sqlite-graph-memory MCP server

sqlite-graph-memory: persistent memory for Claude Code, Cursor and Codex over your own markdown notes

<!-- mcp-name: io.github.tonydzi/sqlite-graph-memory -->

Long-term memory for coding agents that lives in the notes you already keep: an MCP server and a CLI over any folder of markdown, Obsidian vaults included. pip install, point it at the folder, and Claude Code, Cursor, Codex or any other MCP client can recall from it.

Your agent forgets everything between sessions, and plain vector search gives it back flat, unrelated chunks. This gives it associative recall over your own markdown notes: vector search finds the entry points, the [[wikilinks]] you already wrote are the graph. One SQLite file, no graph database, no ETL pass, no server.

Three commands

pip install 'sqlite-graph-memory[embeddings]'
sgm-index ./notes
sgm-ask --ab "In which programming language is the tool behind Project Ouzel written?"

./notes is any folder of markdown notes. To get exactly the output below, use the 202-note synthetic vault from this repo (it is not inside the pip package):

git clone --depth 1 https://github.com/tonydzi/sqlite-graph-memory && cp -R sqlite-graph-memory/bench/vault ./notes

Real output, top 5 of 12:

--- DIRECT memory (vector) ---
 1. [  1.66] project-ouzel
 2. [ -1.33] project-avocet
 3. [ -1.79] tool-cobbleset
 4. [ -2.44] meeting-2025-10-21-lighthouse-sync
 5. [ -2.70] person-severin-ibarrondo

--- ASSOCIATIVE memory (vector+graph) ---
 1. [  1.66] project-ouzel
 2. [  1.14] tool-lanternfish  <== +graph
 3. [ -1.33] project-avocet
 4. [ -1.79] tool-cobbleset
 5. [ -2.44] meeting-2025-10-21-lighthouse-sync

The answer lives in tool-lanternfish ("Lanternfish is written in Elixir"). The question never names Lanternfish, so the vector pass does not find it in the top 12 at all. project-ouzel links to it, so one graph hop puts it second.

If that hop found something your vector search missed, a star is how other people with a notes folder find this repo. sgm-ask --ab asks the same once per machine, in a terminal only; SGMNOSTAR_PROMPT=1 turns it off.

That is the idea in one query, and it is not a blanket win. On this same vault vector-only ranks ahead of graph mode overall; the numbers are in bench/. --ab exists so you can measure which one wins on your own notes.

From any MCP client (Claude Desktop, Cursor, Zed, Windsurf): sgm-mcp after the install above, or uvx --with 'sqlite-graph-memory[embeddings]' sqlite-graph-memory. Set BRAININDEXDIR to the index folder sgm-index wrote. Config: examples/mcp-config.json. Claude Code: claude mcp add sqlite-graph-memory -e BRAININDEXDIR=/path/to/index -- sgm-mcp. Codex: a [mcp_servers.sqlite-graph-memory] table in ~/.codex/config.toml with command = "sgm-mcp". Agents that read files first: llms.txt.

What it is

Graph RAG on SQLite for AI agents — a working pilot, not a framework. It is the extracted memory layer of a personal "second brain" agent setup: vector search finds the entry points, the [[wikilinks]] you already wrote by hand are the graph, and a cross-encoder reranks what the hop dragged in. SQLite is the only database (schema.sql).

Shortened. The full README is on GitHub.