Does Baserow Remember Between Runs?
Does Baserow Remember Between Runs? No. A Baserow automation holds no memory of its own between runs. Each run starts from the database as it stands at that moment: the rows, the field values, and the event that triggered it. Anything a previous run noticed, decided, or worked out survives only if that run wrote it into a row, a field, or another table first. That distinction is easy to miss because a database feels continuous. The same tables, views, and automations are still there tomorrow,
Does Baserow Remember Between Runs?
No. A Baserow automation holds no memory of its own between runs. Each run starts from the database as it stands at that moment: the rows, the field values, and the event that triggered it. Anything a previous run noticed, decided, or worked out survives only if that run wrote it into a row, a field, or another table first.
That distinction is easy to miss because a database feels continuous. The same tables, views, and automations are still there tomorrow, and a scheduled workflow still fires at the same time. The continuity lives in the stored data and in the workflow definition. It does not live in a memory that one run hands to the next.
What does a table actually hold?
A table holds structured data. Rows hold field values: a lead name, a status, an owner, a due date, a checkbox, a linked record, and any notes typed into a long text field. Views filter and sort those rows, and formulas calculate results from the values in front of them. That stored data is real persistence, and automations can read it on every run.
A workflow can check a status field, count rows that match a condition, or read a date field to decide whether something is due. A formula can total a column, look up a related row, or flag a record that needs attention. All of that works from current values.
The scope is the table. What persists is what was deliberately saved as a field value. The reasoning behind a change, the options a run considered and rejected, and the thread of an earlier discussion are not in the table unless someone typed them into a field. A new run reads values. It does not recover the conversation that produced them.
How do Baserow automations actually run?
An automation in Baserow connects a trigger to one or more actions, with formulas, variables, and router nodes for conditions in between. The trigger is what starts the run, such as a row being created, a row being updated, a row being deleted, or specific field values changing. Workflows can also start on a schedule, start manually, or be started by another workflow or an application. The actions are what happens next: update a record, create rows, send a notification, call an API, run an AI step, execute JavaScript in a code node, or branch through a router when a condition is met.
Every run is a fresh execution of that definition. A workflow that summarizes a new support ticket, assigns it to a team, and drafts a follow-up reads the ticket in front of it, does the work, and ends. The next ticket starts the same way, from its own row. The workflow does not retain the last ticket's summary, a note about which assignee already has too much work, or a record of what a customer was promised last time, unless a table stores that state explicitly and the workflow is built to read it back.
The workarounds prove the point. Keeping a first-contact date takes a dedicated field that a run fills once and later runs are told not to overwrite. Tracking progress across stages takes status fields and helper columns that each run updates by hand-designed rules. Batch actions can create or update up to 1,000 rows in a single step, and that scale makes the design more powerful, not more remembering: a thousand rows can be updated from current values, and none of them carries the reasoning from the run before.
What about execution history and Baserow AI?
Execution history is stored for auditing, and node history helps with debugging a workflow. Both are records for a person reviewing what happened. Neither one is memory a workflow carries into its next run. A run does not open its own history to learn why it routed a ticket one way last week, and it does not read a past execution log to recover context before acting.
Formulas follow the same rule. A formula field shows the result calculated from current inputs. Change an input and the result updates. The formula keeps the latest result, not a history of earlier results or the reasons behind them.
Baserow AI works from the data in front of it as well, including AI fields and the Kuma assistant. An AI step can summarize a ticket, classify a lead, or draft a response based on the row it is given. Each use starts from the stored data, not from a remembered conversation across runs, days, or working sessions. The data is shared. The thread is not.
Where this leaves agents working from Baserow
Baserow is often the shared table behind a wider automation setup: an n8n, Make, or Zapier scenario reads and writes the same rows, and an AI agent answers questions or prepares updates from them. Each of those tools sees the same current values, which is genuinely useful. None of them inherits the thread behind the values: why a lead was marked unqualified, what was agreed on a call, or which explanation already failed with a customer.
So the daily work quietly becomes re-briefing. Someone pastes background into a prompt, re-asks a question that was answered in another tool, or writes longer and longer instructions so the next run can act without the missing context. The automation fires reliably. The context arrives manually, or not at all.
One memory that every run can read
The fix is a memory layer that lives outside any single table or workflow, so every run and every tool works from the same past. Vilix AI is built for exactly that job, for scheduled agents and automations that start blank.
Vilix AI is cloud-hosted, with zero infrastructure to run or maintain. Connect it once over MCP and the same memory is available everywhere the work happens: in Baserow automations, in n8n, Make, and Zapier scenarios, and in the AI chat tools used to plan and review the work. What gets stored is full conversation history, not just facts extracted from it, so a later run can recover the exact agreement, the reason a decision was made, and the wording that was already used, not only a status label in a field.
The loop is simple. Before acting, a run retrieves the relevant history for the records it will touch. It acts with that history in hand. Then it saves what it did and what it learned back to the same memory. The table stays the system of record for rows and values. Vilix AI becomes the system of record for the thread behind them, and the re-briefing cycle ends.
Free forever, no credit card. The free plan is free forever, the 7-day Pro trial needs no credit card, and your memory stays under your control: export everything or delete it anytime.