loopat

Self-hosted AI workspace built around context management — works solo, scales to teams

⭐ Star on GitHub · 🚀 Quick start · 📖 Architecture

When humans collaborate with AI, three things only humans can bring:

• Drive — pushing the work forward. AI has no desires, no

ambition of its own; momentum has to come from a human.

• Attention — what matters now, what to ignore. AI doesn't know

what's worth your time.

• Entropy reduction — turning noise into structured knowledge.

AI generates tokens but won't spontaneously simplify.

loopat is built around managing these three as first-class concepts: Loop (drive) · Focus (attention) · Context (entropy reduction). A fourth concept — Chat — coordinates the team on the sync axis.

The agent itself is the Claude Agent SDK; what makes loopat distinct is the context architecture around it — how chat, code, memory, and knowledge interlock so context doesn't get lost across sessions or teammates.

What makes loopat different

• End-to-end context management. Team chat (IM) threads, code

edits, agent decisions, memory — all live in the same context graph and all flow into the next loop. Most AI tools make you copy-paste from Slack into the AI to give it situational context; loopat treats team chat as a first-class context source — spawn a loop from any chat thread and that thread becomes part of the loop's context automatically.