qualixar/superlocalmemory

**One memory for every agent you and your team use** — Claude Code, Codex, Cursor, Copilot, Antigravity, Hermes, ChatGPT, Claude on the web, Grok Bot, Muse and any harness that speaks MCP or runs a command. Decisions, documents, screenshots and PDFs, recalled the way you ask for them, governed like company data, shared through a mesh where your local agents and web bots talk to each other, and answered honestly: when the answer isn't there, your agent hears *"That isn't in SuperLocalMemory."* — not a confident guess.
**Install** · **See it work** · **Full demo** · **Every capability** · **Docs** · **Website**
**⭐ If SuperLocalMemory keeps your agents from guessing, star the repo. It helps other developers find it.**
**Agents forget everything between sessions. The usual fix — a vector store with a save button — makes it worse:** it always returns *something*, even when nothing it holds answers the question, and your agent builds on it. This is a memory **brain**, not a bucket: it understands what was saved (who, what, when, which project), keeps it true over time, governs who may see it, and refuses to bluff. Four arXiv papers describe how it works.
In Mode A, core remember and recall make no model-provider call: no API key, no cloud account, no Docker. Anything that sends data out is a choice you make, and the docs say exactly what goes.
Install
> **The easiest way: let your AI agent install it.** Copy the block below into Claude Code, Codex, Cursor or any agent that can run commands. It reads the setup guide, asks you one question, sets everything up and tells you what works. No terminal commands for you to learn.
**No AI agent? One line in Terminal** (macOS and Linux). It brings its own Python, sets everything up, connects the AI tools it finds and opens the dashboard; no Python or npm needed beforehand:
Add pictures and documents from the start with sh get-slm.sh --media at the end. It downloads the script first, so you can read it before it runs. Windows: use the agent prompt above, or npm or pip below.
**Prefer the terminal?** Both routes end in the same setup: your text memory on this machine, and, if you say yes, your pictures and documents too.
**npm**, the primary route (Node 18+). It installs SLM into a package-owned virtual environment:
**pip**, the other primary route (Python 3.12 to 3.14), in a Python virtual environment you activate:
**uv**: uv tool install --python 3.12 superlocalmemory installs slm as an isolated tool, with nothing to activate; see Linux install.
**Claude Code plugin:** claude plugin marketplace add qualixar/superlocalmemory, then claude plugin install superlocalmemory@qualixar. **Codex:** slm connect codex, then slm codex install.
**Two models, one memory.** Text is searched with a small local text model (nomic). If you turn on pictures and documents, a second local model (Google's EmbeddingGemma 2) reads them, and one recall returns both. The picture model unloads after 30 minutes without use, so it holds no memory while you only work with text. Details: pictures and documents, getting started, agent setup steps.
Repository clone: ./scripts/install.sh install (macOS, Linux) or .\scripts\install.ps1 -Action Install (Windows). Runs on Windows, Linux and Apple Silicon macOS (platforms).
30-second example
Real output from a fresh Mode A install, trimmed. Scores rank memories; they are not probabilities.
Why SuperLocalMemory: twelve superpowers
**1. It knows when it doesn't know.** Every recall comes back in one of three tiers: a **supported** answer; the **closest memories**, labelled *"I don't have this exactly — here are the closest memories"*; or *"That isn't in SuperLocalMemory."* for nonsense. Just after a start it says it is still warming up and shows what it has found, so you never hear "empty" when the truth is "not ready yet". Your agent stops building on the wrong memory.
**2. A second opinion that never leaves your Mac.** On Apple Silicon, SLM installs **Laya**, a small on-device model that judges whether the memories found actually answer the question. Prefer the cloud, or not on a Mac? **Jev** does the same check online, only after you tick its consent.
**3. Your documents and screenshots become a knowledge base you can ask.** Save a photo, a screenshot or a PDF and find it weeks later by describing it — *"the slide with the quarterly numbers"*. The picture model is **Google's EmbeddingGemma 2**, running on your computer: one model reads both your words and your pictures, so a description finds the image. OCR runs locally too, every PDF page is indexed by its picture and its text, and every answer cites its file and page. No generative model ever looks at your files.