Self-hosted AI platform for air-gapped environments
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LeapfrogAI is a self-hosted AI platform designed for deployment in air-gapped, cloud-native, and edge environments. It provides a comprehensive suite of tools including a vector database, model backends, an API compatible with OpenAI's, and a user interface, enabling organizations to leverage generative AI without relying on external services, thereby ensuring data independence and cost-effectiveness.
How It Works
LeapfrogAI is built on Defense Unicorns' secure runtime environment (UDS), facilitating deployment via Kubernetes. It offers a monorepo structure with distinct packages for its API, SDK, UI, and various model backends (e.g., llama-cpp-python, vLLM, Whisper). The platform supports Retrieval Augmented Generation (RAG) and integrates with DeepEval for its evaluation framework. Its architecture prioritizes compatibility with existing OpenAI tooling through a similar API structure.
Quick Start & Requirements
The preferred deployment method is a local Kubernetes deployment using UDS. Detailed system requirements and instructions are available on the LeapfrogAI documentation website.
Highlighted Details
Maintenance & Community
The project is supported by a community including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, and various US military branches. The project is currently pausing new feature development to explore other AI capabilities.
Licensing & Compatibility
The repository's licensing is not explicitly stated in the provided README. Compatibility for commercial use or closed-source linking would require clarification on the license.
Limitations & Caveats
The project is currently pausing new feature development. The vLLM backend has a noted limitation requiring a CUDA-enabled PyTorch build for ARM64, which is not readily available via standard package managers. Some component flavors are not yet available as quick-start bundles.
8 months ago
1+ week