DeepAnalyze
VerifiedOpen-source agentic LLM for fully autonomous data science workflows.
What is DeepAnalyze?
DeepAnalyze is the first agentic large language model built specifically for autonomous data science. It operates without human intervention to handle end-to-end tasks across varied data formats and produce research reports.
The system works by chaining reasoning steps to explore datasets, run analyses, generate visualizations, and compile findings into structured outputs. It supports databases, CSV, Excel, JSON, XML, YAML, TXT, and Markdown files.
It targets data scientists, researchers, and developers who need reliable automation for repetitive analysis work or open-ended data exploration without building custom pipelines from scratch.
What you can build with DeepAnalyze
Full Pipeline Execution
Automatically manage data preparation, modeling, visualization, and report generation on user-provided datasets.
Multi-Format Data Research
Conduct deep analysis on mixed structured and unstructured sources and synthesize results into professional reports.
Local Assistant Deployment
Run the open-source model and code locally to create a private, customizable data analysis agent.
Install DeepAnalyze
cd demo/chat/frontend
npm install
cd ..
bash start.sh
# stop the api and interface
bash stop.sh
# stop vllm if needed- 1Clone the official GitHub repository to obtain the source code and demo files.
- 2Download the DeepAnalyze-8B model weights from the Hugging Face page.
- 3Install the listed Python dependencies and set up the required environment.
- 4Launch the WebUI or API server following the repository instructions.
- 5Load a dataset and submit a task prompt to begin autonomous analysis.
DeepAnalyze: pros & cons
Pros
- +Fully open-source with model, code, and training data released
- +Handles diverse data formats without manual format-specific coding
- +Operates end-to-end with minimal user guidance
- +Includes ready demo and API access options
Cons
- –Requires local setup and GPU resources for the 8B model
- –Performance depends on prompt quality and dataset complexity
- –Still early-stage with ongoing UI and API updates
Frequently asked questions
It works with structured data like CSV and databases, semi-structured files such as JSON and XML, and unstructured text including Markdown and TXT.
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