Store, retrieve, and search persistent AI agent memory across sessions.
npx skills add https://github.com/lidge-jun/cli-jaw-skills --skill memory-lidge-jun
Please help me install this Agent Skill. Skill: memory Source: https://github.com/lidge-jun/cli-jaw-skills/tree/main/memory Command: npx skills add https://github.com/lidge-jun/cli-jaw-skills --skill memory-lidge-jun
This Skill provides a persistent, long-term memory for the AI, allowing it to recall past interactions, decisions, and user preferences across sessions, preventing the need to re-learn information.
Search memory for information about user preferences.
High-intent search queries and answers about installing and using this skill.
You enable persistent long-term memory by saving durable facts and user preferences to specific file paths. The memory system uses SQLite indexing across profile, episodic, and semantic directories to retrieve this knowledge across conversational sessions.
Storing user preferences requires saving them as durable facts into structured memory file paths. Once saved, the AI can search and recall these preferences in future interactions to tailor its responses without re-learning.
Keyword search retrieves context by querying a persistent knowledge base indexed by SQLite. This allows the AI to perform broad searches across stored profile, episodic, and semantic data to find specific information efficiently.
Yes, saving and retrieving long-term memory context requires a defined storage layout. You must set up profile, episodic, and semantic directories indexed by SQLite to ensure durable knowledge is structured for efficient recall.
Yes, you can search past interactions to recall specific project context. The memory system supports broad keyword searching across persistent storage, allowing the AI agent to retrieve previously saved durable facts and decisions.
AmidVoshakulImplement persistent memory architectures with semantic, episodic, and procedural storage layers.
EnigmaWUActivate personal profiles and update user preferences across AI-assisted sessions.
New-dev0Store and retrieve coding project memories using mem0 MCP tools.
ArogyaReddyCombine vector similarity and keyword matching with RRF, Linear, Cross-encoder, or Cascade fusion.
