<table width="100%">
<tr>
<td align="left">
<a href="https://www.apache.org/licenses/LICENSE-2.0">
<img src="https://img.shields.io/badge/license-Apache%202.0-blueviolet?style=for-the-badge" alt="Apache 2.0 License">
</a>
</td>
<td align="right">
<a href="https://datus.ai">Website</a> |
<a href="https://docs.datus.ai/">Docs</a> |
<a href="https://docs.datus.ai/getting_started/Quickstart/">QuickStart</a> |
<a href="https://docs.datus.ai/release_notes/">ReleaseNotes</a>
</td>
</tr>
</table>
## 🎯 Overview
**Datus** is an open-source data engineering agent that builds evolvable context for your data system.
Data engineering needs a shift from "building tables and pipelines" to "delivering scoped, domain-aware agents for analysts and business users.
* Datus-CLI: An AI-powered command-line interface for data engineers—think "Claude Code for data engineers." Write SQL, build subagents, and construct context interactively.
* Datus-Chat: A web chatbot providing multi-turn conversations with built-in feedback mechanisms (upvotes, issue reports, success stories) for data analysts.
* Datus-API: APIs for other agents or applications that need stable, accurate data services.
## 🚀 Key Features
### 🧩 Contextual Data Engineering
Automatically builds a **living semantic map** of your company’s data — combining metadata, metrics, SQL history, and external knowledge — so engineers and analysts collaborate through context instead of raw SQL.
### 💬 Agentic Chat
A **Claude-Code-like CLI** for data engineers.
Chat with your data, recall tables or metrics instantly, and run agentic actions — all in one terminal.
### 🧠 Subagents for Every Domain
Turn data domains into **domain-aware chatbots**.
Each subagent encapsulates the right context, tools, and rules — making data access accurate, reusable, and safe.
### 🔁 Continuous Learning Loop
Every query and feedback improves the model.
Datus learns from success stories and user corrections to evolve reasoning accuracy over time.
---
## 🧰 Installation
```bash
pip install datus-agent==0.2.1
datus-agent init
```
## 🧭 User Journey
### 1️⃣ Initial Exploration
A Data Engineer (DE) starts by chatting with the database using /chat.
They run simple questions, test joins, and refine prompts using @table or @file.
Each round of feedback (e.g., “Join table1 and table2 by PK”) helps the model improve accuracy.
`datus-cli --namespace demo`
`/Check the top 10 bank by assets lost @Table duckdb-demo.main.bank_failures`
### 2️⃣ Building Context
The DE imports SQL history and generates summaries or semantic models:
`/gen_semantic_model xxx`
`@subject`
They edit or refine models in @subject, combining AI-generated drafts with human corrections.
Now, /chat can reason using both SQL history and semantic context.
### 3️⃣ Creating a Subagent
When the context matures, the DE defines a domain-specific chatbot (Subagent):
`.subagent add mychatbot`
They describe its purpose, add rules, choose tools, and limit scope (e.g., 5 tables).
Each subagent becomes a reusable, scoped assistant for a specific business area.
### 4️⃣ Delivering to Analysts
The Subagent is deployed to a web interface:
`http://localhost:8501/?subagent=mychatbot`
Analysts chat directly, upvote correct answers, or report issues for feedback.
Results can be saved via !export.
### 5️⃣ Refinement & Iteration
Feedback from analysts loops back to improve the subagent:
engineers fix SQL, add rules, and update context.
Over time, the chatbot becomes more accurate, self-evolving, and domain-aware.
For detailed guidance, please follow our [tutorial](https://docs.datus.ai/getting_started/contextual_data_engineering/).
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