Semantic Memory

Problem

Without a dedicated semantic store, every fact the agent 'knows' either lives in a static system prompt (frozen, cannot grow with experience) or is re-derived from raw episodes on every turn (slow, lossy, and prone to drift between runs). Mixing facts with raw events also confuses retrieval — 'user prefers dark mode' gets stored as 'on 2026-03-12 the user said: I prefer dark mode' and surfaces only by similarity to that timestamp's wording, not as a stable assertion. The CoALA framework names semantic memory as a distinct long-term type for exactly this reason: the agent needs a layer that holds *what is true*, separately from *what happened* and *how to act*.

Solution

The CoALA framework (Sumers et al. 2023) names semantic memory as one of three long-term memory types alongside episodic and procedural, defined by function rather than storage. Implementations vary by substrate: LangMem's semantic channel uses profile (single JSON document) or collection (many documents) stores; knowledge-graph implementations (cognee, Zep) store assertions as typed triples; vector stores can back it when retrieval is by similarity over fact text. The function is the same regardless: extract durable assertions from interactions, store them with entity/attribute keys and provenance, retrieve them when the situation calls for 'what does the agent know about X'. Refer to [[vector-memory]] and [[knowledge-graph-memory]] as substrate options.

When to use

• The agent needs to remember durable facts (user preferences, domain truths, settled conclusions) across sessions.
• Retrieval by 'what does the agent know about X' must be cheap and substrate-agnostic.
• Facts must be updatable and invalidatable independently of the events that produced them.