--- title: "aikit vs anubis-oss" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kaito-project-aikit-vs-uncsoft-anubis-oss" tools: ["kaito-project-aikit", "uncsoft-anubis-oss"] --- # aikit vs anubis-oss *GraphCanon updated Sep 20, 2026* ## Verdict Pick aikit if aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies; 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. [aikit](https://kaito-project.github.io/aikit/) reports 539 GitHub stars, 57 forks, and 37 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 [aikit's repository](https://github.com/kaito-project/aikit) and [anubis-oss's repository](https://github.com/uncSoft/anubis-oss). | | [aikit](/tools/kaito-project-aikit.md) | [anubis-oss](/tools/uncsoft-anubis-oss.md) | | --- | --- | --- | | Tagline | Fine-tune, build, and deploy open-source LLMs easily! | Local LLM Testing & Benchmarking for Apple Silicon | | Stars | 539 | 207 | | Forks | 57 | 15 | | Open issues | 37 | 1 | | Language | Go | Swift | | Adopt for | Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. | 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 | MIT | GPL-3.0 license ensures that any derivative works related to anubis-oss must also be open source under the same licensing terms. | | Categories | Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aikit](/tools/kaito-project-aikit.md) | [anubis-oss](/tools/uncsoft-anubis-oss.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 15d | | Open issues (now) | 37 | 1 | | Stars delta | +5 (30d) | +9 (30d) | | Open issues delta | -6 (30d) | -3 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/kaito-project-aikit/trust.md) | [trust report](/tools/uncsoft-anubis-oss/trust.md) | ## Decision facts: aikit - **Adopt for:** Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. ## 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 aikit if… - aikit is primarily Go; anubis-oss is Swift. - License: aikit is MIT, anubis-oss is GPL-3.0. - Tags unique to aikit: ai, buildkit, chatgpt, docker. - Also covers LLM Frameworks, Model Training. - aikit ships Docker support for self-hosted deployment. - - You need a flexible solution specifically built using Go and prefer its concurrency model. ### Choose anubis-oss if… - anubis-oss is primarily Swift; aikit is Go. - License: anubis-oss is GPL-3.0, aikit 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 aikit - - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit. - - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these. ## 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 aikit and anubis-oss? aikit: Fine-tune, build, and deploy open-source LLMs easily!. anubis-oss: Local LLM Testing & Benchmarking for Apple Silicon. See the comparison table for live GitHub stats and shared categories. ### When should I choose aikit over anubis-oss? Choose aikit over anubis-oss when aikit is primarily Go; anubis-oss is Swift; License: aikit is MIT, anubis-oss is GPL-3.0; Tags unique to aikit: ai, buildkit, chatgpt, docker; Also covers LLM Frameworks, Model Training; aikit ships Docker support for self-hosted deployment; - You need a flexible solution specifically built using Go and prefer its concurrency model. ### When should I choose anubis-oss over aikit? Choose anubis-oss over aikit when anubis-oss is primarily Swift; aikit is Go; License: anubis-oss is GPL-3.0, aikit 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 aikit? - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit. - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these. ### 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 aikit or anubis-oss more popular on GitHub? aikit has more GitHub stars (539 vs 207). Stars measure visibility, not whether either tool fits your constraints. ### Are aikit and anubis-oss open source? Yes - both are open-source projects on GitHub (aikit: MIT, anubis-oss: GPL-3.0). ### Where can I find alternatives to aikit or anubis-oss? GraphCanon lists graph-backed alternatives at [aikit alternatives](/tools/kaito-project-aikit/alternatives) and [anubis-oss alternatives](/tools/uncsoft-anubis-oss/alternatives) ([aikit markdown twin](/tools/kaito-project-aikit/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/kaito-project-aikit-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, aikit or anubis-oss? aikit: 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 aikit and anubis-oss? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aikit trust report](/tools/kaito-project-aikit/trust); [anubis-oss trust report](/tools/uncsoft-anubis-oss/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kaito-project-aikit`](/api/graphcanon/graph?tool=kaito-project-aikit) - 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/_