AI agents get persistent memory

Short summary

Most AI agents forget because LLM APIs are stateless. Instead of appending conversation history to prompts, treat memory as queryable architecture: extract semantic/episodic patterns into vector stores, embed relevant memories when needed, and inject only pertinent context. This shift from replaying history to retrieving relevance enables agents to maintain persistent, searchable knowledge across sessions.

• •Stateless LLM APIs require explicit memory management to persist context across sessions
• •Memory should be extracted into semantic/episodic types and stored in vector databases, not as full transcripts
• •RAG patterns enable agents to retrieve relevant context selectively rather than replaying conversation history

Generated with AI, which can make mistakes.