Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

A Lightweight Modular Framework for Constructing Autonomous Agents Driven by Large Language Models: Design, Implementation, and Applications in AgentForge

Akbar Anbar Jafari, Cagri Ozcinar, Gholamreza Anbarjafari

TL;DR

AgentForge is presented, a lightweight, open-source Python framework designed to democratize the construction of LLM-driven autonomous agents through a principled modular architecture that achieves competitive task completion rates while reducing development time by 62% compared to LangChain and 78% compared to direct API integration.

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.

A Lightweight Modular Framework for Constructing Autonomous Agents Driven by Large Language Models: Design, Implementation, and Applications in AgentForge

TL;DR

AgentForge is presented, a lightweight, open-source Python framework designed to democratize the construction of LLM-driven autonomous agents through a principled modular architecture that achieves competitive task completion rates while reducing development time by 62% compared to LangChain and 78% compared to direct API integration.

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.
Paper Structure (26 sections, 1 theorem, 7 equations, 3 figures, 4 tables, 1 algorithm)

This paper contains 26 sections, 1 theorem, 7 equations, 3 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. LLM-Based Autonomous Agents
  4. Existing Agent Frameworks
  5. Research Gap and Motivation
  6. System Architecture
  7. Design Principles and Requirements
  8. Modular Skill Architecture
  9. LLM Abstraction Layer
  10. Pipeline Orchestration
  11. Implementation
  12. Core Components
  13. Configuration System
  14. Extension Mechanisms
  15. Experimental Evaluation
  16. ...and 11 more sections

Key Result

Theorem 1

The skill composition mechanism with sequential ($\triangleright$) and parallel ($\|$) operators can express any directed acyclic graph (DAG) of skill invocations.

Figures (3)

  • Figure 1: AgentForge four-layer architecture. The Interface Layer provides user access points; the Orchestration Layer coordinates task execution; the Skill Layer implements discrete capabilities; the Backend Layer abstracts LLM inference.
  • Figure 2: Sequential skill composition flow. Input data flows through WebScraperSkill, DataAnalysisSkill, and ContentGenerationSkill, with each skill's output serving as input to its successor. The composition $f_{1 \triangleright 2 \triangleright 3}(i) = f_3(f_2(f_1(i)))$ produces the final output result.
  • Figure 3: Development time comparison across benchmark tasks. Error bars indicate 95% confidence intervals. AgentForge consistently requires the least implementation effort (n = $250$ participants per framework).

Theorems & Definitions (6)

  • Definition 1: Skill
  • Definition 2: Skill Composition
  • Definition 3: Parallel Composition
  • Theorem 1: Expressiveness
  • proof
  • Definition 4: LLM Backend