{"repo":"carloluisito/mindkeg-mcp","free":true,"listed":false,"github":"https://github.com/carloluisito/mindkeg-mcp","clone":"git clone https://github.com/carloluisito/mindkeg-mcp.git","description":"A persistent memory MCP server for AI coding agents — stores, searches, and retrieves atomic learnings so agents retain knowledge across sessions.","language":"TypeScript","stars":10,"topics":["agent","ai","ai-tools","claude","cursor","embeddings","knowledge-base","mcp","mcp-server","memory"],"license":"MIT","category":"mcp-servers","readme_excerpt":"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 — ","default_branch":null,"files":null,"tree":[],"storefront":"/r/carloluisito","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/carloluisito/mindkeg-mcp/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}