OpenCollab: A Multi-Agent Coding Framework with Programmable Collaboration and Controllable Runtime

arXiv:2609.38345 · cs.SE, cs.AI, cs.CL, cs.LG · Submitted 2026-09-29 · Read on arXiv

cs.SE, cs.AI, cs.CL, cs.LG

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/ag2ai/ag2

Terminology

• Benchmark — 16× in this paper · explained in 2 episodes
• Trace — 12× in this paper · explained in 2 episodes
• Multi-agent Collaboration — 6× in this paper · explained in 2 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Multi-Agent System — 5× in this paper · explained in 6 episodes
• Verification — 4× in this paper · explained in 2 episodes
• Verifier — 4× in this paper · explained in 2 episodes
• AI Agents — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Decoupling — 2× in this paper · explained in 2 episodes
• Selection Bias — 2× in this paper · explained in 4 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Agent Architectures — 1× in this paper · explained in 1 episode
• Agent Interactions — 1× in this paper · explained in 1 episode
• Agentic Coding — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 1× in this paper
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Multi-agent Collaborative Framework — 1× in this paper · explained in 2 episodes
• Multi-hop Reasoning — 1× in this paper · explained in 3 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• State Management — 1× in this paper · explained in 3 episodes

Sources

• The Ringelmann Effect in Multi-Agent LLM Systems: A Scaling Law for Effective Team Size
• Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
• DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks
• How Much Coordination Gain Is Real? A Paired Noise-Floor Protocol for Multi-Agent LLM Benchmarks
• CooperBench: Why Coding Agents Cannot be Your Teammates Yet
• TeamBench: Evaluating Agent Coordination under Enforced Role Separation
• Log analysis is necessary for credible evaluation of AI agents
• Multi-agent Collaboration with State Management
• From Plan to Action: How Well Do Agents Follow the Plan?
• Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets
• Shepherd: Enabling Programmable Meta-Agents via Reversible Agentic Execution Traces
• Stop Comparing LLM Agents Without Disclosing the Harness
• When Does Multi-Agent Collaboration Help? An Entropy Perspective

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