Does Integrately Remember Between Runs? Run History Yes, Agent Memory No

You are an automation operator, and you have probably asked this about every platform you touch: after the run finishes, does the next one know what happened? Integrately is a popular Zapier alternative, with one-click automations, more than 1,500 app integrations, and an AI assistant that builds automations from a plain-English description. The honest answer to the memory question is a split: Integrately remembers that your runs happened, and it keeps the receipts, but each new run starts completely fresh. Anything that looks like an AI agent inside your automation wakes up blind.

The short answer

Integrately keeps a detailed history log of every task execution, so you can always look up what ran, what succeeded, and what failed. AutoRetry, available on the Professional plan and above, even checks that history before retrying failed actions. But none of that history is fed back into the next run as working context. Each automation is linear: when the trigger fires, the steps execute once, in order, and then the run ends. There is no loop support, no parallel branches, and no place where state from one run carries into the next. If your automation calls an AI step, like an OpenAI or ChatGPT action, that AI gets whatever you pass it in that run and nothing else. Yesterday's run might as well not have existed.

What Integrately actually keeps

The platform's memory is administrative, not operational. The history log records each task execution with detailed logs, which is genuinely useful for debugging: when an automation fails at 3 a.m., you can see exactly which step broke and with what data. AutoRetry builds on that log. It checks whether the automation and AutoRetry are both on, reviews what happened, and retries failed actions on a schedule, emailing the account owner if failures persist after ten hours.

The automation definitions themselves also persist, obviously. Your mappings, filters, field transforms, and schedules stay saved. Integrately's AI builder, the feature that turns a plain-English description into a working automation with fields mapped and conditions added, remembers your automation in the sense that it created a durable definition. It does not remember how the automation behaved once it started running.

Pricing context matters here because memory workarounds cost tasks. The Free plan includes 100 tasks and 5 automations per month. Starter is $29.99 per month, or $19.99 billed annually, for 2,000 tasks. Professional is $49 per month, or $39 annually, for 10,000 tasks plus branching, the Iterator, and AutoRetry. Every workaround you bolt on to simulate memory consumes tasks, so the memory question has a direct line to your bill.

What starts fresh every run

Every run begins with a clean slate. When the trigger fires, the automation has no access to what previous runs saw or decided, unless you explicitly pass that information through an app it can read. Data flows down the steps of one run, never across runs.

This is where AI steps get painful. Suppose your automation drafts a weekly sales digest with an AI step. The AI in this week's run has never seen last week's digest. It does not know which leads it already flagged or which anomalies it already reported. If you want continuity, you have to engineer it yourself: fetch last week's output from wherever you stored it and paste it into this week's prompt. That works, but it costs extra steps, extra tasks, and a fragile dependency on whatever storage app you chose.

The workarounds operators actually use

Most operators simulate memory with one of three patterns, and all of them have the same ceiling.

The first is the storage-app pattern: write key facts to Google Sheets or Airtable at the end of each run, and read them back at the start of the next one. It works and is the most common approach. Its ceiling is that the automation now depends on a spreadsheet schema it cannot validate, every read and write costs tasks, and the AI step gets raw rows instead of a summarized, decision-ready past.

The second is the re-fetch pattern: instead of remembering, the automation queries the source system fresh every run and reconstructs context from current data. Fine for volatile facts like ticket status or inventory. Useless for anything the source system does not record, like what the AI decided or what failed last time.

The third is the notes-in-the-prompt pattern: a human curates a running context document and the automation injects it into the AI step each run. Best AI behavior of the three, and the most manual. The moment nobody updates the document, the AI starts operating on a stale past, and stale memory is worse than no memory because it acts with confidence on things that are no longer true.

The cleaner fix: keep the memory outside the platform

The deeper problem is architectural. Integrately's job is moving data between apps on a trigger. Memory is a different job: accumulating what happened across runs, deciding what still matters, and handing the next run a compact, relevant past. When you bolt memory onto a platform built for stateless runs, you pay in tasks, fragility, and maintenance.

The cleaner fix is a memory layer that sits outside any one platform and serves every tool the same past. Vilix AI is built for exactly this: a cloud-hosted memory layer, so there is nothing to install or maintain, and the same memory follows your automations everywhere through MCP. It stores full conversation history, not just extracted facts, so the actual reasoning from a previous run can be revisited instead of a flattened summary. The free plan is free forever, there is a 7-day Pro trial with no credit card required, and your data is portable: export everything or delete it anytime.

The practical pattern looks like this. At the end of each run, the automation writes a short summary of what happened, what was decided, and what needs follow-up into the shared memory. At the start of the next run, it pulls back the relevant slice: the last few run summaries, the durable rules you have set, and any open follow-ups. The automation itself stays simple and linear, which is what Integrately is good at. The memory does the remembering, which is what it is good at.

The bottom line

Does Integrately remember between runs? It remembers that they happened, in a history log you can inspect and retry from. It does not remember what they meant, and it hands nothing to the next run. If your automations only move data, that is fine. The moment they need judgment, continuity, or learning, you need memory that lives outside the run. Your agents forget everything between runs; give them one memory.

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