mlsanityData to 7 Oct 2026

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

arXiv 2308.08155 ↗First posted 16 Aug 2023cs.AI cs.CLcited by 1,906

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks. AutoGen agents are customizable, conversable, and can operate in various modes that employ combinations of LLMs, human inputs, and tools. Using AutoGen, developers can also flexibly define agent interaction behaviors. Both natural language and computer code can be used to program flexible conversation patterns for different applications. AutoGen serves as a generic infrastructure to build diverse applications of various complexities and LLM capacities. Empirical studies demonstrate the effectiveness of the framework in many example applications, with domains ranging from mathematics, coding, question answering, operations research, online decision-making, entertainment, etc.

Key facts. Cited by 1,906 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 28 of its 75 references that are in the map. First posted on arXiv 16 Aug 2023.

Builds on

28 of 75 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Self-collaboration Code Generation via ChatGPT15 Apr

Other references · 27

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  4. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  5. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  6. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  7. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  8. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  9. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
  10. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
  11. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  12. 2016Concrete Problems in AI Safetycited by 1,582
15 more
  1. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
  2. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,143
  3. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
  4. 2018VirtualHome: Simulating Household Activities via Programscited by 411
  5. 2023Language Models can Solve Computer Taskscited by 371
  6. 2023Large Language Models as Tool Makerscited by 293
  7. 2018Reinforcement Learning on Web Interfaces Using Workflow-Guided Explorationcited by 282
  8. 2017StarCraft II: A New Challenge for Reinforcement Learningcited by 268
  9. 2017Deal or No Deal? End-to-End Learning for Negotiation Dialoguescited by 202
  10. 2020iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenescited by 151
  11. 2023Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answeringcited by 150
  12. 2021Retrieval Augmented Code Generation and Summarizationcited by 148
  13. 2019FLAML: A Fast and Lightweight AutoML Librarycited by 104
  14. 2023LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Modelscited by 80
  15. 2023Large Language Models for Supply Chain Optimizationcited by 61

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 13; 2024: 132; 2025: 541; 2026: 1220.

1,906 papers.

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  2. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
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  4. 2025Why Do Multi-Agent LLM Systems Fail?cited by 559
  5. 2025Multi-Agent Collaboration Mechanisms: A Survey of LLMscited by 424
  6. 2025Agent Laboratory: Using LLM Agents as Research Assistantscited by 405
  7. 2025LLMs Get Lost In Multi-Turn Conversationcited by 400
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  11. 2024Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaborationcited by 187
  12. 2025AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challengescited by 167
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  8. 2025Memento: Fine-tuning LLM Agents without Fine-tuning LLMscited by 119
  9. 2025WebShaper: Agentically Data Synthesizing via Information-Seeking Formalizationcited by 118
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  11. 2025Agentic Large Language Models, a surveycited by 105
  12. 2025Multi-Agent Collaboration via Evolving Orchestrationcited by 102
  13. 2024MARG: Multi-Agent Review Generation for Scientific Paperscited by 98
  14. 2024On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agentscited by 92
  15. 2024SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoningcited by 90
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  17. 2024AgentInstruct: Toward Generative Teaching with Agentic Flowscited by 88
  18. 2025On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weightingcited by 88
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  21. 2025Process Reward Models for LLM Agents: Practical Framework and Directionscited by 81
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  23. 2024EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Recordscited by 75
  24. 2025Debate or Vote: Which Yields Better Decisions in Multi-Agent Large Language Models?cited by 74
  25. 2024Self-Evolving Multi-Agent Collaboration Networks for Software Developmentcited by 73
  26. 2024BLADE: Benchmarking Language Model Agents for Data-Driven Sciencecited by 72
  27. 2024Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agentscited by 71
  28. 2024Preble: Efficient Distributed Prompt Scheduling for LLM Servingcited by 68
  29. 2026Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineeringcited by 68
  30. 2025In-the-Flow Agentic System Optimization for Effective Planning and Tool Usecited by 67
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  33. 2024Learning to Route LLMs with Confidence Tokenscited by 66
  34. 2025MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understandingcited by 66
  35. 2024LLM Discussion: Enhancing the Creativity of Large Language Models via Discussion Framework and Role-Playcited by 65
  36. 2025SWE-Exp: Experience-Driven Software Issue Resolutioncited by 64
  37. 2024ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learningcited by 63
  38. 2025Talk Isn't Always Cheap: Understanding Failure Modes in Multi-Agent Debatecited by 63

50 most cited of 1,906 All 1,906 (JSON)

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  4. 2023Towards Autonomous Testing Agents via Conversational Large Language Models
  5. 2024Creating an LLM-based AI-agent: A high-level methodology towards enhancing LLMs with APIs
  6. 2026Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report
  7. 2023Gentopia: A Collaborative Platform for Tool-Augmented LLMs
  8. 2026Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions