AgentRoom: Concurrent Multi-Agent Coding in a CRDT-Backed Shared Workspace
cs.AI, cs.SE
Submitted: 2026-08-24
Updated: 2026-08-24
Code: https://github.com/yjs/yjs
License: http://creativecommons.org/licenses/by/4.0/
The gist: Concurrent multi-agent coding promises division of labor across modules, robustness through redundancy, and parallel exploration at the natural granularity of multi-file projects.
Terminology
Abstract
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.
Sources
- Enhancing LLM Code Generation: A Systematic Evaluation of Multi-Agent Collaboration and Runtime Debugging for Improved Accuracy, Reliability, and Latency
- AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
- MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
- AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation
- CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging
- A Conflict-Free Replicated JSON Datatype
- Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study
- Preference Leakage: A Contamination Problem in LLM-as-a-judge
- AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology
- CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation
- Generative Agents: Interactive Simulacra of Human Behavior
- SEMAG: Self-Evolutionary Multi-Agent Code Generation
- CodeCRDT: Observation-Driven Coordination for Multi-Agent LLM Code Generation
- Reflexion: Language Agents with Verbal Reinforcement Learning
- Mixture-of-Agents Enhances Large Language Model Capabilities
- OpenHands: An Open Platform for AI Software Developers as Generalist Agents
- Self-Consistency Improves Chain of Thought Reasoning in Language Models
- Self-Preference Bias in LLM-as-a-Judge
- AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- Agents' Last Exam
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems