经 AI Skill Hub 精选评估,通用本体定义 获评「强烈推荐」。这款Agent工作流在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 8.0 分,适合有一定技术背景的用户使用。
通用本体定义 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
通用本体定义 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
# 方式一:pip 安装(推荐)
pip install universal-ontology-definition
# 方式二:虚拟环境安装(推荐生产环境)
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install universal-ontology-definition
# 方式三:从源码安装(获取最新功能)
git clone https://github.com/ramphias/universal-ontology-definition
cd universal-ontology-definition
pip install -e .
# 验证安装
python -c "import universal_ontology_definition; print('安装成功')"
# 命令行使用
universal-ontology-definition --help
# 基本用法
universal-ontology-definition input_file -o output_file
# Python 代码中调用
import universal_ontology_definition
# 示例
result = universal_ontology_definition.process("input")
print(result)
# universal-ontology-definition 配置文件示例(config.yml) app: name: "universal-ontology-definition" debug: false log_level: "INFO" # 运行时指定配置文件 universal-ontology-definition --config config.yml # 或通过环境变量配置 export UNIVERSAL_ONTOLOGY_DEFINITION_API_KEY="your-key" export UNIVERSAL_ONTOLOGY_DEFINITION_OUTPUT_DIR="./output"
- 🏗️ Four-Layer Separation — Stable semantic core, pluggable Industry and Domain Extension, free enterprise customization, and multi-platform bindings. - 🛡️ Anti-Entropy by Design — 4 abstract domain roots, hard class caps, governance rules, and CI validation prevent ontology sprawl. - 📐 Standardized Definition Format — Unified JSON Schema with lifecycle management (status, since, deprecated_since). - 🔗 Inheritance & Extension — L2 extends L1, L3 extends L1+L2, with generalized domain/range relations. - ⚙️ Platform Bindings — L0 provides ready-to-use OWL/RDF, JSON-LD, GraphQL, and SQL mappings. - 🌍 Bilingual Support — All concepts include Chinese and English labels and definitions. - 🤝 Community-Driven — Anyone can contribute Industry and Domain Extensions, platform bindings, or improve core definitions. - 🖥️ Ontology Studio — Production-ready visual web workspace for browsing and managing all ontology layers. ---
1. Create a new site on Netlify linked to your GitHub repo. 2. Build settings (auto-detected from netlify.toml): - Base directory: studio - Build command: npm run build - Publish directory: studio/.next 3. Environment variables — add the same variables as above in Netlify's site settings (Settings > Environment variables), but set NEXTAUTH_URL to your deployed URL (e.g. https://your-site.netlify.app). 4. Deploy. The @netlify/plugin-nextjs plugin handles SSR and serverless functions automatically.
Permission data (user roles) is stored in Netlify Blobs — no external database required.
---
For the full step-by-step walkthrough with detailed examples in Chinese, see README_CN.md — Ontology 创建与更新完整指南.
Browse the l2-extensions/ directory and select the appropriate industry package. Each extension declares its parent through the extends field:
{
"layer": "L2_consulting_industry_extension",
"version": "1.0.0",
"extends": "L1_universal_organization_ontology",
"classes": [
{
"id": "ConsultingFirm",
"label_zh": "咨询公司",
"parent": "Organization",
"definition": "An enterprise entity providing professional consulting services"
}
]
}
l2-extensions/_template/ as your starting point.schema/extension_schema.json.---
1. Identify parent dependencies (L1 core, L2 extensions)
2. Copy template → l2-extensions/_template/ or l3-enterprise/_template/
3. Inherit parent classes via the "parent" field
4. Define domain-specific classes (PascalCase IDs, bilingual labels)
5. Define relations with "specializes" inheritance chain (snake_case IDs)
6. Add sample instances (type must reference a concrete, non-abstract class)
7. Validate: JSON Schema → Governance rules → Referential integrity
8. Generate derived formats (OWL/RDF, Neo4j, LLM exports)
9. Version management & release
| Industry | Directory | Classes | Relations | Status |
|---|---|---|---|---|
| **Consulting** | [l2-extensions/consulting/](l2-extensions/consulting/) | 54 | 45 |  |
| **Financial Services** | [l2-extensions/financial-services/](l2-extensions/financial-services/) | 30 | 12 |  |
| **Food & Beverage** | [l2-extensions/fnb/](l2-extensions/fnb/) | 19 | 7 |  |
| **Healthcare** | [l2-extensions/healthcare/](l2-extensions/healthcare/) | 28 | 10 |  |
| **Luxury Goods** | [l2-extensions/luxury-goods/](l2-extensions/luxury-goods/) | 39 | 14 |  |
| **Manufacturing** | [l2-extensions/manufacturing/](l2-extensions/manufacturing/) | 27 | 11 |  |
| **Technology** | [l2-extensions/technology/](l2-extensions/technology/) | 29 | 12 |  |
🌟 We're looking for community contributions! Retail, Education, Real Estate, Logistics, Energy, and more.
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高质量的开源AI工作流框架,具有广泛的应用前景
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议,可商用,需保留版权声明和 NOTICE 文件,含专利授权条款。
AI Skill Hub 点评:通用本体定义 的核心功能完整,质量优秀。对于自动化工程师和运维人员来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。
| 原始名称 | universal-ontology-definition |
| 原始描述 | 开源AI工作流:🌐 An open, standardized four-layer enterprise ontology framework for knowledge 。⭐7 · Python |
| Topics | aiontologyknowledge-graphenterprise-architecture |
| GitHub | https://github.com/ramphias/universal-ontology-definition |
| License | Apache-2.0 |
| 语言 | Python |
收录时间:2026-05-26 · 更新时间:2026-05-26 · License:Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。
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