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withcatai/node-llama-cpp
Run AI models locally on your machine with node.js bindings for llama.cpp. Enforce a JSON schema on the model output on the generation level
Node.js bindings for llama.cpp that let you run GGUF models locally with GPU acceleration via Metal, CUDA, or Vulkan. The standout feature is grammar-based JSON schema enforcement enforced at token-sampling time — not post-processing — so malformed output is structurally impossible. Aimed at TypeScript developers building local-first AI features without a Python stack.
Pre-built binaries ship for 13 platform/backend combinations (mac-arm64-metal, linux-x64-cuda, win-x64-vulkan, etc.) with no node-gyp or Python required — this is a real win over most native Node addons. JSON schema enforcement happens at the grammar/sampler level inside llama.cpp, not as a retry loop, so you get valid output on the first generation. GPU auto-detection picks the best available compute layer at runtime without any configuration. The library tracks upstream llama.cpp releases and lets you pull and compile the latest with a single CLI command.
Native bindings mean the object lifecycle is your problem: models, contexts, and sequences must be explicitly disposed or you leak native memory — there's a whole docs page on this, which is a warning sign for teams not used to managing native resources from JS. The llama.cpp upstream moves extremely fast and sometimes breaks the ABI, so pin your version carefully or expect surprise build failures after updates. This is Node.js-only — if any part of your stack is Python, you're in the wrong place. Build-from-source fallback on unsupported platforms still requires CMake and a working C++ toolchain, which is a worse setup experience than the README implies.