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! --> Quick Overview of AutoAgent. 🔥 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 • 1. (Deep Research Agents) • 2. (Agent Creation without Workflow) • 3. (Agent Creation with Workflow) • ⚡ Quick Start • Installation • API Keys Setup • Start with CLI Mode • ☑️ Todo List • 🔬 How To Reproduce the Results in the Paper • 📖 Documentation • 🤝 Join the Community • 🙏 Acknowledgements • 🌟 Cite 🔍 How to Use AutoAgent • (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) • (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 or mode to start your journey of building agents through conversations. You can use 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. • (Agent Creation with Workflow) You can also create the agent workflows using natural language description with the 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 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 , and set the API keys for the LLMs you want to use. Not every LLM API Key is required, use what you need. 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 to start full part of AutoAgent, including , and . Btw, you can also run to start more lightweight , just like the Auto-Deep-Research project. Some configuration of this command is shown below. • : Name of the Docker container (default: 'deepresearch') • : Port for the container (default: 12346) • : Specify the LLM model to use, you should follow the name of Litellm to set the model name. (Default: ) • : Enable debug mode for detailed logs (default: False) • : The base URL for the LLM provider (default: None) • : 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. • : Clone the AutoAgent repository to the local environment (only support with the command, default: True) • : The name of the test pull. (only support with the command, default: 'autoagent_mirror') More details about and ] In the and…