MyAG: A Graph-Based Framework for Designing and Analyzing Composable LLM Agent Systems
- URL: http://arxiv.org/abs/2607.13474v1
- Date: Wed, 15 Jul 2026 06:03:52 GMT
- Title: MyAG: A Graph-Based Framework for Designing and Analyzing Composable LLM Agent Systems
- Abstract summary: MyAG is a graph-based framework for designing and analyzing composable LLM agent systems.<n>Our framework is publicly available and fully open-source.
- Score: 7.4813890775941125
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present MyAG, a graph-based framework for designing and analyzing composable LLM agent systems. Our framework separates agent system construction into three graph abstractions: a component graph for agents, environments, and modules; a workflow graph for execution control; and a search graph for runtime execution. This separation allows users to flexibly reuse the same components with different strategies. We further support hierarchical composition through recursive system nodes and provide monitoring and visualization tools for inspecting agent execution. Experiments on representative agent applications show that our framework supports flexible agent system design and helps analyze performance-efficiency tradeoffs. Our framework is publicly available and fully open-source.
Related papers
- Inference-Time Graph Engineering for Multi-Agent LLM Workflows [51.56038180639833]
We introduce ReActNet, a training-free framework that compiles a query and a set of role-specialized agents into a sequence of directed communication graphs.<n>We show that ReActNet consistently improves over fixed-topology and learned-topology baselines while maintaining competitive inference cost.
arXiv Detail & Related papers (2026-09-04T23:32:11Z) - Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence [115.91847478507736]
LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks.<n>Many tasks require heterogeneous expertise, interdependent subtasks, parallel execution, independent verification, and persistent state.<n>We call this System Intelligence: an agent system's ability to organize and coordinate multiple intelligent components into a coherent, adaptive whole.
arXiv Detail & Related papers (2026-08-21T14:27:57Z) - Focus Is All You Need: Adaptive Goal-aware Attention Orchestration for Multi-Agent Graph Systems [28.41165257733149]
Large language models (LLMs) enable autonomous agents for reasoning, planning, computation and tool use.<n>Recent systems increasingly organize these agents as graphs of specialized, interconnected nodes.<n>Existing approaches often execute graph components uniformly, wasting resources on irrelevant or low-impact tasks.<n>We introduce textbfAttention Orchestration, a paradigm that extends Transformer-style attention from token representations to workflow-level agent coordination.
arXiv Detail & Related papers (2026-07-26T14:23:33Z) - MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing [29.41980157624285]
Large language model-based (LLM-based) multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role computation and collaboration.<n>We present MASFactory, a graph-centric framework for orchestrating LLM-based MAS.<n>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.
arXiv Detail & Related papers (2026-03-06T08:04:12Z) - El Agente Gráfico: Structured Execution Graphs for Scientific Agents [7.47895130442454]
We present El Agente Grfico, a single-agent framework that embeds large language models (LLMs)-driven decision-making within a type-safe execution environment.<n>Central to our approach is a structured abstraction of scientific concepts and an object-graph mapper that represents computational state as typed Python objects.<n>We evaluate the system by developing an automated benchmarking framework across a suite of university-level quantum chemistry tasks.
arXiv Detail & Related papers (2026-02-19T23:47:05Z) - ChartAgent: A Chart Understanding Framework with Tool Integrated Reasoning [26.725654222717335]
We introduce ChartAgent, a chart understanding framework grounded in Tool-Integrated Reasoning.<n>Inspired by human cognition, ChartAgent decomposes complex chart analysis into a sequence of observable, replayable steps.<n>We show that ChartAgent substantially improves under sparse annotation settings.
arXiv Detail & Related papers (2025-12-16T03:17:04Z) - Multi-Agent GraphRAG: A Text-to-Cypher Framework for Labeled Property Graphs [7.943264761730892]
Multi-Agent GraphRAG serves as a natural language interface to LPG-based graph data.<n>Iterative content-aware correction and normalization, reinforced by an aggregated feedback loop, ensures both semantic and syntactic refinement of generated queries.<n>This highlights how such an approach can bridge AI with real-world applications at scale, enabling industrial digital automation use cases.
arXiv Detail & Related papers (2025-11-11T14:04:00Z) - AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction [60.51319139563509]
We introduce AutoGraph-R1, the first framework to directly optimize KG construction for task performance using Reinforcement Learning (RL)<n>We design two novel, task-aware reward functions, one for graphs as knowledge carriers and another as knowledge indices.<n>Our work shows it is possible to close the loop between construction and application, shifting the paradigm from building intrinsically good'' graphs to building demonstrably useful'' ones.
arXiv Detail & Related papers (2025-10-17T06:03:36Z) - Flash-Searcher: Fast and Effective Web Agents via DAG-Based Parallel Execution [48.7788770680643]
Flash-Searcher is a novel parallel agent reasoning framework.<n>It decomposes complex tasks into subtasks with explicit dependencies, enabling concurrent execution of independent reasoning paths.<n>It achieves 67.7% accuracy on BrowseComp and 83% on xbench-DeepSearch, while reducing agent execution steps by up to 35% compared to current frameworks.
arXiv Detail & Related papers (2025-09-29T17:39:30Z) - Graph-Augmented Large Language Model Agents: Current Progress and Future Prospects [57.53024716739594]
Graph-augmented LLM Agents (GLA) enhance structure, continuity, and coordination in complex agent systems.<n>This paper offers a timely and comprehensive overview of recent advances and highlights key directions for future work.<n>We hope this paper can serve as a roadmap for future research on GLA and foster a deeper understanding of the role of graphs in GLA agent systems.
arXiv Detail & Related papers (2025-07-29T00:27:12Z) - XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation [16.068460356582648]
This research proposes a visual analysis framework that helps RAG developers identify critical recalls of GraphRAG.<n>We develop XGraphRAG, a prototype system incorporating a set of interactive visualizations to facilitate users' analysis process.
arXiv Detail & Related papers (2025-06-10T09:14:30Z) - Follow the Flow: Fine-grained Flowchart Attribution with Neurosymbolic Agents [106.04963073116468]
Flowcharts are a critical tool for visualizing decision-making processes.<n> vision-language models frequently hallucinate nonexistent connections and decision paths when analyzing these diagrams.<n>We introduce Fine-grained Flowchart, which traces specific components grounding a flowchart referring LLM response.<n>We propose FlowPathAgent, a neurosymbolic agent that performs fine-grained post hoc attribution through graph-based reasoning.
arXiv Detail & Related papers (2025-06-02T06:02:41Z) - RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs [58.10503898336799]
We introduce the RAG-on-Graphs Library (RGL), a modular framework that seamlessly integrates the complete RAG pipeline.<n>RGL addresses key challenges by supporting a variety of graph formats and integrating optimized implementations for essential components.<n>Our evaluations demonstrate that RGL not only accelerates the prototyping process but also enhances the performance and applicability of graph-based RAG systems.
arXiv Detail & Related papers (2025-03-25T03:21:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.