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

Agent harnesses — the scaffolding that orchestrates LLMs with tools and data — are inseparable from agent memory. Using a closed or proprietary harness (like Claude Managed Agents or OpenAI's stateful APIs) means surrendering ownership of your agent's memory to a third party, creating deep platform lock-in. Memory is what makes agents sticky and personalized; losing it means losing a proprietary dataset of user interactions. The post argues that harnesses and memory must be open and model-agnostic, and introduces LangChain's Deep Agents as an open-source, model-agnostic harness with pluggable memory backends (Mongo, Postgres, Redis) and self-hostable deployment.

Your harness, your memory

Agent Harnesses are how you build agents, and they’re not going anywhere

if you don't own your harness, you don't own your memory

Memory is important, and it creates lock in

<p>Wrong link,
here: <a href="https://blog.langchain.com/your-harness-your-memory/" target="_blank" rel="noopener nofollow">https://blog.langchain.com/your-harness-your-memory</a></p>
