Summary

Datus-ai/Datus-agent addresses the evolving needs of data engineering by shifting focus from manual pipeline construction to AI-native context engineering. It provides a suite of tools, including a CLI, web chatbot, and API, designed for data engineers and analysts. The project aims to deliver scoped, domain-aware agents that enhance data accessibility, accuracy, and reusability through intelligent context management and continuous learning.

How It Works

Datus constructs a dynamic "living semantic map" of data by integrating diverse sources including metadata, key metrics, historical SQL queries, and external knowledge bases. Its core approach leverages advanced AI to facilitate interactive context building, enabling users to generate SQL, define domain-specific subagents, and iteratively refine data understanding. This methodology promotes seamless collaboration through shared, AI-curated context rather than requiring direct interaction with raw SQL, making data access more precise, reusable, and secure. The system incorporates a sophisticated continuous learning loop where every user interaction, query, and explicit feedback signal (like upvotes or issue reports) iteratively refines the AI's reasoning accuracy and domain-specific knowledge over time.

Quick Start & Requirements

• Primary Install: pip install datus-agent==0.2.1 followed by datus-agent init.
• Prerequisites: Python >= 3.12.
• Documentation: Detailed installation and usage guidance is available via the Quickstart Guide and tutorial links within the README.

Highlighted Details

• Datus-CLI: An AI-powered command-line interface designed as a "Claude Code for data engineers," facilitating interactive SQL writing, subagent creation, and context construction through intuitive commands like /chat, @table, /gen_semantic_model, and .subagent add.
• Subagents for Every Domain: Empowers users to transform data domains into specialized, domain-aware chatbots. Each subagent encapsulates precise context, relevant tools, and defined rules, acting as a reusable, scoped assistant for specific business areas, enhancing data governance and accessibility.
• Continuous Learning Loop: The agent actively learns from every query and user feedback, including success stories and corrections. This mechanism ensures the system continuously evolves its reasoning accuracy, becoming more self-aware and domain-specific over time.
• Datus-Chat: A user-friendly web-based chatbot interface tailored for data analysts, supporting multi-turn conversations, direct feedback mechanisms (upvotes, issue reports), and data export capabilities.

Maintenance & Community

The provided README does not contain specific details regarding maintainers, community channels (like Discord or Slack), sponsorships, or a public roadmap.

Licensing & Compatibility

The README does not explicitly state the project's license type or provide information on compatibility for commercial use or closed-source linking.

Limitations & Caveats

The README focuses on features and user journeys, offering no explicit mention of limitations, alpha/beta status, known bugs, or unsupported platforms. The version number 0.2.1 suggests the project may still be in its early stages of development.