What is Procedural Memory?
Procedural memory in AI agents refers to stored knowledge about how to perform tasks, use tools, or execute procedures. Unlike episodic memory (which stores specific experiences) or semantic memory (which stores facts), procedural memory encodes skills, workflows, and action sequences. This type of memory enables agents to remember and improve at recurring tasks, maintain consistent procedures across sessions, and build up expertise through experience.
In agent implementations, procedural memory might store successful action sequences for common tasks, learned strategies for using particular tools, optimized workflows discovered through trial and error, or procedural knowledge extracted from demonstrations. This memory can be represented in various forms: explicit procedure definitions, example traces of successful task completions, learned policies or action patterns, or templates and workflows that can be instantiated for new situations.
Procedural memory is less commonly implemented than episodic or semantic memory in current AI agent systems, partly because modern LLMs can often learn procedures in-context or through prompting. However, explicit procedural memory becomes valuable for complex multi-step tasks that benefit from stored expertise, scenarios where agents need to improve at repeated tasks over time, or applications requiring consistent execution of organizational procedures. Some systems implement procedural memory through prompt templates, stored chains, or learned tool-use patterns that accumulate over time.