Does Huginn Remember Between Runs? Per-Agent Memory, Events, and What Never Gets Shared

Short answer: yes, but only in one narrow place. Every Huginn agent has a built-in memory that the system loads and saves automatically, so an agent can maintain state between its scheduled intervals and the events it receives. That memory belongs to that single agent instance, holds only what the agent was built to put in it, and is invisible to every other agent, workflow, and AI tool around it.

That distinction matters more than the yes. Huginn remembers like a notebook kept in one desk drawer. The notebook is real, it survives the night, and nobody else in the building can read it.

What Huginn memory actually is

In Huginn, an agent is a small program that runs on a schedule, reacts to events, or both. The official developer documentation describes memory in one plain sentence: agents have memory that can be used to maintain state between scheduled intervals or received events, it is loaded and saved automatically, and it is available to the agent in its memory store.

In practice, that store works well for classic automation state:

• The last item an agent already processed, so a feed watcher does not emit the same story twice.
• A counter, a cursor, or a last-checked timestamp for an API that pages through results.
• A small set of values the agent updates each time its check method runs.

An agent scheduled to run every hour can read what it stored on the previous run, compare it with what it finds now, and write back an updated value before it sleeps again. For that job, Huginn memory does exactly what it promises, with no database to configure and no extra service to run.

Events are not memory, and memory is not shared

Huginn systems are usually described through events: one agent creates an event, other agents receive it, and the event moves through a scenario. Events feel like memory because they carry history forward, but they serve a different purpose. An event is a message about something that happened. Memory is the private state an agent consults before deciding what to do next.

Hold those two apart and three limits come into focus.

First, memory is per agent. Each instance keeps its own memory, separate from every other instance, even instances of the same agent type. A scenario with five agents has five separate memories, not one shared picture of the work.

Second, memory holds only what the code stores. Huginn does not record conversations, reasoning, or outcomes on its own. If an agent talks to a customer, asks a language model for a judgment, or escalates a problem, none of that lands in memory unless the agent was deliberately built to summarize it and write it there.

Third, memory stops at the Huginn instance. Nothing outside that installation can read it: not a second automation platform, not a script running elsewhere, and not the AI assistant used during the day to plan, draft, and troubleshoot the very work the scenario automates.

Where that breaks for AI work

Classic watchers survive fine on per-agent state. AI-driven automations ask for more, because the valuable part of a run is often not a cursor. It is context:

• What was already decided. Which leads were worked, which customers were contacted, what was promised, and what is still waiting on a reply.
• What the conversation actually said. The full thread behind a decision, so the next run can pick up the real exchange instead of a shortened note about it.
• What other tools already know. A preference a customer shared in chat, a rule set in a planning session, a correction made in a different agent entirely.

A per-agent store can be stretched to hold fragments of the first list, one hard-coded field at a time. It was never shaped to hold the second and third, and every scenario that tries ends up maintaining its own private bookkeeping while the rest of the setup starts each run from a narrower picture.

Three ways to give a Huginn scenario a wider memory

1. Use built-in memory for simple state, on purpose. Last-seen IDs, cursors, and counters belong in the agent's own memory. It is automatic, reliable, and the right size for that job. Naming that boundary keeps a scenario honest about what its memory can and cannot do.

2. Write outcomes to a place the next run must read. For work that must never repeat, keep the record outside any single run: what was sent, answered, or completed, checked at the start of every run before the agent acts. The read-before-acting step has to be non-optional, or the first skipped check quietly turns into duplicate work.

3. Add a shared memory layer the scenario and your AI tools both reach. The first two fixes still leave every agent, scenario, and tool holding a separate fragment. A hosted layer changes that shape. Vilix AI is a cloud-hosted memory layer reached over MCP, so there is zero infrastructure to run alongside a self-hosted Huginn install. The same memory follows the work across Huginn, n8n, Make, Claude, ChatGPT, and every other connected tool, and it stores full conversation history, not just extracted facts, so a later run can revisit what was actually said and decided. The free plan is free forever, the 7-day Pro trial needs no credit card, and everything can be exported or deleted at any time.

The bottom line

Huginn remembers between runs in exactly one place: the automatic memory of each individual agent. That memory is genuine and useful for state like cursors and counters. It is also private to that agent, limited to what its code was built to store, and unreachable from anywhere else in your setup.

When a scheduled automation keeps repeating work, missing context from another tool, or starting each run narrower than the last one ended, the missing piece is rarely a bigger local store. It is one memory that every run, every agent, and every tool can read before acting and write to when the work is done.