# ChatDev 2.0 - DevAll

A Zero-Code Multi-Agent Platform for Developing Everything

【English | 简体中文】

【📚 Developers | 👥 Contributors｜⭐️ ChatDev 1.0 (Legacy)】

## 📖 Overview ChatDev has evolved from a specialized software development multi-agent system into a comprehensive multi-agent orchestration platform. - **ChatDev 2.0 (DevAll)** is a **Zero-Code Multi-Agent Platform** for "Developing Everything". It empowers users to rapidly build and execute customized multi-agent systems through simple configuration. No coding is required—users can define agents, workflows, and tasks to orchestrate complex scenarios such as data visualization, 3D generation, and deep research. - **ChatDev 1.0 (Legacy)** operates as a **Virtual Software Company**. It utilizes various intelligent agents (e.g., CEO, CTO, Programmer) participating in specialized functional seminars to automate the entire software development life cycle—including designing, coding, testing, and documenting. It serves as the foundational paradigm for communicative agent collaboration. ## 🎉 News • **Jan 07, 2026: 🚀 We are excited to announce the official release of ChatDev 2.0 (DevAll)!** This version introduces a zero-code multi-agent orchestration platform. The classic ChatDev (v1.x) has been moved to the [`chatdev1.0`](https://github.com/OpenBMB/ChatDev/tree/chatdev1.0) branch for maintenance. More details about ChatDev 2.0 can be found on [our official post](https://x.com/OpenBMB/status/2008916790399701335).

Old News

•Sep 24, 2025: 🎉 Our paper [Multi-Agent Collaboration via Evolving Orchestration](https://arxiv.org/abs/2505.19591) has been accepted to NeurIPS 2025. The implementation is available in the `puppeteer` branch of this repository. •May 26, 2025: 🎉 We propose a novel puppeteer-style paradigm for multi-agent collaboration among large language model based agents. By leveraging a learnable central orchestrator optimized with reinforcement learning, our method dynamically activates and sequences agents to construct efficient, context-aware reasoning paths. This approach not only improves reasoning quality but also reduces computational costs, enabling scalable and adaptable multi-agent cooperation in complex tasks. See our paper in [Multi-Agent Collaboration via Evolving Orchestration](https://arxiv.org/abs/2505.19591).

•June 25, 2024: 🎉To foster development in LLM-powered multi-agent collaboration🤖🤖 and related fields, the ChatDev team has curated a collection of seminal papers📄 presented in a [open-source](https://github.com/OpenBMB/ChatDev/tree/main/MultiAgentEbook) interactive e-book📚 format. Now you can explore the latest advancements on the [Ebook Website](https://thinkwee.top/multiagent_ebook) and download the [paper list](https://github.com/OpenBMB/ChatDev/blob/main/MultiAgentEbook/papers.csv).

•June 12, 2024: We introduced Multi-Agent Collaboration Networks (MacNet) 🎉, which utilize directed acyclic graphs to facilitate effective task-oriented collaboration among agents through linguistic interactions 🤖🤖. MacNet supports co-operation across various topologies and among more than a thousand agents without exceeding context limits. More versatile and scalable, MacNet can be considered as a more advanced version of ChatDev's chain-shaped topology. Our preprint paper is available at [https://arxiv.org/abs/2406.07155](https://arxiv.org/abs/2406.07155). This technique has been incorporated into the [macnet](https://github.com/OpenBMB/ChatDev/tree/macnet) branch, enhancing support for diverse organizational structures and offering richer solutions beyond software development (e.g., logical reasoning, data analysis, story generation, and more).

• May 07, 2024, we introduced "Iterative Experience Refinement" (IER), a novel method where instructor and assistant agents enhance shortcut-oriented experiences to efficiently adapt to new tasks. This approach encompasses experience acquisition, utilization, propagation and elimination across a series of tasks and making the pricess shorter and efficient. Our preprint paper is available at https://arxiv.org/abs/2405.04219, and this technique will soon be incorporated into ChatDev.

• January 25, 2024: We have integrated Experiential Co-Learning Module into ChatDev. Please see the [Experiential Co-Learning Guide](wiki.md#co-tracking). • December 28, 2023: We present Experiential Co-Learning, an innovative approach where instructor and assistant agents accumulate shortcut-oriented experiences to effectively solve new tasks, reducing repetitive errors and enhancing efficiency. Check out our preprint paper at https://arxiv.org/abs/2312.17025 and this technique will soon be integrated into ChatDev.

• November 15, 2023: We launched ChatDev as a SaaS platform that enables software developers and innovative entrepreneurs to build software efficiently at a very low cost and remove the barrier to entry. Try it out at https://chatdev.modelbest.cn/.

• November 2, 2023: ChatDev is now supported with a new feature: incremental development, which allows agents to develop upon existing codes. Try ```--config "incremental" --path "[source_code_directory_path]"``` to start it.

• October 26, 2023: ChatDev is now supported with Docker for safe execution (thanks to contribution from [ManindraDeMel](https://github.com/ManindraDeMel)). Please see [Docker Start Guide](wiki.md#docker-start).

• September 25, 2023: The **Git** mode is now available, enabling the programmer to utilize Git for version control. To enable this feature, simply set ``"git_management"`` to ``"True"`` in ``ChatChainConfig.json``. See [guide](wiki.md#git-mode).

• September 20, 2023: The **Human-Agent-Interaction** mode is now available! You can get involved with the ChatDev team by playing the role of reviewer and making suggestions to the programmer ; try ``python3 run.py --task [description_of_your_idea] --config "Human"``. See [guide](wiki.md#human-agent-interaction) and [example](WareHouse/Gomoku_HumanAgentInteraction_20230920135038).

• September 1, 2023: The **Art** mode is available now! You can activate the designer agent to generate images used in the software; try ``python3 run.py --task [description_of_your_idea] --config "Art"``. See [guide](wiki.md#art) and [example](WareHouse/gomokugameArtExample_THUNLP_20230831122822). • August 28, 2023: The system is publicly available. • August 17, 2023: The v1.0.0 version was ready for release. • July 30, 2023: Users can customize ChatChain, Phasea and Role settings. Additionally, both online Log mode and replay mode are now supported. • July 16, 2023: The [preprint paper](https://arxiv.org/abs/2307.07924) associated with this project was published. • June 30, 2023: The initial version of the ChatDev repository was released.

## 🚀 Quick Start ### 📋 Prerequisites * **OS**: macOS / Linux / WSL / Windows * **Python**: 3.12+ * **Node.js**: 18+ * **Package Manager**: [uv](https://docs.astral.sh/uv/) ### 📦 Installation 1. **Backend Dependencies** (Python managed by `uv`): ```bash uv sync ``` 2. **Frontend Dependencies** (Vite + Vue 3): ```bash cd frontend && npm install ``` ### ⚡️ Run the Application 1. **Start Backend** : ```bash # Run from the project root uv run python server_main.py --port 6400 --reload ``` > Remove `--reload` if output files (e.g., GameDev) trigger restarts, which interrupts tasks and loses progress. 2. **Start Frontend**: ```bash cd frontend VITE_API_BASE_URL=http://localhost:6400 npm run dev ``` > Then access the Web Console at **[http://localhost:5173](http://localhost:5173)**. > **💡 Tip**: If the frontend fails to connect to the backend, the default port `6400` may already be occupied. > Please switch both services to an available port, for example: > > * **Backend**: start with `--port 6401` > * **Frontend**: set `VITE_API_BASE_URL=http://localhost:6401` ### 🔑 Configuration * **Environment Variables**: Create a `.env` file in the project root. * **Model Keys**: Set `API_KEY` and `BASE_URL` in `.env` for your LLM provider. * **YAML placeholders**: Use `${VAR}`（e.g., `${API_KEY}`）in configuration files to reference these variables. --- ## 💡 How to Use ### 🖥️ Web Console The DevAll interface provides a seamless experience for both construction and execution * **Tutorial**: Comprehensive step-by-step guides and documentation integrated directly into the platform to help you get started quickly. * **Workflow**: A visual canvas to design your multi-agent systems. Configure node parameters, define context flows, and orchestrate complex agent interactions with drag-and-drop ease. * **Launch**: Initiate workflows, monitor real-time logs, inspect intermediate artifacts, and provide human-in-the-loop feedback. ### 🧰 Python SDK For automation and batch processing, use our lightweight Python SDK to execute workflows programmatically and retrieve results directly. ```python from runtime.sdk import run_workflow # Execute a workflow and get the final node message result = run_workflow( yaml_file="yaml_instance/demo.yaml", task_prompt="Summarize the attached document in one sentence.", attachments=["/path/to/document.pdf"], variables={"API_KEY": "sk-xxxx"} # Override .env variables if needed ) if result.final_message: print(f"Output: {result.final_message.text_content()}") ``` --- ## ⚙️ For Developers **For secondary development and extensions, please proceed with this section.** Extend DevAll with new nodes, providers, and tools. The project is organized into a modular structure: * **Core Systems**: `server/` hosts the FastAPI backend, while `runtime/` manages agent abstraction and tool execution. * **Orchestration**: `workflow/` handles the multi-agent logic, driven by configurations in `entity/`. * **Frontend**: `frontend/` contains the Vue 3 Web Console. * **Extensibility**: `functions/` is the place for custom Python tools. Relevant reference documentation: * **Getting Started**: [Start Guide](./docs/user_guide/en/index.md) * **Core Modules**: [Workflow Authoring](./docs/user_guide/en/workflow_authoring.md), [Memory](./docs/user_guide/en/modules/memory.md), and [Tooling](./docs/user_guide/en/modules/tooling/index.md) --- ## 🌟 Featured Workflows We provide robust, out-of-the-box templates for common scenarios. All runnable workflow configs are located in `yaml_instance/`. * **Demos**: Files named `demo_*.yaml` showcase specific features or modules. * **Implementations**: Files named directly (e.g., `ChatDev_v1.yaml`) are full in-house or recreated workflows. As follows: ### 📋 Workflow Collection | Category | Workflow | Case | | :--- |:------------------------------------------------------------------------------------------------------------| :--- | | **📈 Data Visualization** | `data_visualization_basic.yaml`
`data_visualization_enhanced.yaml` |
Prompt: *"Create 4–6 high-quality PNG charts for my large real-estate transactions dataset."* | | **🛠️ 3D Generation**
*(Requires [Blender](https://www.blender.org/) & [blender-mcp](https://github.com/ahujasid/blender-mcp))* | `blender_3d_builder_simple.yaml`
`blender_3d_builder_hub.yaml`
`blender_scientific_illustration.yaml` |
Prompt: *"Please build a Christmas tree."* | | **🎮 Game Dev** | `GameDev_v1.yaml`
`ChatDev_v1.yaml` |
Prompt: *"Please help me design and develop a Tank Battle game."* | | **📚 Deep Research** | `deep_research_v1.yaml` |
Prompt: *"Research about recent advances in the field of LLM-based agent RL"* | | **🎓 Teach Video** | `teach_video.yaml` (Please run command `uv add manim` before running this workflow) |
Prompt: *"讲一下什么是凸优化"* | --- ### 💡 Usage Guide For those implementations, you can use the **Launch** tab to execute them. 1. **Select**: Choose a workflow in the **Launch** tab. 2. **Upload**: Upload necessary files (e.g., `.csv` for data analysis) if required. 3. **Prompt**: Enter your request (e.g., *"Visualize the sales trends"* or *"Design a snake game"*). --- ## 🤝 Contributing We welcome contributions from the community! Whether you're fixing bugs, adding new workflow templates, or sharing high-quality cases/artifacts produced by DevAll, your help is much appreciated. Feel free to contribute by submitting **Issues** or **Pull Requests**. By contributing to DevAll, you'll be recognized in our **Contributors** list below. Check out our [Developer Guide](#developers) to get started! ### 👥 Contributors #### Primary Contributors

_NA-Wen

_zxrys

_swugi

_huatl98

#### Contributors

_shiowen

_kilo2127

_AckerlyLau

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## :newspaper: **What's NEW!**     :sparkling_heart: 

Recent released features

### Introducing : LLM-Based Autonomous Evolving Agents for Industrial Data-Driven R&D

We are excited to announce the release of **RD-Agent**📢, a powerful tool that supports automated factor mining and model optimization in quant investment R&D.

RD-Agent is now available on [GitHub](https://github.com/microsoft/RD-Agent), and we welcome your star🌟!

To learn more, please visit our [♾️Demo page](https://rdagent.azurewebsites.net/). Here, you will find demo videos in both English and Chinese to help you better understand the scenario and usage of RD-Agent.

We have prepared several demo videos for you:
| Scenario | Demo video (English) | Demo video (中文) |
| --                      | ------    | ------    |
| Quant Factor Mining | [Link](https://rdagent.azurewebsites.net/factor_loop?lang=en) | [Link](https://rdagent.azurewebsites.net/factor_loop?lang=zh) |
| Quant Factor Mining from reports | [Link](https://rdagent.azurewebsites.net/report_factor?lang=en) | [Link](https://rdagent.azurewebsites.net/report_factor?lang=zh) |
| Quant Model Optimization | [Link](https://rdagent.azurewebsites.net/model_loop?lang=en) | [Link](https://rdagent.azurewebsites.net/model_loop?lang=zh) |

- 📃**Paper**: [R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization](https://arxiv.org/abs/2505.15155)
- 👾**Code**: https://github.com/microsoft/RD-Agent/
```BibTeX
@misc{li2025rdagentquant,
    title={R\&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization},
    author={Yuante Li and Xu Yang and Xiao Yang and Minrui Xu and Xisen Wang and Weiqing Liu and Jiang Bian},
    year={2025},
    eprint={2505.15155},
    archivePrefix={arXiv},
    primaryClass={cs.AI}
}
```
![image](https://github.com/user-attachments/assets/3198bc10-47ba-4ee0-8a8e-46d5ce44f45d)

***

| Feature | Status |
| --                      | ------    |
| [R&D-Agent-Quant](https://arxiv.org/abs/2505.15155) Published | Apply R&D-Agent to Qlib for quant trading | 
| BPQP for End-to-end learning | 📈Coming soon!([Under review](https://github.com/microsoft/qlib/pull/1863)) |
| 🔥LLM-driven Auto Quant Factory🔥 | 🚀 Released in [♾️RD-Agent](https://github.com/microsoft/RD-Agent) on Aug 8, 2024 |
| KRNN and Sandwich models | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/1414/) on May 26, 2023 |
| Release Qlib v0.9.0 | :octocat: [Released](https://github.com/microsoft/qlib/releases/tag/v0.9.0) on Dec 9, 2022 |
| RL Learning Framework | :hammer: :chart_with_upwards_trend: Released on Nov 10, 2022. [#1332](https://github.com/microsoft/qlib/pull/1332), [#1322](https://github.com/microsoft/qlib/pull/1322), [#1316](https://github.com/microsoft/qlib/pull/1316),[#1299](https://github.com/microsoft/qlib/pull/1299),[#1263](https://github.com/microsoft/qlib/pull/1263), [#1244](https://github.com/microsoft/qlib/pull/1244), [#1169](https://github.com/microsoft/qlib/pull/1169), [#1125](https://github.com/microsoft/qlib/pull/1125), [#1076](https://github.com/microsoft/qlib/pull/1076)|
| HIST and IGMTF models | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/1040) on Apr 10, 2022 |
| Qlib [notebook tutorial](https://github.com/microsoft/qlib/tree/main/examples/tutorial) | 📖 [Released](https://github.com/microsoft/qlib/pull/1037) on Apr 7, 2022 | 
| Ibovespa index data | :rice: [Released](https://github.com/microsoft/qlib/pull/990) on Apr 6, 2022 |
| Point-in-Time database | :hammer: [Released](https://github.com/microsoft/qlib/pull/343) on Mar 10, 2022 |
| Arctic Provider Backend & Orderbook data example | :hammer: [Released](https://github.com/microsoft/qlib/pull/744) on Jan 17, 2022 |
| Meta-Learning-based framework & DDG-DA  | :chart_with_upwards_trend:  :hammer: [Released](https://github.com/microsoft/qlib/pull/743) on Jan 10, 2022 | 
| Planning-based portfolio optimization | :hammer: [Released](https://github.com/microsoft/qlib/pull/754) on Dec 28, 2021 | 
| Release Qlib v0.8.0 | :octocat: [Released](https://github.com/microsoft/qlib/releases/tag/v0.8.0) on Dec 8, 2021 |
| ADD model | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/704) on Nov 22, 2021 |
| ADARNN  model | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/689) on Nov 14, 2021 |
| TCN  model | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/668) on Nov 4, 2021 |
| Nested Decision Framework | :hammer: [Released](https://github.com/microsoft/qlib/pull/438) on Oct 1, 2021. [Example](https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution/workflow.py) and [Doc](https://qlib.readthedocs.io/en/latest/component/highfreq.html) |
| Temporal Routing Adaptor (TRA) | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/531) on July 30, 2021 |
| Transformer & Localformer | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/508) on July 22, 2021 |
| Release Qlib v0.7.0 | :octocat: [Released](https://github.com/microsoft/qlib/releases/tag/v0.7.0) on July 12, 2021 |
| TCTS Model | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/491) on July 1, 2021 |
| Online serving and automatic model rolling | :hammer:  [Released](https://github.com/microsoft/qlib/pull/290) on May 17, 2021 | 
| DoubleEnsemble Model | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/286) on Mar 2, 2021 | 
| High-frequency data processing example | :hammer: [Released](https://github.com/microsoft/qlib/pull/257) on Feb 5, 2021  |
| High-frequency trading example | :chart_with_upwards_trend: [Part of code released](https://github.com/microsoft/qlib/pull/227) on Jan 28, 2021  | 
| High-frequency data(1min) | :rice: [Released](https://github.com/microsoft/qlib/pull/221) on Jan 27, 2021 |
| Tabnet Model | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/205) on Jan 22, 2021 |

Features released before 2021 are not listed here.


  


Qlib is an open-source, AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning.

An increasing number of SOTA Quant research works/papers in diverse paradigms are being released in Qlib to collaboratively solve key challenges in quantitative investment. For example, 1) using supervised learning to mine the market's complex non-linear patterns from rich and heterogeneous financial data, 2) modeling the dynamic nature of the financial market using adaptive concept drift technology, and 3) using reinforcement learning to model continuous investment decisions and assist investors in optimizing their trading strategies.

It contains the full ML pipeline of data processing, model training, back-testing; and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution. 
For more details, please refer to our paper ["Qlib: An AI-oriented Quantitative Investment Platform"](https://arxiv.org/abs/2009.11189).



  
    
      Frameworks, Tutorial, Data & DevOps
      Main Challenges & Solutions in Quant Research
    
    
      
        Plans
        Framework of Qlib
        Quick Start
          
            Installation 
            Data Preparation
            Auto Quant Research Workflow
            Building Customized Quant Research Workflow by Code
        Quant Dataset Zoo
        Learning Framework
        More About Qlib
        Offline Mode and Online Mode
        
          Performance of Qlib Data Server
        
Related Reports
        Contact Us
        Contributing
      
      
        Main Challenges & Solutions in Quant Research
          
            Forecasting: Finding Valuable Signals/Patterns
              
                Quant Model (Paper) Zoo
                  
                    Run a Single Model
                    Run Multiple Models
                  
                
              
            
          Adapting to Market Dynamics
          Reinforcement Learning: modeling continuous decisions
          
        
      
    
  


# Plans
New features under development(order by estimated release time).
Your feedbacks about the features are very important.



# Framework of Qlib





The high-level framework of Qlib can be found above(users can find the [detailed framework](https://qlib.readthedocs.io/en/latest/introduction/introduction.html#framework) of Qlib's design when getting into nitty gritty).
The components are designed as loose-coupled modules, and each component could be used stand-alone.

Qlib provides a strong infrastructure to support Quant research. [Data](https://qlib.readthedocs.io/en/latest/component/data.html) is always an important part.
A strong learning framework is designed to support diverse learning paradigms (e.g. [reinforcement learning](https://qlib.readthedocs.io/en/latest/component/rl.html), [supervised learning](https://qlib.readthedocs.io/en/latest/component/workflow.html#model-section)) and patterns at different levels(e.g. [market dynamic modeling](https://qlib.readthedocs.io/en/latest/component/meta.html)).
By modeling the market, [trading strategies](https://qlib.readthedocs.io/en/latest/component/strategy.html) will generate trade decisions that will be executed. Multiple trading strategies and executors in different levels or granularities can be [nested to be optimized and run together](https://qlib.readthedocs.io/en/latest/component/highfreq.html).
At last, a comprehensive [analysis](https://qlib.readthedocs.io/en/latest/component/report.html) will be provided and the model can be [served online](https://qlib.readthedocs.io/en/latest/component/online.html) in a low cost.


# Quick Start

This quick start guide tries to demonstrate
1. It's very easy to build a complete Quant research workflow and try your ideas with _Qlib_.
2. Though with *public data* and *simple models*, machine learning technologies **work very well** in practical Quant investment.

Here is a quick **[demo](https://terminalizer.com/view/3f24561a4470)** shows how to install ``Qlib``, and run LightGBM with ``qrun``. **But**, please make sure you have already prepared the data following the [instruction](#data-preparation).


## Installation

This table demonstrates the supported Python version of `Qlib`:
|               | install with pip      | install from source  |        plot        |
| ------------- |:---------------------:|:--------------------:|:------------------:|
| Python 3.8    | :heavy_check_mark:    | :heavy_check_mark:   | :heavy_check_mark: |
| Python 3.9    | :heavy_check_mark:    | :heavy_check_mark:   | :heavy_check_mark: |
| Python 3.10   | :heavy_check_mark:    | :heavy_check_mark:   | :heavy_check_mark: |
| Python 3.11   | :heavy_check_mark:    | :heavy_check_mark:   | :heavy_check_mark: |
| Python 3.12   | :heavy_check_mark:    | :heavy_check_mark:   | :heavy_check_mark: |

**Note**: 
1. **Conda** is suggested for managing your Python environment. In some cases, using Python outside of a `conda` environment may result in missing header files, causing the installation failure of certain packages.
2. Please pay attention that installing cython in Python 3.6 will raise some error when installing ``Qlib`` from source. If users use Python 3.6 on their machines, it is recommended to *upgrade* Python to version 3.8 or higher, or use `conda`'s Python to install ``Qlib`` from source.

### Install with pip
Users can easily install ``Qlib`` by pip according to the following command.

```bash
  pip install pyqlib
```

**Note**: pip will install the latest stable qlib. However, the main branch of qlib is in active development. If you want to test the latest scripts or functions in the main branch. Please install qlib with the methods below.

### Install from source
Also, users can install the latest dev version ``Qlib`` by the source code according to the following steps:

* Before installing ``Qlib`` from source, users need to install some dependencies:

  ```bash
  pip install numpy
  pip install --upgrade cython
  ```

* Clone the repository and install ``Qlib`` as follows.
    ```bash
    git clone https://github.com/microsoft/qlib.git && cd qlib
    pip install .  # `pip install -e .[dev]` is recommended for development. check details in docs/developer/code_standard_and_dev_guide.rst
    ```

**Tips**: If you fail to install `Qlib` or run the examples in your environment,  comparing your steps and the [CI workflow](.github/workflows/test_qlib_from_source.yml) may help you find the problem.

**Tips for Mac**: If you are using Mac with M1, you might encounter issues in building the wheel for LightGBM, which is due to missing dependencies from OpenMP. To solve the problem, install openmp first with ``brew install libomp`` and then run ``pip install .`` to build it successfully. 

## Data Preparation
❗ Due to more restrict data security policy. The official dataset is disabled temporarily. You can try [this data source](https://github.com/chenditc/investment_data/releases) contributed by the community.
Here is an example to download the latest data.
```bash
wget https://github.com/chenditc/investment_data/releases/latest/download/qlib_bin.tar.gz
mkdir -p ~/.qlib/qlib_data/cn_data
tar -zxvf qlib_bin.tar.gz -C ~/.qlib/qlib_data/cn_data --strip-components=1
rm -f qlib_bin.tar.gz
```

The official dataset below will resume in short future.


----

Load and prepare data by running the following code:

### Get with module
  ```bash
  # get 1d data
  python -m qlib.cli.data qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn

  # get 1min data
  python -m qlib.cli.data qlib_data --target_dir ~/.qlib/qlib_data/cn_data_1min --region cn --interval 1min

  ```

### Get from source

  ```bash
  # get 1d data
  python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn

  # get 1min data
  python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data_1min --region cn --interval 1min

  ```

This dataset is created by public data collected by [crawler scripts](scripts/data_collector/), which have been released in
the same repository.
Users could create the same dataset with it. [Description of dataset](https://github.com/microsoft/qlib/tree/main/scripts/data_collector#description-of-dataset)

*Please pay **ATTENTION** that the data is collected from [Yahoo Finance](https://finance.yahoo.com/lookup), and the data might not be perfect.
We recommend users to prepare their own data if they have a high-quality dataset. For more information, users can refer to the [related document](https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format)*.

### Automatic update of daily frequency data (from yahoo finance)
  > This step is *Optional* if users only want to try their models and strategies on history data.
  > 
  > It is recommended that users update the data manually once (--trading_date 2021-05-25) and then set it to update automatically.
  >
  > **NOTE**: Users can't incrementally  update data based on the offline data provided by Qlib(some fields are removed to reduce the data size). Users should use [yahoo collector](https://github.com/microsoft/qlib/tree/main/scripts/data_collector/yahoo#automatic-update-of-daily-frequency-datafrom-yahoo-finance) to download Yahoo data from scratch and then incrementally update it.
  > 
  > For more information, please refer to: [yahoo collector](https://github.com/microsoft/qlib/tree/main/scripts/data_collector/yahoo#automatic-update-of-daily-frequency-datafrom-yahoo-finance)

  * Automatic update of data to the "qlib" directory each trading day(Linux)
      * use *crontab*: `crontab -e`
      * set up timed tasks:

        ```
        * * * * 1-5 python

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