@CycleDecoded: 这波属于是把 AI Agent 的门槛直接给砸穿了。HKU 又出了个神仙项目,不用写一行代码,用自然语言就能自动组装并演进一整套 AI 工作流。 项目介绍 香港大学 (HKUDS) 开源的 AutoAgent,目前 GitHub 已经拿下…
摘要
香港大学开源 AutoAgent 框架,无需写代码即可通过自然语言自动构建和演进多智能体工作流,并内置深度研究模式,支持多种主流大模型。
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缓存时间: 2026/08/03 15:46
这波属于是把 AI Agent 的门槛直接给砸穿了。HKU 又出了个神仙项目,不用写一行代码,用自然语言就能自动组装并演进一整套 AI 工作流。
项目介绍 香港大学 (HKUDS) 开源的 AutoAgent,目前 GitHub 已经拿下 9.5k+ Star(MIT 协议)。主打全自动、零代码搭建 Multi-Agent 架构,甚至自带类似 Deep Research 的深度研究模式,直接对标那些收费大几百美金的 AI 引擎。
核心卖点
零代码自然语言搭 Agent:直接用大白话提需求,系统自动生成工具、智能体和整个协作工作流。
完全免费替代 Deep Research:内置 User Mode 深度研究助手,调用 Claude 3.5 或 DeepSeek-R1 效果直接拉满。
自我演进与代码生成:遇到不会的功能,它会自己写代码扩展工具库并迭代更新。
无缝对接主流模型:支持 OpenAI、Claude、Gemini、Grok 及各类本地开源大模型 API。
链接 GitHub:https://github.com/HKUDS/AutoAgent
HKUDS/AutoAgent
Source: https://github.com/HKUDS/AutoAgent
AutoAgent: Fully-Automated & Zero-Code LLM Agent Framework
Welcome to AutoAgent! AutoAgent is a Fully-Automated and highly Self-Developing framework that enables users to create and deploy LLM agents through Natural Language Alone.
✨Key Features of AutoAgent
-
💬 Natural Language-Driven Agent Building Automatically constructs and orchestrates collaborative agent systems purely through natural dialogue, eliminating the need for manual coding or technical configuration.
-
🚀 Zero-Code Framework Democratizes AI development by allowing anyone, regardless of coding experience, to create and customize their own agents, tools, and workflows using natural language alone.
-
⚡ Self-Managing Workflow Generation Dynamically creates, optimizes and adapts agent workflows based on high-level task descriptions, even when users cannot fully specify implementation details.
-
🔧 Intelligent Resource Orchestration Enables controlled code generation for creating tools, agents, and workflows through iterative self-improvement, supporting both single agent creation and multi-agent workflow generation.
-
🎯 Self-Play Agent Customization Enables controlled code generation for creating tools, agents, and workflows through iterative self-improvement, supporting both single agent creation and multi-agent workflow generation.
🚀 Unlock the Future of LLM Agents. Try 🔥AutoAgent🔥 Now!
🔥 News
- [2025, Feb 17]: 🎉🎉We've updated and released AutoAgent v0.2.0 (formerly known as MetaChain). Detailed changes include: 1) fix the bug of different LLM providers from issues; 2) add automatic installation of AutoAgent in the container environment according to issues; 3) add more easy-to-use commands for the CLI mode. 4) Rename the project to AutoAgent for better understanding.
- [2025, Feb 10]: 🎉🎉We've released MetaChain!, including framework, evaluation codes and CLI mode! Check our paper for more details.
📑 Table of Contents
- ✨ Features
- 🔥 News
- 🔍 How to Use AutoAgent
- ⚡ Quick Start
- ☑️ Todo List
- 🔬 How To Reproduce the Results in the Paper
- 📖 Documentation
- 🤝 Join the Community
- 🙏 Acknowledgements
- 🌟 Cite
🔍 How to Use AutoAgent
1. user mode (Deep Research Agents)
AutoAgent features a ready-to-use multi-agent system accessible through user mode on the start page. This system serves as a comprehensive AI research assistant designed for information retrieval, complex analytical tasks, and comprehensive report generation.
- 🚀 High Performance: Matches Deep Research using Claude 3.5 rather than OpenAI’s o3 model.
- 🔄 Model Flexibility: Compatible with any LLM (including Deepseek-R1, Grok, Gemini, etc.)
- 💰 Cost-Effective: Open-source alternative to Deep Research’s $200/month subscription
- 🎯 User-Friendly: Easy-to-deploy CLI interface for seamless interaction
- 📁 File Support: Handles file uploads for enhanced data interaction
🎥 Deep Research (aka User Mode)
2. agent editor (Agent Creation without Workflow)
The most distinctive feature of AutoAgent is its natural language customization capability. Unlike other agent frameworks, AutoAgent allows you to create tools, agents, and workflows using natural language alone. Simply choose agent editor or workflow editor mode to start your journey of building agents through conversations.
You can use agent editor as shown in the following figure.
Input what kind of agent you want to create. |
Automated agent profiling. |
Output the agent profiles. |
Create the desired tools. |
Input what do you want to complete with the agent. (Optional) |
Create the desired agent(s) and go to the next step. |
3. workflow editor (Agent Creation with Workflow)
You can also create the agent workflows using natural language description with the workflow editor mode, as shown in the following figure. (Tips: this mode does not support tool creation temporarily.)
Input what kind of workflow you want to create. |
Automated workflow profiling. |
Output the workflow profiles. |
Input what do you want to complete with the workflow. (Optional) |
Create the desired workflow(s) and go to the next step. |
⚡ Quick Start
Installation
AutoAgent Installation
git clone https://github.com/HKUDS/AutoAgent.git
cd AutoAgent
pip install -e .
Docker Installation
We use Docker to containerize the agent-interactive environment. So please install Docker first. You don’t need to manually pull the pre-built image, because we have let Auto-Deep-Research automatically pull the pre-built image based on your architecture of your machine.
API Keys Setup
Create an environment variable file, just like .env.template, and set the API keys for the LLMs you want to use. Not every LLM API Key is required, use what you need.
# Required Github Tokens of your own
GITHUB_AI_TOKEN=
# Optional API Keys
OPENAI_API_KEY=
DEEPSEEK_API_KEY=
ANTHROPIC_API_KEY=
GEMINI_API_KEY=
HUGGINGFACE_API_KEY=
GROQ_API_KEY=
XAI_API_KEY=
Start with CLI Mode
[🚨 News: ] We have updated a more easy-to-use command to start the CLI mode and fix the bug of different LLM providers from issues. You can follow the following steps to start the CLI mode with different LLM providers with much less configuration.
Command Options:
You can run auto main to start full part of AutoAgent, including user mode, agent editor and workflow editor. Btw, you can also run auto deep-research to start more lightweight user mode, just like the Auto-Deep-Research project. Some configuration of this command is shown below.
--container_name: Name of the Docker container (default: ‘deepresearch’)--port: Port for the container (default: 12346)COMPLETION_MODEL: Specify the LLM model to use, you should follow the name of Litellm to set the model name. (Default:claude-3-5-sonnet-20241022)DEBUG: Enable debug mode for detailed logs (default: False)API_BASE_URL: The base URL for the LLM provider (default: None)FN_CALL: Enable function calling (default: None). Most of time, you could ignore this option because we have already set the default value based on the model name.git_clone: Clone the AutoAgent repository to the local environment (only support with theauto maincommand, default: True)test_pull_name: The name of the test pull. (only support with theauto maincommand, default: ‘autoagent_mirror’)
More details about git_clone and test_pull_name]
In the agent editor and workflow editor mode, we should clone a mirror of the AutoAgent repository to the local agent-interactive environment and let our AutoAgent automatically update the AutoAgent itself, such as creating new tools, agents and workflows. So if you want to use the agent editor and workflow editor mode, you should set the git_clone to True and set the test_pull_name to ‘autoagent_mirror’ or other branches.
auto main with different LLM Providers
Then I will show you how to use the full part of AutoAgent with the auto main command and different LLM providers. If you want to use the auto deep-research command, you can refer to the Auto-Deep-Research project for more details.
Anthropic
- set the
ANTHROPIC_API_KEYin the.envfile.
ANTHROPIC_API_KEY=your_anthropic_api_key
- run the following command to start Auto-Deep-Research.
auto main # default model is claude-3-5-sonnet-20241022
OpenAI
- set the
OPENAI_API_KEYin the.envfile.
OPENAI_API_KEY=your_openai_api_key
- run the following command to start Auto-Deep-Research.
COMPLETION_MODEL=gpt-4o auto main
Mistral
- set the
MISTRAL_API_KEYin the.envfile.
MISTRAL_API_KEY=your_mistral_api_key
- run the following command to start Auto-Deep-Research.
COMPLETION_MODEL=mistral/mistral-large-2407 auto main
Gemini - Google AI Studio
- set the
GEMINI_API_KEYin the.envfile.
GEMINI_API_KEY=your_gemini_api_key
- run the following command to start Auto-Deep-Research.
COMPLETION_MODEL=gemini/gemini-2.0-flash auto main
Huggingface
- set the
HUGGINGFACE_API_KEYin the.envfile.
HUGGINGFACE_API_KEY=your_huggingface_api_key
- run the following command to start Auto-Deep-Research.
COMPLETION_MODEL=huggingface/meta-llama/Llama-3.3-70B-Instruct auto main
Groq
- set the
GROQ_API_KEYin the.envfile.
GROQ_API_KEY=your_groq_api_key
- run the following command to start Auto-Deep-Research.
COMPLETION_MODEL=groq/deepseek-r1-distill-llama-70b auto main
OpenAI-Compatible Endpoints (e.g., Grok)
- set the
OPENAI_API_KEYin the.envfile.
OPENAI_API_KEY=your_api_key_for_openai_compatible_endpoints
- run the following command to start Auto-Deep-Research.
COMPLETION_MODEL=openai/grok-2-latest API_BASE_URL=https://api.x.ai/v1 auto main
OpenRouter (e.g., DeepSeek-R1)
We recommend using OpenRouter as LLM provider of DeepSeek-R1 temporarily. Because official API of DeepSeek-R1 can not be used efficiently.
- set the
OPENROUTER_API_KEYin the.envfile.
OPENROUTER_API_KEY=your_openrouter_api_key
- run the following command to start Auto-Deep-Research.
COMPLETION_MODEL=openrouter/deepseek/deepseek-r1 auto main
DeepSeek
- set the
DEEPSEEK_API_KEYin the.envfile.
DEEPSEEK_API_KEY=your_deepseek_api_key
- run the following command to start Auto-Deep-Research.
COMPLETION_MODEL=deepseek/deepseek-chat auto main
After the CLI mode is started, you can see the start page of AutoAgent:
Tips
Import browser cookies to browser environment
You can import the browser cookies to the browser environment to let the agent better access some specific websites. For more details, please refer to the cookies folder.
Add your own API keys for third-party Tool Platforms
If you want to create tools from the third-party tool platforms, such as RapidAPI, you should subscribe tools from the platform and add your own API keys by running process_tool_docs.py.
python process_tool_docs.py
More features coming soon! 🚀 Web GUI interface under development.
☑️ Todo List
AutoAgent is continuously evolving! Here’s what’s coming:
- 📊 More Benchmarks: Expanding evaluations to SWE-bench, WebArena, and more
- 🖥️ GUI Agent: Supporting Computer-Use agents with GUI interaction
- 🔧 Tool Platforms: Integration with more platforms like Composio
- 🏗️ Code Sandboxes: Supporting additional environments like E2B
- 🎨 Web Interface: Developing comprehensive GUI for better user experience
Have ideas or suggestions? Feel free to open an issue! Stay tuned for more exciting updates! 🚀
🔬 How To Reproduce the Results in the Paper
GAIA Benchmark
For the GAIA benchmark, you can run the following command to run the inference.
cd path/to/AutoAgent && sh evaluation/gaia/scripts/run_infer.sh
For the evaluation, you can run the following command.
cd path/to/AutoAgent && python evaluation/gaia/get_score.py
Agentic-RAG
For the Agentic-RAG task, you can run the following command to run the inference.
Step1. Turn to this page and download it. Save them to your datapath.
Step2. Run the following command to run the inference.
cd path/to/AutoAgent && sh evaluation/multihoprag/scripts/run_rag.sh
Step3. The result will be saved in the evaluation/multihoprag/result.json.
📖 Documentation
A more detailed documentation is coming soon 🚀, and we will update in the Documentation page.
🤝 Join the Community
We want to build a community for AutoAgent, and we welcome everyone to join us. You can join our community by:
- Join our Slack workspace - Here we talk about research, architecture, and future development.
- Join our Discord server - This is a community-run server for general discussion, questions, and feedback.
- Read or post Github Issues - Check out the issues we’re working on, or add your own ideas.
Misc
🙏 Acknowledgements
Rome wasn’t built in a day. AutoAgent stands on the shoulders of giants, and we are deeply grateful for the outstanding work that came before us. Our framework architecture draws inspiration from OpenAI Swarm, while our user mode’s three-agent design benefits from Magentic-one’s insights. We’ve also learned from OpenHands for documentation structure and many other excellent projects for agent-environment interaction design, among others. We express our sincere gratitude and respect to all these pioneering works that have been instrumental in shaping AutoAgent.
🌟 Cite
@misc{AutoAgent,
title={{AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents}},
author={Jiabin Tang, Tianyu Fan, Chao Huang},
year={2025},
eprint={202502.05957},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2502.05957},
}
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