AgensFlow: Framework for Multi-Agent LLM Coordination
AgensFlow, a novel open-source framework, approaches multi-agent coordination as a problem of online policy learning amid partial observability. Tailored for systems based on large language models (LLMs), it tackles the complexities of coordination decisions, including skill protocols, agent roles, model binding, interaction topology, retrieval, verification, and step omission. These elements fluctuate according to task regimes and operational limitations, rendering static pipelines inadequate. Instead of depending on fixed designs, AgensFlow enables coordination choices to be observable and learnable through repeated trajectories. The framework has been assessed on two datasets: incident tasks in distributed systems and security tasks. The related paper can be found on arXiv with the identifier 2605.27466.
Key facts
- AgensFlow is an open-source framework for multi-agent LLM coordination.
- It treats coordination as an online policy-learning problem under partial observability.
- The framework makes coordination decisions observable and learnable from repeated trajectories.
- It addresses skill protocols, agent roles, model binding, topology, retrieval, verification, and step omission.
- Evaluated on distributed-systems incident tasks and security tasks.
- Paper available on arXiv with identifier 2605.27466.
Entities
Institutions
- arXiv