Detailed Introduction
DeepAnalyze is an agentic large language model designed for autonomous data science workflows. It can perform end-to-end tasks with minimal or no human intervention, covering data exploration, cleaning, modeling, visualization, and final report generation. By combining task planning with multi-modal data handling, DeepAnalyze can analyze structured data (databases, CSV), semi-structured formats (JSON), and unstructured text, and produce reproducible, structured research reports.
Key Features
End-to-end coverage: supports preprocessing, feature engineering, model training and evaluation, visualization, and report generation;
Agentic planning: built-in task decomposition and scheduling that makes coherent decisions across multiple analysis steps;
Open-source and transparent: code, models and training data are released to facilitate reproduction, deployment and extension;
Multi-source support: automatically recognizes and integrates structured, semi-structured and unstructured data sources for joint analysis.
Use Cases
DeepAnalyze fits scenarios such as automated data-science research, data analyst assistants, internal enterprise data exploration, and teaching examples. It can quickly generate research-grade data reports, automate repetitive analysis tasks, help engineering teams run preliminary modeling with limited resources, or be embedded as a customizable analytic assistant in business workflows.
Technical Characteristics
DeepAnalyze builds on open models and agentic training paradigms, leveraging vLLM-level inference efficiency and instruction-tuning strategies tailored for data science. The training data and evaluation suites are publicly available; local deployment is supported through vLLM or similar runtimes, with example scripts and a demo interface for end-to-end API interaction.