datafoundry — security grade SAFE, quality 66/100

Security audit verdict: SAFE · quality 66/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by datagallery-lab · Agent Tool · ★ 708

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

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About datafoundry

DataFoundry An enterprise-grade Data Agent workbench — it reads business definitions through unified semantics, runs complex multi-table, multi-step analysis inside read-only boundaries, and keeps every step auditable and replayable, turning one question into a trustworthy analysis. 28 datasource types out of the box · Enterprise semantics & context · Self-hosted · Multi-model · Fully auditable English · 简体中文 Quick Start · Docs · Supported Data Sources · Roadmap · Contributing

ag-uiagent-runtimeaiai-agentsdata-analysisdata-sourcesdata-workbenchinkmastra

Quick Facts

Stars708
Forks84
LanguageTypeScript
CategoryAgent Tool
LicenseApache-2.0
Quality Score65.9442449290438/100
Open Issues4
Last Updated2026-08-17
Created2026-06-18
Platformsnode
Est. Tokens~17k

Compatible Skills

These tools work well together with datafoundry for enhanced workflows:

  • alibabacloud-dataworks-mcp-server — semantic(0.29)+complementary+rare_topics+same_lang+similar_pop+shared_platform (65%)
  • ZizkaDB — semantic(0.21)+complementary+rare_topics+same_lang+similar_pop+shared_platform (62%)

datafoundry alternative? Top 6 similar tools

Looking for a datafoundry alternative? If you're comparing datafoundry with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

  • dataagent by datagallery-ai · ⭐ 767

    DataFoundry is an open-source AI workbench for data analysis, unifying data sources, knowledge, tools, and age

  • datafoundry by datagallery-ai · ⭐ 740

    DataFoundry is an open-source AI workbench for data analysis, unifying data sources, knowledge, tools, and age

  • trpc-agent-go by trpc-group · ⭐ 1.8k

    A Go framework for building production agent systems with graph workflows, tools, memory, A2A, AG-UI, MCP, eva

  • golf by golf-mcp · ⭐ 840

    Production-Ready MCP Server Framework • Build, deploy & scale secure AI agent infrastructure • Includes Auth,

  • BambooAI by pgalko · ⭐ 784

    A Python library powered by Language Models (LLMs) for conversational data discovery and analysis.

  • swarmclaw by swarmclawai · ⭐ 636

    Open-source self-hosted AI agent runtime and multi-agent framework for autonomous agent swarms. Agent memory,

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Frequently Asked Questions

What is datafoundry?

datafoundry is 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.. It is categorized as a Agent Tool with 708 GitHub stars.

What programming language is datafoundry written in?

datafoundry is primarily written in TypeScript. It covers topics such as ag-ui, agent-runtime, ai.

How do I install or use datafoundry?

You can find installation instructions and usage details in the datafoundry GitHub repository at github.com/datagallery-lab/datafoundry. The project has 708 stars and 84 forks, indicating an active community.

What license does datafoundry use?

datafoundry is released under the Apache-2.0 license, making it free to use and modify according to the license terms.

What are the best alternatives to datafoundry?

The top alternatives to datafoundry on Agent Skills Hub include dataagent, datafoundry, trpc-agent-go. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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