--- title: "open-r1 vs aikit" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huggingface-open-r1-vs-kaito-project-aikit" tools: ["huggingface-open-r1", "kaito-project-aikit"] --- # open-r1 vs aikit *GraphCanon updated Jul 12, 2026* ## Verdict Pick open-r1 if open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training; 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. [open-r1](https://github.com/huggingface/open-r1) reports 26k GitHub stars, 2.4k forks, and 340 open issues, last pushed Apr 2, 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 [open-r1's repository](https://github.com/huggingface/open-r1) and [aikit's repository](https://github.com/kaito-project/aikit). | | [open-r1](/tools/huggingface-open-r1.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Tagline | Fully open reproduction of DeepSeek-R1 | Fine-tune, build, and deploy open-source LLMs easily! | | Stars | 26,401 | 533 | | Forks | 2,446 | 57 | | Open issues | 340 | 41 | | Language | Python | Go | | Adopt for | Open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training. | 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 | The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution. | MIT | | Categories | Inference & Serving, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [open-r1](/tools/huggingface-open-r1.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 100d | 0d | | Open issues (now) | 340 | 41 | | Full report | [trust report](/tools/huggingface-open-r1/trust.md) | [trust report](/tools/kaito-project-aikit/trust.md) | ## Decision facts: open-r1 - **Requirements:** Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical. - **Adopt for:** Open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training. - **License detail:** The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution. ## 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 open-r1 if… - open-r1 is primarily Python; aikit is Go. - License: open-r1 is Apache-2.0, aikit is MIT. - Requirements: Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical.. - Tags unique to open-r1: cuda, deepseek-r1, flashattention, model distillation. - Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes. ### Choose aikit if… - aikit is primarily Go; open-r1 is Python. - License: aikit is MIT, open-r1 is Apache-2.0. - Tags unique to aikit: ai, buildkit, chatgpt, docker. - Also covers LLM Frameworks. - 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 open-r1 - Avoid Open-R1 if your hardware does not support CUDA 12.4 or cannot run PyTorch `v2.6.0`, as this may lead to errors. - Do not use it if the need for rapid experimentation outweighs the value of detailed replication, since the multi-stage training and datasets curation process can be time-consuming. ## 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 open-r1 and aikit? open-r1: Fully open reproduction of DeepSeek-R1. 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 open-r1 over aikit? Choose open-r1 over aikit when open-r1 is primarily Python; aikit is Go; License: open-r1 is Apache-2.0, aikit is MIT; Requirements: Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical.; Tags unique to open-r1: cuda, deepseek-r1, flashattention, model distillation; Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes. ### When should I choose aikit over open-r1? Choose aikit over open-r1 when aikit is primarily Go; open-r1 is Python; License: aikit is MIT, open-r1 is Apache-2.0; Tags unique to aikit: ai, buildkit, chatgpt, docker; Also covers LLM Frameworks; 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 open-r1? Avoid Open-R1 if your hardware does not support CUDA 12.4 or cannot run PyTorch `v2.6.0`, as this may lead to errors. Do not use it if the need for rapid experimentation outweighs the value of detailed replication, since the multi-stage training and datasets curation process can be time-consuming. ### 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 open-r1 or aikit more popular on GitHub? open-r1 has more GitHub stars (26,401 vs 533). Stars measure visibility, not whether either tool fits your constraints. ### Are open-r1 and aikit open source? Yes - both are open-source projects on GitHub (open-r1: Apache-2.0, aikit: MIT). ### Where can I find alternatives to open-r1 or aikit? GraphCanon lists graph-backed alternatives at [open-r1 alternatives](/tools/huggingface-open-r1/alternatives) and [aikit alternatives](/tools/kaito-project-aikit/alternatives) ([open-r1 markdown twin](/tools/huggingface-open-r1/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/huggingface-open-r1-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, open-r1 or aikit? open-r1: Slowing. 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 open-r1 and aikit? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [open-r1 trust report](/tools/huggingface-open-r1/trust); [aikit trust report](/tools/kaito-project-aikit/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huggingface-open-r1`](/api/graphcanon/graph?tool=huggingface-open-r1) - 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/_