https://github.com/hkuds/auto-deep-research

"Your Fully-Automated Personal AI Assistant, and Open-Source & Cost-Efficient Alternative to OpenAI's Deep Research"
https://github.com/hkuds/auto-deep-research

agent deep-research llm

Last synced: 11 days ago
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"Your Fully-Automated Personal AI Assistant, and Open-Source & Cost-Efficient Alternative to OpenAI's Deep Research"

Host: GitHub
URL: https://github.com/hkuds/auto-deep-research
Owner: HKUDS
Created: 2025-02-16T05:28:50.000Z (about 2 months ago)
Default Branch: main
Last Pushed: 2025-04-01T12:10:00.000Z (14 days ago)
Last Synced: 2025-04-01T13:24:34.671Z (14 days ago)
Topics: agent, deep-research, llm
Language: Python
Homepage: https://arxiv.org/abs/2502.05957
Size: 1020 KB
Stars: 853
Watchers: 22
Forks: 112
Open Issues: 22
Metadata Files:
- Readme: README.md

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README

Auto-Deep-Research: Your Fully-Automated and Cost-Effective Personal AI Assistant

Welcome to Auto-Deep-Research! Auto-Deep-Research is a open-source and cost-efficient alternative to OpenAI's Deep Research, based on [AutoAgent](https://github.com/HKUDS/AutoAgent) framework.

## ✨Key Features

- 🏆 **High Performance**: Ranks the **#1** spot among open-sourced methods, delivering comparable performance to **OpenAI's Deep Research**.
- 🌐 **Universal LLM Support**: Seamlessly integrates with **A Wide Range** of LLMs (e.g., OpenAI, Anthropic, Deepseek, vLLM, Grok, Huggingface ...)
- 🔀 **Flexible Interaction**: Supports both **function-calling** and **non-function-calling** interaction LLMs.
- 💰 **Cost-Efficient**: Open-source alternative to Deep Research's $200/month subscription with your own pay-as-you-go LLM API keys.
- 📁 **File Support**: Handles file uploads for enhanced data interaction
- 🚀 **One-Click Launch**: Get started instantly with a simple `auto deep-research` command - **Zero Configuration** needed, truly out-of-the-box experience.

🚀 Own your own personal assistant with much lower cost. Try 🔥Auto-Deep-Research🔥 Now!

## 🔥 News

[2025, April 1]: 🎉🎉 Click to see what Auto-Deepresearch can do! [Video 1] [Video 2]

[2025, Feb 16]: 🎉🎉We've cleaned up the codebase of AutoAgent, removed the irrelevant parts for Auto-Deep-Research and released the first version of Auto-Deep-Research.

## 📑 Table of Contents

* ✨ Features
* 🔥 News
* 🧐 Why to release Auto-Deep-Research?
* ⚡ Quick Start
* Installation
* API Keys Setup
* Start Auto-Deep-Research
* ☑️ Todo List
* 📖 Documentation
* 🤝 Join the Community
* 🙏 Acknowledgements
* 🌟 Cite

## 🧐 Why to release Auto-Deep-Research?

After releasing AutoAgent (previously known as MetaChain) for a week, we've observed three compelling reasons to introduce Auto-Deep-Research:

1. **Community Interest**
We noticed significant community interest in our Deep Research alternative functionality. In response, we've streamlined the codebase by removing non-Deep-Research related components to create a more focused tool.

2. **Framework Extensibility**
Auto-Deep-Research serves as the first ready-to-use product built on AutoAgent, demonstrating how quickly and easily you can create powerful Agent Apps using our framework.

3. **Community-Driven Improvements**
We've incorporated valuable community feedback from the first week, introducing features like one-click launch and enhanced LLM compatibility to make the tool more accessible and versatile.

Auto-Deep-Research represents our commitment to both the community's needs and the demonstration of AutoAgent's potential as a foundation for building practical AI applications.

## ⚡ Quick Start

### Installation

#### Auto-Deep-Research Installation

```bash
conda create -n auto_deep_research python=3.10
conda activate auto_deep_research
git clone https://github.com/HKUDS/Auto-Deep-Research.git
cd Auto-Deep-Research
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 a 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.

### Start Auto-Deep-Research

#### Command Options:

You can run `auto deep-research` to start Auto-Deep-Research. 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.

#### Different LLM Providers

We will show you how easy it is to start Auto-Deep-Research with different LLM providers.

##### Anthropic

* set the `ANTHROPIC_API_KEY` in the `.env` file.

```bash
ANTHROPIC_API_KEY=your_anthropic_api_key
```