AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

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   AutoAgents: A Framework for Automatic Agent Generation

   Guangyao Chen, Siwei Dong, Yu Shu et al. · 2023 · Artificial Intelligence · AI summary available

   Abstract

   Large language models (LLMs) have enabled remarkable advances in automated task-solving with multi-agent systems. However, most existing LLM-based multi-agent approaches rely on predefined agents to handle simple tasks, limiting the adaptability of multi-agent collaboration to different scenarios. Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks. Specifically, AutoAgents couples the relationship between tasks and roles by dynamically generating multiple required agents based on task content and planning solutions for the current task based on the generated expert …

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   Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents

   Yashar Talebirad, Amirhossein Nadiri · 2023 · Artificial Intelligence · AI summary available

   Abstract

   In this paper, we present a novel framework for enhancing the capabilities of large language models (LLMs) by leveraging the power of multi-agent systems. Our framework introduces a collaborative environment where multiple intelligent agent components, each with distinctive attributes and roles, work together to handle complex tasks more efficiently and effectively. We demonstrate the practicality and versatility of our framework through case studies in artificial general intelligence (AGI), specifically focusing on the Auto-GPT and BabyAGI models. We also examine the "Gorilla" model, which integrates external APIs into the LLM. Our framework addresses limitations and challenges such as loop…

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   EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms

   Siyu Yuan, Kaitao Song, Jiangjie Chen et al. · 2024 · Artificial Intelligence · Not summarized yet

   Abstract

   The rise of powerful large language models (LLMs) has spurred a new trend in building LLM-based autonomous agents for solving complex tasks, especially multi-agent systems. Despite the remarkable progress, we notice that existing works are heavily dependent on human-designed frameworks, which greatly limits the functional scope and scalability of agent systems. How to automatically extend the specialized agent to multi-agent systems to improve task-solving capability still remains a significant challenge. In this paper, we introduce EvoAgent, a generic method to automatically extend expert agents to multi-agent systems via the evolutionary algorithm, thereby improving the effectiveness of LL…

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   AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents

   Ke Yang, Yao Liu, Sapana Chaudhary et al. · 2024 · Artificial Intelligence · AI summary available

   Abstract

   Autonomy via agents using large language models (LLMs) for personalized, standardized tasks boosts human efficiency. Automating web tasks (like booking hotels within a budget) is increasingly sought after. Fulfilling practical needs, the web agent also serves as an important proof-of-concept example for various agent grounding scenarios, with its success promising advancements in many future applications. Prior research often handcrafts web agent strategies (e.g., prompting templates, multi-agent systems, search methods, etc.) and the corresponding in-context examples, which may not generalize well across all real-world scenarios. On the other hand, there has been limited study on the misali…

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   MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

   Sirui Hong, Mingchen Zhuge, Jonathan Chen et al. · 2023 · Artificial Intelligence · AI summary available

   Abstract

   Remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs). Existing LLM-based multi-agent systems can already solve simple dialogue tasks. Solutions to more complex tasks, however, are complicated through logic inconsistencies due to cascading hallucinations caused by naively chaining LLMs. Here we introduce MetaGPT, an innovative meta-programming framework incorporating efficient human workflows into LLM-based multi-agent collaborations. MetaGPT encodes Standardized Operating Procedures (SOPs) into prompt sequences for more streamlined workflows, thus allowing agents with human-like domain expertise to verify intermediat…

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   Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG

   Aditi Singh, Abul Ehtesham, Saket Kumar et al. · 2025 · Artificial Intelligence · AI summary available

   Abstract

   Large Language Models (LLMs) have revolutionized artificial intelligence (AI) by enabling human like text generation and natural language understanding. However, their reliance on static training data limits their ability to respond to dynamic, real time queries, resulting in outdated or inaccurate outputs. Retrieval Augmented Generation (RAG) has emerged as a solution, enhancing LLMs by integrating real time data retrieval to provide contextually relevant and up-to-date responses. Despite its promise, traditional RAG systems are constrained by static workflows and lack the adaptability required for multistep reasoning and complex task management. Agentic Retrieval-Augmented Generation (Agen…

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   AI Agents and Agentic AI-Navigating a Plethora of Concepts for Future Manufacturing

   Yinwang Ren, Yangyang Liu, Tang Ji et al. · 2025 · Artificial Intelligence · AI summary available

   Abstract

   AI agents are autonomous systems designed to perceive, reason, and act within dynamic environments. With the rapid advancements in generative AI (GenAI), large language models (LLMs) and multimodal large language models (MLLMs) have significantly improved AI agents' capabilities in semantic comprehension, complex reasoning, and autonomous decision-making. At the same time, the rise of Agentic AI highlights adaptability and goal-directed autonomy in dynamic and complex environments. LLMs-based AI Agents (LLM-Agents), MLLMs-based AI Agents (MLLM-Agents), and Agentic AI contribute to expanding AI's capabilities in information processing, environmental perception, and autonomous decision-making,…

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   The Rise and Potential of Large Language Model Based Agents: A Survey

   Zhiheng Xi, Wenxiang Chen, Xin Guo et al. · 2023 · Artificial Intelligence · AI summary available

   Abstract

   For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are artificial entities that sense their environment, make decisions, and take actions. Many efforts have been made to develop intelligent AI agents since the mid-20th century. However, these efforts have mainly focused on advancement in algorithms or training strategies to enhance specific capabilities or performance on particular tasks. Actually, what the community lacks is a sufficiently general and powerful model to serve as a starting point for designing AI agents that can adapt to diverse scenarios. Due…