Does Gumloop Remember Between Runs? What Persists and What Starts Blank

Gumloop's answer to "what does my agent remember" depends on which part of Gumloop you mean. The platform has two execution models: chat-style Agents and visual workflow Workbooks (flows). Each keeps a different kind of memory, and each forgets a different kind. If you schedule a Gumloop agent to run every morning, you need to know which facts survive from Monday to Tuesday, because the ones that don't are the ones that will break your automation.

Here is the honest breakdown, based on Gumloop's own documentation.

Gumloop Agents: each task is its own conversation

Gumloop's docs describe Agents as AI-powered assistants where "one piece of work you hand to an agent is a task. Each task is its own conversation with its own history, files, and credit cost." That is the whole answer for scheduled agents: when a Scheduled Trigger fires at 9 AM, the new run starts a fresh task. Anything the agent needs from Monday must be retrievable, not ambient.

Agents have five persistence mechanisms that reach across tasks:

1. Search Past Conversations. An on-by-default ability that searches and retrieves earlier tasks for context. Gumloop's docs call it "the backbone of an agent that learns over time." So an agent on Tuesday's run can pull up what happened in Monday's task, but only if the retrieval succeeds. It is search, not guaranteed recall: the agent asks, the index answers, and whatever ranks highest becomes the context. If Monday's run buried the important detail under hundreds of tool-call logs, the agent may retrieve the noise instead of the signal.

2. Skills. Procedural memory. Skills teach the agent how to do work your way: multi-step processes, templates, domain knowledge. They load only when relevant, and the agent can create and update them on its own ("turn this into a skill," or the AI Skill Editing toggle). This is the durable layer. Where conversation history fades into search results, skills persist as instructions the agent actually follows. The weakness: someone has to do the work well once and distill it into a skill. The agent will not reliably extract its own learnings into skills without being told.

3. Edit Own Instructions. A self-improving instructions feature: the fastest path to a useful agent is to let it do real work, then have it write its own instructions. In reality this is a manual checkpoint, not continuous learning. The instructions update when you ask, not after every run.

4. Knowledge Sources ("Brain"). Attached sources like Google Drive, Notion, Slack, GitHub, and Confluence. When you ask about internal knowledge, the agent searches automatically and answers with citations. This is persistent external memory, but it is document retrieval, not experience memory. Your Brain knows what your docs say. It does not know what your agent learned last Tuesday.

5. Artifacts. Every file, document, and chart the agent produces persists in the agent's Artifacts panel. Files survive; the reasoning behind them survives only if it was written down.

Then there is the mode that remembers nothing at all: Incognito mode. Incognito tasks are not saved to the database, are held in temporary memory, and auto-delete after 24 hours.

Gumloop Workflows: run history is a log, not a memory

Workflows (called Workbooks in the docs) are the visual node-based flows. Every execution is recorded with detailed intermediate outputs in the Previous Runs tab. You can inspect the exact data passing through each wire, see why something failed, and replay a failed run without re-entering inputs. The DataCamp Gumloop tutorial calls the Previous Runs tab "essential for troubleshooting and refinement."

But a run log is not run-to-run memory. Each flow execution starts with fresh inputs from its trigger. Run 47 cannot read Run 46's intermediate outputs unless you deliberately route them somewhere Run 47 can reach. If your flow computes a rolling total, tracks a list of processed items, or accumulates state across days, you have to build the storage yourself:

• Generate File node to write structured output (CSV, PDF, DOCX, HTML, TXT) that a later run can read back.
• Google Sheets or another datastore connector to append rows each run, then read them at the start of the next.
• Checkpoints to snapshot the workflow definition for rollback, which is version control for the flow itself, not memory of what the flow did.

There is also a subtle trap in Loop Mode: looping over a list inside one run works on that run's data. The loop does not persist anything between runs on its own. Statelessness is the default; every persistence is something you wired.

The gap: nothing connects runs automatically

Put the two models together and the pattern is clear. Gumloop Agents remember tasks (searchable) and procedures (skills); Workflows remember logs (run history). Neither remembers accumulated experience across runs on its own. A scheduled agent reviewing tickets every morning can search last week's tasks, but it will not carry forward "these three customers are chronically angry" unless the retrieval surfaces it. A workflow enriching leads nightly will not deduplicate against last night's list unless you build the lookup.

Three practical rules follow:

1. Name your state. Decide what Monday's run owes Tuesday's run: a list, a count, a verdict. If you cannot name it, no system will persist it.
2. Write it to something boring. A sheet, a file, a table. The mechanisms that survive are the ones with a storage backend you can inspect.
3. Read it at the start of the next run. Explicitly. The most common failure is writing state that nothing ever reads back, which is indistinguishable from forgetting.

The bigger picture: one memory across all your tools

Gumloop is one platform in most operators' stacks. The same blindness shows up between tools: what your Gumloop agent learned on Monday is invisible to your n8n workflow on Tuesday. Each platform keeps its own tasks, skills, logs, and run history. That is fine for isolated work, and it breaks the moment a process spans more than one tool.

Vilix AI is built for exactly that gap: one hosted memory layer that follows you across every AI tool. It is cloud-hosted, so there is nothing to deploy and nothing to maintain. The same memory and context follows the user on phone apps, laptop, and every connected AI tool over MCP: one memory, every device, every app. Vilix AI stores actual full conversations, not just extracted facts, so the real conversation can be revisited anytime. The plan is free forever with a 7-day Pro trial that asks for no credit card, and your data is portable: export everything or delete it whenever you want. Give your scheduled agents a memory that survives the platform it runs on, at vilix.ai.

Bottom line

Gumloop Agents carry tasks, skills, and searchable history between runs; Incognito wipes everything within 24 hours. Gumloop Workflows carry logs, not state: every run starts fresh unless you wire persistence yourself. Name your state, store it somewhere inspectable, and read it back at the start of every run.