AutoAgent

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 ## 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 ### 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 ```bash 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](https://www.docker.com/) 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. ```bash # 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](https://github.com/HKUDS/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](https://github.com/BerriAI/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](https://github.com/HKUDS/Auto-Deep-Research) project for more details. ##### Anthropic * set the `ANTHROPIC_API_KEY` in the `.env` file. ```bash ANTHROPIC_API_KEY=your_anthropic_api_key ``` * run the following command to start Auto-Deep-Research. ```bash auto main # default model is claude-3-5-sonnet-20241022 ``` ##### OpenAI * set the `OPENAI_API_KEY` in the `.env` file. ```bash OPENAI_API_KEY=your_openai_api_key ``` * run the following command to start Auto-Deep-Research. ```bash COMPLETION_MODEL=gpt-4o auto main ``` ##### Mistral * set the `MISTRAL_API_KEY` in the `.env` file. ```bash MISTRAL_API_KEY=your_mistral_api_key ``` * run the following command to start Auto-Deep-Research. ```bash COMPLETION_MODEL=mistral/mistral-large-2407 auto main ``` ##### Gemini - Google AI Studio * set the `GEMINI_API_KEY` in the `.env` file. ```bash GEMINI_API_KEY=your_gemini_api_key ``` * run the following command to start Auto-Deep-Research. ```bash COMPLETION_MODEL=gemini/gemini-2.0-flash auto main ``` ##### Huggingface * set the `HUGGINGFACE_API_KEY` in the `.env` file. ```bash HUGGINGFACE_API_KEY=your_huggingface_api_key ``` * run the following command to start Auto-Deep-Research. ```bash COMPLETION_MODEL=huggingface/meta-llama/Llama-3.3-70B-Instruct auto main ``` ##### Groq * set the `GROQ_API_KEY` in the `.env` file. ```bash GROQ_API_KEY=your_groq_api_key ``` * run the following command to start Auto-Deep-Research. ```bash COMPLETION_MODEL=groq/deepseek-r1-distill-llama-70b auto main ``` ##### OpenAI-Compatible Endpoints (e.g., Grok) * set the `OPENAI_API_KEY` in the `.env` file. ```bash OPENAI_API_KEY=your_api_key_for_openai_compatible_endpoints ``` * run the following command to start Auto-Deep-Research. ```bash 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_KEY` in the `.env` file. ```bash OPENROUTER_API_KEY=your_openrouter_api_key ``` * run the following command to start Auto-Deep-Research. ```bash COMPLETION_MODEL=openrouter/deepseek/deepseek-r1 auto main ``` ##### DeepSeek * set the `DEEPSEEK_API_KEY` in the `.env` file. ```bash DEEPSEEK_API_KEY=your_deepseek_api_key ``` * run the following command to start Auto-Deep-Research. ```bash 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](./AutoAgent/environment/cookie_json/README.md) 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](./process_tool_docs.py). ```bash 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 evalu