Mengbi
Quality score 83Bdoc.ruoyiai.chat#coding#productivity

RuoYi AI

RuoYi AI is an open-source AI assistant platform for developers to quickly build their own AI assistant. It supports configuring MCP tools and agents, integrating knowledge bases for augmented chat, and building Supervisor multi-agent workflows.

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About this tool

Overview

RuoYi AI is an open-source platform for developers, backend engineers, and AI application builders, aimed at helping users quickly build their own AI assistant platform.

Key Capabilities

  • MCP Tools and Agent Configuration: Supports three tool types — BUILTIN, LOCAL, and REMOTE. Java tools can be added in the backend via the @Tool annotation, or local and remote tools can be integrated through the MCP protocol.
  • Supervisor Multi-Agent Workflows: New sub-agent roles can be added in the backend under org/ruoyi/agent/, orchestrated by the Supervisor.
  • Knowledge Base Augmented Chat: The client sends the current question along with an agentId, and the backend retrieves relevant material to supplement the model input.
  • Multi-Model Integration: A unified LLM framework supporting services such as PPIO.
  • Charts and Structured Output: Supports echarts with a strict JSON convention, extensible to pie charts, line charts, and more.

Technical

Notes The project is built on a Java backend (ruoyi-modules/ruoyi-chat) and a frontend (ruoyi-web), with internationalized documentation. When invoking tools, note that HTTP 200 does not mean the tool is usable — the response content must be checked.

Best for
  • Developers who want to quickly build their own AI assistant platform
  • Backend engineers who need to configure MCP tools and agents
  • AI application builders constructing Supervisor multi-agent workflows
  • Teams that need knowledge base augmented chat
Use cases
  • Configure MCP tools and bind them to agents
  • Build your own AI assistant platform
  • Integrate a knowledge base to augment chat answers
  • Build Supervisor multi-agent collaboration workflows
  • Add custom Java tools in the backend
  • Display query results through charts
Strengths
  • Open-source project with freely inspectable and modifiable code
  • Supports BUILTIN, LOCAL, and REMOTE tool types for flexible integration
  • Provides Supervisor multi-agent orchestration
  • Unified LLM framework for integrating different model services
  • Supports knowledge base retrieval-augmented chat
Limitations
  • Medium learning curve; requires understanding MCP tool types and agent binding mechanisms
  • New tools are not automatically granted to all agents; they must be bound via configuration such as mcpToolIds
  • Adding a sub-agent requires a new backend role interface and corresponding imports; merely annotating @Agent or creating a same-named record in the admin panel will not let the Supervisor discover it
  • The database description generated by getAllBuiltinTools() is currently empty, so model-side detailed descriptions must be placed on @Tool / @P

Features & use cases

Supports BUILTIN, LOCAL, and REMOTE MCP tool typesRegister Java tools via the @Tool annotationSupervisor multi-agent orchestrationKnowledge base retrieval-augmented chatUnified LLM framework with multi-model integrationecharts charts with a strict JSON output conventionInternationalized project documentation

At a glance

Maturity signals
  • Open source
Learning curve
  • Medium

Resources

技能管理管理 docx、pdf、xlsx 等技能资源，并区分普通聊天选择器与 Coding Harness 的真实执行链路。查看技能管理

Sources

Every profile keeps its sources and latest verification date.
01https://doc.ruoyiai.chat/guide/features/knowledge.html

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