The first agentic LLM for autonomous data science that autonomously completes the entire data science pipeline including data preparation, analysis, modeling, visualization, and report generation. Supports diverse data sources and produces analyst-grade research reports.
One-Minute Overview#
DeepAnalyze is the first agentic LLM designed for autonomous data science, capable of completing data-intensive tasks without human intervention. Whether you're a data scientist, analyst, or researcher, DeepAnalyze helps you quickly process and analyze large datasets, generating professional analysis reports with a single click, significantly boosting productivity.
Core Value: Fully autonomous data science workflow supporting diverse data sources and producing professional-grade analysis reports
Quick Start#
Installation Difficulty: Medium - Requires Python environment and GPU resources, with different deployment options based on configuration
# Create environment and install dependencies
conda create -n deepanalyze python=3.12 -y
conda activate deepanalyze
pip install -r requirements.txt
# Start vLLM service
vllm serve DeepAnalyze-8B
Is this suitable for me?
- ✅ Large-scale data analysis: Automatically processes multiple data formats including CSV, Excel, and databases
- ✅ Complex data science tasks: Complete workflow from data cleaning to modeling and visualization
- ✅ Professional report generation: One-click analysis reports without manual writing
- ❌ Real-time interactive analysis: Better suited for batch processing than real-time interaction
- ❌ Extremely low-resource environments: Requires at least 16GB GPU memory to run
Core Capabilities#
1. Complete Data Science Pipeline - Solving manual coding complexity#
- Automatically executes all data science tasks including data preparation, analysis, modeling, visualization, and report generation Actual Value: Users don't need to write code to complete complex data science analysis workflows
2. Open-Ended Data Research - Solving diverse data sources and analysis depth#
- Supports structured (databases, CSV, Excel), semi-structured (JSON, XML, YAML), and unstructured (TXT, Markdown) data sources
- Can automatically explore and integrate multiple data sources for deep research Actual Value: Users can upload data in any format and the system automatically understands and generates comprehensive analysis reports
3. Multiple Interaction Interfaces - Solving different usage habits#
- Provides WebUI, JupyterUI, and CLI three interaction methods
- Supports Chinese and English interfaces to accommodate different language needs Actual Value: Users can choose the most suitable interaction method based on their habits without changing workflows
4. API Service - Solving integration and expansion#
- Provides OpenAI-style API interface for easy integration with existing systems Actual Value: Developers can integrate DeepAnalyze capabilities into their own applications to build customized data analysis services
5. Fully Open Source - Solving transparency and customization#
- Model, code, training data, and demonstrations are all open-sourced
- Users can deploy or extend their own data analysis assistants Actual Value: Users can modify and extend the system based on their own needs without vendor lock-in
Technology Stack & Integration#
Development Language: Python Key Dependencies: PyTorch, Transformers, vLLM≥0.8.5 Integration Method: API / SDK / Library
Maintenance Status#
- Development Activity: Actively maintained with frequent feature updates
- Recent Updates: Added OpenAI-style API endpoints and JupyterUI functionality
- Community Response: Gained 1000+ GitHub stars and 200K+ Twitter views within a week of release
Documentation & Learning Resources#
- Documentation Quality: Comprehensive, including API guide, usage examples, and developer guides
- Official Documentation: GitHub repository and Feishu Wiki
- Sample Code: Provides example code and case studies for various scenarios