gingugu/gingugu
by Various
[](https://glama.ai/mcp/servers/gingugu/gingugu) π π π πͺ π§ - Persistent memory for AI coding assistants. Local SQLite, no cloud. 16 MCP tools: store, recall, search, relate, c
MCP
gingugu/gingugu
Added 16 June 2026
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
How to use
Install
pip install gingugu Tools exposed
MEMORY_DB_PATHMEMORY_NAMESPACEMEMORY_NAMESPACE_PATHMEMORY_AUTO_CONTEXT_LIMITMEMORY_DECAY_LAMBDAMEMORY_EMBEDDINGS_ENABLEDMEMORY_EMBEDDINGS_BACKENDMEMORY_EMBEDDINGS_MODELMEMORY_EMBEDDINGS_OLLAMA_MODELMEMORY_EMBEDDINGS_OLLAMA_HOSTMEMORY_W_RELEVANCEMEMORY_W_FRESHNESSMEMORY_W_ACCESSMEMORY_W_CONFIDENCEMEMORY_CREDENTIALS_ENABLEDMEMORY_SERVE_HOSTMEMORY_SERVE_PORTMEMORY_SERVE_TOKENMEMORY_LOG_LEVELMEMORY_DEBUG
Tested with
Claude Desktop, Claude Code, Cursor, Windsurf, Cline, ChatGPT
Notes
gingugu/gingugu provides persistent memory for AI coding assistants using a local SQLite database. It runs entirely on the userβs 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.
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
Open-source & AI alternatives
Swap-in tools that solve the same job. Weigh the trade-offs before you commit.
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