Stack AI Remembers the Conversation. Your Scheduled Runs Still Start Blank.

You built a daily briefing agent in Stack AI. Every morning it scans the news, pulls numbers from your tools, and posts a summary to Slack. The first week it feels like magic. By the third week you notice something: every morning the agent writes as if yesterday's briefing never happened. It flags the same story twice. It forgets the format you asked it to use. It re-derives conclusions it already reached.

So you dig into Stack AI's memory options, and the platform clearly thought about this. The LLM node has a Memory setting. There is a Shared Memory node for carrying context across interactions. And yet the agent still starts blank every morning. Here is why, and what to do about it.

What Stack AI's memory actually does

Stack AI splits its memory into two pieces, and both are built for conversations, not runs.

The first is the Memory setting on the LLM node. Stack AI's own RAG guide describes it plainly: conversational memory keeps track of what has already been said so the next query can use it. You can choose the strategy. The simple one concatenates recent turns and feeds them back in. A more advanced one summarizes earlier exchanges into a compact form so long conversations do not blow through token limits. There is also vector memory, where past queries and answers are embedded and recalled on demand. (Stack AI's advanced RAG guide)

The second is the Shared Memory node, which the platform describes as the way to maintain context across multiple interactions. Wire it into the workflow and the agent can reference earlier exchanges instead of treating every message in isolation.

Both of these solve the same problem: a person chatting with the agent should not have to repeat themselves. And for a chatbot on your website, they work. The follow-up question gets understood. The thread stays coherent.

The problem with scheduled runs

A scheduled or triggered workflow run is not a conversation. It is a fresh execution.

When the run fires at 7 AM, there is no open thread to resume. The Memory setting has no recent turns to concatenate, because the last run's turns belonged to a different execution. The summary it might have built has nowhere to live between runs unless you wired the Shared Memory node deliberately, pointed it at a stable key, and made sure every run reads from it before acting.

Most people do not wire it that way, because the builder does not force the question. The workflow canvas is about nodes and connections inside one run. What happens between runs is outside the frame, so it never gets designed. The agent ships, runs daily, and quietly forgets everything that is not baked into the prompt.

This is the gap that shows up as small annoyances first. The agent re-flags stories it already covered. It asks your API for data it already pulled yesterday. It drafts the summary in a different shape every morning. Then one day it acts on stale information that last week's run would have corrected, and the annoyance turns into real work.

The DIY fixes, and their ceiling

Operators usually reach for one of three workarounds.

The first is the prompt. You paste yesterday's state into the instructions, or you make the workflow fetch a "context" document at the start. This works until the state outgrows a prompt, and it always does. Summaries drift. Details get lost. Someone edits the document by hand and the agent never sees it.

The second is the spreadsheet. The run writes its findings to a Google Sheet or an Airtable base at the end, and the next run reads it back at the start. This is the most common pattern in the wild, and it is also the most fragile one. The sheet has no memory semantics. It cannot tell the agent what changed, what matters, or what was already handled. It is a file drawer, not a memory.

The third is the Shared Memory node, used properly. This is the right instinct, and for a single Stack AI agent it goes a long way. But it has a boundary that matters: the memory lives inside Stack AI, inside that agent. Your n8n scenario cannot read it. Your Make scenario cannot write to it. The agent that runs your weekly report in a different tool still starts from zero. Each platform keeps its own notebook, and none of them share.

The question behind the question

"Does Stack AI remember between runs?" is really asking whether your operation remembers. A single agent remembering its own thread is a feature. Every agent, on every platform, reading from and writing to one shared state is an operation. The first is documented. The second is the thing that removes you from the loop.

The durable pattern is boring and it works: keep the memory outside any single run. Each run opens by asking what the current state of the things it cares about is, acts on the answer, and closes by recording what changed. Not a summary sentence. The actual findings: numbers, names, decisions, open questions.

This is the pattern Vilix AI exists to make trivial. It is a cloud-hosted memory layer, so there is nothing for you to run or maintain, and it connects to everything over MCP: Stack AI, n8n, Make, Claude, ChatGPT, your coding agents. One memory, every tool, every device.

Because it stores full conversation history and not just the facts someone decided were worth keeping, a later run can go back to the actual exchange and re-derive what it needs. Your data stays yours: export everything or delete it anytime in a portable format. The free plan is free forever, and the Pro trial runs seven days with no credit card.

The bottom line

Stack AI remembers the conversation. That covers the chat window, not the 7 AM run. If your agent only ever talks to people, the Memory setting and the Shared Memory node are enough. The moment the same agent runs on a schedule and the rest of your stack runs somewhere else, the memory needs to live outside the run, outside the agent, and outside the platform. Build that one layer and the forgetting stops everywhere at once.