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！
Last verified:
What is Deepanalyze?
DeepAnalyze is an agentic large language model built for autonomous data science. It is designed to take raw data sources and carry a task all the way from preparation and analysis through modeling, visualization, and report generation.
The website presents it as a one-size-fits-all solution for both specific data tasks and open-ended data research. It emphasizes that the system can produce analyst-grade deep research reports, not just short answers or isolated charts.
A core theme of the project is end-to-end automation with minimal human intervention. The demo prompt on the site invites users to upload data and let DeepAnalyze perform data-oriented deep research and other data-centric tasks.
The tool is aimed at data scientists, analysts, and researchers who need deeper, more automated handling of structured and unstructured data. It also targets developers and teams who want to deploy or extend an open-source data analysis assistant.
Deepanalyze pricing
Pricing model: Freemium
The website presents DeepAnalyze as open-source and links to the model, code, and training data, which indicates the core project is publicly available at no stated license cost on the homepage. It also provides a demo invitation, but the site does not list any paid plans, subscription tiers, usage quotas, or bundled feature packages.
Deepanalyze pros
- Autonomous end-to-end data science pipeline
- Handles data preparation automatically
- Supports analysis and modeling
- Generates visualizations
- Produces report-style outputs
- Designed for open-ended data research
- Works with structured data sources
- Works with semi-structured data sources
- Works with unstructured data sources
- Aims for analyst-grade research reports
- Open-sourced model weights
- Open-sourced codebase
- Open-sourced training data
- Suitable for deployment or extension
- Built on agentic training
- Uses curriculum-based learning
- Uses data-grounded trajectory synthesis
- Targets real-world data science workflows
Deepanalyze cons
- Website does not show a public pricing page
- No clear paid plan information on the site
- Demo access details are limited
- Case examples are listed but not fully explained on the homepage
- Performance claims are broad rather than fully benchmarked on-page
- Operational limits are not documented on the website
- Supported file formats are described generally, not exhaustively
- Enterprise features are not clearly listed
Frequently asked questions about Deepanalyze
What is DeepAnalyze?
DeepAnalyze is an agentic large language model for autonomous data science. It is built to automatically carry out a complete data workflow, from raw data sources to analyst-grade reports, instead of stopping at one isolated task.
What kinds of tasks can it do?
The website says DeepAnalyze can handle the full data science pipeline, including data preparation, analysis, modeling, visualization, and report generation. It also supports open-ended data research and broader data-centric tasks.
What kinds of data can it work with?
The site describes support for structured, semi-structured, and unstructured data. The examples include databases, CSV, Excel, JSON, XML, YAML, TXT, and Markdown.
How is DeepAnalyze different from workflow-based agents?
The website says workflow-based agents rely on predefined workflows, which limits full autonomy. DeepAnalyze is positioned as agentic and end-to-end, meaning it is intended to decide and execute more of the process autonomously.
What is the main result it produces?
DeepAnalyze is designed to produce analyst-grade deep research reports. The website emphasizes that it can transform uploaded or source data into a polished report after performing analysis and reasoning steps.
What training approach does DeepAnalyze use?
The site says it uses a curriculum-based agentic training paradigm. This approach is meant to help the model progress from single abilities to more comprehensive real-world data science capabilities.
What is data-grounded trajectory synthesis?
The website describes it as a framework that automatically generates high-quality reasoning and interaction trajectories. The goal is to provide better guidance during training across a large solution space.
Is DeepAnalyze open source?
Yes. The site states that the model, code, and training data are open-sourced. It also links to the model and dataset resources for users who want to deploy or extend the system.
Who is DeepAnalyze intended for?
The tool is intended for data scientists, analysts, researchers, and developers who need autonomous help with data tasks. It is especially relevant for people who want deeper research outputs and an open-source data analysis assistant they can extend.
Where can users try it?
The website includes a demo invitation that says users can upload data and let DeepAnalyze perform deep research and other data-oriented tasks. It also points to project resources such as the paper, code, model, and dataset.