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MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing

Yang Liu, Jinxuan Cai, Yishen Li, Qi Meng, Zedi Liu, Xin Li, Chen Qian, Chuan Shi, Cheng Yang

TL;DR

MASFactory is presented, a graph-centric framework for orchestrating LLM-based MAS that introduces Vibe Graphing, a human-in-the-loop approach that compiles natural-language intent into an editable workflow specification and then into an executable graph.

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/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 overcome these limitations, we present MASFactory, a graph-centric framework for orchestrating LLM-based MAS. It introduces Vibe Graphing, a human-in-the-loop approach that compiles natural-language intent into an editable workflow specification and then into an executable graph. In addition, the framework provides reusable components and pluggable context integration, as well as a visualizer for topology preview, runtime tracing, and human-in-the-loop interaction. We evaluate MASFactory on seven public benchmarks, validating both reproduction consistency for representative MAS methods and the effectiveness of Vibe Graphing. Our code (https://github.com/BUPT-GAMMA/MASFactory) and video (https://youtu.be/ANynzVfY32k) are publicly available.

MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing

TL;DR

MASFactory is presented, a graph-centric framework for orchestrating LLM-based MAS that introduces Vibe Graphing, a human-in-the-loop approach that compiles natural-language intent into an editable workflow specification and then into an executable graph.

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/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 overcome these limitations, we present MASFactory, a graph-centric framework for orchestrating LLM-based MAS. It introduces Vibe Graphing, a human-in-the-loop approach that compiles natural-language intent into an editable workflow specification and then into an executable graph. In addition, the framework provides reusable components and pluggable context integration, as well as a visualizer for topology preview, runtime tracing, and human-in-the-loop interaction. We evaluate MASFactory on seven public benchmarks, validating both reproduction consistency for representative MAS methods and the effectiveness of Vibe Graphing. Our code (https://github.com/BUPT-GAMMA/MASFactory) and video (https://youtu.be/ANynzVfY32k) are publicly available.
Paper Structure (23 sections, 6 figures, 2 tables)

This paper contains 23 sections, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Contributions.
  3. Related Work
  4. LLM-based multi-agent systems.
  5. Frameworks for MAS development.
  6. System Design
  7. Basic Components
  8. Key Components
  9. Agent Component
  10. Reusability and Templated Prototyping
  11. Orchestration Interfaces
  12. Visualizer
  13. Evaluation and Analysis
  14. Experimental Setup
  15. Performance of Reproduced Workflows
  16. ...and 8 more sections

Figures (6)

  • Figure 1: Vibe Graphing in MASFactory. MASFactory turns a user's natural-language intent into an executable multi-agent workflow via a three-stage, human-in-the-loop process, then compiles and executes the resulting workflow at runtime.
  • Figure 2: Architecture overview of MASFactory framework.
  • Figure 3: Human-in-the-loop interaction during Vibe Graphing in the visualizer. The user provides feedback to refine the role assignment and structure across stages.
  • Figure 4: Visualizer views used in the Vibe Graphing.
  • Figure 5: MASFactory programming code for the Vibe Graphing.
  • ...and 1 more figures