Store and retrieve LLM memories in SQLite with semantic search.
npx skills add https://github.com/MichielDean/LLMem --skill llmem
Please help me install this Agent Skill. Skill: llmem Source: https://github.com/MichielDean/LLMem/tree/main/skills/llmem Command: npx skills add https://github.com/MichielDean/LLMem --skill llmem
LLMem provides a structured, SQLite-backed memory store with semantic search and relation traversal to empower LLM agents to remember, recall, and relate experiences over time.
Start by installing and running the llmem CLI, then add memories and use search with relation traversal to surface contextual memories.
High-intent search queries and answers about installing and using this skill.
You can add persistent memory to LLM agents by storing structured memory types like facts and events in a SQLite-backed store, then retrieving them using semantic search via embeddings for contextual recall.
A structured memory store for LLMs saves experiences as typed memories and links them in a relations graph, enabling agents to traverse connections and surface related context during complex recall tasks.
You set up semantic search for AI memory by installing the memory CLI, configuring SQLite for storage, and enabling optional embeddings via Ollama with a local vector index to retrieve relevant memories.
Embeddings via Ollama are optional for semantic memory retrieval, but enabling them with a local vector index is required to perform fast semantic search rather than standard full-text search across stored memories.
A background dream cycle maintains long-term memory by running maintenance routines that apply decay to old memories, boost important ones, and merge near-duplicates to keep the memory store fresh.
You can use full-text search instead of semantic search for LLM memory if you do not configure the optional embeddings server, though you will lose the ability to retrieve memories based on semantic similarity.
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