Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research
🤖 AI Summary
Addressing three key challenges in language agent development—high engineering overhead, nonstandardized components, and incomparable evaluations—this paper introduces AGORA, a graph-driven agent orchestration framework. AGORA employs a graph-structured workflow engine to unify LLM inference, memory-aware state management, and multi-granularity task execution. It provides a modular architecture, a full-stack reusable algorithm library, and a multidimensional standardized evaluation protocol covering mathematical reasoning and multimodal tasks. Systematic evaluation across multiple LLMs and diverse tasks demonstrates that Chain-of-Thought achieves optimal trade-offs between robustness and efficiency. AGORA significantly reduces development effort, enhances experimental reproducibility, and improves cross-study comparability.
Application Category
📝 Abstract
Language agents powered by large language models (LLMs) have demonstrated remarkable capabilities in understanding, reasoning, and executing complex tasks. However, developing robust agents presents significant challenges: substantial engineering overhead, lack of standardized components, and insufficient evaluation frameworks for fair comparison. We introduce Agent Graph-based Orchestration for Reasoning and Assessment (AGORA), a flexible and extensible framework that addresses these challenges through three key contributions: (1) a modular architecture with a graph-based workflow engine, efficient memory management, and clean component abstraction; (2) a comprehensive suite of reusable agent algorithms implementing state-of-the-art reasoning approaches; and (3) a rigorous evaluation framework enabling systematic comparison across multiple dimensions. Through extensive experiments on mathematical reasoning and multimodal tasks, we evaluate various agent algorithms across different LLMs, revealing important insights about their relative strengths and applicability. Our results demonstrate that while sophisticated reasoning approaches can enhance agent capabilities, simpler methods like Chain-of-Thought often exhibit robust performance with significantly lower computational overhead. AGORA not only simplifies language agent development but also establishes a foundation for reproducible agent research through standardized evaluation protocols.
Problem
Research questions and friction points this paper is trying to address.
Standardizing components for reproducible language agent research
Reducing engineering overhead in developing robust language agents
Providing a fair evaluation framework for agent comparison
Innovation
Methods, ideas, or system contributions that make the work stand out.
Graph-based workflow engine for modular architecture
Reusable agent algorithms for reasoning approaches
Rigorous evaluation framework for systematic comparison
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