--- title: "aikit vs Eagle" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kaito-project-aikit-vs-nvlabs-eagle" tools: ["kaito-project-aikit", "nvlabs-eagle"] --- # aikit vs Eagle *GraphCanon updated Aug 24, 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 Eagle if eagle: Frontier Vision-Language Models with Data-Centric Strategies. [aikit](https://kaito-project.github.io/aikit/) reports 537 GitHub stars, 57 forks, and 40 open issues, last pushed Aug 24, 2026. [Eagle](https://nvlabs.github.io/Eagle/) has 3.4k stars, 327 forks, and 62 open issues, last pushed Jun 24, 2026. Figures are from public GitHub metadata via [aikit's repository](https://github.com/kaito-project/aikit) and [Eagle's repository](https://github.com/NVlabs/Eagle). | | [aikit](/tools/kaito-project-aikit.md) | [Eagle](/tools/nvlabs-eagle.md) | | --- | --- | --- | | Tagline | Fine-tune, build, and deploy open-source LLMs easily! | Frontier Vision-Language Models with Data-Centric Strategies | | Stars | 537 | 3,407 | | Forks | 57 | 327 | | Open issues | 40 | 62 | | Language | Go | Python | | Adopt for | Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. | Eagle: Frontier Vision-Language Models with Data-Centric Strategies | | Persona | - | - | | Runtime | - | - | | License | MIT | The code is released under Apache 2.0 license, while the pretrained models are under CC BY-NC 4.0 or NVIDIA licenses for non-commercial use only. | | Categories | Inference & Serving, LLM Frameworks, Model Training | Computer Vision, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aikit](/tools/kaito-project-aikit.md) | [Eagle](/tools/nvlabs-eagle.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 54d | | Open issues (now) | 40 | 62 | | Stars delta | +3 (30d) | +199 (30d) | | Open issues delta | -3 (30d) | +3 (30d) | | Full report | [trust report](/tools/kaito-project-aikit/trust.md) | [trust report](/tools/nvlabs-eagle/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: Eagle - **Pricing:** freemium - Free for non-commercial use; requires adherence to licensing agreements - **Requirements:** Min 8 GB RAM; Ensure compliance with all applicable laws and regulations when using the dataset and model weights. - **Adopt for:** Eagle: Frontier Vision-Language Models with Data-Centric Strategies - **License detail:** The code is released under Apache 2.0 license, while the pretrained models are under CC BY-NC 4.0 or NVIDIA licenses for non-commercial use only. ## Choose when ### Choose aikit if… - aikit is primarily Go; Eagle is Python. - License: aikit is MIT, Eagle is Apache-2.0. - Tags unique to aikit: ai, buildkit, chatgpt, docker. - Also covers Inference & Serving, 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 Eagle if… - Eagle is primarily Python; aikit is Go. - License: Eagle is Apache-2.0, aikit is MIT. - Pricing: Free for non-commercial use; requires adherence to licensing agreements. - Requirements: Min 8 GB RAM; Ensure compliance with all applicable laws and regulations when using the dataset and model weights.. - Tags unique to Eagle: data-centric-strategies, gpt4, huggingface, llm-improvements. - Also covers Computer Vision. - When you need advanced vision-language models enhanced by data-centric strategies developed by NVlabs and improved using Qwen. ## 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 Eagle - If your project requires commercial use, as Eagle's models are intended for non-commercial use only under the CC BY-NC 4.0 License or NVIDIA License. - In situations where you require a vision-language model that does not rely on improvements made using Qwen. ## Common questions ### What is the difference between aikit and Eagle? aikit: Fine-tune, build, and deploy open-source LLMs easily!. Eagle: Frontier Vision-Language Models with Data-Centric Strategies. See the comparison table for live GitHub stats and shared categories. ### When should I choose aikit over Eagle? Choose aikit over Eagle when aikit is primarily Go; Eagle is Python; License: aikit is MIT, Eagle is Apache-2.0; Tags unique to aikit: ai, buildkit, chatgpt, docker; Also covers Inference & Serving, 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 Eagle over aikit? Choose Eagle over aikit when Eagle is primarily Python; aikit is Go; License: Eagle is Apache-2.0, aikit is MIT; Pricing: Free for non-commercial use; requires adherence to licensing agreements; Requirements: Min 8 GB RAM; Ensure compliance with all applicable laws and regulations when using the dataset and model weights.; Tags unique to Eagle: data-centric-strategies, gpt4, huggingface, llm-improvements; Also covers Computer Vision; When you need advanced vision-language models enhanced by data-centric strategies developed by NVlabs and improved using Qwen. ### 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 Eagle? If your project requires commercial use, as Eagle's models are intended for non-commercial use only under the CC BY-NC 4.0 License or NVIDIA License. In situations where you require a vision-language model that does not rely on improvements made using Qwen. ### Is aikit or Eagle more popular on GitHub? Eagle has more GitHub stars (3,407 vs 537). Stars measure visibility, not whether either tool fits your constraints. ### Are aikit and Eagle open source? Yes - both are open-source projects on GitHub (aikit: MIT, Eagle: Apache-2.0). ### Where can I find alternatives to aikit or Eagle? GraphCanon lists graph-backed alternatives at [aikit alternatives](/tools/kaito-project-aikit/alternatives) and [Eagle alternatives](/tools/nvlabs-eagle/alternatives) ([aikit markdown twin](/tools/kaito-project-aikit/alternatives.md), [Eagle markdown twin](/tools/nvlabs-eagle/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-nvlabs-eagle.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aikit or Eagle? aikit: Very active. Eagle: Steady. 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 Eagle? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aikit trust report](/tools/kaito-project-aikit/trust); [Eagle trust report](/tools/nvlabs-eagle/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/_