leapfrogai  by defenseunicorns

Self-hosted AI platform for air-gapped environments

created 2 years ago
274 stars

Top 95.2% on sourcepulse

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Project Summary

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

  • OpenAI-compatible API for seamless integration with existing tools.
  • Supports multiple backends including llama-cpp-python, vLLM, and Whisper.
  • Includes an evaluation framework integrated with DeepEval.
  • Offers "flavors" for deployment using upstream or hardened IronBank images.

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.

Health Check
Last commit

8 months ago

Responsiveness

1+ week

Pull Requests (30d)
0
Issues (30d)
0
Star History
5 stars in the last 90 days

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