Aimagutchi
Agent memory built for recurring builder workflows
Builders feel the cost of rebuilding context first. Give each agent a small, durable memory set that reloads when the work does.
The pain
- Stack prefs, voice, and research focus vanish between sessions.
- You burn tokens re-teaching the same agent persona.
- Teammates cannot see what the agent “knows.”
Why chats & prompts fail
- Generic assistants optimize for conversation, not owned agent state.
- Manual paste-ins do not scale across workflows.
- Without inspectability, memory errors go unnoticed.
Persistent memory + playbooks
- Per-agent memory CRUD with clear titles and bodies.
- Seeded starters for research, content, and coding use cases.
- Visible proof when memory is reused on sample and skill runs.
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.
Seed an agent with memoryYour 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
- Who is agent memory for?
- Prosumer builders and small teams running the same AI workflows repeatedly—research briefs, content packs, coding helpers.
- Is personality required?
- Personality amplifies ownership; the core value is memory, history, and reusable playbooks.
Stop rebuilding. Start compounding.
Create a free account, pick a starter, and see memory + history on your first run.
Seed an agent with memory