Langchain is a publication focusing on programming languages, language design, and compiler development. Readers can explore articles covering topics such as language features, syntax design, and compiler optimization techniques. Additionally, they can learn about programming language theory, language implementation challenges, and practical applications of language design principles.

LangChain

Clay, a B2B SaaS platform for go-to-market teams, runs 300 million AI agent runs per month and relies on LangSmith for observability, evaluation, and cost monitoring. Key challenges included unpredictable quality at scale, cost reconciliation across multiple model providers, and rapid model evaluation. LangSmith integration required a single line of code and enabled day-zero tracing, production monitoring with Snowflake export, and structured offline evaluations using both deterministic and LLM-as-a-judge approaches. The result: 99–99.5% cost reconciliation accuracy, faster debugging loops, and the ability to update pricing on the day of a model launch.

How Clay uses LangSmith to debug, evaluate, and monitor 300 million agents runs per month

From chat completions to 300 million agent runs

The challenge: quality, cost, and model proliferation at scale

LangSmith as the observability layer: from day zero to production

Achieving near-perfect cost reconciliation at massive scale

Looking ahead: Agents with longer time horizons