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.

翻译：大型语言模型（LLM）的出现推动了自主智能体开发范式的转变，使系统能够进行推理、规划并执行复杂的多步骤任务。然而，现有智能体框架常受限于架构僵化、供应商锁定以及过高的复杂性，阻碍了快速原型设计与部署。本文提出AgentForge，一个轻量级开源Python框架，旨在通过原则化的模块化架构，降低构建LLM驱动的自主智能体的门槛。AgentForge引入了三项关键创新：（1）可组合的技能抽象机制，支持通过形式化定义的输入-输出契约实现细粒度任务分解；（2）统一的LLM后端接口，支持云端API与本地推理引擎之间的无缝切换；（3）基于YAML的声明式配置系统，将智能体逻辑与实现细节分离。我们将技能组合机制形式化为有向无环图（DAG），并证明其能够表达任意顺序与并行任务工作流。在四个基准场景下的综合实验评估表明，与LangChain相比，AgentForge在保持竞争力的任务完成率的同时，将开发时间减少了62%；与直接API集成相比，开发时间减少了78%。延迟测量证实其编排开销低于100毫秒，表明该框架适用于实时应用。模块化设计便于扩展：我们展示了六个内置技能的集成，并为自定义技能开发提供了完整的文档支持。AgentForge通过为研究者和实践者提供一个生产就绪的基础平台，在不牺牲灵活性或性能的前提下，支持自主智能体的构建、评估与部署，从而填补了LLM智能体生态系统中的一个关键空白。