Aimagutchi
Persistent memory for AI workflows
Recurring workflows need state. Persist the facts that matter, show the runs that worked, and stop paying to re-explain yourself.
The pain
- Repeat tasks still start with a blank prompt.
- Token spend climbs because context is regenerated, not reused.
- Ops buyers cannot show continuity or hygiene improvements.
Why chats & prompts fail
- Stateless chats optimize novelty over continuity.
- Workaround folders do not attach to an agent’s runtime.
- Without history, you cannot audit what changed.
Persistent memory + playbooks
- Durable memory reloaded on every relevant run.
- Chronological work history with summaries.
- Playbooks that encode successful approaches for reuse.
First win in minutes
Day-7 starters ship with seeded memory, sample history, and one reusable workflow—so you see continuity before you build from scratch.
Start a persistent workflowYour starter
Atlas Owl is ready
Memory, work history, and one reusable workflow — no API keys required.
Saved memory
- Brief format preference
Verdict → evidence → next action
Work history
Compared three note-taking tools for long-term retrieval quality
Memory reused on last run
Verdict: prioritize visible history + reusable skills in week one…
Research brief workflow
Run sample35% fewer tokens vs baseline12 skills saved
FAQ
- Does persistence reduce token waste?
- Yes—reusing memory and playbooks avoids regenerating the same setup. That is also a practical AI-hygiene win for teams.
- What is deferred from Day-7 MVP?
- Collectibles, marketplace depth, and complex skill graphs. First value is memory, history, and one reusable template.
Stop rebuilding. Start compounding.
Create a free account, pick a starter, and see memory + history on your first run.
Start a persistent workflow