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

• URL: http://arxiv.org/abs/2408.15247v1
• Date: Fri, 9 Aug 2024 03:27:37 GMT
• Title: AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems
• Authors: Victor Dibia, Jingya Chen, Gagan Bansal, Suff Syed, Adam Fourney, Erkang Zhu, Chi Wang, Saleema Amershi,
• Abstract summary: AUTOGEN STUDIO is a no-code developer tool for rapidly prototyping multi-agent systems. It provides an intuitive drag-and-drop UI for agent specification, interactive evaluation, and a gallery of reusable agent components.
• Score: 31.113305753414913
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

Related papers

• Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks [39.084974125007165]
  We introduce Magentic-One, a high-performing open-source agentic system for solving complex tasks. Magentic-One uses a multi-agent architecture where a lead agent, the Orchestrator, tracks progress, and re-plans to recover from errors. We show that Magentic-One achieves statistically competitive performance to the state-of-the-art on three diverse and challenging agentic benchmarks.
  arXiv  Detail & Related papers  (2024-11-07T06:36:19Z)
• Automated Design of Agentic Systems [5.404186221463082]
  We formulate a new research area, Automated Design of Agentic Systems, which aims to automatically create powerful agentic system designs. We show that our algorithm can progressively invent agents with novel designs that greatly outperform state-of-the-art hand-designed agents.
  arXiv  Detail & Related papers  (2024-08-15T21:59:23Z)
• ReDel: A Toolkit for LLM-Powered Recursive Multi-Agent Systems [39.85101344037394]
  ReDel is a toolkit for building multi-agent systems. It supports custom tool-use, delegation schemes, event-based logging, and interactive replay. Our code, documentation, and PyPI package are open-source and free to use under the MIT license.
  arXiv  Detail & Related papers  (2024-08-05T05:43:23Z)
• Very Large-Scale Multi-Agent Simulation in AgentScope [112.98986800070581]
  We develop new features and components for AgentScope, a user-friendly multi-agent platform. We propose an actor-based distributed mechanism towards great scalability and high efficiency. We also provide a web-based interface for conveniently monitoring and managing a large number of agents.
  arXiv  Detail & Related papers  (2024-07-25T05:50:46Z)
• Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
  Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents. We propose the Internet of Agents (IoA), a novel framework that addresses these limitations. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
  arXiv  Detail & Related papers  (2024-07-09T17:33:24Z)
• SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering [79.07755560048388]
  SWE-agent is a system that facilitates LM agents to autonomously use computers to solve software engineering tasks. SWE-agent's custom agent-computer interface (ACI) significantly enhances an agent's ability to create and edit code files, navigate entire repositories, and execute tests and other programs. We evaluate SWE-agent on SWE-bench and HumanEvalFix, achieving state-of-the-art performance on both with a pass@1 rate of 12.5% and 87.7%, respectively.
  arXiv  Detail & Related papers  (2024-05-06T17:41:33Z)
• AgentKit: Structured LLM Reasoning with Dynamic Graphs [91.09525140733987]
  We propose an intuitive LLM prompting framework (AgentKit) for multifunctional agents. AgentKit offers a unified framework for explicitly constructing a complex "thought process" from simple natural language prompts.
  arXiv  Detail & Related papers  (2024-04-17T15:40:45Z)
• AgentScope: A Flexible yet Robust Multi-Agent Platform [66.64116117163755]
  AgentScope is a developer-centric multi-agent platform with message exchange as its core communication mechanism. The abundant syntactic tools, built-in agents and service functions, user-friendly interfaces for application demonstration and utility monitor, zero-code programming workstation, and automatic prompt tuning mechanism significantly lower the barriers to both development and deployment.
  arXiv  Detail & Related papers  (2024-02-21T04:11:28Z)
• AutoAgents: A Framework for Automatic Agent Generation [27.74332323317923]
  AutoAgents is an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks. Our experiments on various benchmarks demonstrate that AutoAgents generates more coherent and accurate solutions than the existing multi-agent methods.
  arXiv  Detail & Related papers  (2023-09-29T14:46:30Z)
• Agents: An Open-source Framework for Autonomous Language Agents [98.91085725608917]
  We consider language agents as a promising direction towards artificial general intelligence. We release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience.
  arXiv  Detail & Related papers  (2023-09-14T17:18:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.