# AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems

arXiv: https://arxiv.org/abs/2408.15247 · HTML: https://mlsanity.com/paper/2408.15247/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 7 of 7)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023)
- [Augmented Language Models: a Survey](https://mlsanity.com/paper/2302.07842/) (2023)
- [TaskWeaver: A Code-First Agent Framework](https://mlsanity.com/paper/2311.17541/) (2023)

## Built on by (most cited first, 20 of 20)

- [A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives](https://mlsanity.com/paper/2503.13415/) (2025)
- [Challenges in Human-Agent Communication](https://mlsanity.com/paper/2412.10380/) (2024)
- [From Conversation to Orchestration: HCI Challenges and Opportunities in Interactive Multi-Agentic Systems](https://mlsanity.com/paper/2506.20091/) (2025)
- [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026)
- [GeoFlow: Agentic Workflow Automation for Geospatial Tasks](https://mlsanity.com/paper/2508.04719/) (2025)
- [Good Parenting is all you need -- Multi-agentic LLM Hallucination Mitigation](https://mlsanity.com/paper/2410.14262/) (2024)
- [DiLLS: Interactive Diagnosis of LLM-based Multi-agent Systems via Layered Summary of Agent Behaviors](https://mlsanity.com/paper/2602.05446/) (2026)
- [AgentFixer: From Failure Detection to Fix Recommendations in LLM Agentic Systems](https://mlsanity.com/paper/2603.29848/) (2026)
- [Data Analysis in the Era of Generative AI](https://mlsanity.com/paper/2409.18475/) (2024)
- [SwarmSys: Decentralized Swarm-Inspired Agents for Scalable and Adaptive Reasoning](https://mlsanity.com/paper/2510.10047/) (2025)
- [MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems?](https://mlsanity.com/paper/2606.23664/) (2026)
- [Transduction is All You Need for Structured Data Workflows](https://mlsanity.com/paper/2508.15610/) (2025)
- [Dr. Claw: An AI Scientist Workspace for Vibe Research](https://mlsanity.com/paper/2609.00365/) (2026)
- [VIDEE: Visual and Interactive Decomposition, Execution, and Evaluation of Text Analytics with Intelligent Agents](https://mlsanity.com/paper/2506.21582/) (2025)
- [Agentic Meta-Orchestrator for Multi-task Copilots](https://mlsanity.com/paper/2510.22781/) (2025)
- [Don't Vibe Code, Do Skele-Code: Interactive No-Code Notebooks for Subject Matter Experts to Build Lower-Cost Agentic Workflows](https://mlsanity.com/paper/2603.18122/) (2026)
- [Agents in the Wild: Where Research Meets Deployment](https://mlsanity.com/paper/2607.19336/) (2026)
- [TraceGrant: A Contract-Governed Security Framework for the Task-Effect Lifecycle of Networked LLM Agents](https://mlsanity.com/paper/2608.21126/) (2026)
- [Bioinfoysis Technical Report](https://mlsanity.com/paper/2609.03871/) (2026)
- [Understanding Issues, Causes and Solutions in Open-Source LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2610.00905/) (2026)

## Abstract (arXiv)

Multi-agent systems, where multiple agents (generative AI models + tools) collaborate, are emerging as an effective pattern for solving long-running, complex tasks in numerous domains. However, specifying their parameters (such as models, tools, and orchestration mechanisms etc,.) and debugging them remains challenging for most developers. To address this challenge, we present AUTOGEN STUDIO, a no-code developer tool for rapidly prototyping, debugging, and evaluating multi-agent workflows built upon the AUTOGEN framework. AUTOGEN STUDIO offers a web interface and a Python API for representing LLM-enabled agents using a declarative (JSON-based) specification. It provides an intuitive drag-and-drop UI for agent workflow specification, interactive evaluation and debugging of workflows, and a gallery of reusable agent components. We highlight four design principles for no-code multi-agent developer tools and contribute an open-source implementation at https://github.com/microsoft/autogen/tree/main/samples/apps/autogen-studio