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Answers about shared memory for people and AI
Start with the question in front of your team. Each answer explains one part of how shared context reaches the AI before work starts.
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Shared memory for teams that work with AI
Shared memory for an AI team is one place the team curates — decisions, rules and the daily record — that every person's AI reads before it starts working, and writes back to when the work is done. Arroway is that place: the memory belongs to the project, not to a chat window, so the second person's assistant starts from what the first person already settled.
Read the answerShared context across Claude, ChatGPT and Codex
One connection, read by every assistant. Arroway runs as a remote MCP server, so Claude, ChatGPT, Codex and any other client that speaks MCP read and write the same project memory — the context does not live inside one vendor, and moving tools does not restart it.
Read the answerHuman-governed memory for AI work
Governed memory means a person decides what counts as a rule, and the AI cannot promote its own inference to that status. In Arroway an assistant writes freely, but what it writes on its own arrives as a proposal; a human turns proposals into standing rules — or corrects them — in a review that takes minutes and happens after the fact, never as a gate the work waits on.
Read the answerHow team decisions reach the AIs doing the work
A decision reaches an AI when it is served to that AI before it acts, in the read the assistant makes at the start of a task. Arroway does that by holding decisions as project material with a stated scope, ranking them against the task, and pinning the ones that must apply always — and by declaring, in the answer itself, how much fit and what did not.
Read the answerHow to install and use Arroway in the main AI ecosystems
You choose the ecosystem you already work in, connect the Arroway server there, and start working — the installation page shows one route at a time instead of a list of every possibility, and it never asks you to sign in before the moment a sign-in is actually needed.
Read the answerShould team knowledge for AI live in versioned files, or in curated memory?
Files in the repository — a CLAUDE.md, a skills folder, notes committed next to the code — are where most teams put what their AI should know, and they work for what a repository owns: conventions, commands, the shape of the code. What they do not carry is the part that changes and needs a person behind it — which rule is in force, who decided it, whether it is a proposal or settled, and whether the model actually received it. Arroway holds that part as product material: curated by people, ranked against the task, and delivered with a receipt saying how much fit.
Read the answerWhere does team memory fit in an AI-native software lifecycle?
The AI-native loop is well described as a sequence of artefacts: an intent written down, a plan derived from it, code produced against the plan, and review closing the circle. What the loop describes is how a unit of work travels. What it does not carry is what stays true between units — which rule is in force, who decided it, why an approach was abandoned, and what the people who never open the repository already settled. That standing layer is what Arroway holds: curated by people, ranked against the task, and delivered to the assistant with a receipt saying how much of it fit.
Read the answerYour CLAUDE.md outgrew a page. What comes out of it?
The advice to keep a CLAUDE.md under a page is good advice, and the reason it is hard to follow is that two different kinds of knowledge end up in the same file. One belongs there permanently: the commands, the conventions, the mistakes an assistant keeps repeating in this codebase. The other is what a person decided — which rule is in force, why an approach was dropped, what may be promised to a customer — and it grows without limit, because decisions never stop being made. The cut is not between important and unimportant. It is between what a repository owns and what a team owns.
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