How do you implement memory systems for AI agents that persist across sessions?
Agent memory enables continuity across conversations and tasks. Memory types: (1) Short-term/working memory: current conversation context and task state, stored in-memory during execution. (2) Episodic memory: records of past interactions and outcomes, stored as retrievable text entries with timestamps and metadata. (3) Semantic memory: accumulated knowledge and facts, stored in a vector database for retrieval. (4) Procedural memory: learned skills and successful action patterns, stored as templates or few-shot examples. Implementation: episodic memory uses a vector store where each past interaction is embedded and retrievable by similarity (retrieve relevant past experiences when facing similar situations). Semantic memory accumulates facts extracted from conversations (user preferences, project details, domain knowledge) in a structured or vector store. Procedural memory stores successful action sequences as templates that can be retrieved and adapted for similar future tasks. Storage: use a database (PostgreSQL with pgvector, or dedicated vector store) to persist memories across sessions, implement memory consolidation (periodically summarize and compress old memories), and manage memory capacity (implement importance-based retention, discarding low-value memories). Retrieval: query relevant memories at the start of each interaction, include retrieved memories in the agent's context, and weight recent memories higher than older ones. Memory update: after each interaction, extract and store new facts, update existing knowledge if contradicted, and reinforce successful action patterns. Privacy: implement memory deletion capabilities (right to be forgotten), scope memories per user/tenant, and encrypt sensitive memories. Evaluation: measure memory retrieval relevance, assess whether memories improve task performance over time, and monitor memory growth and storage costs.