Agent Memory Architecture

Agent Memory Architecture is the multi-tiered persistence subsystem that endows stateless LLMs with cross-session personalization, long-term fact retention, and evolving skill accumulation; it structures memory into 3 layers: 1) Working/Short-term Memory (the active conversational Context Window managed via sliding summaries); 2) Long-term Episodic & Entity Memory (vector databases and knowledge graphs storing extracted entities and historical episodic events, e.g., Mem0, Zep); 3) Procedural Memory (reusable executable code snippets, tool blueprints, and verified Reflexion rules).

Long-term personalized companion agents, continuously learning software engineering agents (Claude Code, Open-Devin), and multi-month enterprise advisors.

Base LLMs lose all memory upon session reset, while blindly dumping full chat history triggers context window overflow and attention degradation; memory engineering enables selective extraction, decay, contradiction resolution, and precision recall.

Detail the workflow in Mem0 for dynamically extracting facts, updating existing memories, and invalidating contradictory stale facts?

Explain short-term working memory management combining sliding message windows with asynchronous incremental LLM summarization?

Derive the tripartite retrieval scoring formula for Episodic Memory: Semantic Similarity + Time Decay Factor + Importance Weight?

How does Procedural Memory persist successfully authored Python tool scripts into a dynamic skill library (e.g., Voyager paradigm)?

How to implement deterministic hard-deletion across vector/graph memory systems to comply with GDPR 'Right to be Forgotten' mandates?