Minuet is a Neovim plugin that provides AI-powered code completion from multiple LLM providers, including OpenAI, Gemini, Claude, and local models.
Minuet is a Neovim plugin that delivers AI-powered code completions as you type, leveraging popular LLMs like OpenAI, Gemini, Claude, and local models via Ollama or Llama.cpp. It solves the need for intelligent, context-aware code suggestions directly within the editor, without relying on proprietary binaries or services.
Neovim users who want integrated AI code completion, especially developers using multiple LLM providers or preferring self-hosted/local models for privacy or cost reasons.
Developers choose Minuet for its extensive provider support, seamless integration with Neovim ecosystems (like nvim-cmp and blink-cmp), and efficient resource usage—offering a flexible, open-source alternative to commercial AI coding assistants.
💃 Dance with Intelligence in Your Code. Minuet offers code completion as-you-type from popular LLMs including OpenAI, Gemini, Claude, Ollama, Llama.cpp, Codestral, and more.
Supports cloud services like OpenAI, Gemini, and Claude, as well as local models via Ollama and Llama.cpp, offering flexibility in cost, privacy, and performance, as detailed in the provider examples.
Integrates seamlessly with nvim-cmp, blink-cmp, virtual text, and Neovim's built-in LSP, allowing users to choose their preferred workflow, with setup guides for each in the README.
Implements streaming, throttling, and debouncing to sync completions with typed text, reducing unnecessary LLM requests and conserving resources, as highlighted in the features section.
Relies on curl and user-configured LLM providers without proprietary binaries, ensuring transparency and control over data, a key point in the philosophy and requirements.
Setting up local LLMs like Llama.cpp requires manual server launch, custom template adjustments, and bash scripting, which can be error-prone and time-consuming, as shown in the provider examples.
The built-in completion has known indentation problems, especially for Python, and the author acknowledges incomplete workarounds that may introduce edge cases, as warned in the LSP section.
With numerous configuration options for providers, prompts, and frontends, initial setup demands significant effort and understanding of LLM parameters, evident from the detailed configuration table and provider-specific notes.
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