Does Restack Remember Between Runs? Its Agents Persist. Your Other Tools Still Start Blank.

Short answer: a Restack agent remembers, because a Restack agent is not really a run that ends. Restack describes its agents as event-driven background processes that persist state, execute tasks over time, and operate continuously for weeks or even months. That is a different design from a cron job that wakes up, works, and exits. The catch is scope. The memory lives inside that agent, on that Restack deployment. An agent in another tool, a scheduled workflow elsewhere, or an assistant someone opens on a laptop still starts with none of it.

So the honest answer splits in two. Inside one long-lived Restack agent, continuity is real and built in. Across the rest of an automation setup, the forgetting problem is still there, just moved to the boundary between tools.

What Restack actually keeps

State defined in the agent itself. In Restack's Python library, an agent's __init__() is where variables are defined that the agent keeps in its state. Those variables are not recreated from zero on every event. They belong to the running agent, and event handlers update them as work arrives.

Long-term state as a stated feature. Restack's own blueprint guide lists what its agents can do: listen for events to run tasks, maintain long-term state to learn and improve, and run at scale with built-in scheduling and controls. Memory is not an add-on diagram in that pitch. It is part of what the agent is.

Context stored for weeks or months. The chat blueprint puts it plainly: Restack agents store context for weeks or months without extra setup, and they expose OpenAI-compatible endpoints so clients can stream responses from that ongoing conversation. A conversation with a Restack agent can therefore pick up far later without a re-briefing at the start.

A context store underneath. Restack's architecture documentation describes a built-in memory powered by ClickHouse, a context store that holds past interactions and relevant data, with retrieval so agents pull the context they need for every task. That is a real memory layer, scoped to the Restack world.

Restarts that keep identity. Long-running agents eventually need a restart, and Restack handles it with continue-as-new: the agent restarts with the same agent ID and the input passed forward. The check behind it looks at how many events and how much history the running agent has built up, which tells you history is a first-class part of the design.

Where that memory stops

Picture an operation that is bigger than one agent. A Restack agent watches a support queue and drafts answers. A separate scheduled workflow in another tool compiles the weekly report. A person reviews edge cases in a chat assistant. Each piece works. The question is whether they share a past.

The Restack agent's state answers only for itself. Its state variables, its stored context, and its history belong to that agent ID. A child workflow or child agent it starts runs independently of the parent. A scheduled workflow execution elsewhere has no automatic path into that context store. The assistant on someone's phone has none at all, unless a developer builds a bridge and maintains it.

Three gaps follow from that boundary:

• A second agent, built on a different platform, repeats a question the Restack agent already asked and answered, because the answer sits in a store only the first agent reads.
• A scheduled run outside Restack reports on items the agent already handled, because the run checks its own inputs, not the agent's history.
• A human takes over a thread in another tool and re-explains the situation, because the reasoning that led to the draft never left Restack.

None of that makes Restack's persistence weak. It makes it local. Durable, long-lived memory inside one framework is exactly what Restack promises. One memory that every agent and every tool in the operation can read is a different promise, and no single framework's internal store is built to keep it.

Why scheduled work feels the gap most

Scheduled automations are where the boundary bites, because scheduling multiplies tools. The nightly job, the hourly check, and the weekly digest rarely all live in one framework. Each one wakes up and asks the same opening question: what is already true, what changed, and what is still waiting?

If the answer lives inside one agent's state, every other scheduled piece has to rebuild it: query the source systems again, re-derive status, and hope nothing was decided in a conversation it cannot see. That re-derivation is the quiet cost of agent operations. It shows up as duplicate outreach, reports that re-announce old news, and follow-ups sent to people who already replied somewhere else.

The layer built to sit across all of it

Vilix AI is a hosted memory layer built for that cross-tool part. Agents connect to it over MCP, read what earlier work recorded before they act, and save what they decided so the next agent, on the next schedule, in the next tool, starts briefed.

It covers the parts an internal store does not:

Cloud-hosted, with zero infrastructure to run. There is no database, server, or cache to provision, secure, or keep alive. The memory is hosted, and connecting an agent is the whole setup.

The same memory everywhere. Because access runs over MCP, the memory is not locked inside one framework. A Restack agent, a workflow in another automation platform, and an assistant on a phone or laptop can all read and write the same shared memory.

Full conversation history, not just facts. Vilix AI stores the actual history, including what was said, decided, and deferred, so a later run can revisit the real exchange instead of working from a shortened status line.

Free to start, clean to leave. The free plan is free forever. The Pro trial runs for 7 days with no credit card required. Data can be exported or deleted at any time, so the history stays portable.

How the two fit together

Keep Restack for what it is genuinely good at: long-lived agents, event handling, durable state, and reliable execution at scale. Let its agents keep their internal state for the work only they do. Then add one shared memory check at the points where work crosses a boundary: before an agent acts on something another tool may have touched, it looks up the shared record, and after it acts, it writes back what changed.

So, does Restack remember between runs? Better than most: its agents persist state for weeks or months. What it does not do is brief everything else you run. Give the whole operation one memory outside any single framework, and the Restack agent, the scheduled workflow, and the person in the chat finally share the same past.