Loom OS · local-first agent memory
Own your agents' memory. Don't rent it.
Loom OS gives Claude Code, Codex, Cursor, and your in-house agents one shared knowledge graph per project — running on your machine. No SDK, no cloud, no vendor lock-in.
MIT-licensed · single process (no Docker, no Neo4j) · runs 100% local
The problem
Your team runs five coding agents. None of them share a brain.
Every agent keeps its own private memory — Claude Code in one window, Cursor in another, a CI bot somewhere else. Context scatters across tools and gets re-derived from scratch on every task. Worse, most of it is locked inside a vendor's product, where you can't inspect it, move it, or take it with you when the next model wins.
How it works
One process. One graph. Every agent on the same page.
Filesystem inbox
Any agent connects by writing a file. Zero SDK, zero auth.
Single-process daemon
pip install and run. No Docker, Neo4j, external DB, or cloud.
Code-aware graph
Parses your codebase AST — files, functions, classes, call flows — enriched by what your agents learn.
Dashboard control plane
Browse the graph, watch agents live, search, and dispatch work.
MCP + hybrid search
An MCP server plus graph + vector + relational search in one call.
Sandboxed task board
Dispatch tasks that run in isolated git worktrees with a budget cap; review the diff, then merge.
See it run
Demo coming soon
The walkthrough video isn't wired up yet. Want the live version? Book a teardown and I'll screen-share it.
Book a 30-min teardown →Who it's for
AI dev agencies
Ship client agent stacks that don't leak context between engagements.
Engineering teams (5–50 devs)
Give every developer's agents one shared, inspectable project memory.
Local-first builders
Keep your codebase and decisions on your own machine — no cloud round-trips.
The thesis
The model is now a commodity. Your context is the moat.
Models keep getting cheaper and more interchangeable — this quarter's frontier is next quarter's default. The durable lock-in was never the model; it's the context woven around it: your codebase, your decisions, your agents' hard-won memory. Loom keeps that context yours — local, inspectable, and portable across whichever models win next.
Work with me
Done-for-you, from teardown to running fabric.
Agent workflow teardown
A live screen-share. I look at how your agents work today and show you exactly where context is leaking. No pitch.
Book a 30-min teardown →Workflow audit
A written teardown of your multi-agent setup: where memory is siloed, what to share, and the highest-leverage fixes — prioritized.
Setup sprint (done-for-you)
I install Loom OS, wire your agents into one shared graph, and hand back a working local-first memory fabric your team owns.
Support retainer
Ongoing tuning, upgrades, and new agent integrations as your stack — and the models underneath it — keep changing.
Not sure which fits? Start with the free teardown — everything else follows from what we find.
About
I'm Mohamed — a Cairo-based developer with nine years in the trade, building local-first AI dev tools for people who are, like me, allergic to cloud vendor lock-in. I built Loom OS to scratch my own itch: I wanted my agents to share one brain that lives on my machine, not in someone else's cloud. Which means I can set it up for your team faster than anyone.
Read the story: why I choose local AI over cloud APIsStop renting your context.
Thirty minutes, screen to screen. I'll show you where your agents' memory is leaking — and what owning it looks like.