--- title: "aikit vs llmflows" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kaito-project-aikit-vs-stoyan-stoyanov-llmflows" tools: ["kaito-project-aikit", "stoyan-stoyanov-llmflows"] --- # aikit vs llmflows *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 llmflows if lLMFlows is a Python library designed for creating and managing LLM applications with a focus on simplicity and transparency. [aikit](https://kaito-project.github.io/aikit/) reports 539 GitHub stars, 57 forks, and 37 open issues, last pushed Sep 18, 2026. [llmflows](https://llmflows.readthedocs.io) has 708 stars, 35 forks, and 19 open issues, last pushed Feb 20, 2025. Figures are from public GitHub metadata via [aikit's repository](https://github.com/kaito-project/aikit) and [llmflows's repository](https://github.com/stoyan-stoyanov/llmflows). | | [aikit](/tools/kaito-project-aikit.md) | [llmflows](/tools/stoyan-stoyanov-llmflows.md) | | --- | --- | --- | | Tagline | Fine-tune, build, and deploy open-source LLMs easily! | Simple, Explicit and Transparent LLM Apps | | Stars | 539 | 708 | | Forks | 57 | 35 | | Open issues | 37 | 19 | | 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. | LLMFlows is a Python library designed for creating and managing LLM applications with a focus on simplicity and transparency. | | Persona | - | - | | Runtime | - | - | | License | MIT | LLMFlows is distributed under the MIT license, allowing for free use, modification, and distribution. | | 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) | [llmflows](/tools/stoyan-stoyanov-llmflows.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 574d | | Open issues (now) | 37 | 19 | | Stars delta | +5 (30d) | +1 (30d) | | Open issues delta | -6 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/kaito-project-aikit/trust.md) | [trust report](/tools/stoyan-stoyanov-llmflows/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: llmflows - **Pricing:** freemium - The library is free to use, but additional services or support might incur costs. - **Requirements:** Min 2 GB RAM; Python environment is required. - **Adopt for:** LLMFlows is a Python library designed for creating and managing LLM applications with a focus on simplicity and transparency. - **License detail:** LLMFlows is distributed under the MIT license, allowing for free use, modification, and distribution. ## Choose when ### Choose aikit if… - aikit is primarily Go; llmflows is Python. - Tags unique to aikit: buildkit, docker, fine-tuning, finetuning. - 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 llmflows if… - llmflows is primarily Python; aikit is Go. - Pricing: The library is free to use, but additional services or support might incur costs.. - Requirements: Min 2 GB RAM; Python environment is required.. - Tags unique to llmflows: gpt-4, llm, llm-inference, llmops. - When you need a straightforward and transparent approach to building LLM applications, LLMFlows offers a simple framework that prioritizes clarity in the development process. ## 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 llmflows - Avoid LLMFlows if you require advanced customization options that go beyond its simple and explicit design philosophy. - Do not use LLMFlows if your project demands a more complex framework that integrates a wide range of third-party services, as LLMFlows focuses on simplicity and may lack extensive integration features ## Common questions ### What is the difference between aikit and llmflows? aikit: Fine-tune, build, and deploy open-source LLMs easily!. llmflows: Simple, Explicit and Transparent LLM Apps. See the comparison table for live GitHub stats and shared categories. ### When should I choose aikit over llmflows? Choose aikit over llmflows when aikit is primarily Go; llmflows is Python; Tags unique to aikit: buildkit, docker, fine-tuning, finetuning; 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 llmflows over aikit? Choose llmflows over aikit when llmflows is primarily Python; aikit is Go; Pricing: The library is free to use, but additional services or support might incur costs.; Requirements: Min 2 GB RAM; Python environment is required.; Tags unique to llmflows: gpt-4, llm, llm-inference, llmops; When you need a straightforward and transparent approach to building LLM applications, LLMFlows offers a simple framework that prioritizes clarity in the development process. ### 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 llmflows? Avoid LLMFlows if you require advanced customization options that go beyond its simple and explicit design philosophy. Do not use LLMFlows if your project demands a more complex framework that integrates a wide range of third-party services, as LLMFlows focuses on simplicity and may lack extensive integration features ### Is aikit or llmflows more popular on GitHub? llmflows has more GitHub stars (708 vs 539). Stars measure visibility, not whether either tool fits your constraints. ### Are aikit and llmflows open source? Yes - both are open-source projects on GitHub (aikit: MIT, llmflows: MIT). ### Where can I find alternatives to aikit or llmflows? GraphCanon lists graph-backed alternatives at [aikit alternatives](/tools/kaito-project-aikit/alternatives) and [llmflows alternatives](/tools/stoyan-stoyanov-llmflows/alternatives) ([aikit markdown twin](/tools/kaito-project-aikit/alternatives.md), [llmflows markdown twin](/tools/stoyan-stoyanov-llmflows/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-stoyan-stoyanov-llmflows.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aikit or llmflows? aikit: Very active. llmflows: Dormant. 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 llmflows? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aikit trust report](/tools/kaito-project-aikit/trust); [llmflows trust report](/tools/stoyan-stoyanov-llmflows/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/_