ruoyi-ai
Enterprise-grade AI agent framework with multi-provider LLM management, secure knowledge bases and high-precision RAG, visual workflow orchestration, and multi-agent coordination.
No outside reports on file
This is a complex system touching real company data and multiple external AI services, so it requires strong security practices and careful oversight to avoid data leaks or unauthorized agent behavior.
Based on public adoption and transparency signals as of Oct 21, 2026. Our opinion, not a code audit or security guarantee.
What it can access
- Local filesNo local folder access notedLOW
- Command executionNo shell command runner notedLOW
- Network reachLocal or standard API callsLOW
- CredentialsRequires API key or tokenREVIEW
- Package sourceGitHub (ageerle/ruoyi-ai)PUBLIC
Inferred from this listing’s public package details and description. Check the project’s source before installing.
Install
No npm or PyPI package is listed. Install from source: github.com/ageerle/ruoyi-ai ↗
Everything known
We found nothing about this project in the sources we read (last checked 2026-10-30). That does not mean it has no problems.
0 incidents · 0 advisories · 0 news articles · 0 community threads · checked 2026-10-30
Popularity grade: B (from public signals such as stars and downloads, not a code review). Graded 2026-10-21.
Sources we read: OSV, GitHub Advisory Database, CISA Known Exploited Vulnerabilities, 44 security news feeds and our incident library. Last checked 2026-10-30.
About Ruoyi AI
Ruoyi-ai is a framework for building AI agents that can work together and access outside information sources. It handles multiple AI models from different companies, organizes data for AI to search through, and lets you design agent workflows visually.
Who it’s for
Companies building custom AI applications that need multiple agents working together, or teams wanting to connect AI models to their own company data safely. Not for casual users or small projects.
Alternatives
- LangChain: Open-source library for chaining AI model calls and adding memory or tools.
- AutoGen: Microsoft's framework for multi-agent AI collaboration with built-in conversation features.
- Flowise: Visual workflow builder for AI agents with drag-and-drop node connections.
- Dify: Open-source platform for building AI applications with knowledge bases and visual workflows.
Setup
Check these steps against the project’s own instructions before running anything.
Follow the project's README for installation and configuration instructions, as this is an enterprise framework with multiple components.
Why this grade
Signal-based grade (4/7 points): very widely starred on GitHub, public source code. This reflects adoption and transparency, not a code review.
Popularity and upkeep
- GitHub stars
- 5.8k
- Listing last refreshed
- 2026-10-30
These are public popularity signals, not a maintenance check. Look at the repository’s commit history and open issues.
Risks to watch for
- Knowledge base access: The system can connect to company databases or document stores, so compromised credentials could expose sensitive business data.
- LLM model switching: Attackers could manipulate which AI model processes requests, potentially routing data through untrusted providers.
- Workflow injection: Malicious prompts hidden in documents or user input could trick the visual workflow system into executing unintended agent actions.
- Multi-agent coordination: One compromised agent in a chain could send false information to other agents, causing them to make wrong decisions with real data.
- Credential storage: The framework manages API keys for multiple LLM providers and data sources—poor storage could expose them all at once.
How to lower the risk
Least-privilege permissions
- Give agents only access to the specific data they truly need, not entire databases.
- Use separate API keys for each provider and rotate them regularly.
Network isolation
- Run the framework on trusted internal servers, not the open internet.
- Inspect outgoing requests to LLM providers to confirm no sensitive data leaks.
Monitoring
- Log all agent decisions and the data they accessed.
- Alert on unusual workflows or data queries that don't match expected use.
Red flags to watch for
- Unexpected changes to workflow configurations or agent permissions.
- Agents requesting access to new data sources outside their normal scope.
- Unusual patterns in how agents communicate with each other or external services.
Questions
How do I install Ruoyi AI?
Ruoyi AI does not publish a standalone npm or PyPI package. Clone or follow the setup instructions in its source repository at github.com/ageerle/ruoyi-ai.
Are there security reports about Ruoyi AI?
No reports found for this package in our research (last checked 2026-10-30). Ruoyi AI currently holds Grade B (Some risk signals) based on public adoption and transparency signals. Key risk areas to watch include knowledge base access, llm model switching, workflow injection.
Is Ruoyi AI actively maintained?
Ruoyi AI has 5.8k GitHub stars. Our directory data was last refreshed on 2026-10-30. Check recent commit activity, release tags, and open issues at github.com/ageerle/ruoyi-ai before pinning it in production.
What are alternatives to Ruoyi AI?
Notable alternatives to Ruoyi AI include LangChain (Open-source library for chaining AI model calls and adding memory or tools), AutoGen (Microsoft's framework for multi-agent AI collaboration with built-in conversation features), Flowise (Visual workflow builder for AI agents with drag-and-drop node connections), Dify (Open-source platform for building AI applications with knowledge bases and visual workflows). You can also compare Ruoyi AI side by side with other MCP servers in the Related tools section on this page.
README badge and team notes
Maintain or use Ruoyi AI? Show its live grade in your GitHub README, docs site, or run a check from the terminal before you install.
GitHub README Markdown:
[](https://agentarmorhq.com/skills/ageerle-ruoyi-ai)HTML embed:
<a href="https://agentarmorhq.com/skills/ageerle-ruoyi-ai"><img src="https://agentarmorhq.com/api/badge/ageerle-ruoyi-ai" alt="AgentArmorHQ Risk Grade for Ruoyi AI" height="20" /></a>Terminal check:
curl -fsSL https://agentarmorhq.com/api/cli | node --input-type=module - check "ageerle-ruoyi-ai"Share with your security lead
Need sign-off to use ruoyi-ai? Copy this note for Slack, a ticket or a pull request.
### AgentArmorHQ Security Review Memo: ruoyi-ai
- **Listing URL:** https://agentarmorhq.com/skills/ageerle-ruoyi-ai
- **Type:** App connector
- **Risk Grade:** Grade B (Some risk signals)
- **Outside Security Advisories on File:** 0
- **Public Adoption:** 5.8k GitHub stars
#### Permission & Blast-Radius Signals
- **Local files:** No local folder access noted
- **Command execution:** No shell command runner noted
- **Network reach:** Local or standard API calls
- **Credentials:** Requires API key or token
- **Package source:** GitHub (ageerle/ruoyi-ai)
#### Recommended Hardened Setup
- **Source Repo:** https://github.com/ageerle/ruoyi-ai
- **Controls:** Pin an exact version in `.mcp.json`, scope access to a single workspace folder, and use a least-privilege read-only token where supported.
_Based on public adoption and transparency signals as of Oct 21, 2026. Our opinion, not a code audit or security guarantee._Key facts
- Type
- App connector
- Publisher
- ageerle
- Source code
- github.com/ageerle/ruoyi-ai
- Est. context load
- ~700 tokens / turn (~4 tool schemas, 0.35% of 200k context)
- Popularity
- 5.8k GitHub stars
- Last updated
- 2026-10-30
- Last graded
- 2026-10-21
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