gingugu/gingugu

Overview

gingugu/gingugu provides persistent memory for AI coding assistants using a local SQLite database. It runs entirely on the user's machine with no cloud dependency and offers 16 Model Context Protocol (MCP) tools for storing, recalling, searching, and relating information.

Best for

Best for
Developers who want a local, self-hosted memory layer for MCP-compatible coding assistants

Use cases

• Give an AI coding assistant long-term memory across sessions
• Store and search project-specific context or notes locally
• Relate and retrieve past tool outputs or code snippets

Notes

gingugu/gingugu provides persistent memory for AI coding assistants using a local SQLite database. It runs entirely on the user’s machine with no cloud dependency and offers 16 Model Context Protocol (MCP) tools for storing, recalling, searching, and relating information.

3 stars on GitHub. Last updated 2026-06-16. Licensed MIT.

Use cases

• Give an AI coding assistant long-term memory across sessions
• Store and search project-specific context or notes locally
• Relate and retrieve past tool outputs or code snippets

Pros

• Fully local, no cloud or API key required
• Leverages SQLite for reliable, zero-config persistence
• 16 MCP tools provide granular control over memory operations

Cons

• Limited to MCP-compatible AI assistants only
• Requires Python runtime and some setup for integration
• Small community (3 stars) means limited support and documentation

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.