OpenCollab: A Multi-Agent Coding Framework with Programmable Collaboration and Controllable Runtime
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Abstract (arXiv)
Multi-agent coding systems are designed to tackle complex software engineering tasks through collaboration. However, existing evaluations typically assume configured organizations are followed faithfully, whereas reality differs. This behavioral gap, combined with differences in underlying system components, prevents clear attribution of observed gains. To this end, we introduce OpenCollab, a multi-agent coding framework that provides a unified infrastructure for programmable collaboration and controllable runtime. Specifically, OpenCollab unifies organization design, enforces experimental control on a shared runtime, and tracks execution through fine-grained event streams. On this basis, we define Adherence to quantify whether the declared organization is actually realized. Our experiments reveal that agents collaborate very differently across configurations: changing any single dimension shifts Adherence, from 47.2% to as high as 97.2%. Furthermore, extensive agentic coding benchmarks show that OpenCollab (Duo) establishes new SOTA performance compared to the mainstream harnesses such as Mini-SWE-agent, OpenHands, Codex CLI, and Claude Code, showing that a well-designed organization can outperform strong existing harnesses, while OpenCollab's single-agent configuration uses the fewest tokens across all evaluated suites. OpenCollab establishes a unified multi-agent infrastructure for easy programmable collaboration and controlled causal evaluation.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 31 of its 47 references that are in the map. First posted on arXiv 29 Sep 2026.
Builds on
31 of 47 references.
References · 31
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- 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 585
19 more
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- 2024Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?cited by 159
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- 2025Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluationcited by 92
- 2026DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Taskscited by 49
- 2026Stop Comparing LLM Agents Without Disclosing the Harnesscited by 34
- 2026Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgetscited by 30
- 2026CooperBench: Why Coding Agents Cannot be Your Teammates Yetcited by 23
- 2026Shepherd: Enabling Programmable Meta-Agents via Reversible Agentic Execution Tracescited by 12
- 2026TeamBench: Evaluating Agent Coordination under Enforced Role Separationcited by 10
- 2026Log analysis is necessary for credible evaluation of AI agentscited by 10
- 2026Multi-agent Collaboration with State Managementcited by 6
- 2026From Plan to Action: How Well Do Agents Follow the Plan?cited by 4
- 2026When Does Multi-Agent Collaboration Help? An Entropy Perspectivecited by 3
- 2026The Ringelmann Effect in Multi-Agent LLM Systems: A Scaling Law for Effective Team Sizecited by 3
- 2026When Agents Coordinate: Measuring Coordination in Multi-Agent AI Codingcited by 3
- 2026How Much Coordination Gain Is Real? A Paired Noise-Floor Protocol for Multi-Agent LLM Benchmarkscited by 2
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