DeepAnalyze: Agentic Large Language Models for Autonomous Data Science

Authors: Shaolei Zhang, Ju Fan*, Meihao Fan, Guoliang Li, Xiaoyong Du

Renmin University of China, Tsinghua University

DeepAnalyze is the first agentic LLM for autonomous data science. It can autonomously complete a wide range of data-centric tasks without human intervention, supporting: - 🛠 Entire data science pipeline: Automatically perform any data science tasks such as data preparation, analysis, modeling, visualization, and report generation. - 🔍 Open-ended data research: Conduct deep research on diverse data sources, including structured data (Databases, CSV, Excel), semi-structured data (JSON, XML, YAML), and unstructured data (TXT, Markdown), and finally produce analyst-grade research reports. - 📊 Fully open-source: The model, code, training data, and demo of DeepAnalyze are all open-sourced, allowing you to deploy or extend your own data analysis assistant.

🔥 News

• [2026.07]: We look forward to releasing DeepPrep, a data-preparation companion to DeepAnalyze that turns raw tables into analysis-ready data.

More about DeepPrep

DeepPrep is an LLM-powered agentic system for autonomous data preparation. It constructs data-preparation pipelines through execution-grounded interaction with intermediate table states and runtime feedback, helping clean, transform, and standardize raw data before downstream analysis.

▶️ Demo:

https://github.com/user-attachments/assets/6b94927f-5c0c-4cfe-bc33-de56b8e459cd

• [2026.06.15]: We release CoDA-Bench, a benchmark for evaluating whether code agents can handle data-intensive analytical tasks, closely aligned with DeepAnalyze's target scenarios.

More about CoDA-Bench

CoDA-Bench evaluates agents in a Linux sandbox with hundreds of data files. Given a natural-language question, an agent must discover relevant data, write executable code, and produce the final answer. It provides a benchmark setting for the same type of data discovery and code-execution challenges targeted by DeepAnalyze.

▶️ Demo:

https://github.com/user-attachments/assets/34e50a62-744b-4079-8988-6a8bbfe166a0

• [2026.05.31]: DA-Studio, the system behind DeepAnalyze WebUI v2 (demo/chat_v2), has been accepted to the VLDB 2026 Demonstration Track.

• [2026.03.16]: Update DeepAnalyze WebUI v2, featuring a smoother UI, support for the HeyWhale API, and support for Docker-based sandboxed code execution. More details in Readme .

• [2026.01.31]: 🎉🎉🎉DeepAnalyze served as the official agent supporting the 2026年(第19届)中国大学生计算机设计大赛大数据主题赛 (2026 (19th) China Collegiate Computer Design Contest – Big Data Track).

• [2025.12.28] ANNOUNCEMENT: DeepAnalyze API Keys Are Now Available 🎉🎉🎉 You can now apply for your API key via this Google Form or this Feishu Form. For full details and usage instructions, please refer to the Guide or the Feishu Wiki.

• [2025.11.13]: DeepAnalyze now supports OpenAI-style API endpointsis and is accessible through the Command Line Terminal UI. Thanks to the contributor @LIUyizheSDU

• [2025.11.08]: DeepAnalyze is now accessible through the JupyterUI, building based on jupyter-mcp-server. Thanks to the contributor @ChengJiale150.

• [2025.10.28]: We welcome all contributions, including improving the DeepAnalyze and sharing use cases (see CONTRIBUTION.md). All merged PRs will be listed as contributors.

• [2025.10.27]: DeepAnalyze has attracted widespread attention, gaining 1K+ GitHub stars and 200K+ Twitter views within a week.

• [2025.10.21]: DeepAnalyze's paper, code, model, training data are released!

🖥 Demo

WebUI

https://github.com/user-attachments/assets/04184975-7ee7-4ae0-8761-7a7550c5c8fe

Upload the data, DeepAnalyze can perform data-oriented deep research 🔍 and any data-centric tasks 🛠

• Clone this repo and download DeepAnalyze-8B.
• Deploy DeepAnalyze-8B via vllm: vllm serve DeepAnalyze-8B

• Run these scripts to launch the API and interface, and then interact through the browser (http://localhost:4000): ```bash cd demo/chat/frontend npm install cd .. bash start.sh

  stop the api and interface

  bash stop.sh

  stop vllm if needed

  ``` - If you want to deploy under a specific IP, please replace localhost with your IP address in ./demo/chat/backend.py and ./demo/chat/frontend/lib/config.ts

WebUI v2

https://github.com/user-attachments/assets/2dd1d2aa-6fb9-4202-bc8d-cbe874844725

Upload the data, DeepAnalyze can perform data-oriented deep research 🔍 and any data-centric tasks 🛠

• A more streamlined UI
• Added support for HeyWhale API keys
• Added support for a Docker-based sandbox code execution environment.

• The usage method is the same as WebUI.

  ```bash cd demo/chat_v2/frontend  npm install cd .. cp .env.example .env bash start.sh

  stop the api and interface

  bash stop.sh

  stop vllm if needed

  ```

JupyterUI

https://github.com/user-attachments/assets/a2335f45-be0e-4787-a4c1-e93192891c5f

Familiar with Jupyter Notebook? Try DeepAnalyze through the JupyterUI!

• This Demo runs Jupyter Lab as frontend, creating a new notebook, converting <Analyze|Understand|Answer> to Markdown cells, converting <Code> to Code cells and executing them as <Execute>.
• Go to demo/jupyter to see more and try!
• 👏Thanks a lot to the contributor @ChengJiale150.

CLI

https://github.com/user-attachments/assets/018acae5-b979-4143-ae1e-5b74da453c1d

Try DeepAnalyze through the command-line interface

• Deploy DeepAnalyze-8B via vllm: vllm serve DeepAnalyze-8B

• Start the API server and launch the CLI interface: ```bash cd API python start_server.py # In one terminal

  cd demo/cli python api_cli.py # In another terminal (English)

  or

  python api_cli_ZH.py # In another terminal (Chinese) ```

• The CLI provides a Rich-based beautiful interface with file upload support and real-time streaming responses.

• Supports both English and Chinese interfaces .

[!TIP]

Clone this repository to deploy DeepAnalyze locally as your data analyst, completing any data science tasks without any workflow or closed-source APIs.

🔥 The UI of the demo is an initial version. Welcome to further develop it, and we will include you as a contributor.

🚀 Quick Start

🔑 Use the DeepAnalyze API

API keys are now available!

To request your key, please fill out one of the following application forms: * Primary Form (Google) * Alternative Form (Feishu)

📚 For comprehensive usage instructions, please refer to the API guide:

• Documentation
• Feishu Wiki

Model Download

Download model in RUC-DataLab/DeepAnalyze-8B · Hugging Face or DeepAnalyze-8B · 模型库

📊 Memory Configuration Recommended Parameters Table

To obtain the quantized model, you can use ./quantize.py .

🚀 vLLM Launch Command Template

General Command Template

python -m vllm.entrypoints.openai.api_server \
  --model <model_path> \
  --served-model-name DeepAnalyze-8B \
  --max-model-len <select_from_table_above> \
  --gpu-memory-utilization 0.95 \
  --port 8000 \
  <add_fp8_if_required> \
  --trust-remote-code

Command Examples by Scenario

Scenario 1: 16GB GPU Memory Users (Recommended: 4-bit Quantized Version)

python -m vllm.entrypoints.openai.api_server \
  --model /path/to/deepanalyze/4bit \
  --served-model-name DeepAnalyze-8B \
  --max-model-len 49152 \
  --gpu-memory-utilization 0.95 \
  --port 8000 \
  --kv-cache-dtype fp8 \
  --trust-remote-code

Scenario 2: 24GB GPU Memory Users (For Maximum Context Length)

python -m vllm.entrypoints.openai.api_server \
  --model /path/to/deepanalyze/4bit \
  --served-model-name DeepAnalyze-8B \
  --max-model-len 131072 \
  --gpu-memory-utilization 0.95 \
  --port 8000 \
  --kv-cache-dtype fp8 \
  --trust-remote-code

Scenario 3: 80GB GPU Memory Users (Best Performance)

python -m vllm.entrypoints.openai.api_server \
  --model /path/to/original/model \
  --served-model-name DeepAnalyze-8B \
  --max-model-len 131072 \
  --gpu-memory-utilization 0.95 \
  --port 8000 \
  --trust-remote-code

Quick Selection Guide

• Limited Memory (<24GB): Use 4-bit Quantized Version + FP8 KV Cache
• Balanced Configuration (24-40GB): Choose model type based on requirements
• Sufficient Memory (≥40GB): Use Original Model for best precision

After launching, the API service can be accessed via http://localhost:8000/v1/completions.

Requirements

• Install packages: torch, transformers, vllm>=0.8.5 ```bash conda create -n deepanalyze python=3.12 -y conda activate deepanalyze pip install -r requirements.txt

  For training

  (cd ./deepanalyze/ms-swift/ && pip install -e .) (cd ./deepanalyze/SkyRL/ && pip install -e .) `` - [requirements.txt`](requirements.txt) lists the minimal dependencies required for DeepAnalyze inference. For