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

arXiv 2601.13383 ↗First posted 19 Jan 2026cs.AIcited by 3

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 19 of its 33 references that are in the map. First posted on arXiv 19 Jan 2026.

Builds on

19 of 33 references.

Closest ancestors · 3

Same direction, newest first

Other references · 16

2020Language Models are Few-Shot Learnerscited by 32,199
2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
2022Large Language Models are Zero-Shot Reasonerscited by 4,323
2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
2022Emergent Abilities of Large Language Modelscited by 2,483
2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
2025OpenAI GPT-5 System Cardcited by 910
2024ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Modelscited by 200
2024Multi-Step Reasoning with Large Language Models, a Surveycited by 140

4 more

2026Emergent Introspective Awareness in Large Language Modelscited by 73
2023Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Studycited by 43
2025Emergent Abilities in Large Language Models: A Surveycited by 34
2024Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Modelscited by 15

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

Similar, not cited

Close in wording, no citation either way