Persistent memory for AI agents
An AI agent's context window empties when the session ends. Whatever it learned — decisions, research, project state — is gone the next time you start it. Atomo gives agents a memory that is not a context window: a structured workspace they read and write over MCP, which is still there tomorrow.
The problem with session memory
Chat histories are linear and unsearchable in practice; context windows are finite and reset. Teams work around it by pasting summaries between sessions or maintaining a notes file by hand — the agent depends on a human to remember for it.
How Atomo works as agent memory
- Structured, not linear: knowledge lives as entries in sections and spaces — your agent files a finding once and any future session finds it by search or by link, instead of replaying a transcript.
- Read AND write over MCP: agents don't just retrieve — they create entries, append findings, file captures and update status through the same tools every session.
- Survives every session and every tool: the workspace is server-side. A Claude Code session, a claude.ai chat and a Cursor session all see the same memory.
- Attributed and reversible: every change records which agent made it, and entry version history lets you roll anything back — memory you can audit, not a black box.
Check it yourself
The MCP docs list the full live tool catalogue — the exact read/search/write surface an agent gets. The blog shows real workflows built on it, including agents that file captures overnight and post a briefing of what changed.
Try it
The free plan is genuinely free — no card required. Sign up or read the MCP docs.