LangSmith

Building an AI agent is one thing; understanding what it actually does in production — where it's slow, where it's expensive, where it silently fails — is another entirely. LangSmith (from the LangChain team) is built for exactly that, with a mission to let you 'know what your agents are really doing.'

See every step your agent takes

LangSmith delivers complete visibility into AI agent behavior through comprehensive tracing — you can see exactly what an agent does step by step, which is invaluable when an LLM app behaves unexpectedly. Debugging AI is notoriously hard precisely because the reasoning is opaque, and LangSmith makes it legible. It offers native tracing for popular frameworks with SDKs for Python, TypeScript, Go and Java, so it drops into whatever you're building with. It complements AI infrastructure like E2B and Weaviate.

Monitor cost and performance in production

LangSmith provides real-time production monitoring with cost tracking, so you can pinpoint the performance issues affecting latency and cost — critical when LLM calls directly drive your bill and user experience. It also includes LLM-as-judge evaluations, letting you systematically assess quality rather than eyeballing outputs. That combination of cost, latency and quality monitoring is exactly what you need to run AI responsibly at scale.

Discover failure modes automatically

A standout feature is unsupervised clustering that automatically discovers failure modes and common behaviors across your traces — surfacing patterns you'd never find by manually reading logs. That means LangSmith doesn't just record what happened but helps you understand systemic issues, which is where real improvement comes from.

Essential for production AI

As more teams move AI agents from demo to production, observability like LangSmith's shifts from nice-to-have to essential — you can't reliably operate what you can't see.

Who it's for

LangSmith suits teams building and deploying AI agents and LLM applications in production who need deep visibility, debugging and performance monitoring.

Pricing

LangSmith is freemium with a free tier for development and small-scale production, paid plans that scale with trace volume, and enterprise pricing on request. The free tier lets you instrument a real app and see the tracing before committing.

The automatic failure-mode discovery is where LangSmith earns its place in a serious AI stack: manually reading traces to find why an agent misbehaves doesn't scale past a handful of examples, but unsupervised clustering surfaces systemic patterns across thousands of runs. That shifts debugging from anecdotal — fixing the one bad case you happened to notice — to systematic, which is the only way to reliably improve an AI system in production.

Bottom line: LangSmith is observability and evaluation for LLM apps and agents — step-by-step tracing, cost and latency monitoring, LLM-as-judge evals and automatic failure-mode discovery — making it essential tooling for running AI agents reliably in production.