Skip to content
#

Sovereign AI

Sovereign AI describes artificial intelligence systems whose models, training data, and compute are owned and governed by the organization, nation, or individual that operates them, rather than rented from a third-party cloud or foreign provider. It emphasizes data control, local or on-device inference, open model weights, and computational independence.

Projects in this space include self-hosted and local-first assistants, on-device inference runtimes, privacy-preserving and federated training, and tooling for regulatory compliance such as the EU AI Act. The shared goal is to reduce external dependencies and keep sensitive data and decision-making under the operator’s own control.

Here are 458 public repositories matching this topic...

Project Tapestry aims to give every nation and participant frontier AI they can call their own — uniting a global consortium to train a shared frontier model from which partners build and own sovereign models aligned to their national, socio-cultural, and industrial needs.

  • Updated Jul 2, 2026
  • Python

A sovereign cognitive architecture with IIT 4.0 integrated information, residual-stream affective steering (CAA), Global Workspace Theory, active inference, and 72 consciousness modules — running locally on Apple Silicon.

  • Updated Jul 5, 2026
  • Python

Web knowledge is fragmented — duplicated across fonts, embeddings, metadata, and renderings. Humans see pixels, AI sees tokens, neither shares the source. Knowledge3D: a sovereign GPU-native reference implementation for W3C PM-KR, where humans and AI consume the same procedural knowledge from one source.

  • Updated May 17, 2026
  • Python

AegisSovereignAI: The Cross-Ecosystem Trust Layer for the Distributed Enterprise. Verifiable Identity, Hardware-Rooted Integrity, and Sovereign AI Governance - from Silicon to Prompt. Unifying AI, Cloud-Native, and Decentralized architectures.

  • Updated May 2, 2026
  • Go

Build sovereign RAG systems with MAS‑RAG, Dual‑RAG, GraphRAG, Spatial‑RAG, multimodal pipelines, and vector search directly inside Oracle AI Database 26ai and Exadata.

  • Updated May 7, 2026
  • Jupyter Notebook

Open-source evidence layer for AI governance. Gates every AI agent tool call against your policy and writes a hash-chained execution record an auditor verifies offline, without trusting the operator. Root-agnostic, and binds to a TPM 2.0 or SEV-SNP hardware root when present. Your environment, no SaaS, no telemetry. AGPL-3.0.

  • Updated Jul 4, 2026
  • Python