notice the clustered failure and click "Fix," and it only runs against LangChain's own inference. Coolhand's loop runs continuously in the background, is provider-agnostic, and is purpose-built end to end for diagnose → fix → feedback → ROI rather than being a feature bolted onto a trace-first tool.
You need both once you want LangSmith's depth on trace search and prompt management, plus a fix-and- feedback loop that doesn't depend on someone remembering to check the Engine queue.
How to use them together
Keep LangSmith for prompt version control, trace search, and any LangGraph-specific tooling your team relies on. Run Coolhand alongside it against the same production traffic so it's continuously diagnosing and proposing fixes without anyone having to trigger it. If LangSmith Engine flags and fixes something first, great — Coolhand's PRs and LangSmith's are just two independent proposals your team reviews the same way any other PR gets reviewed.
The practical split: LangSmith is where you go to inspect a trace or manage a prompt version by hand; Coolhand is what's already watching in the background so you don't have to.
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.