Store, recall, and search persistent user data with a TypeScript API.
npx skills add https://github.com/shreed27/DAIN --skill memory-shreed27
Please help me install this Agent Skill. Skill: memory Source: https://github.com/shreed27/DAIN/tree/main/CloddsBot-main/src/skills/bundled/memory Command: npx skills add https://github.com/shreed27/DAIN --skill memory-shreed27
This Skill provides a persistent memory system, allowing users to store, recall, and search for preferences, facts, notes, and rules across conversations, ensuring information is never lost.
Use the memory skill to remember that your favorite cryptocurrency is Bitcoin.
High-intent search queries and answers about installing and using this skill.
Persistent memory storage for user preferences is implemented using backends like LanceDB, SQLite, or PostgreSQL to save and recall data across conversations. This ensures user preferences, facts, and rules are never lost.
Semantic search for recalling notes and rules works by using vector embeddings to find information based on meaning rather than exact keywords. This allows the memory system to retrieve relevant context efficiently.
Yes, you can use PostgreSQL or SQLite as a backend for persistent memory. The system supports structured data storage with types and metadata using these databases, alongside LanceDB, for efficient retrieval.
The best way to save and retrieve structured data using a TypeScript API is through the memory system's dedicated methods for remembering, recalling, and forgetting data. It supports structured data storage with types and metadata.
To store and retrieve user preferences and facts in a knowledge base, you use the memory system's API to remember specific details and later recall them. It supports semantic search to find information based on meaning.
nirholasStore and retrieve user memories across conversations with semantic search.
jholhewresStore and retrieve long-term memories with semantic search and category tagging.
Eli5DeFiStore and recall persistent AI agent memory as local Markdown files.
handsomelong922Store and retrieve embeddings with metadata using a vector database.
