--- title: "Rapid-MLX vs anubis-oss" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/raullenchai-rapid-mlx-vs-uncsoft-anubis-oss" tools: ["raullenchai-rapid-mlx", "uncsoft-anubis-oss"] --- # Rapid-MLX vs anubis-oss *GraphCanon updated Sep 20, 2026* ## Verdict 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; pick anubis-oss if anubis-oss, specifically tailored for Apple Silicon devices using Swift, is distinguished by its focus on local large language model evaluation and testing. [Rapid-MLX](https://pypi.org/project/rapid-mlx) reports 3.8k GitHub stars, 416 forks, and 54 open issues, last pushed Sep 18, 2026. [anubis-oss](https://devpadapp.com/leaderboard.html) has 207 stars, 15 forks, and 1 open issues, last pushed Sep 5, 2026. Figures are from public GitHub metadata via [Rapid-MLX's repository](https://github.com/raullenchai/Rapid-MLX) and [anubis-oss's repository](https://github.com/uncSoft/anubis-oss). | | [Rapid-MLX](/tools/raullenchai-rapid-mlx.md) | [anubis-oss](/tools/uncsoft-anubis-oss.md) | | --- | --- | --- | | Tagline | Fast local AI engine for Apple Silicon | Local LLM Testing & Benchmarking for Apple Silicon | | Stars | 3,774 | 207 | | Forks | 416 | 15 | | Open issues | 54 | 1 | | Language | Python | Swift | | 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 | Anubis-oss, specifically tailored for Apple Silicon devices using Swift, is distinguished by its focus on local large language model evaluation and testing within the macOS environment. | | Persona | - | - | | Runtime | - | - | | License | Other | GPL-3.0 license ensures that any derivative works related to anubis-oss must also be open source under the same licensing terms. | | Categories | Developer Tools, Inference & Serving, LLM Frameworks | Evaluation & Observability, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Rapid-MLX](/tools/raullenchai-rapid-mlx.md) | [anubis-oss](/tools/uncsoft-anubis-oss.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 15d | | Open issues (now) | 54 | 1 | | Stars delta | +383 (30d) | +9 (30d) | | Open issues delta | +6 (30d) | -3 (30d) | | Full report | [trust report](/tools/raullenchai-rapid-mlx/trust.md) | [trust report](/tools/uncsoft-anubis-oss/trust.md) | ## 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 ## Decision facts: anubis-oss - **Pricing:** freemium - The tool is free and open-source with no monetary costs for usage or distribution. - **Requirements:** Min 8 GB RAM - **Adopt for:** Anubis-oss, specifically tailored for Apple Silicon devices using Swift, is distinguished by its focus on local large language model evaluation and testing within the macOS environment. - **License detail:** GPL-3.0 license ensures that any derivative works related to anubis-oss must also be open source under the same licensing terms. ## Choose when ### Choose Rapid-MLX if… - Rapid-MLX is primarily Python; anubis-oss is Swift. - License: Rapid-MLX is Other, anubis-oss is GPL-3.0. - Tags unique to Rapid-MLX: claude-code, cursor, deepseek, fastapi. - 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. ### Choose anubis-oss if… - anubis-oss is primarily Swift; Rapid-MLX is Python. - License: anubis-oss is GPL-3.0, Rapid-MLX is Other. - Pricing: The tool is free and open-source with no monetary costs for usage or distribution.. - Requirements: Min 8 GB RAM. - Tags unique to anubis-oss: benchmarking, gpu, local-llm, macos. - Also covers Evaluation & Observability. - When developing and evaluating large language models intended to run natively on Apple Silicon hardware. ## 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 ## When NOT to use anubis-oss - If your development does not involve Apple Silicon or macOS environments as Anubis-oss is tightly integrated with these platforms. - When preferring a language other than Swift, since Anubis-oss depends on this for its operations. ## Common questions ### What is the difference between Rapid-MLX and anubis-oss? Rapid-MLX: Fast local AI engine for Apple Silicon. anubis-oss: Local LLM Testing & Benchmarking for Apple Silicon. See the comparison table for live GitHub stats and shared categories. ### When should I choose Rapid-MLX over anubis-oss? Choose Rapid-MLX over anubis-oss when Rapid-MLX is primarily Python; anubis-oss is Swift; License: Rapid-MLX is Other, anubis-oss is GPL-3.0; Tags unique to Rapid-MLX: claude-code, cursor, deepseek, fastapi; 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 choose anubis-oss over Rapid-MLX? Choose anubis-oss over Rapid-MLX when anubis-oss is primarily Swift; Rapid-MLX is Python; License: anubis-oss is GPL-3.0, Rapid-MLX is Other; Pricing: The tool is free and open-source with no monetary costs for usage or distribution.; Requirements: Min 8 GB RAM; Tags unique to anubis-oss: benchmarking, gpu, local-llm, macos; Also covers Evaluation & Observability; When developing and evaluating large language models intended to run natively on Apple Silicon hardware. ### 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 ### When should I avoid anubis-oss? If your development does not involve Apple Silicon or macOS environments as Anubis-oss is tightly integrated with these platforms. When preferring a language other than Swift, since Anubis-oss depends on this for its operations. ### Is Rapid-MLX or anubis-oss more popular on GitHub? Rapid-MLX has more GitHub stars (3,774 vs 207). Stars measure visibility, not whether either tool fits your constraints. ### Are Rapid-MLX and anubis-oss open source? Yes - both are open-source projects on GitHub (Rapid-MLX: Other, anubis-oss: GPL-3.0). ### Where can I find alternatives to Rapid-MLX or anubis-oss? GraphCanon lists graph-backed alternatives at [Rapid-MLX alternatives](/tools/raullenchai-rapid-mlx/alternatives) and [anubis-oss alternatives](/tools/uncsoft-anubis-oss/alternatives) ([Rapid-MLX markdown twin](/tools/raullenchai-rapid-mlx/alternatives.md), [anubis-oss markdown twin](/tools/uncsoft-anubis-oss/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/raullenchai-rapid-mlx-vs-uncsoft-anubis-oss.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Rapid-MLX or anubis-oss? Rapid-MLX: Very active. anubis-oss: 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 Rapid-MLX and anubis-oss? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Rapid-MLX trust report](/tools/raullenchai-rapid-mlx/trust); [anubis-oss trust report](/tools/uncsoft-anubis-oss/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=raullenchai-rapid-mlx`](/api/graphcanon/graph?tool=raullenchai-rapid-mlx) - 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/_