Same trace stream. Different exits.

The job each one does

LangSmith answers "what happened in this LLM call and how do I fix it?" It catches a regression in production, surfaces a slow span, lets you re-run a trace with a different prompt, scores outputs against a rubric, ships the better prompt. Every team running LLMs in production needs something in this lane and LangSmith is the most polished answer.

kolm answers "given enough of these calls, can the smaller model take over?" The captured pairs aren't just for debug; they're labelled training data. The verifier catches the same regressions LangSmith catches but uses them to filter the training set, not just alert a human. The output isn't a fixed prompt; it's a smaller model that has learned the behavior.

You can run them at the same time on the same traffic. They consume the same trace stream and produce non-overlapping deliverables.

Where LangSmith wins

Honest concession. LangSmith is the better human-in-the-loop tool. The trace UI is mature, the eval harness handles complex multi-step agents, the cost analytics are real, the LangChain integration is native. If your team's bottleneck is "we can't see what the agent is doing in production," LangSmith is the right answer and we're not trying to compete on that surface.

kolm captures less detail per trace and never tries to be a debug surface. We expose /v1/bridges/observations for sanity checks and that's it. The capture is task-shaped, not engineer-shaped.

Side-by-side

When to use LangSmith

Use LangSmith when the question is about the calls themselves - debug, replay, prompt iteration, cost analytics, eval. Anything where a human needs to look at a trace and decide what to change.

# trace every call, browse them, score them:
import { Client } from "langsmith";
const client = new Client();
# LangChain auto-traces; or wrap manually with @traceable

When to use kolm

Use kolm when the question is about the model - "can a smaller, signed, portable model do this task as well as the frontier?" The captured pairs become labelled training data; the verifier becomes the quality gate.

# point traffic at the kolm capture proxy:
ANTHROPIC_BASE_URL=https://kolm.ai/v1/capture/anthropic

# after enough pairs, compile:
kolm compile "summarize support tickets" \
  --namespace support \
  --base qwen2.5-7b

ok wrote support-summarize.kolm  k_score=0.89 signature=hmac-sha256

Can I use both?

Yes - and for most production teams the right move is to run both. LangSmith on the inbound side for visibility on the frontier calls; kolm on the same calls (or a sampled subset) to accumulate captures into a compileable namespace. When a namespace hits the threshold you compile it, sub in the .kolm for the cheap path, and let LangSmith keep watching the long-tail traffic that still goes to the frontier.