Retrieval-Augmented Generation (RAG) used to be an experimental hack. Today, it’s a core production strategy for everything from AI copilots to legal research tools. But if you’ve deployed RAG in the wild, you know the pain: hallucinations, high token costs, inconsistent answers, and brittle pipelines that break under
Technology has long promised to make life easier. Every wave of innovation—desktop software, the internet, mobile apps, cloud platforms, AI tools—has arrived with the implicit narrative that the friction of daily life would lessen, that software would lift the burdens of complexity, coordination, and execution. Yet for all
A deep dive into why stateless AI architectures struggle as complexity grows—and what’s needed to build agents that can sustain multi-step, long-lived interactions. In the rush to deploy AI-powered products and services, developers have overwhelmingly defaulted to stateless architectures. Large language models (LLMs) like GPT-5, Claude, and LLaMA
We’ve all seen it: chatbots that answer fluently in the moment but blank out on anything said yesterday. The “AI memory problem” feels deceptively simple, but solving it is messy - and we’ve been knee-deep in that mess trying to figure it out. Where Chatbots Stand Today Most
AI agents have come a long way—from simple Q&A bots to complex multi-step systems that plan, reason, and act. But there’s still a fundamental flaw: most AI agents forget everything once a session ends. Developers are starting to realize that stateless systems can only go so
As AI apps and agents become more advanced, developers are bumping into a critical limitation: long-term memory. Most large language models (LLMs) like GPT-4, Claude, and LLaMA are stateless—they don't retain any context beyond the current prompt. To solve this, many teams reach for vector databases (like
Most AI Apps Still Forget If you’ve built an AI-powered product—whether it’s a chatbot, assistant, or internal tool—you’ve probably run into a frustrating issue: It forgets. Maybe your app can handle a few turns of conversation, but once the session ends, the context disappears. Even
Recallio is now available as a native integration inside LangChain. A plug-and-play memory API that gives your app scoped, queryable, persistent memory - without managing infra. Why LangChain + Recallio? LangChain gives you the building blocks for intelligent agents and workflows. But “memory” in LangChain has traditionally been: * Session-based * Stored in
If your “AI assistant” still forgets decisions from last week’s meeting, repeats intake questions, or recommends actions that contradict current policy, the issue isn’t the model - it’s context. Models are powerful pattern machines, but they’re amnesic and literal. Unless you systematically feed them the right