A deep dive into LLM agent memory architecture based on the MemRec paper (ACL 2026): collaborative memory, decoupling memory management from reasoning, Information Bottleneck. Comparison of the TOP-3 production tools — Mem0, Letta (MemGPT), and Zep — across nine axes. Highlighting the gap: research outpaces tooling.
Mutex and atomic are not two independent tools — they are one stack of abstractions. I dig through the stack from data race to starvation mode: why x++ is three ARM64 instructions, how CAS became a universal primitive (Herlihy 1991), why Mutex is built on top of atomic CAS, and how Russ Cox’s barging-vs-handoff debate (issue #13086) gave Go its 1ms starvation threshold. With public benchmarks, the bit layout of Mutex state, and a cheatsheet for when to reach for which.
Deep dive into agent memory mechanics: five memory files, four context assembly strategies, TOOLS.md as a dual-source responsibility, and Dreaming — background consolidation from Anthropic (Auto Dream, Dreams API, KAIROS) and OpenAI (Dreaming V3, 82.8% factual recall). Engineering decisions behind a production platform.
Five multi-agent orchestration architectures — Supervisor, Swarm, Debate, Assembly Line, Hierarchical — with Mermaid diagrams, code examples in Eino (Go), and an honest look at limitations. When one agent isn’t enough, and when you’re better off staying solo.
Context window ≠ memory. I break down agent amnesia, the academic taxonomy (Du et al., 2026), five approaches to memory — from OpenClaw’s file-based brain to mem0’s managed layer — and how our team solves this in practice.