Companion videos

Building memory for long-running agents — companion videos

The article is about what "memory" actually means in an agent — short-term thread state, long-term cross-thread store, profiles, lists, instructions — and the engineering choices for each. Long-running-agent memory is still a small audience on YouTube, but these two videos cover the conceptual map and a working implementation more cleanly than anything else available.

Primary pick

6:47

Memory for agents (conceptual video)

LangChain

Short, no-code walkthrough of the short-term-vs-long-term split, the three shapes long-term memory tends to take (instructions, profile, list of objects), and the hot-path-versus-background trade-off for when to write. The article's memory-architecture section assumes exactly this taxonomy.

What you should get from this: Separate short-term and long-term memory decisions and decide when agent memory should be written.

Watch or know first: General agent-building context; no LangChain specifics needed.

AI Expert note: Under our view bar because the niche is small; it stays because the article's memory-architecture section assumes exactly this taxonomy — watch it first so the terms line up.

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Also worth watching

43:31

Building Brain-Like Memory for AI | LLM Agent Memory Systems

Adam Lucek

A longer implementation pass through the cognitive-science-inspired categories — episodic, semantic, working, procedural — wired into an agent in code. Worth watching after the LangChain conceptual video if you want a more opinionated mental model and a working example to crib from.

What you should get from this: You can wire episodic, semantic, working and procedural memory into an agent in code, not just name the categories.

Watch or know first: Comfortable reading agent code; watch the conceptual primary pick first.

AI Expert note: The cognitive-science labels are a mental model, not a spec — use the working example as something to crib from rather than an architecture to adopt wholesale.

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