RAG Frameworks · ageerle

ruoyi-ai

RuoYi AI is a Java-based enterprise AI application development framework supporting multi-vendor LLM integration, RAG knowledge management, visual workflow orchestration, and multi-agent coordination. It provides Docker deployment and a full-stack platform with admin and user interfaces.

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Source: GitHub — github.com/ageerle/ruoyi-ai

5.5k

GitHub stars

1.4k

Forks

Java

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	ageerle/ruoyi-ai
Owner	ageerle
Primary language	Java
License	MIT — OSI-approved
Stars	5.5k
Forks	1.4k
Open issues	21
Latest release	v3.0.0 (2026-04-13)
Last updated	2026-06-10
Source	https://github.com/ageerle/ruoyi-ai

What ruoyi-ai is

Built on Spring Boot 3.5.8 + Langchain4j, it integrates multiple vector databases (Milvus/Weaviate/Qdrant), supports MCP protocol and Agent Skills, offers SSE streaming and WebSocket real-time communication, and includes document parsing (PDF/Word/Excel) with Redis caching and Sa-Token/JWT authentication.

Quickstart

Get the ruoyi-ai source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/ageerle/ruoyi-ai.git2cd ruoyi-ai3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-Vendor LLM Management

Consolidate access to OpenAI, DeepSeek, Alibaba, Zhipu, and MiniMax models through a single framework, simplifying vendor integration and model switching for enterprise teams.

Visual Workflow Automation

Design, drag-and-drop orchestrate, and execute multi-step AI processes (model calls, email, human review) with SSE streaming execution for approval workflows and content generation pipelines.

Enterprise RAG & Knowledge Systems

Build document-grounded AI assistants with local RAG, vector database backing, and multi-format parsing to support internal knowledge bases and retrieval-augmented generation at scale.

Implementation considerations

Requires MySQL 8.0, Redis, and at least one vector database (Milvus/Weaviate/Qdrant); plan infrastructure and networking before deployment.
LLM API keys (OpenAI, DeepSeek, etc.) must be provisioned and managed securely; no built-in secret rotation described.
Docker Compose deployment provided but production use requires customization for resource limits, backups, monitoring, and high-availability setup.
Langchain4j integration is core; review compatibility with specific LLM API versions and Agent protocol maturity before committing to large-scale automation.
Document parsing and RAG retrieval performance depends on vector DB tuning; benchmark with your document corpus and query volumes.

When to avoid it — and what to weigh

Production Security Audit Pending — No security audit, penetration testing results, or formal threat model disclosed. Verify Sa-Token/JWT implementation and vector DB exposure before production deployment in regulated industries.
Non-Java/Spring Boot Stack — Tightly coupled to Java and Spring Boot; unsuitable if your team uses Go, Node.js, or Python-first infrastructure or requires language-agnostic deployment.
Minimal SLA/Support Expectation — Community-driven open-source project; no commercial support, SLA, or guaranteed response times. Not recommended for mission-critical systems requiring vendor accountability.
High Data Sovereignty Requirements — Designed around cloud LLM APIs and external vector DBs (Milvus, Weaviate); on-premise isolated deployments with zero external calls require significant custom work.

License & commercial use

MIT License: permissive open-source license allowing commercial use, modification, and distribution with attribution and no warranty. Full terms in LICENSE file.

MIT license permits commercial use without payment to the original author. However, as a community project with no formal support, you assume all operational, security, and maintenance risk. Consult your legal team regarding liability and IP compliance in regulated industries (finance, healthcare). Consider internal SLA documentation if mission-critical.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

Review Sa-Token + JWT implementation for token expiration and secret rotation. Vector database (Milvus/Weaviate/Qdrant) exposure and authentication require network segmentation. LLM API key management not detailed; ensure use of environment variables or secrets manager. No mention of input validation against prompt injection or rate limiting. Assess multi-agent execution in untrusted contexts.

Alternatives to consider

LangChain (Python) + FastAPI

Language-agnostic, larger ecosystem, better documentation, but requires custom UI/orchestration work vs. RuoYi's full-stack offering.

Dify

Comparable visual workflow and multi-model support, cloud-hosted option available, but RuoYi integrates Dify as a backend option rather than replacing it.

CrewAI / AutoGen

Lightweight, pure Python multi-agent frameworks, easier for teams already in Python; lack enterprise UI and RAG integration.

Software development agency

Build on ruoyi-ai with DEV.co software developers

RuoYi AI offers a full-stack, open-source foundation for LLM integration and workflow automation. Assess infrastructure requirements and security needs, then deploy via Docker or engage a Devco consultant for production customization.

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ruoyi-ai FAQ

Can I deploy RuoYi AI on-premise without cloud LLM APIs?

Partially. You can self-host the backend and vector DB (Milvus on-premise), but LLM providers (OpenAI, DeepSeek, etc.) are cloud-only. A local LLM (e.g., Ollama) can be integrated via Langchain4j, but this requires custom configuration.

What is the performance/scalability limit?

Unknown. No benchmarks provided for concurrent users, query latency, or document corpus size. Scalability depends on MySQL partitioning, Redis cluster setup, vector DB node count, and LLM API rate limits. Load testing required before production.

Is there a commercial support option or managed hosting?

Not disclosed. This is a community project. Consider engaging consulting services or forking for enterprise customization. The sponsor (Atlas Cloud, Volcengine) offer API infrastructure but not RuoYi-specific support.

How do I upgrade from one version to the next?

Not documented in provided data. Typical Java Spring Boot upgrades apply (dependency updates, schema migrations), but specific RuoYi version upgrade paths are unknown. Requires review of release notes and CHANGELOG.

Software developers & web developers for hire

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If ruoyi-ai is part of your rag frameworks roadmap, our team can implement, customize, migrate, and maintain it.

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