What is DeepAnalyze used for?

DeepAnalyze is a research project for an agentic LLM that autonomously handles data science tasks from preparation to modeling, charts and written reports. It is a good fit for automated exploratory data analysis, generating analysis reports from raw datasets and research on agentic data science.

Is DeepAnalyze open source?

Yes. DeepAnalyze is open source under the MIT licence. Its source code is on GitHub at ruc-datalab/DeepAnalyze and is written mainly in Python.

Yes. DeepAnalyze is open source, so the software itself is free to use.

Can I self-host DeepAnalyze?

Yes. DeepAnalyze can be self-hosted on your own server or infrastructure; there is no official hosted version.

What is DeepAnalyze an alternative to?

DeepAnalyze is an open-source alternative to Julius AI. Other open-source alternatives to Julius AI include DB-GPT, Pretzel and Nao.

Is DeepAnalyze actively maintained?

Yes. The most recent commit to DeepAnalyze was on 23 September 2026. The project has 4.7k stars on GitHub.

DeepAnalyze: open-source alternative to Julius AI

About DeepAnalyze

DeepAnalyze is an agentic large language model for autonomous data science, developed by researchers at Renmin University of China and Tsinghua University. The goal is for it to complete data-centered tasks without step-by-step human guidance, acting as an AI data analyst that takes in large amounts of data and produces a professional analysis report.

According to the README it can run the whole data science pipeline, including data preparation, analysis, modeling, visualization and report generation. It also supports open-ended data research across different data sources, from structured data such as databases, CSV files and Excel sheets to other formats. Repository topics mention Qwen, Jupyter, deep research and data visualization, and the code is Python.

DeepAnalyze is released under the MIT license. It is a research-oriented project you run yourself with the provided model and code, rather than a hosted product. It suits data scientists, analysts and researchers exploring autonomous analysis agents.

Key features

Autonomous end-to-end data science pipeline
Data preparation and analysis
Modeling and visualization
Automatic report generation
Open-ended deep research on data
Works with databases, CSV and Excel data

Good fit for

→Automated exploratory data analysis
→Generating analysis reports from raw datasets
→Research on agentic data science

Built with: Python
Tags: data-science; agentic-ai; llm; data-analysis; report-generation; python; research; qwen

DeepAnalyze: questions and answers

What is DeepAnalyze used for?: DeepAnalyze is a research project for an agentic LLM that autonomously handles data science tasks from preparation to modeling, charts and written reports. It is a good fit for automated exploratory data analysis, generating analysis reports from raw datasets and research on agentic data science.
Is DeepAnalyze open source?: Yes. DeepAnalyze is open source under the MIT licence. Its source code is on GitHub at ruc-datalab/DeepAnalyze and is written mainly in Python.
Is DeepAnalyze free?: Yes. DeepAnalyze is open source, so the software itself is free to use.
Can I self-host DeepAnalyze?: Yes. DeepAnalyze can be self-hosted on your own server or infrastructure; there is no official hosted version.
What is DeepAnalyze an alternative to?: DeepAnalyze is an open-source alternative to Julius AI. Other open-source alternatives to Julius AI include DB-GPT, Pretzel and Nao.
Is DeepAnalyze actively maintained?: Yes. The most recent commit to DeepAnalyze was on 23 September 2026. The project has 4.7k stars on GitHub.