DeepAnalyze

// Overview

DeepAnalyze, from the Renmin University of China data lab with Tsinghua collaborators, is an agentic LLM built specifically for data work rather than a general assistant pointed at a CSV. Given a task and a set of files, it runs the whole pipeline without a human in the loop: data preparation, analysis, modelling, visualisation and report generation. It also handles open-ended data research — explore several sources and produce an analyst-grade written report rather than answer one question.

It is explicitly source-agnostic. Structured inputs (databases, CSV, Excel), semi-structured inputs (JSON, XML, YAML) and unstructured inputs (TXT, Markdown) can all be listed in one prompt, and the agent discovers what is in them before deciding how to use them. The whole stack is open: the DeepAnalyze-8B checkpoint on Hugging Face and ModelScope, the training data as DataScience-Instruct-500K, the inference and training code, and a demo — so you can deploy it as-is or continue training your own variant.

Four interfaces ship with the project: a WebUI, a WebUI v2 built on the DA-Studio system (accepted to the VLDB 2026 demonstration track) with Docker-sandboxed code execution, a JupyterUI, and a CLI. Around the core model the group has published companion work — DeepPrep for autonomous data preparation, CoDA-Bench for evaluating code agents on data-intensive tasks, and SkillAdam for automatically improving agent skills — each in its own repository.

// What it does

• Runs the full data-science pipeline end to end — preparation, analysis, modelling, visualisation and report writing
• Open-ended data research across many sources, producing analyst-grade written reports rather than single answers
• Handles structured (databases, CSV, Excel), semi-structured (JSON, XML, YAML) and unstructured (TXT, Markdown) inputs in one task
• Fully open release: DeepAnalyze-8B weights, DataScience-Instruct-500K training data, inference and training code
• Four front ends — WebUI, WebUI v2 (DA-Studio) with Docker-sandboxed execution, JupyterUI and a CLI
• Served through vLLM behind an OpenAI-compatible endpoint, with 4-bit and 8-bit quantised variants for 16–24 GB GPUs
• Curriculum-based agentic training recipes built on ms-swift and SkyRL for training your own variant

// Getting started

DeepAnalyze runs the DeepAnalyze-8B checkpoint locally through vLLM, or you can request an API key from the project instead of hosting it. The README recommends keeping the inference and training environments separate to avoid dependency conflicts.

conda create -n deepanalyze python=3.12 -y
conda activate deepanalyze
pip install -r requirements.txt

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

from deepanalyze import DeepAnalyzeVLLM

prompt = """# Instruction
Generate a data science report.

# Data
File 1: {"name": "person.csv", "size": "10.6KB"}
File 2: {"name": "enlist.csv", "size": "6.7KB"}
File 3: {"name": "male.xlsx", "size": "8.8KB"}
"""

(cd ./deepanalyze/ms-swift/ && pip install -e .)
(cd ./deepanalyze/SkyRL/ && pip install -e .)

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Reach for it when a folder of mixed CSVs, spreadsheets and JSON needs exploring and you do not know yet what the question is
• Reach for it to generate a repeatable analyst-style report from a fixed set of sources rather than a one-off chat answer
• Reach for it when the data must stay on your own hardware — the weights, code and training set are all open
• Reach for it as a base model to fine-tune on your organisation's own data-science workflows

// How DeepAnalyze compares

DeepAnalyze alongside other open-source agent frameworks & builders tools AI/TLDR tracks, ranked by GitHub stars.