A community-driven registry for Claude, Cursor, Windsurf, Cline & more. Not affiliated with Anthropic.
Are you the author? Sign in to claim
DevoxxGenie is a plugin for IntelliJ IDEA that uses local LLM's (Ollama, LMStudio, GPT4All, Jan and Llama.cpp) and Cloud
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
🆕 Skills — Drop portable SKILL.md files into ~/.devoxxgenie/skills/, <project>/.devoxxgenie/skills/, .claude/skills/ or .agents/skills/ and the LLM activates them mid-conversation when relevant. The same files work in Claude Code, Codex and Gemini — and Custom Prompts have been renamed to Commands to make the distinction clear.
🆕 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.
60K+ Downloads
📖 Visit our comprehensive documentation at genie.devoxx.com
Quick links:
📖 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 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:
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.
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.
ExternalPromptService — no compile-time dependency required. Two POC integrations available: SonarLint DevoxxGenie and SpotBugs DevoxxGenie.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
📖 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.
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!
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:
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.
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.
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
You can now add the full project to your prompt IF your selected cloud LLM has a big enough window context.
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.
"But wait," you might say, "my project is HUGE!" 😅
Fear not! We've got options:
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...
The new "Copy Project" panel lets you:
Exclude specific directories Filter by file extensions Remove JavaDocs to slim down your context
Right-click to add only the most relevant parts of your project to the context.
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!
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.
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.
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.
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
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.
Settings -> Plugins -> Marketplace -> Enter 'Devoxx' to find plugin OR Install plugin from Disk./gradlew buildPlugin, and install the plugin from the build/distributions directory and select file 'DevoxxGenie-X.Y.Z.zip'Gradle IntelliJ Plugin prepares a ZIP archive when running the buildPlugin task.
You'll find it in the build/distributions/ directory
./gradlew buildPlugin
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.
Run the automated plugin verification against multiple IDE versions:
./gradlew verifyPlugin
This checks API compatibility without launching the full IDE.
It is recommended to use the publishPlugin task for releasing the plugin
./gradlew publishPlugin
Enjoy!
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:
anthropic, ollama)claude-3-5-sonnet)What is never sent:
A first-launch notification asks for your consent before any data is sent. You can change this at any time in Settings → DevoxxGenie → General.
The DevoxxGenie IDEA Plugin processes user prompts through the following steps:
UserPromptPanel → Captures the prompt from the UI.PromptSubmissionListener.onPromptSubmitted() → Listens for the submission event.PromptExecutionController.handlePromptSubmission() → Starts execution.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.ChatModelFactory.getModels() → Gets the models for the select LLM provider
Cloud-based LLMs:
Local models:
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.Indexing Source Code for Retrieval
ProjectIndexerService.indexFiles() → Indexes project filesChromaDBIndexService.storeEmbeddings() → Stores embeddings in ChromaDB.Retrieval & Augmentation
SemanticSearchService.search() → Fetches relevant indexed code.SemanticSearchReferencesPanel → Displays retrieved results.ChatResponsePanel with:
ResponseHeaderPanel → Shows metadata (LLM name, execution time).ResponseDocumentPanel → Formats text & code snippets.MetricExecutionInfoPanel → Displays token usage and cost.Below is a detailed flow diagram illustrating this workflow:
PromptExecutionController.java to see how prompts are routed.ChatResponsePanel.java if you want to enhance response rendering.chatmodel/cloud/ or chatmodel/local/.Want to contribute? Submit a PR! 🚀
干净、强大、属于你的 AI Agent 平台 --AI agents, without the clutter.
Native macOS app to monitor Claude AI usage limits and watch your coding sessions live
An AI-powered custom node for ComfyUI designed to enhance workflow automation and provide intelligent assistance
A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)