Mind Keg MCP: Persistent Memory for Claude Code and MCP-Compatible Agents

What Mind Keg MCP Does
Mind Keg MCP addresses the common frustration where Claude Code forgets everything between sessions. It's an MCP server that acts as persistent memory for Claude Code and any MCP-compatible agent. Claude stores small learnings as it works with you, then retrieves them via semantic search in future sessions.
The more you use Claude Code, the more it actually knows about your codebase, your preferences, and your patterns. Sessions stop feeling like day one. When Claude hits a problem it's seen before, it searches its memory, gets the relevant context back automatically, and doesn't repeat the same mistake.
Technical Implementation
Under the hood:
- Semantic search runs locally with ONNX embeddings (FastEmbed)
- No API keys, no cost, fully offline
- Also supports OpenAI embeddings or FTS5 keyword search if you prefer
- Three scoping levels: repo-specific, workspace-wide, and global
- SQLite storage, zero external dependencies
- API key auth with SHA-256 hashing
- TypeScript, Node.js 22, MIT license
Compatibility and Setup
Works with Claude Code, Cursor, Windsurf, Codex CLI, Gemini CLI, and GitHub Copilot.
Quick start:
npm install -g mindkeg-mcp
mindkeg api-key create --name "My Laptop"
# Add the MCP config to Claude Code. Done.Development Background
The developer built this entirely using Claude Code as their coding partner, from architecture decisions to implementation to debugging. The tool is 100% free and open source.
📖 Read the full source: r/ClaudeAI
👀 See Also

Nudge: A local-first app that surfaces Claude-generated plans via contextual triggers
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SwiftUI Agent Skill: Enhancing View Development with AI
SwiftUI Agent Skill is an open-source tool that uses AI to improve SwiftUI view development by embedding best practices and optimizations.

Detecting Silent Tool Failures in AI Coding Agents with Vibeyard
Vibeyard is a tool that detects when AI coding agents experience silent tool failures—where agents fall back to alternative strategies without alerting developers—and surfaces these inefficiencies during sessions. It can suggest fixes to prevent repeated inefficient workflows.

Claude Code Used to Simulate 4,000+ Blind Werewolf Games with LLMs
A developer used Claude Code to build a simulator where LLMs play blind one-night Werewolf, running ~4,600 games across OpenAI and xAI models. The experiment revealed consistent name-based voting patterns despite minimal game signals.