Does the Albato AI Agent Remember Between Runs? (And What 100 Interactions Actually Means)

The short answer is right there in Albato's own help center, stated in one plain sentence: by default, the agent does not remember anything between runs. Each run starts from scratch.

There is an optional memory setting, and it works exactly as documented. But it has a ceiling, a scope, and an intended use case. Does the Albato AI agent remember between runs? No, not by default. Yes, optionally, but only the last 1 to 100 interactions, only inside one thread, and Albato's own guidance says it is meant for chatbot-style flows.

What Albato's memory setting actually is

Albato's AI agent is a step you drop inside an automation: it reads data from earlier steps, decides which action to run, and can call roughly 5,000 actions across connected apps as tools. In the setup guide, "configure agent memory" is marked optional, and the behavior without it is explicit: nothing persists between runs.

When you do turn it on, two settings define the mechanism:

Memory size. The number of recent interactions the agent takes into account, from 1 to 100. One interaction equals one input plus one output.

Thread ID. This separates memory between different users or conversations. The docs' example is a Telegram chatbot: pass the chat ID so each user has their own context.

That is the full mechanism. A recent-interactions window, capped at 100, keyed by thread. It is chat memory: the agent stays coherent inside a conversation. Nothing in the design carries run-level knowledge forward.

Do the math on 100 interactions

The interaction definition is where scheduled automation work quietly breaks. In a chatbot, one interaction is one back-and-forth with a person. In a scheduled automation, the agent talks to records, not people.

Take a nightly lead-triage agent built on Albato. Every evening it pulls the day's new leads, scores each one, routes the good ones, and marks the junk. For every lead it processes, the input is the lead record and the output is the decision. That is one interaction per lead.

A typical night processes 250 leads. The memory window holds 100. By run 101 the oldest interactions start falling off, and when the run finishes it has already forgotten the first 150 decisions from the same night.

And forgetting is the least of it. The window is a sliding buffer that drops the oldest first, so it can never accumulate anything. The agent never learns that leads from one landing page never convert, because the evidence ages out before a pattern can form. Every run is structurally incapable of getting smarter. It can execute the task forever without ever improving at it.

This is the core mismatch: Albato's memory keeps the recent conversation. A scheduled agent does not need the recent conversation. It needs last week's decisions, last month's patterns, and the open items it left unfinished.

The thread-ID problem: memory that never leaves its island

The second limitation is the Thread ID. Memory is keyed to it, so each thread is its own island. In the Telegram example, user A's chat ID gets one memory and user B's gets another.

Now apply that to a real automation setup. You have a Telegram support bot with memory per chat ID, and a scheduled nightly agent that summarizes the day's support conversations and updates your docs. Those are two different threads, so the nightly agent cannot see what the support bot learned.

You could technically route every scheduled run through one shared thread ID. But then you hit the first problem at full speed: one thread stuffed with every run's history, capped at 100 interactions, dropping the oldest first. A shared thread is just a sliding window shared by everyone, which is barely better than a sliding window nobody shares.

Albato's own guidance draws the line

This is not a hidden flaw; Albato says it outright. In their comparison of AI agents, chatbots, and workflow automation, they give this tip: turn on optional memory only for conversational, multi-turn agents like chatbots. For a lead-scoring or routing agent that runs once per record, memory adds cost and context you do not need.

That sentence draws the line. The built-in memory is for chatbots. It is not for the agent that runs once per record on a schedule.

And the cost part is real, not theoretical. Each AI Agent run costs 3 transactions regardless of how many tools it calls, and with Albato AI you pay 1 transaction per 2,000 tokens of input and output combined. A bigger memory window means more tokens injected into every run, which means more transactions on every run. Memory is metered context, not free context, and the platform itself advises against using it for once-per-record agents.

So the honest answer to the target question has three layers:

1. Off by default: nothing persists; every run starts from scratch.
2. On optionally: the last 100 interactions inside one thread, built for chatbots.
3. For scheduled batch agents: neither setting carries run-level knowledge forward.

What scheduled runs actually need

A scheduled automation agent needs something the chat model of memory cannot express: run-to-run state. Not the last 100 turns of a conversation, but the decisions the previous run made, the patterns it noticed, and the items it left open. The pattern that works is three steps:

1. State what the run found. Not a summary sentence. The actual findings: numbers, names, decisions, open questions.
2. Store it in one place every tool can reach. If the Albato agent writes it and the n8n scenario cannot read it, the wall just moved.
3. Read it before the next run acts. The first action of every run is a lookup, not an assumption. Yesterday's context is cheaper than today's re-computation, and far cheaper than the hours spent being your own automation's memory.

The memory has to live outside the run, not inside the agent step, because runs are disposable by design. The agent step is born at trigger time and dies when the scenario finishes.

The one-line fix

This is the pattern Vilix AI exists to make trivial. It is a cloud-hosted memory layer, so there is zero infrastructure to run, and everything connects over MCP: Albato scenarios, ChatGPT, Claude, n8n, Make, 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. Yesterday's decisions, last week's patterns, the open items from three runs ago: all of it is a lookup, not a re-computation. 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.

Out of the box, the Albato AI agent remembers nothing. With memory on, it remembers the last 100 turns of one thread: right for a chatbot, wrong for a scheduled agent. The run that wakes up knowing last week is not a feature of the agent step. It is a feature of the layer underneath it.