Why I stopped putting LLMs in my agent memory retrieval path

<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

<p>Most AI agents have amnesia.</p> <p>You close the chat window. They forget everything. Every session starts from zero. Every context window fills up and dumps the earliest

<p>`---<br /> title: "Why I Built a Brain for My AI Agents — and What It Taught Me About Memory"<br /> published: true<br /> description: "Anamnesis v0.3.0: The cognitive

<p>This post originally appeared on <a href="https://tokenjam.dev/blog/2026-05-13-agent-memory?utm_source=devto&amp;utm_medium=referral&amp;utm_campaign=cross-post" rel="noopener

<p>If you are building agents in 2026, you have already hit the wall. Bigger models do not fix forgetfulness. Context windows can grow forever, and the agent still cannot remember

<!-- SC_OFF --><div class="md"><p>We spent the last year building a code indexing system and tried pretty much every retrieval approach along the way. Sharing what actually worked

<!-- SC_OFF --><div class="md"><p>I’ve been building Signet, an open-source memory substrate for AI agents.</p> <p>The problem is that most agent memory systems are still

<p><strong>TL;DR:</strong> I built the same browser agent twice — once with 500 lines of Python, once with 7 lines of JSON. The second one took 5 minutes. The agent harness layer