SuperLocalMemory MCP server

The governed memory brain for every AI agent. It lives on your machine. It never bluffs.

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.

Install SuperLocalMemory on this computer for me. Follow
https://github.com/qualixar/superlocalmemory/blob/main/SETUP_FOR_AGENTS.md
step by step and show me what each step printed. Ask me only one question,
before turning on anything optional: whether I also want to find my pictures,
screenshots and PDFs by describing them. Connect SuperLocalMemory to the agent
I am using. When you finish, run slm doctor and tell me in plain words what works.

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:

curl -fsSLo get-slm.sh https://raw.githubusercontent.com/qualixar/superlocalmemory/main/scripts/get-slm.sh && sh get-slm.sh

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:

npm install -g superlocalmemory   # add --media to also turn on pictures and documents
slm setup                         # pick Mode A to keep everything on this machine
slm connect claude-code           # or codex, cursor, windsurf, zed, claude-desktop, jetbrains, opencode ...

pip, the other primary route (Python 3.12 to 3.14), in a Python virtual environment you activate:

python3 -m venv .venv
source .venv/bin/activate         # Windows: .venv\Scripts\activate
python -m pip install superlocalmemory   # or, without a venv: pipx install superlocalmemory
slm setup
slm connect claude-code
slm media enable                  # optional: pictures and documents (about 1.5 GB; needs 16 GB of memory)

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

$ slm remember "We deploy the API blue-green; rollback is a DNS flip." --kind decision
Queryable ✓ 1 facts
$ slm remember "Staging DB is Postgres 16 on port 5433."

$ slm recall "how do we roll back the API"
  1. [0.67] We deploy the API blue-green; rollback is a DNS flip.

$ slm recall "zxqv plorth wimble"
  That isn't in SuperLocalMemory.

$ slm remember "Staging DB moved to Postgres 17 on port 5434." --replaces <id>
Replaced ✓ 1 fact(s). To undo: slm review-correction ... rollback 1
$ slm recall "which port does staging postgres use"
  1. [0.68] Staging DB moved to Postgres 17 on port 5434.

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.

4. Karpathy's LLM-wiki idea, built in — with search kept. Andrej Karpathy's LLM-wiki pattern turns raw documents into an organised knowledge base with an index and a health check. SLM does that from the data it already stores — a document index of every file with its people, projects and dates, and a document health check that finds empty pages, unlinked documents, duplicate pages and contradicted facts — while keeping real hybrid search for the moment you need to find something.

5. It looks after itself. Memory health describes your older memories in the background — dates, names, projects, kinds — only ever by adding, never touching what you wrote. Anything that moves or deletes waits for your Fix, and Undo puts it back.

6. Governed, not just stored. Every save passes an admission check and gets a receipt. Every read respects roles, workspaces and scopes. Every erasure reaches every copy — summaries, caches, indexes, picture mirrors — and comes with a verifiable receipt. Every action lands in a hash-chained audit trail. Built for teams and for GDPR, not bolted on.

7. Memory with a sense of time. Every fact knows when it happened and when SLM learned it. Ask what was true last month, or what SLM knew before a date. Corrections replace a fact and stay undoable; unused memories fade the way human memory does.

8. It never floods your context window. Layered recall gives an agent a one-line glance, the facts, or the full surrounding context — under a hard token budget it sets. The agent digs deeper with expand only when it needs to, and cache and reversible compression stop you paying for the same output twice.

9. Many agents, one brain. Every write records which agent made it, with trust scores against memory poisoning, so ten agents can share one memory without poisoning it.

10. Your agents talk to each other — even across the internet. Through SLM-Mesh, the agents on your computer and the bots on the web finally share one conversation. Claude Code, Codex and Hermes sessions exchange messages, waits, locks and shared state, on one machine or several, so one agent can hand work to another and two never edit the same file at once. And the bots you allow — ChatGPT, Claude on the web, Grok Bot, Muse — join the same mesh: a bot in your browser can message the agent on your laptop by name and read its reply. Every message arrives labelled as data, never as instructions; a web bot reads new messages when its next turn starts.

11. It learns every day, and it can't quietly get worse. Every reported outcome teaches it: ranking learns which memories actually helped, stable habits become soft prompts for new sessions, and agent skills are measured and revised. A new ranker is promoted only on a statistically valid win and rolled back automatically if it slips. Work runs as bounded loops that end only when an independent gate passes — never on an agent's word — and multi-step pipelines run as bounded graphs with Bounded Loops, whose finished runs SLM keeps as read-only evidence.

12. Science, not vibes. Four arXiv papers back every layer: information-geometric retrieval, biologically inspired forgetting, Bayesian trust against memory poisoning, and governed memory with measured costs. The core math runs without a language model, and the published benchmarks state exactly how they were measured.

SuperLocalMemory is part of Qualixar's AI Reliability Engineering work: agent memory that is observable, bounded and honest about what it doesn't know.

Watch it work

The full eight-minute demo: one memory shared by your coding agents and bots, every tab.

Add SuperLocalMemory to your app

You do not need to wait for an app store. Every app below can take SuperLocalMemory straight from this GitHub repository, qualixar/superlocalmemory, in two steps.

Step 1, once per computer. Install SuperLocalMemory: npm install -g superlocalmemory (or pipx install superlocalmemory). Your memory lives on this computer from then on.

Step 2, in each app you use:

Your app	What to do
Claude Code (terminal and desktop app)	Type /plugin marketplace add qualixar/superlocalmemory, then /plugin install superlocalmemory@qualixar
Claude Desktop	Run slm connect claude-desktop, then restart Claude Desktop
ChatGPT, Claude on the web, Muse, Composio	Open the dashboard (slm dashboard) → Web access → turn it on, then add the link it shows as a connector in that app (step-by-step)
Codex (terminal and desktop app)	Run codex plugin marketplace add qualixar/superlocalmemory --ref main, then codex plugin add superlocalmemory-codex@qualixar
Cursor (editor and Agents window)	Run slm connect cursor, then restart Cursor
VS Code (Copilot chat in the editor)	Install the SuperLocalMemory extension (.vsix from the latest release: Extensions → ... → Install from VSIX)
VS Code Agents window, GitHub Copilot CLI	Run slm connect copilot-cli; for the skills, add qualixar/superlocalmemory in the Agents window's Plugins screen
Antigravity	Run agy plugin install "$(npm root -g)/superlocalmemory/antigravity-plugin"
Grok CLI	Run grok plugin install 'qualixar/superlocalmemory#plugin'
Grok Bot	In Grok's Plugins screen, add the marketplace qualixar/superlocalmemory and add superlocalmemory
Muse CLI	Run slm connect muse, then muse plugins install "$(npm root -g)/superlocalmemory/muse-plugin" and muse plugins approve superlocalmemory
Hermes (terminal and desktop app)	Run hermes plugins install 'https://github.com/qualixar/superlocalmemory.git#hermes-plugin', then slm upgrade-hosts --host hermes --apply
OpenClaw	Run slm connect openclaw
Windsurf, Zed, JetBrains, Gemini CLI, OpenCode, Continue	Run slm connect <app>, for example slm connect windsurf
Anything else that speaks MCP	Add a server with the command slm and the argument mcp

Restart the app after adding SuperLocalMemory. Then ask it something like "What do you remember about this project?" The plugin guide has the details for each app, and slm doctor checks your setup.

Works with your agents

Every harness, every bot, one memory. Any harness that speaks MCP (slm mcp, or http://127.0.0.1:8765/mcp/) or can run a command (slm remember, slm recall) can use SuperLocalMemory. The packaged plugins are ready-made examples, not a limit.

Host	Connect	You get
Claude Code	claude plugin install superlocalmemory@qualixar	MCP, 16 skills, 4 agents, /slm-loop, session and recall hooks
Codex	slm connect codex, then slm codex install	MCP, 16 skills, 4 agents, /slm-loop, lifecycle hooks
GitHub Copilot (VS Code, Visual Studio, JetBrains, CLI)	slm connect vscode-copilot --here	MCP, agent rules, 16 prompts, 4 agents, hooks
Antigravity	agy plugin install the bundled plugin	MCP, 16 skills, 4 agents, /slm-loop, hooks
Hermes	Native plugin, then slm upgrade-hosts --host hermes --apply to grant it the memory server	16 skills, advisor agents, /slm <command>, recall and session hooks
VS Code	The SuperLocalMemory extension (.vsix on the release page)	The memory server in the agent's tool list, nothing to edit
Grok CLI	slm connect grok-cli, then grok plugin install the bundled plugin	MCP, 16 skills, 4 agents, session and recall hooks
VS Code Agents window, GitHub Copilot CLI	slm connect copilot-cli; skills and agents from the Plugins screen	MCP, 16 skills, 4 agents
OpenClaw	slm connect openclaw	MCP and 14 skills
Muse CLI	slm connect muse, then muse plugins install the bundled plugin	MCP, 16 skills, 4 agents, /slm-loop, session and recall hooks
Grok Bot and Cursor-format hosts	Add the qualixar plugin marketplace	A lean plugin built for shared bot computers (guide)
Cursor, Windsurf, Zed, JetBrains, Gemini CLI, Claude Desktop, OpenCode, Continue	slm connect <name>	MCP in that tool's own config
ChatGPT, Claude on the web, Muse, Composio	Optional Web access from the dashboard: sign in per app, read-only or read-and-save, remove any app at once	Your local memory, from the web; the database never leaves your computer (guide)
LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, Semantic Kernel, Microsoft Agent Framework, Google ADK, OpenAI Agents	Framework adapters	Memory inside your own code (adapters)
Anything else	Paste the universal agent rules and give it the CLI or MCP	Memory discipline for any agent

What the plugins teach your agent. Every plugin is built from one source, so every host gets the same 16 skills (recall, remember, sessions, governance, scopes and profiles, the knowledge graph, mesh, bounded loops, cache, compression, Web access, pictures, and two for bot computers) and 4 agents (a memory advisor, an optimize advisor, a governance advisor and a loop runner). Hooks run at session start, after tool use and at the end of a session, and every hook fails open: a hook problem never blocks your agent. Plugins, skills and hooks

Everything SLM does

Recall that knows when it doesn't know

Capability	What you get	Docs
Three honest tiers	Supported; closest memories (up to three, labelled); or "That isn't in SuperLocalMemory." Hooks that inject memory automatically only ever inject supported answers	Recall
Warming up, said plainly	While models load, recall shows what keyword search found under "SuperLocalMemory is still warming up — results may be incomplete. Try again in a few minutes."	Recall
Layered recall	depth 0 (a one-line glance), 1 (the facts), 2 (with source, session neighbours, related entities and corrections); a hard max_tokens cap; expand drills into one result	Layered recall
Hybrid search	Semantic, keyword (BM25), entity-graph, temporal, associative (Hopfield), spreading-activation and picture channels, fused and reranked; slm trace shows each channel's score	Recall
Nine memory kinds	Fact, event, status, opinion, rule, decision, how-to, plan, correction. "What did we decide" favours decisions; confirmed rules and decisions load at every session start	Memory kinds
Time travel	--valid-at (what was true then), --known-as-of (what SLM knew then), --window 7d	Recall
Precise filters	By project, agent, subject, kind and exact tags; an empty result says why	Recall
Corrections	--replaces retires an old fact, kept and undoable; edits go through reviewed corrections with rollback	Corrections
Summaries and saved views	Session, day and project summaries that cite their memory ids; saved views like "what did I ship this week"	Recall
Prompt-injection defence	Recalled text is treated as untrusted evidence: secrets redacted, forged markers neutralised, provenance attached	Security

The answer check: Laya and Jev

Laya — on your Mac

A small on-device model for Apple Silicon (about 1.1 GB). Installed by default by setup and offered after an upgrade; nothing ever leaves the machine. Opt out with slm setup --no-laya or SLM_SKIP_LAYA=1. Calibrate it to your own memories in the background with slm embedder calibrate.

Jev — online, any computer

The same check on Windows, Linux or macOS, through TypeSafe or OpenRouter with your own key. It sends the question and the top three memories, and only after you tick its consent. It never turns itself on.

The verdict is one input to the support decision, with its own time budget, and the Answer Check tab shows verdicts, timing and how often memory had no answer — without storing your questions. Answer check