What is l0-memory?
l0-memory is a high-performance, local-first long-term memory system designed specifically for AI assistants. It provides a persistent, structured, and searchable store for facts, preferences, and relationships that would otherwise be lost between chat sessions.
The Mission
The goal of l0-memory is to bridge the gap between ephemeral chat history and a permanent knowledge base. By using the Model Context Protocol (MCP), it allows different AI tools (like Claude Code, Cursor, and Claude Desktop) to share a single, unified memory store.
Why l0-memory?
- Privacy First: Your memories stay on your machine. The store is a local SQLite database—no cloud sync, no telemetry, and no third-party APIs (unless you explicitly configure an optional embedding provider).
- Zero CGO Dependencies: The server is written in pure Go, making it incredibly fast and easy to cross-compile for any platform without external C libraries.
- Structured Knowledge: Unlike simple key-value stores, l0-memory supports typed links, allowing your assistant to build a sophisticated knowledge graph of your projects and workflows.
- Freshness & Trust: Built-in mechanisms for pinning, verification, and archiving ensure that the assistant always prioritizes current information over stale data.
Key Capabilities
Persistence Across Tools
Because l0-memory speaks MCP, a memory saved while working in Claude Code is immediately available to your assistant in VS Code or Claude Desktop. This creates a seamless "second brain" that follows you across your entire development environment.
Knowledge Graph
Memories aren't just isolated snippets. They can be linked with meaningful relationships like depends_on, implements, or supersedes. This allows the AI to traverse related context, just like a human developer would.
Flexible Scoping
Memories can be global (user), project-specific (repo:name), or host-specific (desktop). This prevents "context pollution" while still allowing global facts to be available everywhere.
Use Cases
- Storing project-specific architectural decisions.
- Remembering personal preferences for code style or documentation.
- Tracking the progress of long-running tasks across different sessions.
- Mapping complex relationships between different parts of a codebase.