Episodic Memory

Problem

If the agent has only a fact store, it can answer 'what is true' but not 'what happened' — it loses the ability to learn from specific past interactions, to surface relevant prior episodes by recency or salience, or to reflect on its own behaviour. If the agent collapses every interaction into facts at write-time, it destroys the causal chain — the user said this, then the agent did that, then it broke — that makes debugging and reflection possible. The CoALA framework names episodic memory as a distinct long-term type for this reason: the agent needs a layer that preserves events as events, with their temporal structure intact.

Solution

Park et al.'s Generative Agents memory stream (2023) is the canonical implementation: every observation is logged with a timestamp and an importance score; retrieval combines recency, relevance, and importance; a periodic reflection pass derives higher-level insights from clusters of recent episodes. LangMem's episodic channel stores past interactions for few-shot retrieval and procedure distillation. Substrate is orthogonal to function: vector store ([[vector-memory]]), append-only log ([[append-only-thought-stream]]), or structured journal can all back episodic memory. Compaction is typically delegated to [[episodic-summaries]]; consolidation into facts feeds [[semantic-memory]]; consolidation into skills feeds [[procedural-memory]].

When to use

• The agent needs to recall specific past interactions, not just distilled facts.
• Reflection or consolidation passes need raw episodes as input to derive insights or procedures.
• Temporal queries ('what did I do yesterday', 'what changed since last week') matter.