MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing
arXiv:2603.06007v2 Announce Type: replace-cross Abstract: Large language model-based (LLM-based) multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration. MAS workflows can be naturally modeled as directed computation graphs, where nodes execute agents or sub-workflows and edges encode dependencies and message passing. However, implementing complex graph workflows in current frameworks still requires substantial manual effort, offers limited reuse, and makes it difficult to integrate heterogeneous external context sources. To o
The proliferation of LLMs creates an immediate need for sophisticated orchestration frameworks to manage increasingly complex multi-agent systems, moving beyond manual integration challenges.
This development allows for more efficient and scalable deployment of LLM-based multi-agent systems, accelerating progress in autonomous problem-solving and workflow automation.
Current fragmented and manual approaches to building multi-agent systems will be replaced by more integrated, reusable, and graph-centric frameworks, standardizing development.
- · AI developers
- · SaaS companies
- · Enterprises adopting AI
- · Cloud computing providers
- · Manual workflow integrators
- · Legacy automation platforms
Increased adoption and complexity of multi-agent AI systems across various industries.
Demand for specialized tools and expertise in designing, deploying, and managing agent graph architectures.
Automation of highly complex, multi-step white-collar tasks, leading to significant productivity gains but also job displacement.
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Read at arXiv cs.AI