Does SuperAGI Remember Between Runs? What Its Memory Actually Covers
Does SuperAGI Remember Between Runs? What Its Memory Actually Covers Short answer: yes, more than most agent platforms. SuperAGI ships real cross-run memory: tool-level learning that persists in a vector database and carries over into future runs of the agent workflow. If you are comparing agent frameworks, that puts it ahead of the blank-slate crowd. But "remembers between runs" and "gives your scheduled agent everything it needs" are different things. SuperAGI's memory is tool learnings in v
Does SuperAGI Remember Between Runs? What Its Memory Actually Covers
Short answer: yes, more than most agent platforms. SuperAGI ships real cross-run memory: tool-level learning that persists in a vector database and carries over into future runs of the agent workflow. If you are comparing agent frameworks, that puts it ahead of the blank-slate crowd.
But "remembers between runs" and "gives your scheduled agent everything it needs" are different things. SuperAGI's memory is tool learnings in vector form, scoped to the agent workflow, living inside SuperAGI's own deployment. For an automation operator running agents on a schedule, the gaps that remain are exactly the ones that cause duplicate work and blind re-runs.
What SuperAGI actually keeps
SuperAGI's memory design is built around tools, not conversations. Every tool an agent uses gets two kinds of memory.
Dedicated memory lets a tool look back at its own past iterations. If the SearchTool ran three times during a run, the third iteration can fetch what the first one found and build on it instead of starting over. SuperAGI calls this "Feed Memory": as the run progresses, tools pull the latest memory from other tools and the history of their own earlier iterations, so later steps work with everything earlier steps learned.
Then there is shared memory: a larger vector database that all the tools can query. This is the long-term part. SuperAGI's documentation describes LTM, long-term memory, as storing the learnings of every tool used in a run, across all iterations, so they can be accessed during future agent runs within the agent workflow. The supported vector stores include Weaviate, Pinecone, and Qdrant, so the memory survives restarts and redeploys as long as the database does.
This is genuinely more than most platforms offer. Short-term context lives in the execution run; long-term knowledge persists across runs in the vector database. An agent that learned which search queries return useful results, or which code patterns passed review, carries that forward.
Where the memory stops
The first boundary is what the memory contains. SuperAGI remembers tool learnings: outputs, findings, patterns that helped a tool do its job. It does not keep an operational ledger of the agent's decisions. Your scheduled agent cannot ask it "did I already email this prospect on Tuesday?" or "which leads did I disqualify last week and why?" Vector embeddings of tool outputs are not a record of what the run decided. A scheduled lead-research agent that wakes up on Monday has the benefit of past tool learnings, but no memory of its own past actions.
The second boundary is scope. The LTM is described as learnings stored "throughout the whole agent workflow," usable in future runs "within an agent workflow." The memory belongs to the workflow and its tools. A second agent, a different workflow, or a human operator asking "what did the research agent conclude last week?" has no channel to that memory. The knowledge is real but fenced in.
The third boundary is the deployment wall. SuperAGI's memory lives in SuperAGI's vector database, inside SuperAGI's deployment. Your n8n workflow that acts on the agent's findings cannot read it. Your chat assistant cannot read it. If the research agent runs in SuperAGI and the follow-up sequence runs in another tool, the context crosses the gap by copy and paste, or not at all. Every platform outside the wall starts from zero.
Why scheduled operators still get bitten
Put a SuperAGI agent on a nightly schedule and the gaps show up in familiar shapes. The agent researches the same companies twice because it remembers how to research, not who it already researched. It drafts outreach to a prospect that already replied, because the reply lives in your inbox, not in its tool memory. It re-runs an enrichment pass on records that were already enriched, because "enriched" is a decision the run made, and decisions are not what the vector database stores.
None of this is a flaw in SuperAGI's design. Tool-level learning memory is a real capability, and it makes agents better at their jobs over time. But a scheduled automation needs a different kind of memory on top: the run's own history. What it did, what it decided, what it already handled, in a form any tool can read.
The memory-layer answer
The fix is to separate that operational memory from the agent framework. The agent keeps its tools and its learning memory; a dedicated memory layer holds the run history, the decisions, and the business context, outside any single platform. Before each scheduled run, the agent checks the layer: what did I already do about this? What was decided last time, and why? When the run ends, it writes back what happened.
This is the shape Vilix AI takes: a cloud-hosted memory layer with zero infrastructure to manage. Nothing to install, no database to provision, no vector store to operate. The memory is shared across tools over MCP, so the SuperAGI agent, the n8n workflow, and the chat assistant all read the same record instead of each keeping a private one.
Two properties matter here. Vilix AI stores full conversation history, not just facts or embeddings, so the reasoning behind a decision survives alongside the decision itself. And because it is one hosted layer rather than a database inside one deployment, the scheduled agent and everything around it finally share a brain. The free plan is free forever, there is a 7-day Pro trial with no credit card, and your data stays portable: export everything or delete it anytime.
Good memory, wrong layer for the job
SuperAGI deserves credit: its tool-level long-term memory is one of the more serious memory designs in the agent-framework world. If your question is "does my agent get better at using its tools over time," the answer is yes.
But the scheduled operator's question is different: "does my agent know what it already did?" That needs operational memory, shared across tools, kept outside the run. Give the agent that layer, and the nightly run stops being a talented amnesiac. It becomes a colleague with a notebook.