🧠 Autonomous Agent Second Brain — Graph-RAG Memory Core

A zero-dependency, offline-first Graph-RAG memory system that enables autonomous AI agents to share long-term memories, accumulate research, and coordinate workflows.

Designed to operate natively as an Obsidian Vault on the filesystem and exposed as a clean REST API and Javascript SDK. By representing memories as interconnected nodes ([[wikilinks]]) and fusing graph traversal, keyword search, optional semantic similarity, and recency into one ranked signal, it filters and delivers targeted context to LLMs, returning only the notes relevant to a query, within a token budget, instead of whole files or directories.

⚡ Quick start (MCP)

Give Claude Code, Claude Desktop, Cursor or any MCP client a shared, persistent memory in one line. Needs Node 22.5+; no install step, no API keys.

claude mcp add second-brain -- npx -y mind-mri-second-brain

Other clients: add {"mcpServers":{"second-brain":{"command":"npx","args":["-y","mind-mri-second-brain"]}}} to their MCP config. Your notes are plain markdown in ~/.second-brain/vault, so you can open the folder in Obsidian and read or edit everything your agents remember. See MCP Server for the tool list and options.

What a session looks like (real output from the tools, trimmed for length):

brain_remember   title "Decision - Use SQLite for the cache", type decision,
                 content "We chose SQLite over Redis ... See [[Project - Atlas]]."
                 → { "success": true, "id": "agents/atlas/Decision - Use SQLite for the cache", "version": 1 }

brain_recall     query "what database does Atlas use and why", hops 1
                 → 2 notes, ~77 tokens:
                   ### Decision - Use SQLite for the cache [DECISION]
                   We chose SQLite over Redis for the local cache: zero ops, one file ...
                   ### Project - Atlas [MEMORY]            ← pulled in through the [[wikilink]]
                   Atlas is the internal reporting service. Stack: Node, SQLite ...

brain_node_update  id "agents/atlas/Decision - Use SQLite ...", content "Update: confirmed after load test, p95 read latency 3 ms."
                 → { "success": true, "mode": "append" }

Recall returns just the relevant notes plus the notes they link to, inside a token budget, and frames them to the model as reference data rather than instructions.

🚀 Why This Project Stands Out (Portfolio Highlights)

This project showcases several advanced backend engineering principles and data modeling architectures:

• Zero-Dependency Node.js Backend: Built using pure native Node.js ES Modules (http, fs, path, child_process, node:sqlite, node:test). Instantly boots in milliseconds, is highly secure against dependency vulnerability chains, and is extremely portable. Every capability added since 1.0 — semantic search, dedup, task queue, messaging, sandboxed execution — follows the same rule: no required npm dependency, ever. An optional one (a cloud embedding key, Docker, @huggingface/transformers) simply degrades gracefully to "unavailable" when absent, never a crash.
• Graph-RAG vs. Flat-RAG: Flat RAG systems (using simple vector similarity) yield discon

... View full README on GitHub