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

Continual learning for AI agents goes beyond updating model weights. It can happen at three distinct layers: the model (weight updates via SFT/RL), the harness (the code and instructions driving the agent, optimizable via meta-harness techniques), and the context (persistent memory/instructions configurable per agent, user, or org). Each layer has different update mechanisms — offline batch jobs or real-time hot-path updates. Traces are the foundational data source powering improvements at all three layers, with LangSmith serving as the collection platform and Deep Agents as a reference harness supporting production-ready context learning.