OpenAgency: A Self-Evolving Multi-Agent System via Governed Collaboration Protocols

Large language model (LLM) agents have demonstrated strong capabilities in solving complex tasks, motivating the development of multi-agent systems (MAS) that coordinate agents with specialized roles. However, existing multi-agent collaboration paradigms are often static, with collaboration patterns determined a priori and remaining unchanged during execution. Enabling continuously improving collaboration requires protocols that balance flexibility and controllability: flexibility allows agents to explore diverse coordination strategies at runtime, while controllability ensures that such decisions can be used to improve collaboration. Existing collaboration protocols typically fall into two extremes: they are either rigidly predefined, limiting adaptation, or fully free-form, sacrificing oversight. In this paper, we introduce OpenAgency, a self-evolving multi-agent system built upon governed collaboration protocols. These protocols enable agents to dynamically select their next collaborators within a predefined yet extensible space at runtime, transforming the collaboration pattern itself into an evolvable object. To evolve collaboration protocols, OpenAgency maintains execution traces from agent interactions and introduces a collaboration-driven evolving agent that distills these experiences into a bounded number of atomic protocol improvements. Through this mechanism, OpenAgency allows multi-agent systems to continuously refine their collaboration strategies and adapt to diverse complex tasks while preserving control over the evolution process.Experiments across fourteen benchmarks spanning general question answering, long-horizon reasoning tasks, and production-oriented workloads demonstrate that OpenAgency achieves improved collaboration performance and continues to enhance its effectiveness through self-evolution.