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arxiv:2608.23740

AgentRoom: Concurrent Multi-Agent Coding in a CRDT-Backed Shared Workspace

Published on Aug 24
· Submitted by
Seonglae Cho
on Aug 26
Authors:

Abstract

AgentRoom enables concurrent multi-agent coding through real-time collaborative editing and shared filesystem coordination, reducing task abandonment and improving consistency compared to solo or uncoordinated parallel approaches.

Concurrent multi-agent coding promises division of labor across modules, robustness through redundancy, and parallel exploration at the natural granularity of multi-file projects. Realtime collaborative editing protocols solve this coordination problem for human teams via Conflict-free Replicated Data Types (CRDTs), but the LLMs underneath generate one token at a time and existing multi-agent coding systems inherit this serial limit: they either sequence agents through phase handoffs or pool independent samples without coordination, and a single agent abandons up to half of hard tasks with a one-file stub-and-exit. AgentRoom is a realtime collaborative editing protocol for concurrent coding agents. Its runtime layer exposes file-level claim, status, and broadcast as MCP tools on a CRDT-merged shared filesystem. Five frontier coding-CLI models ran four backend coding tasks, with cross-language checks in Python DevBench and Rust+axum. For CLI-stable models, AgentRoom with 2 agents abandons fewer tasks than Solo and has less run-to-run variation. At matched-compute, one positive mean LLM-judge contrast puts AgentRoom over parallel-merge. The other contrast, a bundle probe, puts full AgentRoom above each partial case: an ordering rather than a percentage split. Coordination, not parallelism or CRDT-merge, bears the load.

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Our research question: can we make a channel that helps real-time collaboration for AI coding work? We leveraged Conflict-free Replicated Data Type called CRDT for seamless communication. We call this channel a Room.

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