Does SnapLogic Remember Between Runs? What AgentCreator's Memory Actually Covers

Short answer first: inside one conversation, yes. Between pipeline runs, no. If you run AI agents on SnapLogic, that distinction decides how much plumbing you have to build before the agent is actually useful.

SnapLogic's AgentCreator is a serious agent builder. You design an Agent pipeline that receives the user request, talks to the LLM, and orchestrates the work. That pipeline calls Tool pipelines that do the real jobs: pulling data, hitting APIs, writing to systems. SnapLogic's own docs describe how memory works across both halves. Short-term context, the user prompt plus the conversation history, is managed through the Prompt Composer in the Agent pipeline. Longer-term retrieval, like querying a knowledge base or an indexed data store, is handled by tool pipelines that connect to those sources with Snaps.

That is a solid in-session memory model. The agent does not lose the thread mid-conversation. The Prompt Composer gives you one unified view of the input data, the schema, and the prompt result, so you can iterate without re-running the whole pipeline. AgentCreator 3.0 added human-in-the-loop, where a pipeline can pause for human input and resume with full context intact. Nothing about that is the problem.

The problem is what happens when the session ends.

Where the memory stops

A scheduled pipeline that runs every morning is not one long session. It is a fresh execution each time. The conversation history the Prompt Composer held so carefully yesterday lives inside yesterday's session. Today's run starts with an empty context window, plus whatever the pipeline explicitly loads from somewhere durable.

Nothing in SnapLogic's documented memory model describes the platform persisting run outcomes so the next run can read them. Memory operates across the Agent pipeline and the Tool pipelines, in SnapLogic's words, and both halves exist inside a run. When the run ends, so does the memory.

This is the same amnesia every automation platform gives you, and it shows up in predictable ways. Your morning triage agent re-reads every edge case it already resolved last week. Your reconciliation agent re-investigates the same mismatches it cleared yesterday. The agent is not broken. It simply never got to keep what it learned, because no one gave it anywhere to keep it.

The two workarounds operators actually use

Operators who hit this wall on SnapLogic generally pick one of two paths.

The first is hand-rolled state. You add Snaps that write run outcomes, decisions, and exceptions to a database, a file, or a config store, then add retrieval steps at the top of the pipeline that load that state back in and feed it to the LLM. This works. It is also plumbing you now own: schemas to maintain, writes to keep idempotent, cleanup jobs so the state does not grow forever, and a separate pattern for every pipeline that needs memory.

The second is knowledge-base retrieval. You turn the knowledge into indexed documents, and the agent's Tool pipelines query them with Snaps during the run. SnapLogic documents exactly this pattern. It is good for stable reference knowledge, policies, product docs, past solutions. It is less natural for the running record of what this agent did yesterday and what it decided, which is the thing scheduled operators actually need.

Both paths put you in the business of building and maintaining memory infrastructure instead of building agents.

What a hosted memory layer changes

There is a third option: stop building memory inside each pipeline and give every run a memory that lives outside all of them.

That is what Vilix AI is for. It is a cloud-hosted memory layer, so there is no infrastructure to manage on your side, and every AI tool you use talks to the same memory over MCP. Your SnapLogic agent pipeline reads from that memory when it starts, writes back what it learned when it finishes, and the next run, in SnapLogic or in any other tool, picks up where this one left off. Not just facts, either. Vilix AI stores full conversation history, so the real exchange can be revisited, not only a summary somebody decided was enough.

The practical shape of it is simple. Run N finishes and saves: the decisions it made, the edge cases it resolved, the context behind the call. Run N+1 wakes up, reads that back, and skips the re-investigation. The pipeline design does not change. The agent just stops starting from zero.

It is free forever, with a 7-day Pro trial that needs no credit card, and your data is portable: export everything or delete it anytime. There is nothing to lose by wiring a trial pipeline to it and watching one run teach the next.

The verdict

So, does SnapLogic remember between runs? It remembers the conversation while it is happening, through the Prompt Composer, the Agent pipeline, and retrieval Snaps that reach your knowledge bases. It does not carry what one run learned into the next. That part is yours to build, or yours to hand to a memory layer that already does it.

Learn more at https://vilix.ai/?utm_source=vilix-blog&utm_medium=article&utm_campaign=does-snaplogic-remember-between-runs.