Comparison
Rapid-MLX vs anubis-oss
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.
Markdown twin · Rapid-MLX alternatives · anubis-oss alternatives
GraphCanon updated Sep 20, 2026
5views this month
vs
Trust & integrity
| Signal | Rapid-MLX | anubis-oss |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 | Active (15d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 15, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | No public record from this source As of Oct 4, 2026 · openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- Rapid-MLX
- Fast local AI engine for Apple Silicon
- anubis-oss
- Local LLM Testing & Benchmarking for Apple Silicon
Stars
- Rapid-MLX
- 3.8k
- anubis-oss
- 207
Forks
- Rapid-MLX
- 416
- anubis-oss
- 15
Open issues
- Rapid-MLX
- 54
- anubis-oss
- 1
Language
- Rapid-MLX
- Python
- anubis-oss
- Swift
Adopt for
- Rapid-MLX
- 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
- 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
- Rapid-MLX
- -
- anubis-oss
- -
Runtime
- Rapid-MLX
- -
- anubis-oss
- -
License
- Rapid-MLX
- Other
- anubis-oss
- GPL-3.0 license ensures that any derivative works related to anubis-oss must also be open source under the same licensing terms.
Last pushed
- Rapid-MLX
- Sep 18, 2026
- anubis-oss
- Sep 5, 2026
Categories
- Rapid-MLX
- Developer Tools, Inference & Serving, LLM Frameworks
- anubis-oss
- Evaluation & Observability, Inference & Serving
Trust and health
Maintenance
- Rapid-MLX
- Very active (96%)
- anubis-oss
- Active (82%)
Days since push
- Rapid-MLX
- 0d
- anubis-oss
- 15d
Open issues (now)
- Rapid-MLX
- 54
- anubis-oss
- 1
Stars delta
- Rapid-MLX
- +383 (30d)
- anubis-oss
- +9 (30d)
Open issues delta
- Rapid-MLX
- +6 (30d)
- anubis-oss
- -3 (30d)
OpenSSF Scorecard
- Rapid-MLX
- No public record from this source
- anubis-oss
- Not queried
Full report
- Rapid-MLX
- Trust report
- anubis-oss
- Trust report
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.
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
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (raullenchai/Rapid-MLX) · observed Sep 20, 2026
- GitHub forks (raullenchai/Rapid-MLX) · observed Sep 20, 2026
- Last push (raullenchai/Rapid-MLX) · observed Sep 18, 2026
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (uncSoft/anubis-oss) · observed Sep 20, 2026
- GitHub forks (uncSoft/anubis-oss) · observed Sep 20, 2026
- Last push (uncSoft/anubis-oss) · observed Sep 5, 2026
- License file (GPL-3.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: Rapid-MLX 3.8k · anubis-oss 207 (synced Sep 20, 2026).
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 and anubis-oss alternatives (Rapid-MLX markdown twin, anubis-oss markdown twin), 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 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; anubis-oss trust report.