ageerle/ruoyi-ai
An enterprise AI development framework for building AI agents. It provides unified management of multi-provider LLMs, secure enterprise knowledge bases with high-precision retrieval, visual workflow orchestration and multi-agent coordination. Compatible with mainstream Agent Skill standards, it enables developers to efficiently build production-gra observed · 2026-08-28
Health v2 · maintenance only
91/100
- Activity 98
- Release rhythm 96
- Longevity 68
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: 19.5
- age_days: 960
- days_rel: 29
- days_push: 17
- n_releases_24m: 7
Adoption not part of the score
5666 stars · 1397 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
RuoYi AI is an open-source, enterprise-grade full-stack AI assistant platform built in Java on the RuoYi ecosystem and LangChain4j. It unifies multi-provider LLM management, RAG knowledge bases with vector stores (Milvus/Qdrant/Weaviate), MCP tool integration, visual workflow orchestration, and multi-agent coordination with Supervisor mode.
Use cases
- build an enterprise AI assistant platform
- self-host a chatgpt-like app with my own knowledge base
- manage multiple LLM providers behind one API
- build RAG search over company documents
- orchestrate multi-agent workflows visually
- integrate MCP tools into an AI chat app
- build a multi-tenant AI SaaS with billing
When to choose
- you want an out-of-the-box full-stack AI platform with admin panel and user frontend
- you need enterprise features like multi-tenancy, auth, rate limiting, and billing out of the box
- you want a complete RAG pipeline with document parsing, chunking, vector stores, and reranking
- you prefer a Java/Spring Boot stack with LangChain4j
- you need MCP tool integration and multi-agent orchestration
When to avoid
- you need a lightweight library to embed in an existing app rather than a full platform
- your stack is Python-centric (LangChain, FastAPI) and you want native Python tooling
- you only need a simple chatbot without admin, tenancy, or workflow features
- you want a minimal, unopinionated agent framework
Facets
framework · maturity active
agent-framework rag mcp chatbot llm-inference workflow-automation chat-interface web-framework vector-database auth artificial-intelligence large-language-models chatbots web-development developer-tools self-hosted jvm self-hosted cross-platform langchain4j multi-agent knowledge-base workflow-orchestration spring-boot ruoyi enterprise-ai mcp-tools supervisor-mode full-stack multi-tenant ai-agents retrieval-augmented-generation docker web-server
10 sources
- readme: https://github.com/ageerle/ruoyi-ai · fetched 2026-08-28 · 40b721ee2dea
- homepage: https://doc.ruoyiai.chat · fetched 2026-08-29 · f274f4c48991
- site_page: https://doc.ruoyiai.chat/guide/getting-started/projection.html · fetched 2026-08-29 · 6ec7326078e0
- site_page: https://doc.ruoyiai.chat/guide/changelog/202508_changeLog.html · fetched 2026-08-29 · 53f2f9135711
- site_page: https://doc.ruoyiai.chat/guide/features/model.html · fetched 2026-08-29 · 652ce90547e7
- site_page: https://doc.ruoyiai.chat/guide/features/knowledge.html · fetched 2026-08-29 · fd082f36cfb3
- site_page: https://doc.ruoyiai.chat/guide/features/tools.html · fetched 2026-08-29 · 532104bc39d5
- site_page: https://doc.ruoyiai.chat/guide/features/memory.html · fetched 2026-08-29 · 9de204cd2443
- site_page: https://doc.ruoyiai.chat/guide/features/skills.html · fetched 2026-08-29 · 9d217f5521ff
- site_page: https://doc.ruoyiai.chat/guide/features/agent.html · fetched 2026-08-29 · 35a3c020a79a
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ageerle/ruoyi-ai | main | 91 |
For agents
markdown · JSON · MCP: product_card(name="ageerle/ruoyi-ai")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem