LAMBDA - Multi-Agent Data Analysis System
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.
- 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.
LAMBDA has demonstrated strong performance on various machine learning datasets, enhancing data science practice and analysis paradigms by seamlessly integrating human and artificial intelligence.
The performance of LAMBDA in solving data science problems is demonstrated in several case studies including:
Note: All code files will be released soon. We recommend starring this repository to stay updated with the latest developments.
This project is licensed under the MIT License - see the LICENSE file for details.
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We thank the contributors and the community for their support and feedback.
LAMBDA is an open-source project aimed at making data analysis more accessible, effective, and efficient for individuals from diverse backgrounds.
If you find our work useful in your research, consider citing our paper by:
@misc{sun2024lambdalargemodelbased,
title={LAMBDA: A Large Model Based Data Agent},
author={Maojun Sun and Ruijian Han and Binyan Jiang and Houduo Qi and Defeng Sun and Yancheng Yuan and Jian Huang},
year={2024},
eprint={2407.17535},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2407.17535},
}