gabrielmahia/offline-mcp

Local AI inference infrastructure for East Africa — Ollama, open weights, degraded-mode fallbacks. 6 tools. Thesis L4. # offline-mcp --- **Compatible with `claude-sonnet-5`** (released 2026-06-30) — Anthropic's most agentic Sonnet yet. Runs multi-step tool chains end-to-end without stopping short. Install: `pip install offline-mcp` · Use with any MCP client. --- > Local AI inference infrastructure — Ollama wrapper, open weights directory, degraded-mode guide for East Africa. **Why:** Never assume OpenAI survives, Anthropic stays accessible, or export controls disappear. This matters more in Africa than anywhere else. **1st world equivalent:** Ollama, LLaMA, Mistral local deployment ## Why This Exists: Data Sovereignty > *"If you take the deal, you're going to be exploited. If you don't take it, you're going to die."* > — Frank Ssekamwa, Ugandan digital rights expert Across the Global South, AI and health data from communities is being extracted, processed abroad, and used to build models whose value flows away from the communities that generated it. `offline-mcp` is the **sovereignty floor** of the East Africa coordination stack. When this runs on a Raspberry Pi 4 with a 50W solar panel and a 256GB SD card: - Health data stays in the clinic. Health guidance comes from local models. - Land records stay in the land office. Queries don't touch foreign servers. - Civic data stays in the county. AI assistance runs without internet. **No API key. No cloud dependency. No data leaving the community.** The models available via `offline-mcp` (Llama 3.2, Qwen 2.5) run entirely on device. Community data used to generate AI outputs creates no dataset sent back to model providers. This is not a privacy feature. It is the architectural foundation of digital independence. --- ## Research Foundation This server implements patterns validated by peer-reviewed research on offline-first AI for bandwidth-constrained environments: **arXiv:2603.03339** (2026) — *Offline-First LLM Architecture for Adaptive Learning in Low-Connectivity Environments* — confirms that meaningful AI su …