{"repo":"JohnnyQ-commits/Aqueduct","free":true,"listed":false,"github":"https://github.com/JohnnyQ-commits/Aqueduct","clone":"git clone https://github.com/JohnnyQ-commits/Aqueduct.git","description":"LLM-powered data engineering agent: converts requirement docs and natural language into production-ready SQL, automating data pipeline workflows for ETL and analytics tasks. Open-source, MIT licensed.可嵌入的 LLM 驱动数据开发 Agent：将需求文档和自然语言转换为生产级 SQL，自动化 ETL 和分析任务的数据流水线工作流。开源项目，采用 MIT 协议。","language":"Python","stars":17,"topics":["automation","data-agent","data-engineering","etl","llm-agent","python","sql-generation","workflow"],"license":"MIT","category":"data-pipelines","readme_excerpt":"Aqueduct Data Engineering Automation Agent Framework English 中文 --- Overview The problem : Data engineers spend 60-70% of their time on repetitive work — understanding requirements, writing boilerplate SQL, creating DDL, writing DQC tests, generating documentation. The creative part (business logic) is small; the mechanical part is huge. Aqueduct automates the mechanical part. Give it a requirement document. Get back 11 standardized deliverables: DDL, ETL SQL, DQC test cases, field-level lineage, design documents, and a comprehensive report. What makes it different Principle What it means ----------- --------------- Framework, not tool Embed into Claude Code, LangChain, or your own app via from aqueduct import Aqueduct 7-layer architecture Clean separation: MCP / LLM / Tools / Skills / Engine / Memory / Config Platform agnostic Connects to any data platform via standard MCP protocol ( .mcp.json ) Ontology knowledge Business domains modeled as typed JSON — entities, relationships, metrics, axioms DAG orchestration StateGraph-based workflow with interactive checkpoints and error recovery Review-fix loop Code review finds bugs → auto-fix SQL → re-review → production-ready quality Full observability Per-task log files, phase timing, LLM call tracing, tool execution audit Prompt-code decoupled Prompts as .tpl.md files — edit without touching code, i18n-ready Output: 11 mandatory deliverables Every pipeline run produces a standardized output directory: # File Description --- ------","default_branch":null,"files":null,"tree":[],"storefront":"/r/JohnnyQ-commits","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/JohnnyQ-commits/Aqueduct/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}