Learning desk

MultiAgent EDU StackGather good sources. Teach what matters.
T3Mermaid to ASCII art (mermaid-ascii)T3Kimi K3, and what we can still learn from the pelican benchmarkT3Firefox in WebAssemblyT3Spot birds not golfT3[AINews] Kimi K3 2.8T-A50B: the largest open model ever released; Opus 4.8-class at Sonnet 5 pricingT1From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphologyT1DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge GraphsT1GenTL: A General Transfer Learning Model for Building Thermal DynamicsT1A short review on the maximum clique problem algorithms with classical, AI, and quantum methodsT1Man, Machine, and Masterpiece: Artistic Ownership in the AI EraT1HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective OptimizationT1How Well Does AI-Generated Feedback Work? Intrinsic and Extrinsic Evaluation across more than 20,000 EFL Essay DraftsT3Mermaid to ASCII art (mermaid-ascii)T3Kimi K3, and what we can still learn from the pelican benchmarkT3Firefox in WebAssemblyT3Spot birds not golfT3[AINews] Kimi K3 2.8T-A50B: the largest open model ever released; Opus 4.8-class at Sonnet 5 pricingT1From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphologyT1DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge GraphsT1GenTL: A General Transfer Learning Model for Building Thermal DynamicsT1A short review on the maximum clique problem algorithms with classical, AI, and quantum methodsT1Man, Machine, and Masterpiece: Artistic Ownership in the AI EraT1HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective OptimizationT1How Well Does AI-Generated Feedback Work? Intrinsic and Extrinsic Evaluation across more than 20,000 EFL Essay Drafts
← Dispatches

MyAG: A Graph-Based Framework for Designing and Analyzing Composable LLM Agent Systems

Primary research

#22

T1digested
Topic
Agent Evaluation
First seen
2026-07-16 19:07:58
Last seen
2026-07-16 19:07:58

Source raw items (1)

  • arXiv2026-07-16 19:06:49
    MyAG: A Graph-Based Framework for Designing and Analyzing Composable LLM Agent Systems

    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.