LangChain.rb — Chains, Agents, and Memory for Ruby AI Apps

<p>You ask a chatbot: "What's the capital of France?"</p> <p>It says: "Paris."</p> <p>You ask: "What's the population there?"</p> <p>It says: "Where?"</p> <p>That's a stateless

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<h2> The Problem Nobody Talks About </h2> <p>You've built an agent. You've wired up 100+ tools. You feel good about it.</p> <p>Then it starts hallucinating. Picking the wrong

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<h1> The Agent Loop </h1> <p><em>The entire AI agent stack in 60 lines of Python.</em></p> <p><a class="article-body-image-wrapper"

<blockquote> <p><strong>What is LangChain? It's the glue that connects large language models with tools — you write a few lines of code, and the Agent handles everything from

<h1> 8 AI Agent Memory Patterns for Production Systems (Beyond Basic RAG) </h1> <p>Every AI agent tutorial shows stateless request-response. User asks, agent answers, context

<p>Most agent frameworks treat memory as an afterthought. They give your agent tools, prompts, and orchestration patterns — but when you restart the conversation, everything