Store and retrieve knowledge across sessions using vector memory.
npx skills add https://github.com/HeadyAI/heady-context --skill heady-memory
Please help me install this Agent Skill. Skill: heady-memory Source: https://github.com/HeadyAI/heady-context/tree/main/heady-skills/heady-memory Command: npx skills add https://github.com/HeadyAI/heady-context --skill heady-memory
Memory persists across conversations by storing facts, embeddings, and context so the AI can recall important information later, enabling continuity and faster responses.
Tell the AI to remember something for future conversations.
High-intent search queries and answers about installing and using this skill.
To persist memory across conversations, you use a vector memory system that stores facts and embeddings as persistent context. This enables cross-session recall so the AI can retrieve important information and maintain continuity in later interactions.
Semantic search uses vector embeddings to find information by meaning rather than exact keyword matches. By applying this to a knowledge base, the system retrieves conceptually related facts and links information across stored sessions for fast recall.
You store and retrieve user preferences by saving them as tagged facts in a vector store. When the user starts a new conversation, the system performs semantic search to recall those preferences and enrich the current response context.
Yes, you can use vector embeddings to link related information across conversations. By storing facts with embeddings in a persistent vector store, the system matches and retrieves connected knowledge regardless of which session originally saved it.
Cross-session memory recall requires a vector store to persistently hold embeddings and facts. This system integrates directly with vector memory tools to handle storage and retrieval, enabling knowledge management without manually configuring an external database.
A vector memory system relies on semantic similarity, so exact string matching or precise numerical recall may be less reliable. Memory persistence is best suited for recalling facts, preferences, and contextual knowledge rather than structured relational data queries.
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