on-device-memory-layer

An MCP server provides unified memory operations for on-device AI agents by offering remember, recall, forget, and search functions without external dependencies. It enables cross-session continuity and memory retention for long-running tasks across platforms like Hermes and Claude Code.

No, the on-device memory layer uses a file-based storage backend instead of a database. This allows AI agents to persist and retrieve memories locally without external dependencies or database setup.

You can automatically capture memories from code using the Hooks API integration provided by the on-device memory layer. This allows AI agents on platforms like Claude Code and Hermes to automatically persist memories during long-running tasks without manual intervention.

Yes, you can use a hybrid RAG memory engine with semantic search for on-device AI agents. It applies memory decay to keep relevant memories and automatically decay outdated ones, ensuring accurate recall without external dependencies.

The best way to persist AI agent memory offline is using a fully local, zero-external-dependency memory layer based on the ClawMem architecture. This approach uses file-based storage and a local MCP server to ensure cross-session continuity without requiring external databases or network access.

File-based storage for AI agent memory lacks database-level concurrency and indexing at scale. However, it satisfies architectural requirements for a fully local MCP server and offline operation, supporting hybrid RAG memory engines without external dependencies.