Roo Code
Open-source multi-agent AI coding extension for VS Code. Customizable modes for coding, architecture, debugging, and review — with a smaller system prompt than Cline.
Coding Agent
Yes
Yes
Yes
Yes — for local inference
Structured plan-then-execute coding workflows
Medium
VS Code
Open source — free
Roo Code is Open-source multi-agent AI coding extension for VS Code. Customizable modes for coding, architecture, debugging, and review — with a smaller system prompt than Cline. Roo Code is a fork of Cline with 22K+ GitHub stars that adds multi-agent modes (Code, Architect, Ask, Debug) with customizable system prompts per mode.
Roo Code works with both local models and cloud APIs. It supports OpenRouter for unified access to 300+ models from a single API. Ollama integration lets you run models locally on your own GPU. Roo Code is open source (https://github.com/RooVetGit/Roo-Code), so you can inspect the code and self-host. 24 GB VRAM recommended for local coding models. Roo Code's ~8-9K system prompt is smaller than Cline's, making it slightly more efficient with limited VRAM, but 12 GB is still insufficient for serious agentic work.
Can it run on my hardware?
Minimum
24 GB VRAM recommended for local coding models. Roo Code's ~8-9K system prompt is smaller than Cline's, making it slightly more efficient with limited VRAM, but 12 GB is still insufficient for serious agentic work.
Recommended
RTX 3090 or 4090 (24 GB) for Qwen3-Coder 30B at Q4 with 40K+ context. Roo Code's smaller system prompt means more context headroom for your actual code.
Approximate VRAM needed for recommended local models at Q4 with 8K context:
| Model | Params | Q4 VRAM | Min GPU |
|---|---|---|---|
| Qwen3 30B-A3B (MoE) | 30B | ~19.8 GB | 24 GB |
| Qwen 2.5 Coder 32B Instruct | 32.5B | ~22.9 GB | 24 GB |
| DeepSeek R1 Distill Qwen 32B | 32.5B | ~22.9 GB | 24 GB |
| Qwen3 32B | 32.8B | ~22.2 GB | 24 GB |
App compatibility
| Feature | Supported |
|---|---|
| Local models | Yes |
| OpenRouter | Yes |
| OpenAI-compatible API | Yes |
| Ollama | Yes |
| LM Studio | Yes |
| Anthropic API | Yes |
| Google API | Yes |
| Mistral API | No |
| Docker | No |
| Works offline | No |
| Needs GPU | Yes |
Recommended models
Best local models
Local vs cloud: which should you use?
Use local models if
- You want privacy — data never leaves your machine
- You already have a GPU with sufficient VRAM
- You want zero per-token API costs
- You need offline access
- You have at least 16-24 GB VRAM for recommended models
Use cloud/API if
- Your GPU has insufficient VRAM for the models you need
- You want access to frontier model quality
- You need maximum coding/reasoning performance
- You don't want to manage local model downloads and updates
- OpenRouter lets you switch between 300+ models with one API key
Setup overview
Setting up Roo Code is moderate in complexity. It runs on vscode. Full documentation is available at https://docs.roocode.com.
Limitations
- Beginners — custom mode system adds complexity
- Teams wanting Cursor-like simplicity
Related
Recommended GPUs
Compatible models
Frequently asked questions
- What is Roo Code?
- Roo Code is Open-source multi-agent AI coding extension for VS Code. Customizable modes for coding, architecture, debugging, and review — with a smaller system prompt than Cline. Roo Code is a fork of Cline with 22K+ GitHub stars that adds multi-agent modes (Code, Architect, Ask, Debug) with customizable system prompts per mode.
- Does Roo Code need a GPU?
- 24 GB VRAM recommended for local coding models. Roo Code's ~8-9K system prompt is smaller than Cline's, making it slightly more efficient with limited VRAM, but 12 GB is still insufficient for serious agentic work.
- Can Roo Code use OpenRouter?
- Yes. Roo Code supports OpenRouter for accessing 300+ models through a single API. Configure OpenRouter as a provider in Roo Code's settings with your API key.
- Can Roo Code use local models via Ollama?
- Yes. Roo Code works with Ollama for running models locally. Install Ollama, pull your model (e.g., `ollama pull qwen2.5:7b`), and connect Roo Code to the local Ollama server. GPU requirements depend on the model you choose, not Roo Code itself.
- What is the best local model for Roo Code?
- For Roo Code, the community-verified best local model is Qwen3 30B-A3B (MoE). RTX 3090 or 4090 (24 GB) for Qwen3-Coder 30B at Q4 with 40K+ context. Roo Code's smaller system prompt means more context headroom for your actual code.
- Can I run Roo Code on 12 GB VRAM?
- 12 GB VRAM is generally not sufficient for serious agentic coding with Roo Code. You can run smaller models (7B-14B at Q4) but tool-calling reliability and context handling will be limited. For the best experience, 24 GB VRAM (RTX 3090/4090) is the community-recommended minimum for local agentic coding.
- Is Roo Code free and open source?
- Yes. Roo Code is open source and completely free. You can find the source code on GitHub at https://github.com/RooVetGit/Roo-Code.