mlsanityData to 7 Oct 2026

ReDel: A Toolkit for LLM-Powered Recursive Multi-Agent Systems

arXiv 2408.02248 ↗First posted 5 Aug 2024cs.CL cs.MA cs.SEcited by 8

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Recently, there has been increasing interest in using Large Language Models (LLMs) to construct complex multi-agent systems to perform tasks such as compiling literature reviews, drafting consumer reports, and planning vacations. Many tools and libraries exist for helping create such systems, however none support recursive multi-agent systems -- where the models themselves flexibly decide when to delegate tasks and how to organize their delegation structure. In this work, we introduce ReDel: a toolkit for recursive multi-agent systems that supports custom tool-use, delegation schemes, event-based logging, and interactive replay in an easy-to-use web interface. We show that, using ReDel, we are able to easily identify potential areas of improvements through the visualization and debugging tools. Our code, documentation, and PyPI package are open-source and free to use under the MIT license at https://github.com/zhudotexe/redel.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 12 of its 19 references that are in the map. First posted on arXiv 5 Aug 2024.

Builds on

12 of 19 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Symbolic Learning Enables Self-Evolving Agents26 Jun
  2. 2024EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms20 Jun
  3. 2024TravelPlanner: A Benchmark for Real-World Planning with Language Agents2 Feb
  4. 2024Investigate-Consolidate-Exploit: A General Strategy for Inter-Task Agent Self-Evolution25 Jan

Other references · 8

  1. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  2. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
  3. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  4. 2022Decomposed Prompting: A Modular Approach for Solving Complex Taskscited by 554
  5. 2023ADaPT: As-Needed Decomposition and Planning with Language Modelscited by 172
  6. 2024AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planningcited by 81
  7. 2023Recursion of Thought: A Divide-and-Conquer Approach to Multi-Context Reasoning with Language Modelscited by 24
  8. 2023Kani: A Lightweight and Highly Hackable Framework for Building Language Model Applicationscited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challenges
  2. 2026DiLLS: Interactive Diagnosis of LLM-based Multi-agent Systems via Layered Summary of Agent Behaviors
  3. 2025LLMs Working in Harmony: A Survey on the Technological Aspects of Building Effective LLM-Based Multi Agent Systems
  4. 2026LLM-Enabled Multi-Agent Systems: Empirical Evaluation and Insights into Emerging Design Patterns & Paradigms
  5. 2026LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review
  6. 2024A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Application
  7. 2026Training-Free Agentic AI: Probabilistic Control and Coordination in Multi-Agent LLM Systems
  8. 2026Provable Coordination for LLM Agents via Message Sequence Charts