Comparison
LLM.swift vs anubis-oss
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 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 within the macOS environment.
Markdown twin · LLM.swift alternatives · anubis-oss alternatives
GraphCanon updated Sep 20, 2026
11views this month
vs
Trust & integrity
| Signal | LLM.swift | anubis-oss |
|---|---|---|
| Maintenance | Steady (62d since push) As of Sep 20, 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 20, 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 Jul 11, 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 | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- LLM.swift
- LLM.swift enables local interaction with large language models for multiple Apple platforms.
- anubis-oss
- Local LLM Testing & Benchmarking for Apple Silicon
Stars
- LLM.swift
- 877
- anubis-oss
- 207
Forks
- LLM.swift
- 126
- anubis-oss
- 15
Open issues
- LLM.swift
- 10
- anubis-oss
- 1
Language
- LLM.swift
- Swift
- anubis-oss
- Swift
Adopt for
- LLM.swift
- LLM.swift is a cross-platform C++ library for Apple systems that simplifies local interaction with large language models.
- 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
- LLM.swift
- -
- anubis-oss
- -
Runtime
- LLM.swift
- -
- anubis-oss
- -
License
- LLM.swift
- MIT license allows for free use and distribution, both commercially and in open source projects, with attribution preferred but not mandatory.
- 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
- LLM.swift
- Jul 19, 2026
- anubis-oss
- Sep 5, 2026
Categories
- LLM.swift
- Inference & Serving
- anubis-oss
- Evaluation & Observability, Inference & Serving
Trust and health
Maintenance
- LLM.swift
- Steady (60%)
- anubis-oss
- Active (82%)
Days since push
- LLM.swift
- 62d
- anubis-oss
- 15d
Open issues (now)
- LLM.swift
- 10
- anubis-oss
- 1
Stars delta
- LLM.swift
- +12 (30d)
- anubis-oss
- +9 (30d)
Open issues delta
- LLM.swift
- +1 (30d)
- anubis-oss
- -3 (30d)
Full report
- LLM.swift
- Trust report
- anubis-oss
- Trust report
Choose LLM.swift if…
- License: LLM.swift is MIT, anubis-oss is GPL-3.0.
- Pricing: Free to use under the MIT License. Premium support may vary..
- Tags unique to LLM.swift: gguf, ios, llm-inference, swift.
- Choose LLM.swift when you need to integrate large language model functionalities into apps targeting multiple Apple platforms, including macOS and mobile OSes.
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.
Choose anubis-oss if…
- License: anubis-oss is GPL-3.0, LLM.swift is MIT.
- 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: apple-silicon, benchmarking, gpu, inference.
- 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 (eastriverlee/LLM.swift) · observed Sep 20, 2026
- GitHub forks (eastriverlee/LLM.swift) · observed Sep 20, 2026
- Last push (eastriverlee/LLM.swift) · observed Jul 19, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 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: LLM.swift 877 · anubis-oss 207 (synced Sep 20, 2026).
Common questions
- What is the difference between LLM.swift and anubis-oss?
- LLM.swift: LLM.swift enables local interaction with large language models for multiple Apple platforms.. anubis-oss: Local LLM Testing & Benchmarking for Apple Silicon. See the comparison table for live GitHub stats and shared categories.
- When should I choose LLM.swift over anubis-oss?
- Choose LLM.swift over anubis-oss when License: LLM.swift is MIT, anubis-oss is GPL-3.0; Pricing: Free to use under the MIT License. Premium support may vary.; Tags unique to LLM.swift: gguf, ios, llm-inference, swift; 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 anubis-oss over LLM.swift?
- Choose anubis-oss over LLM.swift when License: anubis-oss is GPL-3.0, LLM.swift is MIT; 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: apple-silicon, benchmarking, gpu, inference; Also covers Evaluation & Observability; When developing and evaluating large language models intended to run natively on Apple Silicon hardware.
- 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 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 LLM.swift or anubis-oss more popular on GitHub?
- LLM.swift has more GitHub stars (877 vs 207). Stars measure visibility, not whether either tool fits your constraints.
- Are LLM.swift and anubis-oss open source?
- Yes - both are open-source projects on GitHub (LLM.swift: MIT, anubis-oss: GPL-3.0).
- Where can I find alternatives to LLM.swift or anubis-oss?
- GraphCanon lists graph-backed alternatives at LLM.swift alternatives and anubis-oss alternatives (LLM.swift 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, LLM.swift or anubis-oss?
- LLM.swift: Steady. 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 LLM.swift and anubis-oss?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLM.swift trust report; anubis-oss trust report.