At the base it does what you’d expect: log once, recall everywhere. The reason we built it is the three layers that sit on top: a decision ledger with verdicts, a learning engine that watches how your best work gets done, and a silent distribution layer that makes every AI in your org run on your best judgment.
When an AI tool needs background, Memory Stack doesn't hand it your entire history. It retrieves the slice that's relevant to what you're doing right now and injects that. You stop pasting the same context into every prompt, and you spend far fewer tokens doing it.
What you tell Claude is usually invisible to ChatGPT, and what your coding agent learns stays locked inside that agent. Memory Stack sits across all of them, so the same context is there whether you're in Claude, ChatGPT, Cursor, or an agent you wrote yourself.
Memories are private by default. But when something is worth sharing — a hard-won debugging approach, an approach to a tricky problem type, a way of framing a particular kind of decision — you can share it to a team or push it into the wider Commons. That's where the value stops scaling by person and starts scaling by organisation.
The skill-transfer story
The way your sharpest person handles a problem — the framing, the tradeoffs they reach for, the things they refuse to do — doesn't have to live only in their head. Confirm it as a best practice, share it to the team, and it becomes the default standard every agent on the team works from.
Three scopes
A memory flows outward only when you say so. The centre is always private.
Every tool now stores something. Memory Stack decides: which decisions are still holding, which rules were earned, which constraints need enforcing before an agent acts. Three moves that no capture layer makes on its own.
Decisions are first-class objects: who decided, when, what it superseded, what’s counting on it. Each carries a verdict — holding, walked back, under pressure, untested — that updates as reality tests it. When two decisions collide, the contradiction surfaces before the customer finds it, with both sources cited. Agents check the ledger before acting; refusals leave a receipt.
Your best people work under constraints they earned the hard way. Memory Stack sits alongside the work and notices the patterns. Someone states a rule twice — the engine clusters it into a standing constraint. Someone corrects their AI — the correction generalises. A project keeps succeeding under a specific discipline — the discipline gets extracted with the evidence that earned it. You confirm what rings true. Nothing becomes law without a human saying so. This observes the work, not the worker.
Every significant decision gets a ledger entry with a verdict: holding, walked back, under pressure, or untested. The Decision Dossier shows provenance — where the decision came from, what has contradicted it since, whether it's actually guiding current work. Commitments that drift without anyone noticing don't stay invisible here.
Once a rule is confirmed, distribution is free. The constraint your best engineer earned over five years gets injected into every AI session that touches relevant work — for the new hire, the contractor, and the support agent fleet. Nobody attends a training. Nobody reads a wiki. The junior engineer’s AI refuses the Friday deploy, cites the rule, and shows where it came from. A weekly receipt proves it’s working: rules applied, contradictions caught, decisions that held versus walked back.
Weekly receipt
Every Sunday: contradictions caught, rules applied, decisions that held vs. walked back, work completed across every tool. Not a summary someone wrote — a structured receipt pulled from the actual trace of what happened. 206 working memories became 54 patterns, which became 7 enforced rules. That progression is auditable, step by step.
This week
Memory Stack looks for where different people or projects are circling the same thing. Context Overlaps surface those collisions before duplicate work happens. The Memory Graph shows how your memories relate to each other, so you see the shape of what you know instead of a flat list.
This is where the value shifts from personal to organizational. The same knowledge lines up across boundaries even when people describe it in different words.
Memory graph
Organisations deploying AI have a real question: what does our AI actually know, and who put it there? Memory Stack gives admins the visibility and controls to answer that — without having to ask anyone.
See what shared memories exist across your organisation — which teams have shared what, when it was created, and when it was last used. No more shadow context drifting through your AI tools unchecked.
Before a memory is promoted to a shared scope, Memory Stack automatically scans for personal identifiable information and strips it. Private details from one person's context stay out of the team layer.
Every recall has a record: which agent called it, which memories were returned, what it was used for. When you need to understand why an agent behaved the way it did, you have something concrete to look at.
For organisations that need it, memory can be stored in your own infrastructure rather than on a shared cloud. Your data doesn't leave your environment. Enterprise deployments get logical isolation at minimum; dedicated VPC for teams that need it.
When you run agents, each one can record a receipt of what it did: which agent, which tool, what changed, how much it saved. Every receipt stitches into one timeline across every runtime, so work that happened in Claude, then Cursor, then ChatGPT reads as a single thread you can click into.
It turns "the AI remembered" from a claim into something you can look at and verify.
Every major AI tool now ships some form of built-in memory. The catch is the same in each case: what it learns about you stays inside that product. Switch tools, run a different agent, start a new workflow — and you're briefing from scratch again.
You don't have to take the savings on faith. Memory Stack tracks the tokens you've avoided re-sending and your recall activity over time, so the return is a number you can point at.
Log once across every tool. The decision ledger keeps verdicts current and surfaces conflicts. The learning engine watches recurrence, generalises corrections, extracts proven constraints. Confirmed rules get enforced everywhere — for every employee and agent — and a weekly receipt closes the loop: what held, what was caught, what changed.
Works with Claude, ChatGPT, Gemini, Cursor, and any AI tool via API. Free to start — no credit card needed.