MyAG: A Graph-Based Framework for Designing and Analyzing Composable LLM Agent Systems
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 13 of its 22 references that are in the map. First posted on arXiv 15 Jul 2026.
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References · 13
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023Let's Verify Step by Stepcited by 3,598
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
- 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
- 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
- 2023GAIA: a benchmark for General AI Assistantscited by 1,084
- 2025Survey on Evaluation of LLM-based Agentscited by 190
- 2024AgentSquare: Automatic LLM Agent Search in Modular Design Spacecited by 141
- 2024WebCanvas: Benchmarking Web Agents in Online Environmentscited by 119
- 2024A Review of Prominent Paradigms for LLM-Based Agents: Tool Use (Including RAG), Planning, and Feedback Learningcited by 74
- 2025WebRollback: Enhancing Web Agents with Explicit Rollback Mechanismscited by 14
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- 2026Architectural Design, Not Only Model Intelligence, Governs Multi-Agent LLM Performance
- 2026GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration
- 2026From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents
- 2026Inference-Time Graph Engineering for Multi-Agent LLM Workflows
- 2026Scaling LLM-Driven Multi-Agent Systems: Design Principles and Architectural Scalability Analysis