GuideIntermediate

Agent Memory And State

This guide gives a Reservo agent memory that survives the end of a session — and directly resolves the limitation left open by the Agent Fundamentals and Tool Calling Guide: there, Ana would book Focus pro, and in a new session the agent remembered nothing because the history only lived as long as the process did. This guide separates that working/short-term memory (already built) from long-term/persistent memory, its actual subject, and within long-term memory distinguishes episodic memory (what happened, dated events) from semantic memory (stable facts and preferences), grounded in real research (Generative Agents, Park et al. 2023; MemGPT, Packer et al. 2023) as its conceptual framework. You build a real persistent store with `sqlite3` (a file on disk, never `:memory:`), extract facts and episodes from an already-run session — never the raw transcript — retrieve relevant memory by user key, recency, and lexical keyword matching (deliberately without embeddings or semantic search, which are named as the production option covered by other guides), update and expire facts that change, and let a member's memory be forgotten entirely on request. Persistence is demonstrated between genuinely separate Python processes, not a variable cleared within the same process. All the engineering runs on Python 3.14 and its standard library; the LLM call is treated as a concept with realistic examples.