Manage a persistent SQLite memory store with HNSW vector indexing.
npx skills add https://github.com/thecloudtips/founder-os --skill memory-engine
Please help me install this Agent Skill. Skill: memory-engine Source: https://github.com/thecloudtips/founder-os/tree/main/plugin/skills/infrastructure/memory Command: npx skills add https://github.com/thecloudtips/founder-os --skill memory-engine
This Skill provides a robust, persistent memory store that allows AI agents to learn from user interactions, maintain context across sessions, and personalize their behavior over time.
preference memory. Future interactions can then automatically archive newsletters without explicit instruction.Initialize the memory store if it doesn't exist by running the provided bash command.
High-intent search queries and answers about installing and using this skill.
Persistent memory for an AI agent is created by storing user preferences and behavioral patterns in a SQLite database with HNSW vector indexing. This enables the agent to maintain context and personalize interactions across different sessions.
Semantic search for AI context management uses HNSW vector indexing to find similar content in stored memory. It is needed when an AI agent must retrieve previously learned user preferences, business facts, or workflow knowledge based on meaning rather than exact keywords.
You store user preferences by saving them as structured entries in a SQLite memory database, then retrieve them using semantic similarity search or exact key lookups. The system detects behavioral patterns and stores them for future automated retrieval.
Yes, SQLite can be used for AI agent context management by combining it with HNSW vector indexing. This setup supports efficient similarity searches on memory content, alongside structured storage for business facts and contact information.
The best way to query company-specific facts is using a persistent memory store that supports company-specific memory retrieval alongside semantic search. This allows an AI agent to filter and recall business facts relevant to a specific organization.
The memory engine operates with no external dependencies, utilizing a built-in SQLite database for structured storage and HNSW vector indexing for semantic search. It initializes directly via a bash command when the memory store does not yet exist.
ricableManage agent memory with hybrid SQLite and AgentDB backends using HNSW indexing.
RockaRhymeLLCStore and retrieve facts, preferences, and decisions using YAML frontmatter.
winsorllcStore and retrieve agent memory using vector embeddings and keyword search in SQLite.
zechenzhangAGIPerform billion-scale k-NN similarity search with FAISS index types and GPU acceleration.