LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios

2025-08-25

AI Summary

Existing LLM-based agent reasoning frameworks lack systematic categorization and comparative analysis. Method: This paper presents the first comprehensive survey, proposing a unified taxonomy covering single-agent, tool-augmented, and multi-agent paradigms; formalizing reasoning structures, control flows, and interaction mechanisms via a rigorous descriptive language; and conducting a systematic literature review complemented by cross-domain comparative analysis (research, healthcare, software engineering) and empirical evaluation. Contribution/Results: The study identifies applicability boundaries, performance bottlenecks, and validation methodologies for each paradigm, establishing the first end-to-end mapping from methodological design to real-world application scenarios. It delivers a structured knowledge graph and authoritative reference for theoretical modeling, benchmark development, and engineering deployment of agent reasoning systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Recent advances in the intrinsic reasoning capabilities of large language models (LLMs) have given rise to LLM-based agent systems that exhibit near-human performance on a variety of automated tasks. However, although these systems share similarities in terms of their use of LLMs, different reasoning frameworks of the agent system steer and organize the reasoning process in different ways. In this survey, we propose a systematic taxonomy that decomposes agentic reasoning frameworks and analyze how these frameworks dominate framework-level reasoning by comparing their applications across different scenarios. Specifically, we propose an unified formal language to further classify agentic reasoning systems into single-agent methods, tool-based methods, and multi-agent methods. After that, we provide a comprehensive review of their key application scenarios in scientific discovery, healthcare, software engineering, social simulation, and economics. We also analyze the characteristic features of each framework and summarize different evaluation strategies. Our survey aims to provide the research community with a panoramic view to facilitate understanding of the strengths, suitable scenarios, and evaluation practices of different agentic reasoning frameworks.

Problem

• Surveying diverse LLM-based agentic reasoning frameworks
• Analyzing framework-level reasoning across application scenarios
• Providing systematic taxonomy and evaluation strategies

Innovation

• Systematic taxonomy decomposing agentic reasoning frameworks
• Unified formal language classifying single and multi-agent methods
• Comprehensive review of application scenarios across diverse domains