# taichengguo/LLM_MultiAgents_Survey_Papers

Large Language Model based Multi-Agents: A Survey of Progress and Challenges (In IJCAI 2024)

Repository: https://github.com/taichengguo/LLM_MultiAgents_Survey_Papers
Canonical: https://ross.abutalabs.com/products/llm_multiagents_survey_papers
Homepage: https://arxiv.org/abs/2402.01680
License Family: other
Topics: agent, llm, large-language-models, llms, multi-agents, survey, llms-reasoning
Last push: 2026-08-21T20:08:53+00:00

## Health v2 (maintenance only)
Score: 70/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 35, longevity 69
- inputs: {"age_days": 971, "days_push": 12, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1307, forks 67 (observed 2026-08-28T04:04:19.005693+00:00)

## What it is
A curated, regularly updated paper list accompanying the IJCAI 2024 survey 'Large Language Model based Multi-Agents: A Survey of Progress and Challenges'. It organizes LLM-based multi-agent research into frameworks, orchestration, problem solving, world simulation, and datasets/benchmarks.

## Use cases
- find papers on LLM multi-agent systems
- survey of multi-agent frameworks for LLMs
- research reading list for AI agents
- benchmarks for multi-agent LLM systems
- learn how LLM agents communicate and cooperate
- world simulation with LLM agents papers

## When to choose
- you need an academic overview of LLM-based multi-agent research
- you want a categorized, periodically updated reading list
- you are starting research on multi-agent LLM systems

## When to avoid
- you need production-ready agent software rather than papers
- you want a maintained framework or library instead of a paper index

## Facets
- artifact type: learning-resource
- maturity: active
- function: agent-framework, documentation
- domain: artificial-intelligence, large-language-models, tutorials, awesome-lists
- platform: -
- tags: awesome-list, survey-paper, multi-agent-systems, research-papers, ijcai-2024, llm-agents, ai-agents, web-server

## Member repositories
- taichengguo/LLM_MultiAgents_Survey_Papers (main) score 70

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:19.005693+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:50:17.219175+00:00, confidence not recorded.
  - readme: https://github.com/taichengguo/LLM_MultiAgents_Survey_Papers (fetched 2026-08-28T04:04:19.005693+00:00, sha 9246ae09839d)
  - homepage: https://arxiv.org/abs/2402.01680 (fetched 2026-08-29T12:09:06.437959+00:00, sha 7aabc01fdf8e)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T12:09:06.447417+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T12:09:06.451324+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T12:09:06.453226+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T12:09:06.449443+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.