What the word memory covers across eight products, who can save and retrieve each kind, and the pilot that shows whether a teammate really inherits your context
Aug 18, 2026 · 13 min read
A team choosing for shared memory should judge the products on scope, retrieval, permissions and ownership rather than on how many models each one carries. The setup earns its place once several people work on the same customers, products or campaigns and keep handing work to each other. The eight workspaces compared on the same criteria below are Playgram, WorkLLM, nexos.ai, Langdock, TeamAI, Aymo, Magai and TypingMind.
The word memory covers four different products in this market, and a demo makes them look identical. A chat holds what is in front of the model. A project applies shared files and instructions to several chats. A knowledge base retrieves from documents somebody added on purpose. Automatic memory stores reusable facts and brings them back later without being asked.
This guide sets out those four shapes, what each product actually documents, and what to ask about approval, correction and ownership. It ends with a pilot built around one question, which is whether a teammate can continue your work without being sent a summary first.
Brand rules, product facts and campaign history get explained again in every new chat.
Each client needs its own context, and none of it should surface in another client's work.
Product research, decisions and customer feedback move between people every week.
One or two people drafting alone, with no handoff for shared context to serve.
Work is split across products whose ownership rules differ, and the split produces four costs a team can name by the end of a month.
Separate subscriptions buy the same model access twice over, because one person may hold four business plans while much of the work overlaps. As an example, four single-vendor team plans came to about $101 per person a month at July 2026 list prices. That total takes ChatGPT Business at $25, Claude Team at $25, Gemini Business at $21 and Grok Business at $301, 2, 3, 4. Read it as an illustration rather than a rate, because a cheaper mix is easy to assemble. Per-seat plans also charge for provisioned access rather than for activity, so an occasional user costs a full seat, and Claude Team bills a five-member minimum whether or not five people are working2.
People move between tabs, rewrite prompts and upload the same files again. When a researcher starts in one product and an editor prefers another, the second person receives a document, a copied chat or a summary written by hand. That step has a name inside teams, which is context forwarding, and it means one person spends real time converting their own history into a brief somebody else can use.
Ordinary chat history belongs to the person who created it, and sharing a chat does not make the facts in it reusable in later work. Four things get called the same word, so it is worth separating them. A chat holds the messages currently supplied to the model. A project applies shared files and instructions across several chats. A knowledge base retrieves passages from documents somebody added deliberately. Automatic memory stores reusable facts outside the original conversation and brings them back later. Treating those as equivalent is what creates a false expectation about what a teammate inherits, and it is the reason a demo of any of them looks the same.
Separate subscriptions leave nobody able to say who is actively using each service, which models generate the cost, or which projects hold sensitive material. Ownership of shared prompts, agents, files and saved entries is spread across accounts, so a departure raises a question nobody has answered in advance. An admin also needs to know whether a memory can be corrected or deleted, and whether one client's information can be kept out of another client's work. Without those answers a memory feature is knowledge sharing rather than something the company governs.
Five checks on any candidate. The middle three are where products in this category differ most, and the vendor pages describe them least precisely.
A person should be able to pick a model that suits the stage without rebuilding the project brief for it, and switching partway through should not discard the working context. Vendors document the switch far more often than they document what the next model actually receives.
List the work beyond chat: research with citations you can open, document editing, PDF and presentation analysis, spreadsheet work and charts, code review, and chats that leave nothing behind. Add the connectors the team lives in, such as Drive, SharePoint, Slack, GitHub, Jira and calendars.
Files, instructions, approved outputs and decisions should be reusable by project members, and a person should be able to inspect what the system kept. An owner then has to be able to correct or delete an entry that is out of date, because a stored mistake is retrieved as confidently as a fact.
Personal, project, client and organisation context should not share one audience by default, and chats, projects, knowledge bases, agents and saved entries can each have a different owner. An admin should also see usage by person, model, project and period, and cap spending before an overage.
Sensitive or exploratory sessions need a mode that leaves no history, or people do that work somewhere you cannot see. A new teammate should inherit the approved project context without reaching unrelated material, and the pricing shape should let an occasional reviewer join without a full seat.
The multi-model workspaces a team is most likely to weigh up, on the same criteria and to one standard. Where a vendor does not document something, the cell says so.
This table compares multi-model team workspaces with each other. The single-vendor plans a workspace replaces are priced further down, under 'Priced per seat', and are not rows here. Pricing is the lowest-priced paid plan that covers five users, at the monthly rate, so a product whose entry plan holds fewer than five people is shown on the plan that holds them. Each cell cites the page that documents that cell rather than one pricing page per row. Figures checked August 2026 against each provider's own pages, and cells marked 'Manual test required' could not be confirmed from public documentation.
The same products on the criteria that decide daily use: what each one does besides chat, what it connects to, what an admin can see and cap, and where your data goes.
These criteria decide daily use more than the model list does, and vendors document them very unevenly. 'Not publicly documented' means the official sources checked did not state it, and it does not mean the feature is absent, so read those cells as questions to put to the vendor. Checked August 2026.
The published per-seat price of each major single-vendor team plan, billed monthly. Each one keeps its own record of the work and none of them can read another's.
Buying all four for one person came to about $101 a month at July 2026 list prices. Read that as one example stack rather than a going rate, because a team can assemble a cheaper mix and the four plans do not buy the same amount of use. Prices change often and vary by annual against monthly billing and by region, so confirm current pricing with each provider before purchase. The estimator further down compares this shape with usage pricing on your own numbers.
One of these arrives on an invoice and three arrive as time, which is why a team usually notices them in the wrong order.
Six setups, from a workspace that stores decisions for the group down to a person writing a briefing document. The memory options come first because they are the subject here.
Approved context is saved where permitted teammates retrieve it later, so a handoff stops depending on somebody writing a summary. WorkLLM documents organisation memory with an approval step and nexos.ai documents memory inside shared Projects7, 11.
Best for: Teams whose handoffs repeat the same context.
Strengths
Trade-offs
Files, instructions and chats belong to a project that named members can open, without anything being extracted automatically. It is the common shape in this category, and it covers continuity while leaving the filing to people.
Best for: Teams that can keep a project tidy as they work.
Strengths
Trade-offs
Documents are uploaded, chunked and retrieved when relevant, which most products in this category offer under some name. It is predictable and auditable, and everything in it got there because a person put it there.
Best for: Teams with stable reference material and an owner for it.
Strengths
Trade-offs
Standardise on a single vendor and use whatever memory it ships. Governance gets simpler because there is one policy to read, and the memory on offer is whatever that vendor decided to build.
Best for: Teams already standardised on one model family.
Strengths
Trade-offs
No new product. The team keeps a brief, a decision log and the current source files, and updates it whenever work changes hands. It costs nothing and it is the fallback every other option should beat.
Best for: Two or three people with occasional handoffs.
Strengths
Trade-offs
Engineers build the retrieval and permission layers themselves, so what gets stored, who can read it and how it is retrieved are all decisions the team makes rather than reads in someone's documentation.
Best for: Engineering teams that can own retrieval and permissions.
Strengths
Trade-offs
A quarterly launch with the approved context set once and permissions on it, so the sales teammate at the end is never privately briefed by the researcher who started.
A person decides at the approve stage what enters the project and sends the rest back to the research stage, which is how saved knowledge stays different from raw chat. The brief, the open questions and the decision record then stay in the project, so the next teammate reads them instead of asking the researcher what was decided.
Every product here uses the word, and a demo makes all four look alike, so these are the distinctions to draw before trusting any of them with project knowledge.
A context window is the material supplied to a model for one answer, and it is finite and gone when the chat ends. Chat history is a record of past messages. Memory is context stored outside the thread and retrieved in later work, which is the only one of the three a teammate inherits.
Some keep history alone. Some retrieve from documents somebody added on purpose. Some learn automatically and keep it private to one account, as Langdock does with a 50-entry personal store. Some save it where the team can retrieve it, which WorkLLM documents with an approval step7, 32.
Personal, project, client and organisation context need boundaries that match how the team works, and some products draw only one of them. Ask whether an approved entry keeps the permission of the file behind it, because WorkLLM notes that organisation memory reaches everyone once approved7.
Can one teammate create approved context that another teammate's future chat retrieves, while unrelated colleagues cannot reach it? Then check who can inspect, correct and delete an entry, and what happens to a project when its owner leaves, because ownership of chats and entries often differs.
A vendor-neutral plan for the one behaviour a demo cannot show honestly, which is what a second person inherits. It runs about two weeks.
Record every AI subscription and API account with its owner, department, price and billing term, then note active usage and the overlap between providers. Add the files and sensitive data already uploaded, the shared prompts, agents and knowledge bases, and the workflows that depend on one person's chat history.
Choose three to five pieces of real work that change hands. Research into an article draft, discovery notes into a proposal, product research into a specification, support analysis into a report, or a client brief into campaign assets all work. Prompts written for a demo prove nothing about a handoff.
Give each candidate the same material, then run a project with no memory, a shared project, and a project one teammate starts and another continues. Change model midway, remove a fact that is out of date, restrict a file to part of the team, and transfer the project owner as if they had left.
Track time to a useful first answer, manual edits, repeated context explanations and repeated uploads, and record which model suited each stage. Then count retrieval errors and stale entries, permission failures, and the time it took somebody to inspect and correct saved context.
Ask what the vendor calls memory and whether it is automatic, saved by hand or document retrieval, which scopes exist, and who can approve, edit and delete an entry. Then ask whether the original file permission still applies, whether a person can see why a fact was used, and whether the training policy is stated for the exact plan you would buy.
A team shopping for shared memory should decide on scope, retrieval, permissions and ownership rather than on the model count. WorkLLM publishes the clearest account of automatic organisation memory with approval, nexos.ai documents project memory that holds across a model change, and Langdock separates a personal automatic store from explicitly shared projects. The others mainly document shared workspaces, chats, agents or knowledge bases, which support continuity without being automatic.
Four limits apply. Plans with similar names include different usage, analytics, permissions and integrations, so a comparison by name is worthless. Stored context helps only when people can see what was kept and admins can control its audience. Not everyone needs the same access, and the useful scope is usually the project rather than the whole company. And prices and plan caps move faster than any page can track.
So the choice comes down to what a second person can retrieve tomorrow, and to who is allowed to correct it. Test that on your own handoffs, with one restricted file and one departure, because those two cases are where the difference between the four shapes stops being a word and starts being visible.
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