Local N-Gram Code Suggester

Fast, private, comment-aware code completion that runs locally.

Highlights

• Inline and IntelliSense completion without an account or subscription.
• Comments are excluded from code context and training.
• Variable-order n-grams with exact backoff instead of model-wide fuzzy scans.
• Bounded multi-token suggestions with Fast, Balanced, and Quality presets.
• Focused automatic frequency control and shorter five-token suggestions.
• Conservative cross-project learning from saved, error-free files. A pattern needs support from multiple projects and never outweighs the bundled model.
• Receiver-aware API member patterns and useful numeric expressions.
• Optional local project adaptation and free language-pack support.
• Local diagnostics with no telemetry.

Supported profiles: C#, Java, JavaScript, TypeScript, JSON/JSONC, Python, JSX/TSX, Vue, and Razor.

Source, releases, and issue tracking: kevinski119/local-ngram-code-suggester.

Offline model demo

The following animation is a deterministic Pillow-rendered editor simulation. Its completion is queried from the bundled n-gram model; it is not an API response or a recording containing personal desktop data.

Run Local N-Gram: Show Model Diagnostics to inspect the active model and latency. Run Local N-Gram: Manage Language Packs to import a local pack or use a configured trusted catalog. No pack is downloaded without user action.

Privacy

Tokenization, completion, project context, and diagnostics stay on your machine. Network access is limited to user-initiated pack catalog/download operations or explicitly opted-in pack update checks.

Cross-project learning stays in VS Code global storage on this machine. It stores hashed project identities, caps each project's contribution, ignores files with reported errors, and requires multiple-project support before a pattern can be suggested. Learned library identifiers are namespaced by detected pip, npm, Java, or .NET imports and activate only when the same dependency is present. Run Local N-Gram: Clear Cross-Project Learning at any time to erase it.

Attribution

Derived from Erik Klabukov's amest/vscode-ngram-code-suggester under the MIT License. The original Marketplace listing is maintained separately.

License

MIT. See the included license.