--- title: "aikit vs LMFlow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kaito-project-aikit-vs-optimalscale-lmflow" tools: ["kaito-project-aikit", "optimalscale-lmflow"] --- # aikit vs LMFlow *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 LMFlow if lMFlow is an extensible Python toolkit for fine-tuning and inference on large foundation models with Gradio-based chatbot deployment. [aikit](https://kaito-project.github.io/aikit/) reports 537 GitHub stars, 57 forks, and 40 open issues, last pushed Aug 24, 2026. [LMFlow](https://optimalscale.github.io/LMFlow/) has 8.5k stars, 825 forks, and 88 open issues, last pushed May 22, 2026. Figures are from public GitHub metadata via [aikit's repository](https://github.com/kaito-project/aikit) and [LMFlow's repository](https://github.com/OptimalScale/LMFlow). | | [aikit](/tools/kaito-project-aikit.md) | [LMFlow](/tools/optimalscale-lmflow.md) | | --- | --- | --- | | Tagline | Fine-tune, build, and deploy open-source LLMs easily! | An Extensible Toolkit for Finetuning and Inference of Large Foundation Models | | Stars | 537 | 8,486 | | Forks | 57 | 825 | | Open issues | 40 | 88 | | 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. | LMFlow is an extensible Python toolkit for fine-tuning and inference on large foundation models with Gradio-based chatbot deployment. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Inference & Serving, LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aikit](/tools/kaito-project-aikit.md) | [LMFlow](/tools/optimalscale-lmflow.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 72d | | Open issues (now) | 40 | 88 | | Stars delta | +3 (30d) | Unknown | | Open issues delta | -3 (30d) | Unknown | | Full report | [trust report](/tools/kaito-project-aikit/trust.md) | [trust report](/tools/optimalscale-lmflow/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: LMFlow - **Adopt for:** LMFlow is an extensible Python toolkit for fine-tuning and inference on large foundation models with Gradio-based chatbot deployment. - **License detail:** Apache-2.0 ## Choose when ### Choose aikit if… - aikit is primarily Go; LMFlow is Python. - License: aikit is MIT, LMFlow is Apache-2.0. - Tags unique to aikit: ai, buildkit, docker, fine-tuning. - Also covers 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 LMFlow if… - LMFlow is primarily Python; aikit is Go. - License: LMFlow is Apache-2.0, aikit is MIT. - Tags unique to LMFlow: deep-learning, instruction-following, language-model, pretrained-models. - You require an extendable framework to fine-tune or conduct inference operations on large foundational models where a user-friendly chatbot UI can be integrated using Gradio. ## 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 LMFlow - You do not need a Python-based solution for your large foundation model tasks, or if your projects specifically require languages other than Python. - Your project requires commercial use with simplified authorization processes, since LMFlow demands signing a specific document to obtain authorization for commercial use. ## Common questions ### What is the difference between aikit and LMFlow? aikit: Fine-tune, build, and deploy open-source LLMs easily!. LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose aikit over LMFlow? Choose aikit over LMFlow when aikit is primarily Go; LMFlow is Python; License: aikit is MIT, LMFlow is Apache-2.0; Tags unique to aikit: ai, buildkit, docker, fine-tuning; Also covers 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 LMFlow over aikit? Choose LMFlow over aikit when LMFlow is primarily Python; aikit is Go; License: LMFlow is Apache-2.0, aikit is MIT; Tags unique to LMFlow: deep-learning, instruction-following, language-model, pretrained-models; You require an extendable framework to fine-tune or conduct inference operations on large foundational models where a user-friendly chatbot UI can be integrated using Gradio. ### 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 LMFlow? You do not need a Python-based solution for your large foundation model tasks, or if your projects specifically require languages other than Python. Your project requires commercial use with simplified authorization processes, since LMFlow demands signing a specific document to obtain authorization for commercial use. ### Is aikit or LMFlow more popular on GitHub? LMFlow has more GitHub stars (8,486 vs 537). Stars measure visibility, not whether either tool fits your constraints. ### Are aikit and LMFlow open source? Yes - both are open-source projects on GitHub (aikit: MIT, LMFlow: Apache-2.0). ### Where can I find alternatives to aikit or LMFlow? GraphCanon lists graph-backed alternatives at [aikit alternatives](/tools/kaito-project-aikit/alternatives) and [LMFlow alternatives](/tools/optimalscale-lmflow/alternatives) ([aikit markdown twin](/tools/kaito-project-aikit/alternatives.md), [LMFlow markdown twin](/tools/optimalscale-lmflow/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-optimalscale-lmflow.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aikit or LMFlow? aikit: Very active. LMFlow: 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 LMFlow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aikit trust report](/tools/kaito-project-aikit/trust); [LMFlow trust report](/tools/optimalscale-lmflow/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/_