🤖 AI Summary
This work proposes AgentForge, a lightweight and open-source Python framework designed to overcome the limitations of existing large language model (LLM) agent frameworks—namely architectural rigidity, vendor lock-in, and high complexity—that hinder rapid development. AgentForge employs a modular design to enable flexible construction of LLM-driven autonomous agents, featuring composable skill abstractions, a unified LLM backend interface, and declarative YAML-based configuration that expresses arbitrary sequential and parallel task flows as directed acyclic graphs (DAGs). The framework supports both cloud APIs and local inference engines, offering six built-in skills alongside an extensible mechanism for custom implementations. Experimental results demonstrate competitive task completion rates across four benchmark scenarios, with development time reduced by 62% compared to LangChain and by 78% versus direct API integration, while maintaining orchestration overhead below 100 ms—making it suitable for real-time applications.
📝 Abstract
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.