轻量模块化框架让大模型驱动的智能体开发更简单高效。
A Lightweight Modular Framework for Constructing Autonomous Agents Driven by Large Language Models: Design, Implementation, and Applications in AgentForge
- 用可组合技能和有接口约束的任务分解，实现灵活构建
- 任务完成率媲美主流框架，开发时间减少62%~78%
- 适合研究者和开发者快速搭建、测试和部署智能体
大语言模型（LLM）推动了自主智能体的发展，使其具备推理、规划与执行复杂多步骤任务的能力。然而现有框架普遍存在架构僵化、厂商锁定及复杂度过高问题，阻碍快速原型设计与部署。本文提出AgentForge，一个轻量级、开源的Python框架，通过模块化设计推动基于LLM的智能体构建民主化。其核心创新包括：（1）可组合的技能抽象，支持细粒度任务分解并定义正式的输入输出契约；（2）统一的LLM后端接口，无缝切换云端API与本地推理引擎；（3）声明式YAML配置系统，分离智能体逻辑与实现细节。我们形式化技能组合机制为有向无环图（DAG），并证明其可表达任意顺序与并行任务流程。在四个基准场景中的综合评估表明，AgentForge在任务完成率上达到竞争力水平，开发时间相比LangChain减少62%，相比直接调用API减少78%。延迟测量显示编排开销低于100ms，适用于实时应用。模块化设计便于扩展：已集成六个内置技能，并提供完整文档支持自定义技能开发。AgentForge填补了大模型智能体生态的关键空白，为研究人员与实践者提供了可生产使用的灵活、高性能基础平台。
原文摘要 · Abstract (English)
The emergence of LLMs has catalyzed a paradigm shift in autonomous agent development, enabling systems capable of reasoning, planning, and executing complex multi-step tasks. However, existing agent frameworks often suffer from architectural rigidity, vendor lock-in, and prohibitive complexity that impedes rapid prototyping and deployment. This paper presents AgentForge, a lightweight, open-source Python framework designed to democratize the construction of LLM-driven autonomous agents through a principled modular architecture. AgentForge introduces three key innovations: (1) a composable skill abstraction that enables fine-grained task decomposition with formally defined input-output contracts, (2) a unified LLM backend interface supporting seamless switching between cloud-based APIs and local inference engines, and (3) a declarative YAML-based configuration system that separates agent logic from implementation details. We formalize the skill composition mechanism as a directed acyclic graph (DAG) and prove its expressiveness for representing arbitrary sequential and parallel task workflows. Comprehensive experimental evaluation across four benchmark scenarios demonstrates that AgentForge achieves competitive task completion rates while reducing development time by 62% compared to LangChain and 78% compared to direct API integration. Latency measurements confirm sub-100ms orchestration overhead, rendering the framework suitable for real-time applications. The modular design facilitates extension: we demonstrate the integration of six built-in skills and provide comprehensive documentation for custom skill development. AgentForge addresses a critical gap in the LLM agent ecosystem by providing researchers and practitioners with a production-ready foundation for constructing, evaluating, and deploying autonomous agents without sacrificing flexibility or performance.
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