Does Drip Remember Between Runs?
Does Drip Remember Between Runs? No. A Drip workflow holds no memory of its own between runs. When a person enters a workflow, that workflow reads the person exactly as the record stands at that moment: the tags currently applied, the custom fields currently stored, and the events currently recorded on that person. Anything an earlier run worked out survives only if that run wrote it back first, as a tag, a custom field update, or an event a later step can check. That distinction is easy to mi
Does Drip Remember Between Runs?
No. A Drip workflow holds no memory of its own between runs. When a person enters a workflow, that workflow reads the person exactly as the record stands at that moment: the tags currently applied, the custom fields currently stored, and the events currently recorded on that person. Anything an earlier run worked out survives only if that run wrote it back first, as a tag, a custom field update, or an event a later step can check.
That distinction is easy to miss because Drip feels continuous. People, tags, and workflows are all still there tomorrow, and a person halfway through a workflow resumes at the right step. The continuity lives in stored records and workflow definitions, not in a memory one run hands to the next.
What does a Drip person actually hold?
Drip stores three different kinds of data about a person, and the split matters. Custom fields are current state: the values the account currently believes about someone, such as a name, a plan, a signup source, or a lifetime value total. A form, an import, a rule, or a workflow step sets a custom field, and a later write replaces the earlier value. The profile shows the latest value, not the history of values.
Tags work differently. A tag is a label applied to a person, such as a product interest, a customer status, or a campaign response. Tags accumulate until they are removed, and segments, trigger filters, and workflow decisions can all check whether a person carries a tag right now. A tag records that a label applies. It does not record why it was applied, when the reasoning changed, or what was discussed before it was added.
Events are the third shape. An event records that something happened: subscribed, placed an order, visited a page, completed a form, or performed a custom action sent in through an integration. Events keep their timestamps and their properties, and they stack up on the person timeline. A workflow or a segment can ask whether an event happened, how often, and how recently.
All of that is stored data a step can check. A later workflow reads tag lists, field values, and event counts. It does not recover why a subscriber was given a special offer, or what was promised outside Drip, unless someone stored that detail as a field, a tag, or an event first.
How do Drip workflows and rules actually run?
Drip splits automation into two tools. Rules are single switches: a trigger fires and one or more actions happen at once, such as applying a tag, updating a custom field, or recording an event. Workflows are sequential paths: a trigger starts the path, then delays, decisions, goals, and actions guide a person step by step toward an outcome, often a series of emails over time.
Every workflow starts with a trigger, an event that defines how people enter. Triggers can be a form submission, an order, a page visit, a tag applied, a custom event, a date arriving, or a person entering a segment. Triggers are forward-facing: they only apply to actions that happen after the workflow is turned on, and a person cannot enter a workflow they are already in. A trigger filter can narrow entry further, so a person must perform the event and also match extra criteria, such as carrying a tag or matching a segment condition.
Inside a workflow, position persists. A person in a three-week welcome path resumes in week two, decisions branch on current tags, fields, and events, and a goal can pull a person out when the point is reached. That persistence ends at the workflow boundary. A person can sit inside a welcome, cart, and win-back workflow at the same time, and each tracks its own position and ignores the others. A rule that fires tonight does not know what a workflow decided yesterday. It only sees the trigger in front of it and the record as it stands now.
What about segments and the person timeline?
A person profile shows a useful record: current tags and custom fields, the events recorded, the emails sent, and the workflows the person has entered. Segments group people by those same signals, rebuilding as tags, fields, and events change. That record answers what the account currently believes about a person and what that person has done.
That record is not working memory the next run consults before acting. A workflow does not open last month's workflow notes to learn why a branch underperformed, and an AI step drafting the next campaign does not recover the earlier draft, the rejected subject line, or the reasoning behind a split from the profile. The history is there for a person to review, and for conditions to count. The next automated run starts from the profile in front of it.
The gap shows up in routine work. A scheduled agent that reviews workflow performance weekly and drafts the next test has to be handed the numbers, the angle already used, and the promise already made, every week. The context arrives by hand, or not at all.
Where this leaves agents working from Drip
Drip is often one part of a wider setup. A store sends orders in, an n8n, Make, or Zapier scenario updates tags and fields, and planning happens in a chat tool somewhere else. Each part can read the same current tags, fields, and events. None of them inherits the conversation behind those values. So the work turns into re-briefing: background gets pasted into a prompt again, and a question answered in another tool gets asked again.
One memory that every run can read
The fix is a memory layer that lives outside any single person 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: alongside Drip workflows, 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 tag name or a field value on a profile.
The loop is simple. Before acting, a run retrieves the relevant history for the work 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. Drip stays the system of record for people, tags, and sends. 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.