# 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, 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.

## Feature comparison

| 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) — 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 deep in the
LangChain/LangGraph ecosystem — there's no reason to give that up. LangSmith Engine is real, 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.

## 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. 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.

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