[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-71967":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":23,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":28,"readmeContent":29,"aiSummary":30,"trendingCount":16,"starSnapshotCount":16,"syncStatus":31,"lastSyncTime":32,"discoverSource":33},71967,"AutoAgent","HKUDS\u002FAutoAgent","HKUDS","\"AutoAgent: Fully-Automated and Zero-Code LLM Agent Framework\"","https:\u002F\u002Farxiv.org\u002Fabs\u002F2502.05957",null,"Python",9379,1312,116,55,0,4,13,75,12,40.35,"MIT License",false,"main",[26,27],"agent","llms","2026-06-12 02:02:56","\u003Ca name=\"readme-top\">\u003C\u002Fa>\n\n\u003Cdiv align=\"center\">\n  \u003Cimg src=\".\u002Fassets\u002FAutoAgent_logo.svg\" alt=\"Logo\" width=\"200\">\n  \u003Ch1 align=\"center\">AutoAgent: Fully-Automated & Zero-Code\u003C\u002Fbr> LLM Agent Framework \u003C\u002Fh1>\n\u003C\u002Fdiv>\n\n\n\n\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fautoagent-ai.github.io\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FProject-Page-blue?style=for-the-badge&color=FFE165&logo=homepage&logoColor=white\" alt=\"Credits\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fjoin.slack.com\u002Ft\u002Fmetachain-workspace\u002Fshared_invite\u002Fzt-2zibtmutw-v7xOJObBf9jE2w3x7nctFQ\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSlack-Join%20Us-red?logo=slack&logoColor=white&style=for-the-badge\" alt=\"Join our Slack community\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fdiscord.gg\u002FjQJdXyDB\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDiscord-Join%20Us-purple?logo=discord&logoColor=white&style=for-the-badge\" alt=\"Join our Discord community\">\u003C\u002Fa>\n  \u003C!-- \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FHKUDS\u002FAutoAgent\u002Fblob\u002Fmain\u002Fassets\u002Fautoagent-wechat.jpg\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWechat-Join%20Us-green?logo=wechat&logoColor=white&style=for-the-badge\" alt=\"Join our Wechat community\">\u003C\u002Fa> -->\n  \u003Ca href=\".\u002FCommunication.md\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F💬Feishu-Group-07c160?style=for-the-badge&logoColor=white&labelColor=1a1a2e\">\u003C\u002Fa>\n  \u003Ca href=\".\u002FCommunication.md\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWeChat-Group-07c160?style=for-the-badge&logo=wechat&logoColor=white&labelColor=1a1a2e\">\u003C\u002Fa>\n  \n  \u003Cbr\u002F>\n  \u003Ca href=\"https:\u002F\u002Fautoagent-ai.github.io\u002Fdocs\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDocumentation-000?logo=googledocs&logoColor=FFE165&style=for-the-badge\" alt=\"Check out the documentation\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2502.05957\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPaper%20on%20Arxiv-000?logoColor=FFE165&logo=arxiv&style=for-the-badge\" alt=\"Paper\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgaia-benchmark-leaderboard.hf.space\u002F\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGAIA%20Benchmark-000?logoColor=FFE165&logo=huggingface&style=for-the-badge\" alt=\"Evaluation Benchmark Score\">\u003C\u002Fa>\n  \u003Chr>\n\u003C\u002Fdiv>\n\n\u003Cdiv align=\"center\">\n\u003Ca href=\"https:\u002F\u002Ftrendshift.io\u002Frepositories\u002F13954\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Ftrendshift.io\u002Fapi\u002Fbadge\u002Frepositories\u002F13954\" alt=\"HKUDS%2FAutoAgent | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"\u002F>\u003C\u002Fa>\n\u003C\u002Fdiv>\n\nWelcome 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**. \n\n## ✨Key Features of AutoAgent\n\n* 💬 **Natural Language-Driven Agent Building** \n\u003C\u002Fbr>Automatically constructs and orchestrates collaborative agent systems purely through natural dialogue, eliminating the need for manual coding or technical configuration.\n\n* 🚀 **Zero-Code Framework**\n\u003C\u002Fbr>Democratizes AI development by allowing anyone, regardless of coding experience, to create and customize their own agents, tools, and workflows using natural language alone.\n\n* ⚡ **Self-Managing Workflow Generation**\n\u003C\u002Fbr>Dynamically creates, optimizes and adapts agent workflows based on high-level task descriptions, even when users cannot fully specify implementation details.\n\n* 🔧 **Intelligent Resource Orchestration**\n\u003C\u002Fbr>Enables controlled code generation for creating tools, agents, and workflows through iterative self-improvement, supporting both single agent creation and multi-agent workflow generation.\n\n* 🎯 **Self-Play Agent Customization** \n\u003C\u002Fbr>Enables controlled code generation for creating tools, agents, and workflows through iterative self-improvement, supporting both single agent creation and multi-agent workflow generation.\n\n🚀 Unlock the Future of LLM Agents. Try 🔥AutoAgent🔥 Now!\n\n\u003Cdiv align=\"center\">\n  \u003C!-- \u003Cimg src=\".\u002Fassets\u002FAutoAgentnew-intro.pdf\" alt=\"Logo\" width=\"100%\"> -->\n  \u003Cfigure>\n    \u003Cimg src=\".\u002Fassets\u002Fautoagent-intro.svg\" alt=\"Logo\" style=\"max-width: 100%; height: auto;\">\n    \u003Cfigcaption>\u003Cem>Quick Overview of AutoAgent.\u003C\u002Fem>\u003C\u002Ffigcaption>\n  \u003C\u002Ffigure>\n\u003C\u002Fdiv>\n\n\n\n## 🔥 News\n\n\u003Cdiv class=\"scrollable\">\n    \u003Cul>\n      \u003Cli>\u003Cstrong>[2025, Feb 17]\u003C\u002Fstrong>:  🎉🎉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.\u003C\u002Fli>\n      \u003Cli>\u003Cstrong>[2025, Feb 10]\u003C\u002Fstrong>:  🎉🎉We've released \u003Cb>MetaChain!\u003C\u002Fb>, including framework, evaluation codes and CLI mode! Check our \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2502.05957\">paper\u003C\u002Fa> for more details.\u003C\u002Fli>\n    \u003C\u002Ful>\n\u003C\u002Fdiv>\n\u003Cspan id='table-of-contents'\u002F>\n\n## 📑 Table of Contents\n\n* \u003Ca href='#features'>✨ Features\u003C\u002Fa>\n* \u003Ca href='#news'>🔥 News\u003C\u002Fa>\n* \u003Ca href='#how-to-use'>🔍 How to Use AutoAgent\u003C\u002Fa>\n  * \u003Ca href='#user-mode'>1. `user mode` (Deep Research Agents)\u003C\u002Fa>\n  * \u003Ca href='#agent-editor'>2. `agent editor` (Agent Creation without Workflow)\u003C\u002Fa>\n  * \u003Ca href='#workflow-editor'>3. `workflow editor` (Agent Creation with Workflow)\u003C\u002Fa>\n* \u003Ca href='#quick-start'>⚡ Quick Start\u003C\u002Fa>\n  * \u003Ca href='#installation'>Installation\u003C\u002Fa>\n  * \u003Ca href='#api-keys-setup'>API Keys Setup\u003C\u002Fa>\n  * \u003Ca href='#start-with-cli-mode'>Start with CLI Mode\u003C\u002Fa>\n* \u003Ca href='#todo'>☑️ Todo List\u003C\u002Fa>\n* \u003Ca href='#reproduce'>🔬 How To Reproduce the Results in the Paper\u003C\u002Fa>\n* \u003Ca href='#documentation'>📖 Documentation\u003C\u002Fa>\n* \u003Ca href='#community'>🤝 Join the Community\u003C\u002Fa>\n* \u003Ca href='#acknowledgements'>🙏 Acknowledgements\u003C\u002Fa>\n* \u003Ca href='#cite'>🌟 Cite\u003C\u002Fa>\n\n\u003Cspan id='how-to-use'\u002F>\n\n## 🔍 How to Use AutoAgent\n\n\u003Cspan id='user-mode'\u002F>\n\n### 1. `user mode` (Deep Research Agents)\n\nAutoAgent 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.\n\n- 🚀 **High Performance**: Matches Deep Research using Claude 3.5 rather than OpenAI's o3 model.\n- 🔄 **Model Flexibility**: Compatible with any LLM (including Deepseek-R1, Grok, Gemini, etc.)\n- 💰 **Cost-Effective**: Open-source alternative to Deep Research's $200\u002Fmonth subscription\n- 🎯 **User-Friendly**: Easy-to-deploy CLI interface for seamless interaction\n- 📁 **File Support**: Handles file uploads for enhanced data interaction\n\n\u003Cdiv align=\"center\">\n  \u003Cvideo width=\"80%\" controls>\n    \u003Csource src=\".\u002Fassets\u002Fvideo_v1_compressed.mp4\" type=\"video\u002Fmp4\">\n  \u003C\u002Fvideo>\n  \u003Cp>\u003Cem>🎥 Deep Research (aka User Mode)\u003C\u002Fem>\u003C\u002Fp>\n\u003C\u002Fdiv>\n\n\n\n\u003Cspan id='agent-editor'\u002F>\n\n### 2. `agent editor` (Agent Creation without Workflow)\n\nThe 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.\n\nYou can use `agent editor` as shown in the following figure.\n\n\u003Ctable>\n\u003Ctr align=\"center\">\n    \u003Ctd width=\"33%\">\n        \u003Cimg src=\".\u002Fassets\u002Fagent_editor\u002F1-requirement.png\" alt=\"requirement\" width=\"100%\"\u002F>\n        \u003Cbr>\n        \u003Cem>Input what kind of agent you want to create.\u003C\u002Fem>\n    \u003C\u002Ftd>\n    \u003Ctd width=\"33%\">\n        \u003Cimg src=\".\u002Fassets\u002Fagent_editor\u002F2-profiling.png\" alt=\"profiling\" width=\"100%\"\u002F>\n        \u003Cbr>\n        \u003Cem>Automated agent profiling.\u003C\u002Fem>\n    \u003C\u002Ftd>\n    \u003Ctd width=\"33%\">\n        \u003Cimg src=\".\u002Fassets\u002Fagent_editor\u002F3-profiles.png\" alt=\"profiles\" width=\"100%\"\u002F>\n        \u003Cbr>\n        \u003Cem>Output the agent profiles.\u003C\u002Fem>\n    \u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftable>\n\u003Ctable>\n\u003Ctr align=\"center\">\n    \u003Ctd width=\"33%\">\n        \u003Cimg src=\".\u002Fassets\u002Fagent_editor\u002F4-tools.png\" alt=\"tools\" width=\"100%\"\u002F>\n        \u003Cbr>\n        \u003Cem>Create the desired tools.\u003C\u002Fem>\n    \u003C\u002Ftd>\n    \u003Ctd width=\"33%\">\n        \u003Cimg src=\".\u002Fassets\u002Fagent_editor\u002F5-task.png\" alt=\"task\" width=\"100%\"\u002F>\n        \u003Cbr>\n        \u003Cem>Input what do you want to complete with the agent. (Optional)\u003C\u002Fem>\n    \u003C\u002Ftd>\n    \u003Ctd width=\"33%\">\n        \u003Cimg src=\".\u002Fassets\u002Fagent_editor\u002F6-output-next.png\" alt=\"output\" width=\"100%\"\u002F>\n        \u003Cbr>\n        \u003Cem>Create the desired agent(s) and go to the next step.\u003C\u002Fem>\n    \u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftable>\n\n\u003Cspan id='workflow-editor'\u002F>\n\n### 3. `workflow editor` (Agent Creation with Workflow)\n\nYou 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.)\n\n\u003Ctable>\n\u003Ctr align=\"center\">\n    \u003Ctd width=\"33%\">\n        \u003Cimg src=\".\u002Fassets\u002Fworkflow_editor\u002F1-requirement.png\" alt=\"requirement\" width=\"100%\"\u002F>\n        \u003Cbr>\n        \u003Cem>Input what kind of workflow you want to create.\u003C\u002Fem>\n    \u003C\u002Ftd>\n    \u003Ctd width=\"33%\">\n        \u003Cimg src=\".\u002Fassets\u002Fworkflow_editor\u002F2-profiling.png\" alt=\"profiling\" width=\"100%\"\u002F>\n        \u003Cbr>\n        \u003Cem>Automated workflow profiling.\u003C\u002Fem>\n    \u003C\u002Ftd>\n    \u003Ctd width=\"33%\">\n        \u003Cimg src=\".\u002Fassets\u002Fworkflow_editor\u002F3-profiles.png\" alt=\"profiles\" width=\"100%\"\u002F>\n        \u003Cbr>\n        \u003Cem>Output the workflow profiles.\u003C\u002Fem>\n    \u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftable>\n\u003Ctable>\n\u003Ctr align=\"center\">\n    \u003Ctd width=\"33%\">\n        \u003Cimg src=\".\u002Fassets\u002Fworkflow_editor\u002F4-task.png\" alt=\"task\" width=\"66%\"\u002F>\n        \u003Cbr>\n        \u003Cem>Input what do you want to complete with the workflow. (Optional)\u003C\u002Fem>\n    \u003C\u002Ftd>\n    \u003Ctd width=\"33%\">\n        \u003Cimg src=\".\u002Fassets\u002Fworkflow_editor\u002F5-output-next.png\" alt=\"output\" width=\"66%\"\u002F>\n        \u003Cbr>\n        \u003Cem>Create the desired workflow(s) and go to the next step.\u003C\u002Fem>\n    \u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftable>\n\n\u003Cspan id='quick-start'\u002F>\n\n## ⚡ Quick Start\n\n\u003Cspan id='installation'\u002F>\n\n### Installation\n\n#### AutoAgent Installation\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FHKUDS\u002FAutoAgent.git\ncd AutoAgent\npip install -e .\n```\n\n#### Docker Installation\n\nWe use Docker to containerize the agent-interactive environment. So please install [Docker](https:\u002F\u002Fwww.docker.com\u002F) 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**.\n\n\u003Cspan id='api-keys-setup'\u002F>\n\n### API Keys Setup\n\nCreate 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.\n\n```bash\n# Required Github Tokens of your own\nGITHUB_AI_TOKEN=\n\n# Optional API Keys\nOPENAI_API_KEY=\nDEEPSEEK_API_KEY=\nANTHROPIC_API_KEY=\nGEMINI_API_KEY=\nHUGGINGFACE_API_KEY=\nGROQ_API_KEY=\nXAI_API_KEY=\n```\n\n\u003Cspan id='start-with-cli-mode'\u002F>\n\n### Start with CLI Mode\n\n> [🚨 **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.\n\n#### Command Options:\n\nYou 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](https:\u002F\u002Fgithub.com\u002FHKUDS\u002FAuto-Deep-Research) project. Some configuration of this command is shown below. \n\n- `--container_name`: Name of the Docker container (default: 'deepresearch')\n- `--port`: Port for the container (default: 12346)\n- `COMPLETION_MODEL`: Specify the LLM model to use, you should follow the name of [Litellm](https:\u002F\u002Fgithub.com\u002FBerriAI\u002Flitellm) to set the model name. (Default: `claude-3-5-sonnet-20241022`)\n- `DEBUG`: Enable debug mode for detailed logs (default: False)\n- `API_BASE_URL`: The base URL for the LLM provider (default: None)\n- `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.\n- `git_clone`: Clone the AutoAgent repository to the local environment (only support with the `auto main` command, default: True)\n- `test_pull_name`: The name of the test pull. (only support with the `auto main` command, default: 'autoagent_mirror')\n\n#### More details about `git_clone` and `test_pull_name`] \n\nIn 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.\n\n#### `auto main` with different LLM Providers\n\nThen 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](https:\u002F\u002Fgithub.com\u002FHKUDS\u002FAuto-Deep-Research) project for more details.\n\n##### Anthropic\n\n* set the `ANTHROPIC_API_KEY` in the `.env` file.\n\n```bash\nANTHROPIC_API_KEY=your_anthropic_api_key\n```\n\n* run the following command to start Auto-Deep-Research.\n\n```bash\nauto main # default model is claude-3-5-sonnet-20241022\n```\n\n##### OpenAI\n\n* set the `OPENAI_API_KEY` in the `.env` file.\n\n```bash\nOPENAI_API_KEY=your_openai_api_key\n```\n\n* run the following command to start Auto-Deep-Research.\n\n```bash\nCOMPLETION_MODEL=gpt-4o auto main\n```\n\n##### Mistral\n\n* set the `MISTRAL_API_KEY` in the `.env` file.\n\n```bash\nMISTRAL_API_KEY=your_mistral_api_key\n```\n\n* run the following command to start Auto-Deep-Research.\n\n```bash\nCOMPLETION_MODEL=mistral\u002Fmistral-large-2407 auto main\n```\n\n##### Gemini - Google AI Studio\n\n* set the `GEMINI_API_KEY` in the `.env` file.\n\n```bash\nGEMINI_API_KEY=your_gemini_api_key\n```\n\n* run the following command to start Auto-Deep-Research.\n\n```bash\nCOMPLETION_MODEL=gemini\u002Fgemini-2.0-flash auto main\n```\n\n##### Huggingface\n\n* set the `HUGGINGFACE_API_KEY` in the `.env` file.\n\n```bash\nHUGGINGFACE_API_KEY=your_huggingface_api_key\n```\n\n* run the following command to start Auto-Deep-Research.\n\n```bash\nCOMPLETION_MODEL=huggingface\u002Fmeta-llama\u002FLlama-3.3-70B-Instruct auto main\n```\n\n##### Groq\n\n* set the `GROQ_API_KEY` in the `.env` file.\n\n```bash\nGROQ_API_KEY=your_groq_api_key\n```\n\n* run the following command to start Auto-Deep-Research.\n\n```bash\nCOMPLETION_MODEL=groq\u002Fdeepseek-r1-distill-llama-70b auto main\n```\n\n##### OpenAI-Compatible Endpoints (e.g., Grok)\n\n* set the `OPENAI_API_KEY` in the `.env` file.\n\n```bash\nOPENAI_API_KEY=your_api_key_for_openai_compatible_endpoints\n```\n\n* run the following command to start Auto-Deep-Research.\n\n```bash\nCOMPLETION_MODEL=openai\u002Fgrok-2-latest API_BASE_URL=https:\u002F\u002Fapi.x.ai\u002Fv1 auto main\n```\n\n##### OpenRouter (e.g., DeepSeek-R1)\n\nWe recommend using OpenRouter as LLM provider of DeepSeek-R1 temporarily. Because official API of DeepSeek-R1 can not be used efficiently.\n\n* set the `OPENROUTER_API_KEY` in the `.env` file.\n\n```bash\nOPENROUTER_API_KEY=your_openrouter_api_key\n```\n\n* run the following command to start Auto-Deep-Research.\n\n```bash\nCOMPLETION_MODEL=openrouter\u002Fdeepseek\u002Fdeepseek-r1 auto main\n```\n\n##### DeepSeek\n\n* set the `DEEPSEEK_API_KEY` in the `.env` file.\n\n```bash\nDEEPSEEK_API_KEY=your_deepseek_api_key\n```\n\n* run the following command to start Auto-Deep-Research.\n\n```bash\nCOMPLETION_MODEL=deepseek\u002Fdeepseek-chat auto main\n```\n\n\nAfter the CLI mode is started, you can see the start page of AutoAgent: \n\n\u003Cdiv align=\"center\">\n  \u003C!-- \u003Cimg src=\".\u002Fassets\u002FAutoAgentnew-intro.pdf\" alt=\"Logo\" width=\"100%\"> -->\n  \u003Cfigure>\n    \u003Cimg src=\".\u002Fassets\u002Fcover.png\" alt=\"Logo\" style=\"max-width: 100%; height: auto;\">\n    \u003Cfigcaption>\u003Cem>Start Page of AutoAgent.\u003C\u002Fem>\u003C\u002Ffigcaption>\n  \u003C\u002Ffigure>\n\u003C\u002Fdiv>\n\n### Tips\n\n#### Import browser cookies to browser environment\n\nYou 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](.\u002FAutoAgent\u002Fenvironment\u002Fcookie_json\u002FREADME.md) folder.\n\n#### Add your own API keys for third-party Tool Platforms\n\nIf 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](.\u002Fprocess_tool_docs.py). \n\n```bash\npython process_tool_docs.py\n```\n\nMore features coming soon! 🚀 **Web GUI interface** under development.\n\n\n\n\u003Cspan id='todo'\u002F>\n\n## ☑️ Todo List\n\nAutoAgent is continuously evolving! Here's what's coming:\n\n- 📊 **More Benchmarks**: Expanding evaluations to **SWE-bench**, **WebArena**, and more\n- 🖥️ **GUI Agent**: Supporting *Computer-Use* agents with GUI interaction\n- 🔧 **Tool Platforms**: Integration with more platforms like **Composio**\n- 🏗️ **Code Sandboxes**: Supporting additional environments like **E2B**\n- 🎨 **Web Interface**: Developing comprehensive GUI for better user experience\n\nHave ideas or suggestions? Feel free to open an issue! Stay tuned for more exciting updates! 🚀\n\n\u003Cspan id='reproduce'\u002F>\n\n## 🔬 How To Reproduce the Results in the Paper\n\n### GAIA Benchmark\nFor the GAIA benchmark, you can run the following command to run the inference.\n\n```bash\ncd path\u002Fto\u002FAutoAgent && sh evaluation\u002Fgaia\u002Fscripts\u002Frun_infer.sh\n```\n\nFor the evaluation, you can run the following command.\n\n```bash\ncd path\u002Fto\u002FAutoAgent && python evaluation\u002Fgaia\u002Fget_score.py\n```\n\n### Agentic-RAG\n\nFor the Agentic-RAG task, you can run the following command to run the inference.\n\nStep1. Turn to [this page](https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fyixuantt\u002FMultiHopRAG) and download it. Save them to your datapath.\n\nStep2. Run the following command to run the inference.\n\n```bash\ncd path\u002Fto\u002FAutoAgent && sh evaluation\u002Fmultihoprag\u002Fscripts\u002Frun_rag.sh\n```\n\nStep3. The result will be saved in the `evaluation\u002Fmultihoprag\u002Fresult.json`.\n\n\u003Cspan id='documentation'\u002F>\n\n## 📖 Documentation\n\nA more detailed documentation is coming soon 🚀, and we will update in the [Documentation](https:\u002F\u002FAutoAgent-ai.github.io\u002Fdocs) page.\n\n\u003Cspan id='community'\u002F>\n\n## 🤝 Join the Community\n\nWe want to build a community for AutoAgent, and we welcome everyone to join us. You can join our community by:\n\n- [Join our Slack workspace](https:\u002F\u002Fjoin.slack.com\u002Ft\u002FAutoAgent-workspace\u002Fshared_invite\u002Fzt-2zibtmutw-v7xOJObBf9jE2w3x7nctFQ) - Here we talk about research, architecture, and future development.\n- [Join our Discord server](https:\u002F\u002Fdiscord.gg\u002Fz68KRvwB) - This is a community-run server for general discussion, questions, and feedback. \n- [Read or post Github Issues](https:\u002F\u002Fgithub.com\u002FHKUDS\u002FAutoAgent\u002Fissues) - Check out the issues we're working on, or add your own ideas.\n\n\u003Cspan id='acknowledgements'\u002F>\n\n\n\n## Misc\n\n\u003Cdiv align=\"center\">\n\n[![Stargazers repo roster for @HKUDS\u002FAutoAgent](https:\u002F\u002Freporoster.com\u002Fstars\u002FHKUDS\u002FAutoAgent)](https:\u002F\u002Fgithub.com\u002FHKUDS\u002FAutoAgent\u002Fstargazers)\n\n[![Forkers repo roster for @HKUDS\u002FAutoAgent](https:\u002F\u002Freporoster.com\u002Fforks\u002FHKUDS\u002FAutoAgent)](https:\u002F\u002Fgithub.com\u002FHKUDS\u002FAutoAgent\u002Fnetwork\u002Fmembers)\n\n[![Star History Chart](https:\u002F\u002Fapi.star-history.com\u002Fsvg?repos=HKUDS\u002FAutoAgent&type=Date)](https:\u002F\u002Fstar-history.com\u002F#HKUDS\u002FAutoAgent&Date)\n\n\u003C\u002Fdiv>\n\n## 🙏 Acknowledgements\n\nRome 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](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fswarm), while our user mode's three-agent design benefits from [Magentic-one](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Ftree\u002Fmain\u002Fpython\u002Fpackages\u002Fautogen-magentic-one)'s insights. We've also learned from [OpenHands](https:\u002F\u002Fgithub.com\u002FAll-Hands-AI\u002FOpenHands) 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.\n\n\n\u003Cspan id='cite'\u002F>\n\n## 🌟 Cite\n\n```tex\n@misc{AutoAgent,\n      title={{AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents}},\n      author={Jiabin Tang, Tianyu Fan, Chao Huang},\n      year={2025},\n      eprint={202502.05957},\n      archivePrefix={arXiv},\n      primaryClass={cs.AI},\n      url={https:\u002F\u002Farxiv.org\u002Fabs\u002F2502.05957},\n}\n```\n\n\n\n\n\n","AutoAgent 是一个全自动且无需编写代码的大规模语言模型（LLM）代理框架。它通过自然语言驱动的方式自动构建和协调协作代理系统，用户无需手动编码或技术配置即可创建复杂的代理应用。其核心功能包括通过自然对话自动生成和管理代理工作流程，极大降低了AI开发的门槛，使得任何用户都能轻松定制自己的代理、工具和工作流。AutoAgent 适用于需要快速部署智能代理但又缺乏编程经验的场景，如企业自动化、客户服务、教育辅助等领域。",2,"2026-06-11 03:39:44","high_star"]