I run IT and cybersecurity ops by day and tinker in a homelab at night. The problem I kept hitting: useful context from Cursor and Claude Code sessions evaporates when the chat ends. Notes end up scattered. RAG demos are cloud-first. I wanted something I own.

So I built Brain AI Hub — a portable second brain with a local LLM, markdown vault, semantic search, and MCP hooks for IDE agents.

What it does

1. Local LLM — Ollama (qwen2.5, nomic-embed). OpenAI-compatible API on :11434.
2. Knowledge store — Obsidian-style vault, PDF/EPUB library, sqlite-vec RAG, lightweight knowledge graph.
3. Agent bridge — three MCP servers (brain-vault, brain-library, brain-rag) with one-click deploy to Cursor, Claude Code, VS Code.
4. Transcript pipeline — distills exports from Claude/Cursor/Antigravity into vault markdown, dedupes, indexes code, runs scheduled jobs.
5. Dashboard — FastAPI UI on :7860 for services, chat, GPU/VRAM, API keys, pipeline status.

Two editions

Install scripts speak English and Polish ({% raw %}locale.env: LANG=en or LANG=pl).

MCP in practice

On Windows, Brain deploys stdio MCP configs from the dashboard. On Linux, point Cursor at the SSE gateway:

{
  "mcpServers": {
    "brain-rag": {
      "url": "http://192.168.1.10:7862/sse/brain-rag"
    }
  }
}

Agents can search your vault, pull library chunks, and run skills without sending data to a third-party memory API.

Why MCP instead of only RAG?

RAG answers retrieval. MCP gives agents tools — write a note, list vault files, trigger a skill, query the code index. That matches how Cursor and Claude Code actually work: function calls mid-session, not a single embedding search at prompt time.

Stack

Python, FastAPI, Ollama, sqlite-vec, PowerShell (Windows), systemd (Linux). Homelab-friendly: MikroTik/UniFi networking, WireGuard, Docker where it helps.

Try it

git clone https://github.com/lobrzut/brain.git
cd brain
# Windows: Install.bat && Start.bat
# Linux:  curl -fsSL https://raw.githubusercontent.com/lobrzut/brain/main/linux/bootstrap.sh | sudo bash

Open http://127.0.0.1:7860, connect MCP from the Tools tab, drop a PDF in the library, run a distill job on an old chat export.

Related homelab projects: AI Studio (ComfyUI + ACE-Step) and NetDash (LAN service dashboard).

Feedback and issues welcome on GitHub.