ruc-datalab/deepanalyze

DeepAnalyze is the first agentic LLM for autonomous data science. 🎈你的AI数据分析师,自动分析大量数据,一键生成专业分析报告!

View on GitHub ↗Jump to charts ↓Open shareable report

Summary Information

Updated 28 minutes ago
Added to GitGenius on September 14th, 2026
Created on October 11th, 2025
Open Issues & Pull Requests: 25 (+0)
GitHub issues: Enabled
Number of forks: 733
Total Stargazers: 4,620 (+0)
Total Subscribers: 51 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 17.7 hours
Mean response time: 5.5 days
90th percentile: 20.2 days
Tracked items: 54

Most active contributors

Sign in to see contributor activity.

How this project is maintained

Around half of the issues opened in the past year never receive a reply. Only 14% of issues opened in the past year have been closed. Three people close 77% of everything that gets resolved.

Charts & Analytics

Fetching additional details & charts...

Issue Activity (beta)

Open issues: 17
New in 7 days: 1
Closed in 7 days: 1
Avg open age: 235 days
Stale 30+ days: 15
Stale 90+ days: 15

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • question (3)
  • enhancement (1)
  • good first issue (1)

Detailed Description

DeepAnalyze is an agentic large language model for autonomous data science that can independently execute complete data analysis workflows without human intervention.

The tool addresses the need for end-to-end automation in data science by combining an LLM agent with capabilities to handle data preparation, exploratory analysis, statistical modeling, visualization, and report generation. It works across multiple data formats including structured sources like databases and CSV files, semi-structured formats such as JSON and XML, and unstructured text and markdown documents. The agent autonomously plans and executes analysis tasks, generating analyst-grade research reports as output.

DeepAnalyze suits teams seeking to automate repetitive data analysis work and reduce the manual effort required to produce comprehensive data reports. It is particularly valuable for organizations that need to process diverse data sources and generate professional documentation without dedicated data science resources. The project is fully open-source, including the underlying model, training data, and code, allowing users to deploy it locally or extend it for custom requirements.

The project maintains active development with recent releases of complementary tools. A plugin called SkillAdam was released to automatically improve agent capabilities and integrates with existing coding agents and development environments. A companion tool named DeepPrep is in development to handle data preparation as a specialized component of the analysis pipeline. The codebase is written in Python and documented with a dedicated project website.