--- title: "aikit vs openpi" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kaito-project-aikit-vs-physical-intelligence-openpi" tools: ["kaito-project-aikit", "physical-intelligence-openpi"] --- # aikit vs openpi *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 openpi if openpi is a repository for running AI models with specific GPU requirements for inference and fine-tuning, including LoRA and full fine-tuning modes. [aikit](https://kaito-project.github.io/aikit/) reports 539 GitHub stars, 57 forks, and 37 open issues, last pushed Sep 18, 2026. [openpi](https://github.com/Physical-Intelligence/openpi) has 14k stars, 2.5k forks, and 345 open issues, last pushed Aug 24, 2026. Figures are from public GitHub metadata via [aikit's repository](https://github.com/kaito-project/aikit) and [openpi's repository](https://github.com/Physical-Intelligence/openpi). | | [aikit](/tools/kaito-project-aikit.md) | [openpi](/tools/physical-intelligence-openpi.md) | | --- | --- | --- | | Tagline | Fine-tune, build, and deploy open-source LLMs easily! | Repository for running AI models with GPU requirements specified for inference and fine-tuning. | | Stars | 539 | 13,874 | | Forks | 57 | 2,467 | | Open issues | 37 | 345 | | 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. | openpi is a repository for running AI models with specific GPU requirements for inference and fine-tuning, including LoRA and full fine-tuning modes. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Inference & Serving, LLM Frameworks, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aikit](/tools/kaito-project-aikit.md) | [openpi](/tools/physical-intelligence-openpi.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 24d | | Open issues (now) | 37 | 345 | | Stars delta | +5 (30d) | +776 (30d) | | Open issues delta | -6 (30d) | +29 (30d) | | Full report | [trust report](/tools/kaito-project-aikit/trust.md) | [trust report](/tools/physical-intelligence-openpi/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: openpi - **Requirements:** The repository requires an NVIDIA GPU with at least 8 GB of memory for inference, 22.5 GB for fine-tuning with LoRA, and 70 GB for full fine-tuning. - **Adopt for:** openpi is a repository for running AI models with specific GPU requirements for inference and fine-tuning, including LoRA and full fine-tuning modes. ## Choose when ### Choose aikit if… - aikit is primarily Go; openpi is Python. - License: aikit is MIT, openpi 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. ### Choose openpi if… - openpi is primarily Python; aikit is Go. - License: openpi is Apache-2.0, aikit is MIT. - Requirements: The repository requires an NVIDIA GPU with at least 8 GB of memory for inference, 22.5 GB for fine-tuning with LoRA, and 70 GB for full fine-tuning.. - Tags unique to openpi: gpu, inference, lora, model parallelism. - Use openpi when you need to run AI models with precise GPU memory requirements for inference and fine-tuning, particularly with LoRA and full fine-tuning modes. ## 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 openpi - Avoid using openpi if your project requires multi-node training, as the current training script does not support this feature. - Do not use openpi if you are working on an operating system other than Ubuntu 22.04, as the repository has not been tested with other systems. ## Common questions ### What is the difference between aikit and openpi? aikit: Fine-tune, build, and deploy open-source LLMs easily!. openpi: Repository for running AI models with GPU requirements specified for inference and fine-tuning.. See the comparison table for live GitHub stats and shared categories. ### When should I choose aikit over openpi? Choose aikit over openpi when aikit is primarily Go; openpi is Python; License: aikit is MIT, openpi 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 choose openpi over aikit? Choose openpi over aikit when openpi is primarily Python; aikit is Go; License: openpi is Apache-2.0, aikit is MIT; Requirements: The repository requires an NVIDIA GPU with at least 8 GB of memory for inference, 22.5 GB for fine-tuning with LoRA, and 70 GB for full fine-tuning.; Tags unique to openpi: gpu, inference, lora, model parallelism; Use openpi when you need to run AI models with precise GPU memory requirements for inference and fine-tuning, particularly with LoRA and full fine-tuning modes. ### 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 openpi? Avoid using openpi if your project requires multi-node training, as the current training script does not support this feature. Do not use openpi if you are working on an operating system other than Ubuntu 22.04, as the repository has not been tested with other systems. ### Is aikit or openpi more popular on GitHub? openpi has more GitHub stars (13,874 vs 539). Stars measure visibility, not whether either tool fits your constraints. ### Are aikit and openpi open source? Yes - both are open-source projects on GitHub (aikit: MIT, openpi: Apache-2.0). ### Where can I find alternatives to aikit or openpi? GraphCanon lists graph-backed alternatives at [aikit alternatives](/tools/kaito-project-aikit/alternatives) and [openpi alternatives](/tools/physical-intelligence-openpi/alternatives) ([aikit markdown twin](/tools/kaito-project-aikit/alternatives.md), [openpi markdown twin](/tools/physical-intelligence-openpi/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-physical-intelligence-openpi.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aikit or openpi? aikit: Very active. openpi: 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 openpi? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aikit trust report](/tools/kaito-project-aikit/trust); [openpi trust report](/tools/physical-intelligence-openpi/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/_