--- title: "LLM.swift vs Rapid-MLX" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eastriverlee-llm-swift-vs-raullenchai-rapid-mlx" tools: ["eastriverlee-llm-swift", "raullenchai-rapid-mlx"] --- # LLM.swift vs Rapid-MLX *GraphCanon updated Sep 20, 2026* ## Verdict Pick LLM.swift if lLM.swift is a cross-platform C++ library for Apple systems that simplifies local interaction with large language models; pick Rapid-MLX if rapid-MLX is a high-performance local AI engine optimized for Apple Silicon, providing faster inference times and support for tool calling, prompt caching, and reasoning separation. It serves as a drop-in replacement for. [LLM.swift](https://github.com/eastriverlee/LLM.swift) reports 877 GitHub stars, 126 forks, and 10 open issues, last pushed Jul 19, 2026. [Rapid-MLX](https://pypi.org/project/rapid-mlx) has 3.8k stars, 416 forks, and 54 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [LLM.swift's repository](https://github.com/eastriverlee/LLM.swift) and [Rapid-MLX's repository](https://github.com/raullenchai/Rapid-MLX). | | [LLM.swift](/tools/eastriverlee-llm-swift.md) | [Rapid-MLX](/tools/raullenchai-rapid-mlx.md) | | --- | --- | --- | | Tagline | LLM.swift enables local interaction with large language models for multiple Apple platforms. | Fast local AI engine for Apple Silicon | | Stars | 877 | 3,774 | | Forks | 126 | 416 | | Open issues | 10 | 54 | | Language | Swift | Python | | Adopt for | LLM.swift is a cross-platform C++ library for Apple systems that simplifies local interaction with large language models. | Rapid-MLX is a high-performance local AI engine optimized for Apple Silicon, providing faster inference times and support for tool calling, prompt caching, and reasoning separation. It serves as a drop-in replacement for | | Persona | - | - | | Runtime | - | - | | License | MIT license allows for free use and distribution, both commercially and in open source projects, with attribution preferred but not mandatory. | Other | | Categories | Inference & Serving | Developer Tools, Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLM.swift](/tools/eastriverlee-llm-swift.md) | [Rapid-MLX](/tools/raullenchai-rapid-mlx.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 62d | 0d | | Open issues (now) | 10 | 54 | | Stars delta | +12 (30d) | +383 (30d) | | Open issues delta | +1 (30d) | +6 (30d) | | Full report | [trust report](/tools/eastriverlee-llm-swift/trust.md) | [trust report](/tools/raullenchai-rapid-mlx/trust.md) | ## Decision facts: LLM.swift - **Pricing:** freemium - Free to use under the MIT License. Premium support may vary. - **Adopt for:** LLM.swift is a cross-platform C++ library for Apple systems that simplifies local interaction with large language models. - **License detail:** MIT license allows for free use and distribution, both commercially and in open source projects, with attribution preferred but not mandatory. ## Decision facts: Rapid-MLX - **Adopt for:** Rapid-MLX is a high-performance local AI engine optimized for Apple Silicon, providing faster inference times and support for tool calling, prompt caching, and reasoning separation. It serves as a drop-in replacement for ## Choose when ### Choose LLM.swift if… - LLM.swift is primarily Swift; Rapid-MLX is Python. - License: LLM.swift is MIT, Rapid-MLX is Other. - Pricing: Free to use under the MIT License. Premium support may vary.. - Tags unique to LLM.swift: gguf, ios, llm-inference, macos. - Choose LLM.swift when you need to integrate large language model functionalities into apps targeting multiple Apple platforms, including macOS and mobile OSes. ### Choose Rapid-MLX if… - Rapid-MLX is primarily Python; LLM.swift is Swift. - License: Rapid-MLX is Other, LLM.swift is MIT. - Tags unique to Rapid-MLX: apple-silicon, claude-code, cursor, deepseek. - Also covers Developer Tools, LLM Frameworks. - When you need a fast local AI engine specifically optimized for Apple Silicon devices, offering up to 4.2x faster performance compared to Ollama. ## When NOT to use LLM.swift - Avoid LLM.swift if your application must run on non-Apple systems or if compatibility across various operating systems is prioritized over ease-of-use on Apple platforms. - Do not use this library if you require advanced server-side functionalities, as it focuses specifically on local interactions within Apple’s ecosystem. ## When NOT to use Rapid-MLX - If you are using Cursor, as it routes BYOK requests through its own servers, which cannot reach a Rapid-MLX endpoint on localhost, unless you intentionally expose the server through a public HTTPS ## Common questions ### What is the difference between LLM.swift and Rapid-MLX? LLM.swift: LLM.swift enables local interaction with large language models for multiple Apple platforms.. Rapid-MLX: Fast local AI engine for Apple Silicon. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLM.swift over Rapid-MLX? Choose LLM.swift over Rapid-MLX when LLM.swift is primarily Swift; Rapid-MLX is Python; License: LLM.swift is MIT, Rapid-MLX is Other; Pricing: Free to use under the MIT License. Premium support may vary.; Tags unique to LLM.swift: gguf, ios, llm-inference, macos; Choose LLM.swift when you need to integrate large language model functionalities into apps targeting multiple Apple platforms, including macOS and mobile OSes. ### When should I choose Rapid-MLX over LLM.swift? Choose Rapid-MLX over LLM.swift when Rapid-MLX is primarily Python; LLM.swift is Swift; License: Rapid-MLX is Other, LLM.swift is MIT; Tags unique to Rapid-MLX: apple-silicon, claude-code, cursor, deepseek; Also covers Developer Tools, LLM Frameworks; When you need a fast local AI engine specifically optimized for Apple Silicon devices, offering up to 4.2x faster performance compared to Ollama. ### When should I avoid LLM.swift? Avoid LLM.swift if your application must run on non-Apple systems or if compatibility across various operating systems is prioritized over ease-of-use on Apple platforms. Do not use this library if you require advanced server-side functionalities, as it focuses specifically on local interactions within Apple’s ecosystem. ### When should I avoid Rapid-MLX? If you are using Cursor, as it routes BYOK requests through its own servers, which cannot reach a Rapid-MLX endpoint on localhost, unless you intentionally expose the server through a public HTTPS ### Is LLM.swift or Rapid-MLX more popular on GitHub? Rapid-MLX has more GitHub stars (3,774 vs 877). Stars measure visibility, not whether either tool fits your constraints. ### Are LLM.swift and Rapid-MLX open source? Yes - both are open-source projects on GitHub (LLM.swift: MIT, Rapid-MLX: Other). ### Where can I find alternatives to LLM.swift or Rapid-MLX? GraphCanon lists graph-backed alternatives at [LLM.swift alternatives](/tools/eastriverlee-llm-swift/alternatives) and [Rapid-MLX alternatives](/tools/raullenchai-rapid-mlx/alternatives) ([LLM.swift markdown twin](/tools/eastriverlee-llm-swift/alternatives.md), [Rapid-MLX markdown twin](/tools/raullenchai-rapid-mlx/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/eastriverlee-llm-swift-vs-raullenchai-rapid-mlx.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLM.swift or Rapid-MLX? LLM.swift: Steady. Rapid-MLX: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for LLM.swift and Rapid-MLX? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM.swift trust report](/tools/eastriverlee-llm-swift/trust); [Rapid-MLX trust report](/tools/raullenchai-rapid-mlx/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eastriverlee-llm-swift`](/api/graphcanon/graph?tool=eastriverlee-llm-swift) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_