A memory layer for AI agents that organizes itself
Background decay loops keep agent memory from growing forever without manual cleanup.

Self-initiated AI maintenance workers defined in Markdown files.
Engineering teams using AI coding agents
GitHub Copilot · Dependabot · Sweep
The one thing we've noticed over the last 3 months is that the more you use agents, the more work they create. Dozens of pull requests means older code gets out of date quickly. Documentation drifts. Dependencies become stale. Developers are so focused on pushing out new code that this crucial work falls through the cracks. That's why we pivoted away from agents and invented what we think is the necessary next step for AI powered software development.
Today, we're introducing Daemons: a new product category built for teams dealing with operational drag from agent-created output. Named after the familiar background processes from Linux, Daemons are added to your codebase by adding an .md file to your repo, and run in a set-it-and-forget-it way that will make your lives easier and accelerate any project. For teams that use Claude, Codex, Cursor, Cline, or any other agent, we think you'll really enjoy what Daemons bring to the table.
Background decay loops keep agent memory from growing forever without manual cleanup.
Stop hook feeds code review back to Claude while context is still hot.
Self-hosted coding agent runner when GitHub Copilot already does this in cloud.
E2B sandbox isolation prevents agent chaos while streaming realtime previews before PR creation.
Self-hosted alternative to Stripe Minions for teams avoiding cloud-only agents.
No OAuth, tokens, or bot accounts — agents just join the network directly.