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AI工具

DevoxxGenie IDEA插件

基于 Java · 开源免费，本地部署，数据完全自主可控

英文名：DevoxxGenieIDEAPlugin

⭐ 653 Stars 🍴 89 Forks 💻 Java 📄 MIT 🏷 AI 8.0分

8.0AI 综合评分

AIIntelliJ IDEA代码辅助

🌐 访问官网

✦ AI Skill Hub 推荐

经 AI Skill Hub 精选评估，DevoxxGenie IDEA插件获评「强烈推荐」。这款AI工具在功能完整性、社区活跃度和易用性方面表现出色，AI 评分 8.0 分，适合有一定技术背景的用户使用。

📚 深度解析

DevoxxGenie IDEA插件是一款基于 Java 的开源工具，在 GitHub 上收获 1k+ Star，是AI、IntelliJ IDEA、代码辅助领域中的优质开源项目。开源工具的最大优势在于代码完全透明，你可以审计每一行代码的安全性，也可以根据自身需求进行二次开发和定制。

**为什么要使用开源工具而非商业 SaaS？**
对于个人开发者和有隐私需求的用户，本地部署的开源工具意味着数据不离本机，不受第三方服务商的数据政策约束。同时，开源工具通常没有使用次数限制和月度费用，一次安装即可长期使用，对于高频使用场景的总拥有成本（TCO）远低于订阅制商业工具。

**安装与环境准备**
DevoxxGenie IDEA插件依赖 Java 运行环境。建议通过 pyenv（Python）或 nvm（Node.js）管理 Java 版本，避免全局环境污染。对于新手用户，推荐先创建虚拟环境（python -m venv venv && source venv/bin/activate），再安装依赖，这样即使出现问题也可以随时删除虚拟环境重新开始，不影响系统稳定性。

**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues（已关闭的问题），大多数常见问题都已有解答。遇到 Bug 时，提供 pip list 的输出、完整错误堆栈和最小可复现示例，能显著提高开发者响应速度。AI Skill Hub 将持续追踪 DevoxxGenie IDEA插件的版本更新，及时通知重要功能变化。

📋 工具概览

DevoxxGenie IDEA插件是一款基于 Java 开发的开源工具，专注于 AI、IntelliJ IDEA、代码辅助等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。

GitHub Stars

⭐ 653

开发语言

Java

支持平台

Windows / macOS / Linux / Android

维护状态

正常维护，社区驱动

开源协议

MIT

AI 综合评分

8.0 分

工具类型

AI工具

Forks

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理，如需查看完整原始文档请访问底部「原始来源」。

📌 核心特色

开源免费，支持本地部署，数据完全自主可控
活跃的 GitHub 开源社区，持续迭代更新
提供详细文档和使用示例，新手友好
支持自定义配置，灵活适配不同使用环境
可作为基础组件集成进现有技术栈或进行二次开发

🎯 主要使用场景

本地部署运行，保护数据隐私，满足合规要求
自定义集成到现有系统，扩展技术栈能力
作为开源基础组件进行商业化二次开发

以下安装命令基于项目开发语言和类型自动生成，实际以官方 README 为准。

安装命令

# 克隆仓库
git clone https://github.com/devoxx/DevoxxGenieIDEAPlugin
cd DevoxxGenieIDEAPlugin

# 查看安装说明
cat README.md

# 按 README 完成环境依赖安装后即可使用

📋 安装步骤说明

访问 GitHub 仓库页面
按照 README 文档完成依赖安装
根据系统环境完成初始化配置
参考官方示例或文档开始使用
遇到问题可在 GitHub Issues 中查找解答

以下用法示例由 AI Skill Hub 整理，涵盖最常见的使用场景。

常用命令 / 代码示例

# 查看帮助
devoxxgenieideaplugin --help

# 基本运行
devoxxgenieideaplugin [options] <input>

# 详细使用说明请查阅文档
# https://github.com/devoxx/DevoxxGenieIDEAPlugin

以下配置示例基于典型使用场景生成，具体参数请参照官方文档调整。

配置示例

# devoxxgenieideaplugin 配置说明
# 查看配置选项
devoxxgenieideaplugin --config-example > config.yml

# 常见配置项
# output_dir: ./output
# log_level: info
# workers: 4

# 环境变量（覆盖配置文件）
export DEVOXXGENIEIDEAPLUGIN_CONFIG="/path/to/config.yml"

📑 README 深度解析真实文档完整度 62/100 查看 GitHub 原文 →

以下内容由系统直接从 GitHub README 解析整理，保留代码块、表格与列表结构。

Key Features:

🔒 Security Scanning (v0.9.17+): Run Gitleaks (secret detection), OpenGrep (SAST) and Trivy (SCA/CVEs) as LLM agent tools. Each finding is auto-created as a prioritised Backlog.md task. Enable in Settings → Security Scanning.
📋 Spec Driven Development (v0.9.7+): Define tasks in Backlog.md, browse them in the Spec Browser (Task List + Kanban Board), and let the Agent implement them. 17 built-in backlog tools for full CRUD on tasks, documents, and milestones. Use the Agent Loop to run multiple tasks in batch with dependency ordering (v0.9.8+).
🆕 ACP Runners (v0.9.10+): Communicate with external agents (Kimi, Gemini CLI, Kilocode, Claude Code, Copilot) via the Agent Communication Protocol with structured streaming, conversation history, and capability negotiation.
🔌 Plugin Integration API (v0.9.12+): Let other IntelliJ plugins send prompts or create Backlog tasks via a reflection-based ExternalPromptService — no compile-time dependency required. Two POC integrations available: SonarLint DevoxxGenie and SpotBugs DevoxxGenie.
🖥️ CLI Runners (v0.9.9+): Execute prompts and spec tasks via external CLI tools (Claude Code, GitHub Copilot, Codex, Gemini CLI, Kimi) directly from the chat interface or the Spec Browser.
✨ Inline Code Completion: (v0.9.6+) AI-powered code suggestions as you type using Fill-in-the-Middle (FIM) models. Supports both Ollama and LM Studio with models like StarCoder2, Qwen2.5-Coder, and DeepSeek-Coder.
🤖 Agent Mode (v0.9.4+): Autonomous code exploration and modification with built-in tools (read, write, edit, search files). Parallel sub-agents investigate multiple areas of your codebase concurrently, each with configurable provider/model. Enable in Agent Settings!
🖥️ Local AI Cluster: Pool multiple Apple Silicon devices into a single LLM inference cluster with Exo. Run models like Llama 3.1 405B across your machines with automatic device discovery, zero cloud costs, and full data privacy.
🔥️ MCP Support with Marketplace: Browse and install MCP servers from the integrated marketplace. Add MCP servers and use them in your conversations!
🗂️ DEVOXXGENIE.md: By incorporating this into the system prompt, the LLM will gain a deeper understanding of your project and provide more relevant responses.
📸 DnD images: You can now DnD images with multimodal LLM's.
🧐 RAG Support: Retrieval-Augmented Generation (RAG) support for automatically incorporating project context into your prompts.
👀 Chat History: Your chats are stored locally, allowing you to easily restore them in the future.
🧠 Project Scanner: Add source code (full project or by package) to prompt context when using Anthropic, OpenAI or Gemini.
💰 Token Cost Calculator: Calculate the cost when using Cloud LLM providers.
🔍 Web Search: Search the web for a given query using Google or Tavily.
🏎️ Streaming responses: See each token as it's received from the LLM in real-time.
🧐 Abstract Syntax Tree (AST) context: Automatically include parent class and class/field references in the prompt for better code analysis.
💬 Chat Memory Size: Set the size of your chat memory, by default its set to a total of 10 messages (system + user & AI msgs).
☕️ 100% Java: An IDEA plugin using local and cloud based LLM models. Fully developed in Java using Langchain4J
👀 Code Highlighting: Supports highlighting of code blocks.
💬 Chat conversations: Supports chat conversations with configurable memory size.
📁 Add files & code snippets to context: You can add open files to the chat window context for producing better answers or code snippets if you want to have a super focused window

🔥 RAG Feature

📖 Full RAG Documentation

Devoxx Genie now includes starting from v0.4.0 a Retrieval-Augmented Generation (RAG) feature, which enables advanced code search and retrieval capabilities. This feature uses a combination of natural language processing (NLP) and machine learning algorithms to analyze code snippets and identify relevant results based on their semantic meaning.

With RAG, you can:

Search for code snippets using natural language queries
Retrieve relevant code examples that match your query's intent
Explore related concepts and ideas in the codebase

We currently use Ollama and Nomic Text embedding to generates vector representations of your project files. These embedding vectors are then stored in a Chroma DB (v0.6.2) running locally within Docker. The vectors are used to compute similarity scores between search queries and your code all running locally.

The RAG feature is a significant enhancement to Devoxx Genie's code search capabilities, enabling developers to quickly find relevant code examples and accelerate their coding workflow.

Requirements:

IntelliJ minimum version is 2023.3.4
Java minimum version is JDK 17

Installation:

📖 Full Installation Guide

From IntelliJ IDEA: Go to Settings -> Plugins -> Marketplace -> Enter 'Devoxx' to find plugin OR Install plugin from Disk
From Source Code: Clone the repository, build the plugin using ./gradlew buildPlugin, and install the plugin from the build/distributions directory and select file 'DevoxxGenie-X.Y.Z.zip'

Build

Gradle IntelliJ Plugin prepares a ZIP archive when running the buildPlugin task. You'll find it in the build/distributions/ directory

./gradlew buildPlugin

🗂️ Video Tutorials:

The Power of Full Context: A Real-World Example

The DevoxxGenie project itself, at about 70K tokens, fits comfortably within most high-end LLM context windows. This allows for incredibly nuanced interactions – we're talking advanced queries and feature requests that leave tools like GitHub Copilot scratching their virtual heads!

Usage:

1) Select an LLM provider from the DevoxxGenie panel (right corner) 2) Select some code 4) Enter shortcode command review, explain, generate unit tests of the selected code or enter a custom prompt.

Enjoy!

Privacy & Anonymous Usage Analytics

To guide which LLM providers and models receive engineering investment, DevoxxGenie collects anonymous usage data when you run a prompt or change models.

What is sent: - An anonymous install ID (UUID), generated once and stored locally - A per-launch session ID (random 10-digit number) - Plugin version and IDE version - LLM provider name (e.g. anthropic, ollama) - LLM model name (e.g. claude-3-5-sonnet)

What is never sent: - Prompt text, response text, conversation history - File content, file paths, project name, git remote - API keys, credentials, user name, email - Token counts or cost data

A first-launch notification asks for your consent before any data is sent. You can change this at any time in Settings → DevoxxGenie → General.