NodeTool is an open-source visual workflow builder for creating AI applications without coding. It targets engineers and researchers, enabling drag-and-drop node connections for tasks like image generation and document analysis. Benefits include a no-code interface, local model execution for privacy, cloud API integration, and portable workflows runnable across diverse environments.

How It Works

The platform uses a visual, node-based architecture for defining data flow and processing logic. It supports multimodal inputs/outputs (text, image, audio, video) and allows local model execution on hardware like Apple Silicon or NVIDIA GPUs, or via cloud services. Workflows are portable, running consistently locally, on cloud platforms (RunPod, Cloud Run), or self-hosted infrastructure.

Quick Start & Requirements

• Installation: Downloadable installers for Windows/macOS; AppImage for Linux. Development setup requires Python 3.11, Conda, Node.js LTS.
• Prerequisites:
  • Hardware: Nvidia GPU recommended (Win/Linux), M1+ Apple Silicon (macOS). Specific VRAM/RAM for local AI (e.g., 8GB+ VRAM for images, 12GB+ for video).
  • Software: CUDA Driver >= 525.60.13 (Linux) or >= 527.41 (Windows) for HuggingFace pack.
• Links: Getting Started, Documentation, Node Packs, Custom Nodes, Deployment, API Reference.

Highlighted Details

• Multimodal AI: Supports text, image, audio, and video processing.
• Model Integration: HuggingFace (500k+ models), local models (Flux, SDXL, LLMs on Apple Silicon/NVIDIA/CPU), cloud APIs (OpenAI, Anthropic, Gemini, Replicate, kie.ai).
• Advanced Models: Direct access to Sora 2 Pro, Veo 3.1, FLUX.2, etc.
• Deployment: RunPod, Cloud Run, self-hosted.
• Personal AI Stack: Mobile access to self-hosted AI infrastructure via VPN.

Maintenance & Community

The project is active on GitHub and maintains a Discord community. Contact emails are provided for general inquiries and team members.

Licensing & Compatibility

NodeTool is licensed under the AGPL-3.0 license. This strong copyleft license requires derivative works to be shared under the same terms, potentially impacting commercial use or proprietary integration.

Limitations & Caveats

Running advanced local AI models, especially for video, requires substantial hardware (e.g., 12GB+ VRAM). The AGPL-3.0 license imposes significant obligations on modifications and distribution. The development setup involves multiple package managers and CUDA driver considerations, potentially increasing initial complexity.