Practical overview
When it fits
Use it when an AI application needs more than a model interface, particularly when provider management, document retrieval, visual workflows or multi-agent coordination are central requirements.
Who it is for
- Developers building self-hosted or server-based AI-agent applications
- Teams needing retrieval, tool integration, workflows and agent coordination in one application framework
Documented uses
- Route requests across multiple LLM providers
- Build knowledge bases and retrieval-augmented question-answering applications
- Design visual workflows for LLM-powered processes
- Coordinate multiple agents through Supervisor-mode orchestration
- Connect agents and applications to MCP-compatible tools
Requirements & limits
The repository is public and displays an MIT License. Docker deployment instructions are provided for Linux, macOS and Windows Docker environments. No account or pricing details are documented in the supplied materials.
- The framework is Java-based and uses Spring Boot with LangChain4j.
- RAG storage options documented include Milvus, Weaviate and Qdrant.
- Workflow capabilities include visual node orchestration, SSE streaming, model calls, email sending and manual review.
- The project documents MCP integration and an extensible tool ecosystem.
- The repository is not archived and provides separate frontend and administration repositories.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Start Ruoyi AI with Docker Compose
Launch the documented all-services deployment from the v3.1.0 release.
Before you start
- Docker Engine on Linux or macOS, or Docker Desktop on Windows
- Docker Compose V2
Steps
Clone the v3.1.0 release and change into the repository directory.
Copy the Docker environment example to an .env file.
Set RUIYI_VERSION to v3.1.0 in that environment file.
Pull the pre-built images and start the services with docker compose using docker-compose-all.yaml.
Check service status with docker compose ps.
Expected outcome
The documented backend, admin panel, user frontend and dependencies are started as an all-services deployment.
Guide limitations
- The documented commands start the services but do not describe configuring model providers, knowledge bases or application-specific workflows.
- The access addresses use a server address placeholder and the README lists default admin credentials for the documented deployment.
Compare options for Agent orchestration
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Ruoyi AI | Route requests across multiple LLM providers | Cloud, Self-hosted, Server | The repository is public and displays an MIT License. Docker deployment instructions are provided for Linux, macOS and Windows Docker environments. No account or pricing details are documented in the supplied materials. |
| Agenite | Define typed tools with JSON Schema validation and error handling. | CLI | |
| Veldra | Create an agent with a policy, tools, skills, knowledge base and reasoning method. | Self-hosted | The documented deployment is self-hosted and uses a sign-in gate after a first-run administrator account is created. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 1 October 2026
- Original website title
- ageerle/ruoyi-ai · Official homepage title · retained in its source language