DevoxxGenieIDEAPlugin

DevoxxGenie

Devoxx Genie is a fully Java-based LLM Code Assistant plugin for IntelliJ IDEA, designed to integrate with local LLM providers such as Ollama, LMStudio, GPT4All, Llama.cpp and Exo but also cloud based LLM's such as OpenAI, Anthropic, Mistral, Groq, Gemini, DeepInfra, DeepSeek, Kimi, GLM, OpenRouter, Azure OpenAI and Amazon Bedrock

🆕 Security Scanning — Run Gitleaks (secret detection), OpenGrep (SAST) and Trivy (dependency CVEs) directly from the LLM agent. Findings are automatically created as prioritised tasks in the Spec Browser for tracking and remediation!

🆕 Spec Driven Development (SDD) — Define tasks in Backlog.md, browse them in the Spec Browser with Task List and Kanban Board views, then let the Agent implement them autonomously! Use the Agent Loop to run multiple tasks in a single batch with dependency ordering and automatic advancement.

🆕 AI-powered Inline Code Completion — Get context-aware code suggestions as you type using Fill-in-the-Middle (FIM) models via Ollama or LM Studio!

🆕 ACP Runners — Communicate with external agents (Kimi, Gemini CLI, Kilocode, Claude Code, Copilot) via the Agent Communication Protocol (JSON-RPC 2.0 over stdin/stdout) with structured streaming, conversation history, and capability negotiation!

🆕 CLI Runners — CLI Runners let you execute prompts and spec tasks directly from DevoxxGenie's chat interface or Spec Browser using external CLI tools like Claude Code, GitHub Copilot, Codex, Gemini CLI, and Kimi.

🆕 Plugin Integration API — Other IntelliJ plugins can integrate with DevoxxGenie at runtime via a reflection-based ExternalPromptService — no compile-time dependency required. Two real-world POCs show it in action: a SonarLint fork and a SpotBugs fork that each send code-quality findings to DevoxxGenie with a single click, or defer them as Backlog tasks for the SDD workflow.

With Agent Mode, MCPs and frontier models like Claude Opus 4.6, Gemini Pro, DevoxxGenie isn't just another developer tool — it's a glimpse into the future of agentic programming. One thing's clear: we're in the midst of a paradigm shift in AI-Augmented Programming (AAP) 🐒

We also support RAG-based prompt context based on your vectorized project files, Git Diff viewer, and LLM-driven web search with Google and Tavily.

53K+ Downloads

📚 Documentation

📖 Visit our comprehensive documentation at genie.devoxx.com

Quick links:

• Getting Started - Start using DevoxxGenie in minutes
• Installation Guide - Local and cloud LLM setup
• Configuration - API keys, settings, and customization
• Features - Explore all capabilities
• Security Scanning - Gitleaks, OpenGrep and Trivy as LLM agent tools with auto-backlog task creation
• Agent Mode - Autonomous code tools with parallel sub-agents
• Spec Driven Development - Task management with Backlog.md, Kanban Board, and Agent implementation
• Agent Loop - Batch task execution with dependency ordering and progress tracking
• ACP Runners - Agent Communication Protocol integration with external agents
• CLI Runners - Execute prompts and spec tasks via external CLI tools
• Plugin Integration API - Integrate other IntelliJ plugins with DevoxxGenie at runtime
• Inline Code Completion - AI-powered code suggestions as you type
• Local AI Cluster (Exo) - Distributed LLM inference across Apple Silicon devices
• MCP Support - Model Context Protocol integration
• RAG Setup - Retrieval-Augmented Generation guide
• Troubleshooting - Common issues and solutions

🔒 Security Scanning

📖 Full Security Scanning Documentation

DevoxxGenie integrates three best-in-class open-source security scanners as LLM agent tools. When Agent Mode is active, the LLM can invoke them on demand, interpret the results in context, and automatically create prioritised backlog tasks for every finding.

Scanner	What it detects	Install
Gitleaks	Hardcoded secrets, API keys, tokens	brew install gitleaks
OpenGrep	SAST issues — injection flaws, insecure patterns	brew install opengrep
Trivy	Dependency CVEs (SCA)	brew install trivy

Ask the agent: "Run a full security scan and create backlog tasks for everything you find."

Enable in Settings → DevoxxGenie → Security Scanning. Each scanner has a path browser, a Test button, and install guidance. Findings are deduplicated — re-running a scan will not create duplicate tasks.

Spec Driven Development (SDD)

📖 Full SDD Documentation

Spec Driven Development brings structured task management directly into your IDE. Instead of ad-hoc prompts, define your tasks in Backlog.md files, browse them in the Spec Browser, and let the Agent implement them autonomously.

How it works:

1. Create tasks — Use natural language prompts or write Backlog.md task specs manually
2. Browse in Specs — View tasks in a Task List or visual Kanban Board with drag-and-drop
3. Implement with Agent — Click "Implement with Agent" and let the AI do the work

The Kanban Board gives you a visual overview of task status with drag-and-drop support:

17 built-in backlog tools provide full CRUD operations on tasks, documents, and milestones — all accessible to the LLM agent for autonomous project management.

Agent Loop — Batch Task Execution

Select multiple tasks (or click "Run All To Do") and the Agent Loop executes them sequentially in a single batch. Tasks are automatically sorted by dependencies using topological ordering, and each task gets a fresh conversation. The agent implements each task autonomously, and when it marks a task as Done the runner advances to the next one — with progress tracking and notifications throughout.

📖 Agent Loop Documentation

Agentic Programming with DevoxxGenie

Unlocking AI Coding Assistants: Real World Use Cases by Gunter Rotsaert

Building full-stack AI agents: From project generation to code execution by Stephan Janssen

🗂️ Video Tutorials:

• Building full-stack AI agents: From project generation to code execution (Devoxx France 2025)
• Agentic programming with DevoxxGenie (VoxxedDays Bucharest 2025)
• DevoxxGenie in action (Devoxx Belgium 2024)
• How ChatMemory works
• Hands-on with DevoxxGenie
• The Era of AAP: Ai Augmented Programming using only Java
• DevoxxGenie Demo 2024

Blog Posts:

• DevoxxGenie: Your AI Assistant for IDEA
• The Devoxx Genie IntelliJ Plugin Provides Access to Local or Cloud Based LLM Models
• 10K+ Downloads Milestone for DevoxxGenie!

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

Start in 5 Minutes with local LLM

• Download and start Ollama
• Open terminal and download a model using command "ollama run llama3.2"
• Start your IDEA and go to plugins > Marketplace and enter "Devoxx"
• Select "DevoxxGenie" and install plugin
• In the DevoxxGenie window select Ollama and available model
• Start prompting

Start in 2 Minutes using Cloud LLM

• Start your IDEA and go to plugins > Marketplace and enter "Devoxx"
• Select "DevoxxGenie" and install plugin
• Click on DevoxxGenie cog (settings) icon and click on Cloud Provider link icon to create API KEY
• Paste API Key in Settings panel
• In the DevoxxGenie window select your cloud provider and model
• Start prompting

🗂️ Model Context Protocol servers support

📖 Full MCP Documentation

Initial support for Model Context Protocol (MCP) server tools including debugging of MCP requests & responses! MCP support is a crucial feature towards ful Agentic support within DevoxxGenie. Watch short demo of MCP in action using DevoxxGenie

Example of the Filesystem-server MCP which allows you to interact with the given directory.

Go to the DevoxxGenie settings to enable and add your MCP servers. Browse the MCP Marketplace to discover and install servers with just a few clicks!

When configured correctly you can see the tools that the MCP brings to your LLM conversations

Agentic Magic in action 👀✨🧠

🗂️ DEVOXXGENIE.md

📖 DEVOXXGENIE.md Documentation

You can now generate a DEVOXXGENIE.md file directly from the "Prompts" plugin settings page or just use /init in the prompt input field.

By incorporating this into the system prompt, the LLM will gain a deeper understanding of your project and provide more relevant responses. This is a first step toward enabling agentic AI features for DevoxxGenie 🔥

Once generated, you can edit the DEVOXXGENIE.md file and add more details about your project as needed.

📸 "I can see" DnD images

You can now drag and drop images (and project files) directly into the input field when working with multimodal LLMs like Google Gemini, Anthropic Claude, ChatGPT 4.x, or even local models such as LLaVA

You can even combine screenshots together with some code and then ask related questions!

🔥 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.

See also Demo

Expecting to add also GraphRAG in the near future.

LLM Settings

In the IDEA settings you can modify the REST endpoints and the LLM parameters. Make sure to press enter and apply to save your changes.

We now also support Cloud based LLMs, you can paste the API keys on the Settings page.

Smart Model Selection and Cost Estimation

The language model dropdown is not just a list anymore, it's your compass for smart model selection.

See available context window sizes for each cloud model View associated costs upfront Make data-driven decisions on which model to use for your project

Add Project to prompt & clipboard

You can now add the full project to your prompt IF your selected cloud LLM has a big enough window context.

Calc Cost

Leverage the prompt cost calculator for precise budget management. Get real-time updates on how much of the context window you're using.

See the input/output costs and window context per Cloud LLM. Eventually we'll also allow you to edit these values.

Handling Massive Projects?

"But wait," you might say, "my project is HUGE!" 😅

Fear not! We've got options:

1. Leverage Gemini's Massive Context:

Gemini's colossal 1 million token window isn't just big, it's massive. We're talking about the capacity to digest approximately 30,000 lines of code in a single go. That's enough to digest most codebases whole, from the tiniest scripts to some decent projects.

But if that's not enough you have more options...

2. Smart Filtering:

The new "Copy Project" panel lets you:

Exclude specific directories Filter by file extensions Remove JavaDocs to slim down your context

3. Selective Inclusion

Right-click to add only the most relevant parts of your project to the context.

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!

Support for JLama & LLama3.java

DevoxxGenie now also supports the 100% Modern Java LLM inference engines: JLama.

JLama offers a REST API compatible with the widely-used OpenAI API. Use the Custom OpenAI URL to connect.

You can also integrate it seamlessly with Llama3.java but using the Spring Boot OpenAI API wrapper coupled with the JLama DevoxxGenie option.

Local AI Cluster with Exo

📖 Full Exo Documentation

Pool the compute power of multiple Apple Silicon devices into a single, unified LLM inference cluster using Exo. Run large models like Llama 3.1 405B that don't fit on a single machine — split across your MacBook, Mac Studio, and Mac Mini over Thunderbolt or network.

• Automatic Device Discovery: Exo detects nearby devices automatically and distributes model layers using pipeline or tensor parallelism — no manual configuration needed.
• OpenAI-Compatible API: DevoxxGenie connects to your Exo cluster just like any other local provider. Run powerful models entirely on your own hardware with zero cloud costs and full data privacy.
• Supported Models: Llama 3.x (8B–405B), DeepSeek, Qwen, Mistral, and more — see the full list.

Test Driven Generation (TDG) - Experimental

Write a unit test and let DevoxxGenie generated the implementation for that unit test. This approach was explained by Bouke Nijhuis in his Devoxx Belgium presentation

An demo on how to accomplish this can be seen in this 𝕏 post.

DeepSeek R1 & DevoxxGenie 🔥

As of today (February 2, 2025), alongside the DeepSeek API Key, you can access the full 671B model for FREE using either Nvidia or Chutes! Simply update the Custom OpenAI URL, Model and API Key on the Settings page as follows:

Chutes URL : https://chutes-deepseek-ai-deepseek-r1.chutes.ai/v1/

Nvidia URL : https://integrate.api.nvidia.com/v1

Grok & DevoxxGenie

Create an account on Grok and generated an API Key. Now open the DevoxxGenie settings and enter the OpenAI compliant URL for Grok, the model you want to use and your API Key.

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'

Requirements:

• IntelliJ minimum version is 2023.3.4
• Java minimum version is JDK 17

Build

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

./gradlew buildPlugin 

Testing on Different IntelliJ Versions

You can test the plugin on multiple IntelliJ IDEA versions to ensure compatibility:

# Test on IntelliJ 2024.3 (default baseline)
./gradlew runIde

# Test on IntelliJ 2025.1
./gradlew runIde -PideVersion=2025.1.1

# Test on IntelliJ 2025.2
./gradlew runIde -PideVersion=2025.2.2

# Test on IntelliJ 2025.3 (latest stable)
./gradlew runIde -PideVersion=2025.3.3

Note: First run on a new version will download the IDE (~500MB-1GB), which may take several minutes.

Automated Compatibility Verification

Run the automated plugin verification against multiple IDE versions:

./gradlew verifyPlugin

This checks API compatibility without launching the full IDE.

Publish plugin

It is recommended to use the publishPlugin task for releasing the plugin

./gradlew publishPlugin

Usage:

1. Select an LLM provider from the DevoxxGenie panel (right corner)
2. Select some code
3. 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.

Contribute

📖 Contributing Guide

Understanding the Prompt Flow

The DevoxxGenie IDEA Plugin processes user prompts through the following steps:

1️⃣ User Inputs a Prompt

• UserPromptPanel → Captures the prompt from the UI.
• PromptSubmissionListener.onPromptSubmitted() → Listens for the submission event.
• PromptExecutionController.handlePromptSubmission() → Starts execution.

2️⃣ Processing the Prompt

• PromptExecutionService.executeQuery() → Handles token usage calculations and checks RAG/GitDiff settings.
• ChatPromptExecutor.executePrompt() → Dispatches the prompt to the selected LLM provider.
• LLMProviderService.getAvailableModelProviders() → Retrieves the appropriate model from ChatModelFactory.

3️⃣ LLM Model Inference

• ChatModelFactory.getModels() → Gets the models for the select LLM provider

• Cloud-based LLMs:

  • OpenAIChatModelFactory.createChatModel()
  • AnthropicChatModelFactory.createChatModel()
  • ...

• Local models:

  • OllamaChatModelFactory.createChatModel()
  • GPT4AllChatModelFactory.createChatModel()
  • ...

4️⃣ Response Handling

• If streaming is enabled:

  • StreamingPromptExecutor.execute() → Begins token-by-token streaming.
  • ChatStreamingResponsePanel.createHTMLRenderer() → Updates UI in real time.

• If non-streaming:

  • PromptExecutionService.executeQuery() → Formats the full response.
  • ChatResponsePanel.displayResponse() → Renders the text and code blocks.

5️⃣ Enhancements (RAG)

RAG (Retrieval-Augmented Generation)

• Indexing Source Code for Retrieval

  • ProjectIndexerService.indexFiles() → Indexes project files
  • ChromaDBIndexService.storeEmbeddings() → Stores embeddings in ChromaDB.

• Retrieval & Augmentation

  • SemanticSearchService.search() → Fetches relevant indexed code.
  • SemanticSearchReferencesPanel → Displays retrieved results.

6️⃣ Final Display

• The response is rendered in ChatResponsePanel with:
  • ResponseHeaderPanel → Shows metadata (LLM name, execution time).
  • ResponseDocumentPanel → Formats text & code snippets.
  • MetricExecutionInfoPanel → Displays token usage and cost.

Understanding the Flow

Below is a detailed flow diagram illustrating this workflow:

---

How to Get Started

• Start by exploring PromptExecutionController.java to see how prompts are routed.
• Modify ChatResponsePanel.java if you want to enhance response rendering.
• To add a new LLM provider, create a factory under chatmodel/cloud/ or chatmodel/local/.

Want to contribute? Submit a PR! 🚀