Give your agents a real database.
Say "log this bug" in Claude and a validated record appears: status, owner, history. Query it next week from Cursor and the same record answers, even if an agent changed it in between, because every change is attributed. One hosted database, every MCP client, no server to run.
Your agents can do the work. They can't keep the state.
Every session starts from zero. The ticket your agent triaged, the lead it enriched, the decision you made together: stranded in a transcript, gone when the context window closes. So you improvise. A markdown file here, a scratch SQLite server there. Structure drifts, agents clobber each other's writes, and none of it exists outside one machine. Agent-driven work needs a data layer built for it.
You've probably already hacked around this. Four ways it breaks.
Everyone building with agents hits the state problem, and everyone reaches for the same workarounds. Each one trades away something a real database was always better at:
"Just use AI memory"
Memory holds facts, not work. You can't ask a transcript for "open tickets grouped by owner," and what Claude remembers, ChatGPT has never heard of.
"Just use a spreadsheet"
Your AI can edit a file, but nothing enforces structure: statuses drift, columns mutate, one sheet can't reference another, and there's no record of who changed what. And the file lives on one machine, not in every chat.
"Just keep the tools"
Jira, Linear, Trello, Asana, Notion, a CRM: real structure, but one silo per product, built for clicking, with MCP bolted on after. A ticket in one app can't link to a customer in another, and you're paying per seat for UIs your agents never look at.
"Just roll my own SQLite MCP"
You could, and it works, on one machine. But your local server isn't there from ChatGPT on your phone, nothing validates a field or logs which agent changed what, and every schema change is a migration you now own. You'd be maintaining infrastructure instead of shipping your agent.
Statey is what you'd build, minus the maintenance. One hosted database: validated records, versioned schema, every change attributed, collections that relate to each other, and native to MCP from day one.
Under the hood: every workspace is its own isolated database (MongoDB today), every write is transactional and logged to an append-only activity stream, and your records come back as plain JSON from a single query. Leaving is a query, not an export request.
A real database your AI runs for you. No UI, because you don't need one.
Statey holds your work as proper records: tickets with statuses, customers with plans, notes linked to both. Your AI creates, updates, and queries them mid-conversation. Want to see the board? Ask, and Claude draws a fresh table or kanban from live data, right in the chat. That's why Statey ships no interface of its own: the view is drawn on demand, the records stay put.
Real records, not chat memory
A ticket has a status. A customer has a plan. Statey validates every field, so "done" never drifts into five spellings of done.
Know who changed what
Every edit is logged with who made it, human or agent. Ask "why is this ticket closed?" and get a real answer.
You and your agents, same data
Update a ticket in Claude while an agent updates it from Cursor. Nobody silently overwrites anybody.
Data changes wake your agents
A new bug lands in tickets and your triage agent picks it up. No cron job, no polling, no you.
Store. Query. React.
Store
Say "track this bug" or "log this lead." Your AI files it as a validated record. No setup, no schema design, no forms.
Query
Ask "what's still open?" in this chat or any other, today or next month, and get the live answer as a board, a table, or one number.
React
A new record lands and a trigger wakes your agent to handle it and write the result back. You see the whole chain of who did what.
Connect once. It's there from every surface.
Add Statey to a client once and your records are simply there: in Claude on your laptop, Cursor at work, ChatGPT on your phone. Create a ticket in one, close it from another. Agents you run see the same data you do, and every change says who made it.
Bend it into anything. Change your mind anytime.
You don't hand-design the schema. Describe what you're tracking and Statey builds the structure: typed fields, enums, relations. Want it shaped differently next week? Say so: add a field, split a collection, rename a status. The schema is versioned, so changes apply in place, with no migration scripts and no lost history.
Agent memory that survives the session
Facts, findings, and decisions your agents write as records and query back from any client, weeks later.
A task queue for a crew of agents
Work items multiple agents claim, update, and hand off, with attribution instead of clobbered writes.
Run logs & eval results
Every agent run stored as a queryable record: inputs, outcome, cost. Ask for the failure rate, not the scrollback.
A research vault
Sources, findings, and citations that compound instead of scrolling away.
Bring the work that doesn't fit in a chat window.
Tasks & tickets
The tracker you'd build in Jira, Linear, or Trello, run end to end by your agents.
Customer records
CRM data your agents read, enrich, and keep current.
Markdown docs
Endless structured documents, versioned and queryable.
Inventory & ops
Counts, statuses, and operations that stay in sync.
The way Airtable was the system of record for browser-driven work, Statey is the system of record for agent-driven work.
Pay for what you keep.
Statey is priced on the records you store, not the reads and writes flying back and forth. Your agents can work the workspace as hard as they like; you only pay as your data grows. Set a hard spend cap and Statey holds the line: breach it and writes pause, nothing is ever deleted.
See full pricing →a generous free tier, then simple usage-based storage as you scale.
Get started in your MCP client.
Add it to Claude, Claude Code, Cursor, or any MCP client. Sign in and your workspace is ready.