--- title: LAMBDA app_file: LAMBDA.py sdk: gradio sdk_version: 6.14.0 ---

# LAMBDA - LArge Model-based Data Analysis System [![Docs](https://img.shields.io/badge/Docs-Online-blue)](https://ama-cmfai.github.io/LAMBDA-Docs/#/) [![Project](https://img.shields.io/badge/Project-Webpage-brightgreen)](https://www.polyu.edu.hk/ama/cmfai/lambda.html) [![Paper](https://img.shields.io/badge/Paper-arXiv-red)](https://arxiv.org/pdf/2407.17535) [![MacOS](https://img.shields.io/badge/Download-macOS-black?logo=apple)](https://github.com/AMA-CMFAI/LAMBDA/releases/download/app/LAMBDA-MacOS-beta-v0.0.2.zip) [![Windows](https://img.shields.io/badge/Download-Windows-blue?logo=windows)](https://github.com/AMA-CMFAI/LAMBDA/releases/download/app/LAMBDA-Windows-beta-v0.0.2.zip)

![LAMBDA_mix_250710](https://github.com/user-attachments/assets/5cdc113b-7d26-4328-8911-d421081f98ce) We introduce **LAMBDA**, a novel open-source, code-free multi-agent data analysis system that harnesses the power of large models. LAMBDA is designed to address data analysis challenges in complex data-driven applications through the use of innovatively designed data agents that operate iteratively and generatively using natural language. ## News - LAMBDA App for macOS and Windows has been released. Details can be found in [Released](https://github.com/AMA-CMFAI/LAMBDA/releases/tag/app). (Hint: There are some problems with the kernel installation in the APP. You should run `ipython kernel install --name lambda --user` to install the kernel in advance.) - [Docs site](https://ama-cmfai.github.io/LAMBDA-Docs/#/) is available! ## Key Features - **Code-Free Data Analysis**: Perform complex data analysis tasks through human language instruction. - **Multi-Agent System**: Utilizes two key agent roles, the programmer and the inspector, to generate and debug code seamlessly. - **User Interface**: This includes a robust user interface that allows direct user intervention in the operational loop. - **Model Integration**: Flexibly integrates external models and algorithms to cater to customized data analysis needs. - **Automatic Report Generation**: Concentrate on high-value tasks, rather than spending time and resources on report writing and formatting. - **Jupyter Notebook Exporting**: Export the code and the results to Jupyter Notebook for reproduction and further analysis flexibly. ## Getting Started ### Installation First, clone the repository. ```bash git clone https://github.com/AMA-CMFAI/LAMBDA.git cd LAMBDA ``` Then, we recommend creating a [Conda](https://docs.conda.io/en/latest/) environment for this project and installing the dependencies by following the commands: ```bash conda create -n lambda python=3.10 conda activate lambda ``` Then, install the required packages: ```bash pip install -r requirements.txt ``` Next, you should install the Jupyter kernel to create a local Code Interpreter: ```bash ipython kernel install --name lambda --user ``` ### Configuration to Easy Start 1. To use the Large Language Models, you should have an API key from [OpenAI](https://openai.com/api/pricing/) or other companies. Besides, we support OpenAI-Style interface for your local LLMs once deployed, available frameworks such as [Ollama](https://ollama.com/), [LiteLLM](https://docs.litellm.ai/docs/), [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory). > Here are some products that offer free APIkeys for your reference: [OpenRouter](https://openrouter.ai/) and [SILICONFLOW](https://siliconflow.cn/) 2. Set your API key, models and working path in the config.yaml: ```bash #================================================================================================ # Config of the LLMs #================================================================================================ conv_model : "gpt-4.1-mini" # Choose the model you want to use. We highly recommned using the advanced model. programmer_model : "gpt-4.1-mini" inspector_model : "gpt-4.1-mini" api_key : "sk-xxxxxxx" # The API Keys you buy. base_url_conv_model : 'https://api.openai.com/v1' # The base url from the provider. base_url_programmer : 'https://api.openai.com/v1' base_url_inspector : 'https://api.openai.com/v1' #================================================================================================ # Config of the system #================================================================================================ streaming : True project_cache_path : "cache/conv_cache/" # Local cache path max_attempts : 5 # The max attempts of self-correcting max_exe_time: 18000 # The maximum time for the execution #knowledge integration retrieval : False # Whether to start a knowledge retrieval. If you don't create your knowledge base, you should set it to False ``` Finally, run the following command to start the LAMBDA with GUI: ```bash python lambda_app.py ``` ## Demonstration Videos The performance of LAMBDA in solving data science problems is demonstrated in several case studies, including: - **[Data Analysis](https://www.polyu.edu.hk/ama/cmfai/files/lambda/lambda.mp4)** - **[Integrating Human Intelligence](https://www.polyu.edu.hk/ama/cmfai/files/lambda/knw.mp4)** - **[Education](https://www.polyu.edu.hk/ama/cmfai/files/lambda/LAMBDA_education.mp4)** ## Planning Works - [ ] Create a Logger for log. - [ ] Pre-installation of popular packages in the kernel. - [ ] Replace Gradio UI with OpenWebUI. - [ ] Refactor the Knowledge Integration and Knowledge base module by ChromaDB. - [ ] Add a Docker image for easier use. - [x] Docsite. ## Updating History See [Docs site](https://ama-cmfai.github.io/LAMBDA-Docs/#/). ## Related Works If you are interested in Data Agent, you can take a look at : - Our survey paper [[A Survey on Large Language Model-based Agents for Statistics and Data Science]](https://www.arxiv.org/pdf/2412.14222) - and a reading list: [[Paper List of LLM-based Data Science Agents]](https://github.com/Stephen-SMJ/Reading-List-of-Large-Language-Model-Based-Data-Science-Agent) ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## Acknowledgements Thank the contributors and the communities for their support and feedback. --- > If you find our work useful in your research, consider citing our paper by: ```bash @article{sun2025lambda, title={Lambda: A large model based data agent}, author={Sun, Maojun and Han, Ruijian and Jiang, Binyan and Qi, Houduo and Sun, Defeng and Yuan, Yancheng and Huang, Jian}, journal={Journal of the American Statistical Association}, pages={1--13}, year={2025}, publisher={Taylor \& Francis} } @article{sun2025survey, title={A survey on large language model-based agents for statistics and data science}, author={Sun, Maojun and Han, Ruijian and Jiang, Binyan and Qi, Houduo and Sun, Defeng and Yuan, Yancheng and Huang, Jian}, journal={The American Statistician}, pages={1--14}, year={2025}, publisher={Taylor \& Francis} } ``` ## Star History [![Star History Chart](https://api.star-history.com/svg?repos=AMA-CMFAI/LAMBDA&type=Date)](https://www.star-history.com/#AMA-CMFAI/LAMBDA&Date)