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 w8123 · MCP Server · ★ 747
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is EnterpriseAgentFramework safe to install? View the security audit →
快速、安全完成业务系统智能化改造：让 AI 能查数据、填表单、办业务 适用于 OA、ERP、CRM、eHR、采购、工单、合同等已有业务系统。 ReachAI · 面向 Java 企业系统的企业智能体开发与运行底座 改造完成后，业务系统能做什么 ReachAI 将 AI 助手嵌入 OA、eHR、采购、CRM 等现有业务页面。员工无需切换系统，即可通过自然语言查询数据、填写表单、办理业务： ReachAI 会结合当前登录身份、页面状态与业务上下文理解需求，仅调用已授予当前用户的业务能力。页面筛选、跳转与表单操作由已注册的页面动作执行；真实数据查询与写入仍由后端 Capability 或业务 API 完成。涉及写入、提交和审批等关键操作时，必须经用户确认后执行。 从一个界面掌握智能体运营状态 智能体运营中心按项目汇总 Agent、运行样本、可识别用户、Token、技术完成率、风险待办和业务系统贡献，让管理者先看到真实使用效果，再进入具体项目、版本或运行记录排查。 从现有系统到上线使用 连接业务系统并接入智能运行底座 → 扫描页面、接口和能力 → 推荐改造项 → AI Coding 实施 → 发布与验收 → 原系统使用 AI ReachAI 覆盖业务系统接入、系统盘点、改造实施、能力发布、业务验收和上线运行： 识别项目技术栈，生成标准接入任务，由 AI Coding 集成后端连接、网关通信、页面助手、身份传递和安全校验
| Stars | 747 |
| Forks | 62 |
| Language | Java |
| Category | MCP Server |
| License | MIT |
| Quality Score | 59.6935496084641/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-18 |
| Created | 2026-04-02 |
| Platforms | java, mcp |
| Est. Tokens | ~20k |
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EnterpriseAgentFramework is ReachAI企业级智能体开发平台：快速、安全完成已有业务系统智能化改造，让 AI 在 OA、ERP、CRM 等原系统中查数据、填表单、办业务。ReachAI: Quickly and securely bring AI to existing enterprise systems, enabling AI to query data, fill out forms, and execute bu. It is categorized as a MCP Server with 747 GitHub stars.
EnterpriseAgentFramework is primarily written in Java. It covers topics such as a2a, agent, agent-framework.
You can find installation instructions and usage details in the EnterpriseAgentFramework GitHub repository at github.com/w8123/EnterpriseAgentFramework. The project has 747 stars and 62 forks, indicating an active community.
EnterpriseAgentFramework is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to EnterpriseAgentFramework on Agent Skills Hub include trpc-agent-go, ai-devkit, agents. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
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: