Procedural Memory
Maintain a third agent memory type alongside episodic (past events) and semantic (facts): procedural memory captures *learned how-to* — reusable skills, workflows, and self-rewritten system instructions that map situations directly to actions.
Problem
Episodic memory stores 'on 2026-03-12 I did X'; semantic memory stores 'X is true'. Neither stores 'when situation S arises, the right action sequence is A1, A2, A3'. Without a procedural store, the agent re-derives skills from raw episodes on every invocation, or relies on a frozen system prompt that cannot improve. LangChain's LangMem SDK explicitly names this gap and provides three memory types; the arXiv ProcMEM paper shows learned procedural memory outperforms episodic-only retrieval on reusable-skill tasks.
Solution
Implement a procedural-memory store as a first-class memory type alongside episodic and semantic. Entries are (situation pattern, action sequence, success record). The agent reads at planning time and appends after successful workflows. Updates are gated — naïvely letting the agent overwrite its own playbook risks rogue-drift, so add provenance and review. Common implementations: LangChain's LangMem 'procedural' channel, Claude Agent Skills (manually authored), ProcMEM-style learned skill libraries.
When to use
- Agents that operate across many sessions and encounter recurring task shapes.
- Workflows where the cost of re-deriving the right action sequence is high.
- Long-running agents that benefit from accumulated playbooks.
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Diagram, neighbourhood map, code examples, related patterns and full provenance.