How is 'memory' typically implemented in agents?

medium

Answer

$(1) Short-term: full conversation in the context window (up to model's context limit).$
$(2) Summarized memory: periodically summarize old turns to compress.$
$(3) Long-term / episodic: store past traces + facts in a vector DB, retrieve relevant ones per new query (RAG-over-memory).$
$(4) Structured memory: extract entities/facts into a database (name, address, preferences) and query it.$
$(5) Reflection memory: agent writes 'lessons learned' after each task and retrieves them next time.$
$Modern frameworks (LangGraph, LlamaIndex, Mem0) provide these primitives.$

Check yourself — multiple choice

Only context window
Short (in-context) + summarized + long-term (vector DB) + structured (DB) + reflection — layered memory
Only file storage
Impossible

Agent memory: layered — context / summarized / long-term / structured / reflection.

#agents

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