AI agent dev stack (LangGraph + LangSmith)

Install a skill or read the field note below to see how we apply this pattern in real Claude Code projects.

verified 2 months ago5 min
Install the workflow

For building, debugging, and evaluating multi-step LLM agents. The skills that catch the problems traces-first: cache misses, regression in eval scores, trace forensics. Run this command to install a skill and start from a working baseline instead of rebuilding the setup from scratch.

$npx frenxt-cables add stack-ai-agent
Did install work?
Files this command writes (4 files)
  • .claude/skills/stack-ai-agent/SKILL.md artifact/SKILL.md
  • .claude/skills/analyse-langsmith-trace/SKILL.md artifact/skills/analyse-langsmith-trace/SKILL.md
  • .claude/skills/debug-prompt-caching/SKILL.md artifact/skills/debug-prompt-caching/SKILL.md
  • .claude/skills/benchmark/SKILL.md artifact/skills/benchmark/SKILL.md

AI agent dev stack installed. Pair with `stack-fullstack` if the agent is part of a full product surface.

AI agent dev stack (LangGraph + LangSmith)

For building, debugging, and evaluating multi-step LLM agents. The skills that catch the problems traces-first: cache misses, regression in eval scores, trace forensics.

One line

npx -y frenxt-cables stack stack-ai-agent --force

What it installs

Claude plugins (4):

  • superpowers. Systematic debugging, TDD, verification discipline
  • claude-md-management. Keep the agent project's CLAUDE.md current
  • vercel. AI SDK, AI Gateway routing, Workflow DevKit for durable runs, Sandbox for untrusted code
  • sentry. AI agent monitoring (LLM call tracing, token cost, latency)

Skills:

  • analyse-langsmith-trace. Paste a LangSmith URL, get a structured triage
  • debug-prompt-caching. Forensics on cache misses and cost spikes
  • benchmark. Run LangGraph benchmarks, read traces manually

When to use this

New agent project where traces and evals will be your day-two tools. Works alongside stack-fullstack if the agent is part of a larger product.

When not

Pure UI work → stack-ux. Full product surface → layer stack-fullstack on top.

Quick answers

What do I get from this cable?

You get a skill plus a dated field note that explains how we use it in real Claude Code workflows.

How much time should I budget?

Typical effort is 5 min. The cable is marked intermediate.

How do I install the artifact?

Run npx frenxt-cables add stack-ai-agent. The install block shows the files it writes and any prerequisites before you run it.

How fresh is the guidance?

The cable is explicitly last verified on 2026-04-18, and includes source links for traceability.

Work with FRE|Nxt

We build the production AI systems we write about.

Cables are the field notes. The playbooks come from client engagements — multi-agent systems, RAG pipelines, and LLM cost cuts that ship and hold up in production. If something here maps to a problem on your roadmap, two ways in:

Audit capacity: 5 slots/month · No pitch deck · NDA on request

Same shelf · Fix a specific problem
Share this cable