Multi-Agent Research Outline

Comprehensive Outline of Large Language Model-based Multi-Agent Research

This project presents an interactive eBook that compiles an extensive collection of research papers on large language model (LLM)-based multi-agent systems. Organized into multiple chapters and continuously updated with significant research, it strives to provide a comprehensive outline for both researchers and enthusiasts in the field. We welcome ongoing contributions to expand and enhance this resource.

Initiated by the ChatDev Group at Tsinghua University.

Multi-Agent Directions

Multi-agent systems are currently classified into two categories based on whether the agents are designed to achieve specific task goals under external human instructions: task-solving-oriented systems and social-simulation-oriented systems.

Task Solving
Social Simulation

Task solving-oriented multi-agent systems employ autonomous agents working collaboratively to tackle complex problems. Cutting-edge research in this direction revolves around three primary areas: facilitating communication among agents, designing effective organizational structures for interaction, and exploring how agents co-evolve over time.

Social simulation-oriented multi-agent systems concentrate on modeling and analyzing the social behaviors of agents, offering valuable insights into human dynamics and enhances the ability to analyze or predict social phenomena.

Learn More

In addition to the aforementioned resources, we also feature recent research from our lab. If you find our work of interest, we invite you to read, extend, or collaborate.

Optima

Enhances Agent Communication Efficiency

Paper

Code

iAgents

Bijective Social Networks of Humans and Agents

Paper

Code

IoA

Networking Heterogeneous Agents

Paper

Code

ChatDev

Multi-Agent Collaboration for Software Development

Paper

Code

AgentVerse

General-Purpose Multi-Agent Framework

Paper

Code

Co-Learning

Cross-Task Experience Co-Leaning for Mutual Growth

Paper

Code

Co-Evolving

Continuous Experience Refinement over Time

Paper

Code

MacNet

Exploring Collaborative Scaling Law

Paper

Code

CTC

Cross-Team Multi-Agent Orchestration

Paper

Code

ChatEval

Communication for Automated Evaluation

Paper

Code

AutoForm

Finding Effective Communication Protocals

Paper

Code

Frequently Asked Questions

This ebook gathers leading research on LLM-powered multi-agent systems since 2023, categorized by key perspectives in the field. As this area rapidly evolves, updates will be ongoing.

We encourage open-source collaboration on this project. You can contribute by submitting a pull request with detailed metadata for notable papers in the table.

You can download all ebook content in CSV format directly from here.

Comprehensive Outline of Large Language Model-based Multi-Agent Research

Multi-Agent Directions

Dive into Each Chapter

§1: Communication

§2: Organization

§3: Evolution

§4: Simulation

Learn More

Optima

iAgents

IoA

ChatDev

AgentVerse

Co-Learning

Co-Evolving

MacNet

CTC

ChatEval

AutoForm

Frequently Asked Questions