Add Long-Term Memory to a Conversational Agent
Design a memory layer so an agent remembers user preferences and prior decisions across sessions without bloating the context window.
Decide what to remember, where to store it, and how to retrieve only the relevant memories at the right moment.
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Prompt objective
Decide what to remember, where to store it, and how to retrieve only the relevant memories at the right moment.
Real use case
A personal-assistant agent forgets a user's timezone and project names every new chat. The team needs a memory design that is cheap, private, and actually improves answers.
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Prompt
Act as an applied AI engineer specializing in agent memory. Design a long-term memory system for the agent below. Agent purpose: [PURPOSE] What a user would expect it to remember: [EXAMPLES] Constraints: [PRIVACY, STORAGE, COST, LATENCY] Deliver: 1. Memory taxonomy: which facts are short-term (this session), which are durable (across sessions), and which should never be stored. 2. Write policy: when and how the agent decides a fact is worth saving, and how to summarize it. 3. Storage design: structure of a memory record (key, value, source, timestamp, confidence) and where it lives (vector store, key-value, profile table). 4. Retrieval policy: how the agent fetches only relevant memories per turn to keep the context window lean. 5. Update/forget rules: handling stale or contradicting memories and honoring deletion requests. 6. A short example: 3 turns showing what gets written and what gets retrieved. Call out privacy and consent risks explicitly.
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- 1Replace the key placeholders first: PURPOSE, EXAMPLES, PRIVACY, STORAGE, COST, LATENCY.
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