langchain-ai/langsmith-cookbook resource

Health v2 · maintenance only

Flags: no_releases archived no_license

Adoption not part of the score

1036 stars · 185 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of practical Jupyter notebook recipes and examples for using LangSmith to debug, trace, evaluate, and improve LLM applications. It complements the official LangSmith documentation with real-world patterns for tracing, feedback collection, and dataset management.

Use cases

• learn how to trace LLM applications with LangSmith
• add tracing to non-LangChain Python code
• collect user feedback on LLM runs in a Next.js app
• evaluate and test LLM outputs with datasets
• log runs via the LangSmith REST API
• manage prompts with the LangChain Hub
• debug nested tool calls in LLM agents

When to choose

• you use LangSmith and want practical, example-driven guidance beyond the docs
• you need copy-paste notebooks for tracing, evaluation, or feedback workflows
• you want real-world recipes for LLM observability

When to avoid

• you want the LangSmith SDK or product itself rather than examples
• you need a production observability tool, not tutorials
• you don't use LangChain/LangSmith tooling

Facets

learning-resource · maturity active

monitoring tracing testing llm-inference developer-tools large-language-models artificial-intelligence developer-tools tutorials monitoring python jvm-scripting langsmith langchain llm-observability llm-evaluation cookbook jupyter-notebooks prompt-engineering nodejs

Member repositories

For agents

markdown · JSON · MCP: product_card(name="langchain-ai/langsmith-cookbook")