Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
DataFoundry is an open-source AI workbench for data analysis, unifying data sources, knowledge, tools, and agent runtime into a governed workspace for interactive analytics.
| Date | Stars |
|---|---|
| 2026-07-31 | 557 |
| 2026-08-06 | 629 |
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+72 stars today
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<h1 align="center">DataFoundry</h1> <p align="center"> An enterprise-grade Data Agent workbench — it reads business definitions through unified semantics, runs complex multi-table, multi-step analysis inside read-only boundaries,<br /> and keeps every step auditable and replayable, turning one question into a trustworthy analysis. </p> <p align="center"> <strong>28 datasource types out of the box · Enterprise semantics & context · Self-hosted · Multi-model · Fully auditable</strong> </p> <p align="center"> <strong>English</strong> · <a href="README_zh.md">简体中文</a> </p> <p align="center"> <a href="#-formal-deploy"><strong>Quick Start</strong></a> · <a href="https://datagallery-lab.github.io/datafoundry/"><strong>Docs</strong></a> · <a href="docs/en/reference/supported-datasources.md"><strong>Supported Data Sources</strong></a> · <a href="#️-roadmap"><strong>Roadmap</strong></a> · <a href="#-contributing"><strong>Contributing</strong></a> </p> <p align="center"> <img src="https://img.shields.io/badge/license-Apache--2.0-blue" alt="Apache-2.0" /> <img src="https://img.shields.io/badge/TypeScript-5.x-3178c6?logo=typescript&logoColor=white" alt="TypeScript" /> <img src="https://img.shields.io/badge/self--hostable-local%20first-2ea44f" alt="Self-hostable" /> <img src="https://img.shields.io/badge/PRs-welcome-ff69b4" alt="PRs welcome" /> <img src="https://img.shields.io/badge/status-early%20but%20usable-orange" alt="Status" /> <br /> <a href="https://github.com/mastra-ai/mastra"><img src="https://img.shields.io/badge/Mastra-agent%20runtime-111827" alt="Mastra agent runtime" /></a> <a href="https://github.com/ag-ui-protocol/ag-ui"><img src="https://img.shields.io/badge/AG--UI-event%20stream-6f42c1" alt="AG-UI event stream" /></a> <a href="https://github.com/vadimdemedes/ink"><img src="https://img.shields.io/badge/Ink-terminal%20UI-0f766e" alt="Ink terminal UI" /></a> </p> <p align="center"> <img src="docs/assets/readme/gui-demo.gif" alt="DataFoundry Web workbench demo" width="100%"> </p> --- ## 🤔 What Is DataFoundry When teams let AI query enterprise databases, the real worry is never "can the model write SQL." It is: **does it understand business definitions? Could it mutate production data? Could credentials leak into context? Can a conclusion be verified after the fact?** Most tools reduce the problem to `prompt → SQL → answer` — impressive in a demo, dead on arrival in the enterprise. DataFoundry takes a different path: **it puts the agent inside a semantic, policy-aware, evidence-preserving data task system**, upgrading natural-language analytics into controllable, trustworthy, verifiable data work. ## ✨ Core Capabilities - 🗄️ **28 datasource types, ready out of the box** — From PostgreSQL, MySQL, Snowflake, BigQuery, and ClickHouse to MongoDB, Redis, and Elasticsearch: connect your existing data stack quickly, cut integration cost, and get the agent into real business analysis faster. - 🧠 **Enterprise semantics and context organization** — Manage schema, metric definitions, and field relationships in one place, so terms like "GMV" and "retention" resolve to enterprise-approved tables, fields, and definitions — fewer guessed fields, wrong joins, and definition drift, and fundamentally better accuracy. - 🏠 **Self-hosted deployment and multi-model support** — Run it inside your own boundary so data never leaves; on the model side, any OpenAI-compatible provider works (Qwen, DeepSeek, GPT, ...), letting you balance security, cost, latency, and quality per scenario. - 🔒 **Safe by default, auditable throughout** — Read-only queries, credential isolation, field masking, row limits, and timeouts by default; SQL, tool calls, and event streams are fully persisted and replayable, so every conclusion is backed by evidence. - 🧩 **Deep optimization for complex data tasks** — Built for multi-table, multi-field, long-horizon analysis and multi-step reasoning: complex questions get
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d32c266428429873, topic:ai-agents