Observability and Evaluation
Use LangSmith with LangChain
LangSmith connects to LangChain through a built in by the framework vendor path (LangSmith (same vendor LangChain ecosystem)). This page records what that connection looks like and links the evidence behind it.
Pairing facts
Why this pairing
Agent and LLM observability platform from LangChain for tracing, monitoring, and evaluating applications in production.
Best for: Teams building with LangChain or LangGraph, or any framework, that want tracing connected to datasets, evals, and prompt workflows in one managed platform.
Not ideal for: Teams that require open source self-hosting on lower tiers, or that want flat usage pricing without per-seat charges.
Configuration
export LANGSMITH_API_KEY="<your_langsmith_api_key>"Source: https://github.com/langchain-ai/langchain. Examples use placeholders only.
Evidence
The LangChain repository README lists LangSmith for agent evals, observability and debugging, and for developing, debugging and deploying AI agents and LLM applications.
https://github.com/langchain-ai/langchain
Compatibility here means the cited page documents the pairing. It does not mean AgentsUse benchmarked the combination or that every version works unchanged. Pin versions, run the tool on a small job first, and review the first outputs before widening access.
Watch for
- Self-hosted and hybrid deployment are Enterprise-only according to the product FAQ and pricing summaries, while Developer and Plus are cloud-hosted.
- Pricing combines per-seat fees with per-trace usage and retention upgrades, so cost grows with both team size and trace volume.
- Deepest zero-config tracing is for LangChain and LangGraph stacks, while other frameworks rely on SDK wrappers or OpenTelemetry instrumentation.