--- title: "UER-py vs aikit" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dbiir-uer-py-vs-kaito-project-aikit" tools: ["dbiir-uer-py", "kaito-project-aikit"] --- # UER-py vs aikit *GraphCanon updated Aug 24, 2026* ## Verdict Pick UER-py if uER-py, an open-source PyTorch framework with a diverse model zoo for training and fine-tuning language models; 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. [UER-py](https://github.com/dbiir/UER-py/wiki) reports 3.1k GitHub stars, 520 forks, and 136 open issues, last pushed May 9, 2024. [aikit](https://kaito-project.github.io/aikit/) has 537 stars, 57 forks, and 40 open issues, last pushed Aug 24, 2026. Figures are from public GitHub metadata via [UER-py's repository](https://github.com/dbiir/UER-py) and [aikit's repository](https://github.com/kaito-project/aikit). | | [UER-py](/tools/dbiir-uer-py.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Tagline | Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo | Fine-tune, build, and deploy open-source LLMs easily! | | Stars | 3,112 | 537 | | Forks | 520 | 57 | | Open issues | 136 | 40 | | Language | Python | Go | | Adopt for | UER-py, an open-source PyTorch framework with a diverse model zoo for training and fine-tuning language models. | 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 | LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [UER-py](/tools/dbiir-uer-py.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 836d | 0d | | Open issues (now) | 136 | 40 | | Stars delta | +2 (30d) | +3 (30d) | | Open issues delta | 0 (30d) | -3 (30d) | | Full report | [trust report](/tools/dbiir-uer-py/trust.md) | [trust report](/tools/kaito-project-aikit/trust.md) | ## Decision facts: UER-py - **Pricing:** freemium - The framework itself is free and open-source under Apache 2.0 license providing flexibility for modification with no costs. - **Requirements:** Min 8 GB RAM; - Requires Python environment setup; - Needs PyTorch installation - **Adopt for:** UER-py, an open-source PyTorch framework with a diverse model zoo for training and fine-tuning language models. ## 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 UER-py if… - UER-py is primarily Python; aikit is Go. - License: UER-py is Apache-2.0, aikit is MIT. - Pricing: The framework itself is free and open-source under Apache 2.0 license providing flexibility for modification with no costs.. - Requirements: Min 8 GB RAM; - Requires Python environment setup; - Needs PyTorch installation. - Tags unique to UER-py: albert, bart, bert, chinese. - - When you need to work exclusively within the PyTorch ecosystem, UER-py provides extensive support for various pre-trained models and tasks without the necessity of switching frameworks. ### Choose aikit if… - aikit is primarily Go; UER-py is Python. - License: aikit is MIT, UER-py is Apache-2.0. - Tags unique to aikit: ai, buildkit, chatgpt, docker. - Also covers 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 UER-py - - When you require more framework flexibility and are open to using TensorFlow or other deep learning libraries outside PyTorch. - - If your project is sensitive to maintenance updates but the UER-py repository has not seen recent active contribution, preferring a tool actively maintained might be better. ## 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 UER-py and aikit? UER-py: Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo. 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 UER-py over aikit? Choose UER-py over aikit when UER-py is primarily Python; aikit is Go; License: UER-py is Apache-2.0, aikit is MIT; Pricing: The framework itself is free and open-source under Apache 2.0 license providing flexibility for modification with no costs.; Requirements: Min 8 GB RAM; - Requires Python environment setup; - Needs PyTorch installation; Tags unique to UER-py: albert, bart, bert, chinese; - When you need to work exclusively within the PyTorch ecosystem, UER-py provides extensive support for various pre-trained models and tasks without the necessity of switching frameworks. ### When should I choose aikit over UER-py? Choose aikit over UER-py when aikit is primarily Go; UER-py is Python; License: aikit is MIT, UER-py is Apache-2.0; Tags unique to aikit: ai, buildkit, chatgpt, docker; Also covers 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 UER-py? - When you require more framework flexibility and are open to using TensorFlow or other deep learning libraries outside PyTorch. - If your project is sensitive to maintenance updates but the UER-py repository has not seen recent active contribution, preferring a tool actively maintained might be better. ### 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 UER-py or aikit more popular on GitHub? UER-py has more GitHub stars (3,112 vs 537). Stars measure visibility, not whether either tool fits your constraints. ### Are UER-py and aikit open source? Yes - both are open-source projects on GitHub (UER-py: Apache-2.0, aikit: MIT). ### Where can I find alternatives to UER-py or aikit? GraphCanon lists graph-backed alternatives at [UER-py alternatives](/tools/dbiir-uer-py/alternatives) and [aikit alternatives](/tools/kaito-project-aikit/alternatives) ([UER-py markdown twin](/tools/dbiir-uer-py/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/dbiir-uer-py-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, UER-py or aikit? UER-py: Dormant. 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 UER-py and aikit? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [UER-py trust report](/tools/dbiir-uer-py/trust); [aikit trust report](/tools/kaito-project-aikit/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dbiir-uer-py`](/api/graphcanon/graph?tool=dbiir-uer-py) - 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/_