--- title: "ROLL vs aikit" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alibaba-roll-vs-kaito-project-aikit" tools: ["alibaba-roll", "kaito-project-aikit"] --- # ROLL vs aikit *GraphCanon updated Jul 12, 2026* ## Verdict Pick ROLL when rOLL is primarily Python; aikit is Go; pick aikit when aikit is primarily Go; ROLL is Python. [ROLL](https://alibaba.github.io/ROLL/) reports 3.3k GitHub stars, 295 forks, and 119 open issues, last pushed Jul 11, 2026. [aikit](https://kaito-project.github.io/aikit/) has 533 stars, 57 forks, and 41 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [ROLL's repository](https://github.com/alibaba/ROLL) and [aikit's repository](https://github.com/kaito-project/aikit). | | [ROLL](/tools/alibaba-roll.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Tagline | Efficient and user-friendly scaling library for RL with LLMs | Fine-tune, build, and deploy open-source LLMs easily! | | Stars | 3,292 | 533 | | Forks | 295 | 57 | | Open issues | 119 | 41 | | Language | Python | Go | | Adopt for | - | Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Model Training, Evaluation & Observability | LLM Frameworks, Model Training, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ROLL](/tools/alibaba-roll.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Open issues (now) | 119 | 41 | | Full report | [trust report](/tools/alibaba-roll/trust.md) | [trust report](/tools/kaito-project-aikit/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. ## Choose when ### Choose ROLL if… - ROLL is primarily Python; aikit is Go. - License: ROLL is Apache-2.0, aikit is MIT. - Tags unique to ROLL: rlhf, rlvr, agentic. - Also covers Evaluation & Observability. ### Choose aikit if… - aikit is primarily Go; ROLL is Python. - License: aikit is MIT, ROLL is Apache-2.0. - Tags unique to aikit: gemma, fine-tuning, ai, docker. - Also covers LLM Frameworks, Inference & Serving. - aikit ships Docker support for self-hosted deployment. - - You need a flexible solution specifically built using Go and prefer its concurrency model. ## When NOT to use ROLL - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## 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. ## Common questions ### What is the difference between ROLL and aikit? ROLL: Efficient and user-friendly scaling library for RL with LLMs. aikit: Fine-tune, build, and deploy open-source LLMs easily!. See the comparison table for live GitHub stats and shared categories. ### When should I choose ROLL over aikit? Choose ROLL over aikit when ROLL is primarily Python; aikit is Go; License: ROLL is Apache-2.0, aikit is MIT; Tags unique to ROLL: rlhf, rlvr, agentic; Also covers Evaluation & Observability. ### When should I choose aikit over ROLL? Choose aikit over ROLL when aikit is primarily Go; ROLL is Python; License: aikit is MIT, ROLL is Apache-2.0; Tags unique to aikit: gemma, fine-tuning, ai, docker; Also covers LLM Frameworks, Inference & Serving; 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 avoid ROLL? Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### 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. ### Is ROLL or aikit more popular on GitHub? ROLL has more GitHub stars (3,292 vs 533). Stars measure visibility, not whether either tool fits your constraints. ### Are ROLL and aikit open source? Yes - both are open-source projects on GitHub (ROLL: Apache-2.0, aikit: MIT). ### Where can I find alternatives to ROLL or aikit? GraphCanon lists graph-backed alternatives at [ROLL alternatives](/tools/alibaba-roll/alternatives) and [aikit alternatives](/tools/kaito-project-aikit/alternatives) ([ROLL markdown twin](/tools/alibaba-roll/alternatives.md), [aikit markdown twin](/tools/kaito-project-aikit/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/alibaba-roll-vs-kaito-project-aikit.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ROLL or aikit? ROLL: Very active. aikit: 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 ROLL and aikit? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ROLL trust report](/tools/alibaba-roll/trust); [aikit trust report](/tools/kaito-project-aikit/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alibaba-roll`](/api/graphcanon/graph?tool=alibaba-roll) - 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/_