Why it matters

Memoryless agents feel dumb — they forget everything the moment a session ends. Memory unlocks personalization and continuous improvement. Getting it right is what makes agents feel intelligent over time.

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The architecture

MemoryService: pluggable backend for memory storage. Semantic memory (vector DB for retrieval), episodic memory (event log), custom implementations.

Semantic memory: agent stores facts as embeddings; retrieval by semantic similarity when relevant to current turn.

Memory pipelineStore factembed + saveRetrieve on next turnsimilarity searchInject into contextfor grounded responseVector DB (Vertex AI Vector Search, Pinecone) as backend for semantic memory
Memory read/write cycle.
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How it works end to end

Storage: when agent learns something worth remembering (user preference, prior conversation summary), store to memory service.

Retrieval: at start of each turn, query memory for facts relevant to current input. Inject retrieved facts into agent context.

Curation: not everything belongs in memory. Design what triggers a memory write; prune stale or wrong memories.