AgentRoom: Concurrent Multi-Agent Coding in a CRDT-Backed Shared Workspace
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 15 of its 31 references that are in the map. First posted on arXiv 24 Aug 2026.
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15 of 31 references.
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- 2026SEMAG: Self-Evolutionary Multi-Agent Code Generation16 Mar
- 2025Evaluating Software Process Models for Multi-Agent Class-Level Code Generation12 Nov
- 2025CodeCRDT: Observation-Driven Coordination for Multi-Agent LLM Code Generation18 Oct
- 2025Enhancing LLM Code Generation: A Systematic Evaluation of Multi-Agent Collaboration and Runtime Debugging for Improved Accuracy, Reliability, and Latency4 May
- 2025CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging8 Feb
- 2025CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation14 Jan
- 2024Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization2 Apr
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation16 Aug
Other references · 7
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
- 2024Self-Preference Bias in LLM-as-a-Judgecited by 248
- 2025Preference Leakage: A Contamination Problem in LLM-as-a-judgecited by 119
- 2024AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodologycited by 45
- 2024Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Studycited by 40
- 2016A Conflict-Free Replicated JSON Datatypecited by 6
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