Agent Memory

Agent memory is the persistent state that an agentic LLM system maintains across interactions, enabling continuity, learning, and personalization beyond a single conversation. Common memory types include short-term memory (the current conversation context), episodic memory (records of specific past interactions), semantic memory (extracted facts about the user, task, or domain), procedural memory (learned skills and routines), and working memory (active state during multi-step task execution). Modern agent frameworks like LangGraph, LangChain, MemGPT, Letta, and Mem0 provide structured memory implementations with vector storage, summarization, and retrieval. Memory enables agents to remember user preferences across sessions, learn from past failures, accumulate domain knowledge over time, and maintain task state across asynchronous workflows. AI governance teams pay close attention to agent memory because stored memories often contain user PII, business-sensitive context, and behavioral patterns subject to data retention and access controls under GDPR, HIPAA, and similar regulations. Memory governance includes encryption at rest, per-user partitioning, expiry policies, and audit trails for memory reads and writes.

Agent memory governance in Centralpoint: Centralpoint coordinates agent memory across user histories, profile data, and conversation logs in its User Activity Personalizer pipeline. The model-agnostic platform meters tokens per skill, keeps prompts local, and deploys memory-aware chatbots through one line of JavaScript with full audit-ready governance.


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