Mind Keg MCP

A persistent memory MCP server for AI coding agents. Stores atomic learnings — debugging insights, architectural decisions, codebase conventions — so every agent session starts with relevant institutional knowledge.

Problem

AI coding agents (Claude Code, Cursor, Windsurf) lose context between sessions. Hard-won insights are forgotten the moment a conversation ends. Developers repeatedly re-explain the same things; agents repeatedly make the same mistakes.

Mind Keg solves this with a centralized, persistent brain that any MCP-compatible agent can query and contribute to.

How It Works

Mind Keg implements a RAG (Retrieval-Augmented Generation) pattern for AI coding agents:

1. Retrieval — Agent searches the brain for relevant learnings using semantic or keyword search
2. Augmentation — Retrieved learnings are injected into the agent’s conversation context
3. Generation — The agent responds with awareness of past discoveries and decisions

Unlike traditional RAG systems that chunk large documents, Mind Keg stores pre-curated atomic learnings (max 500 chars each). No chunking strategy needed — each learning IS the retrieval unit. The agent controls both retrieval and storage, creating a feedback loop where knowledge improves over time.

Features

• Store and retrieve atomic learnings (max 500 chars, one insight per entry)
• Semantic search with three provider options:
  • FastEmbed (free, local, ONNX-based — BAAI/bge-small-en-v1.5, 384 dims)
  • OpenAI (paid, best quality — text-embedding-3-small, 1536 dims)
  • None (FTS5 keyword fallback — zero external dependencies)
• Six categories: architecture, conventions, debugging, gotchas, dependencies, decisions
• Free-form tags and group linking
• Three scoping levels: repository-specific, workspace-wide, and global learnings
• Dual transport: stdio (local) + HTTP+SSE (remote)
• Auth-free stdio for local use; API key authentication with per-repository access control for HTTP
• SQLite storage (zero dependencies, zero config)
• Import/export for backup and migration
• Smarter knowledge management: auto-categorization (KNN voting), conflict detection, smart staleness scoring, access tracking with relevance decay, near-duplicate merging, typed learning relationships
• Enterprise security: encryption at rest, audit logging, TTL/data retention, Prometheus monitoring, rate limiting, content integrity verification

Quick Start

npx mindkeg-mcp init

That’s it. This installs Mind Keg globally for your AI agent (Claude Code, Cursor, Windsurf). Open any project and your agent has persistent memory – no API keys, no per-project setup.

For Claude Code, a SessionStart hook is also installed – your agent loads prior knowledge automatically at the start of every session.

Options:

npx mindkeg-mcp init --agent cursor    # Target a specific agent