AutoAgent: Fully-Automated & Zero-Code LLM Agent Framework

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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

<ul>
  <li><strong>[2025, Feb 17]</strong>: &nbsp;🎉🎉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.</li>
  <li><strong>[2025, Feb 10]</strong>: &nbsp;🎉🎉We've released <b>MetaChain!</b>, including framework, evaluation codes and CLI mode! Check our <a href="https://arxiv.org/abs/2502.05957">paper</a> for more details.</li>
</ul>

📑 Table of Contents

• ✨ Features
• 🔥 News
• 🔍 How to Use AutoAgent
• 1. user mode (Deep Research Agents)
• 2. agent editor (Agent Creation without Workflow)
• 3. workflow editor (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

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 the auto main command, default: True)
• test_pull_name: The name of the test pull. (only support with the auto main command, 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_KEY in the .env file.

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_KEY in the .env file.

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_KEY in the .env file.

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_KEY in the .env file.

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_KEY in the .env file.

```bash HUGGINGFACE_API_K