---
title: "open-llms vs aikit"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/eugeneyan-open-llms-vs-kaito-project-aikit"
tools: ["eugeneyan-open-llms", "kaito-project-aikit"]
---

# open-llms vs aikit

*GraphCanon updated Aug 17, 2026*

## Verdict

Pick open-llms if critical Facts for 'open-llms' Tool Usage; 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.

[open-llms](https://github.com/eugeneyan/open-llms) reports 13k GitHub stars, 985 forks, and 11 open issues, last pushed Feb 13, 2025. [aikit](https://kaito-project.github.io/aikit/) has 534 stars, 57 forks, and 43 open issues, last pushed Jul 20, 2026. Figures are from public GitHub metadata via [open-llms's repository](https://github.com/eugeneyan/open-llms) and [aikit's repository](https://github.com/kaito-project/aikit).

| | [open-llms](/tools/eugeneyan-open-llms.md) | [aikit](/tools/kaito-project-aikit.md) |
| --- | --- | --- |
| Tagline | A list of open LLMs available for commercial use. | Fine-tune, build, and deploy open-source LLMs easily! |
| Stars | 12,849 | 534 |
| Forks | 985 | 57 |
| Open issues | 11 | 43 |
| Language | - | Go |
| Adopt for | Critical Facts for 'open-llms' Tool Usage | 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 | The repository itself is licensed under the permissive Apache-2.0 license; however, it aggregates data about various LLMs that may have differing licensing conditions including but not limited to the  | MIT |
| Categories | LLM Frameworks | Inference & Serving, LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [open-llms](/tools/eugeneyan-open-llms.md) | [aikit](/tools/kaito-project-aikit.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Very active (96%) |
| Days since push | 549d | 4d |
| Open issues (now) | 11 | 43 |
| Stars delta | +18 (30d) | Unknown |
| Open issues delta | -2 (30d) | Unknown |
| Owner type | User | Organization |
| Full report | [trust report](/tools/eugeneyan-open-llms/trust.md) | [trust report](/tools/kaito-project-aikit/trust.md) |

## Decision facts: open-llms

- **Pricing:** freemium - Free and open-source models for commercial use are listed here. Usage of the models themselves varies by license, with some possibly requiring contributions or acknowledgements.
- **Adopt for:** Critical Facts for 'open-llms' Tool Usage
- **License detail:** The repository itself is licensed under the permissive Apache-2.0 license; however, it aggregates data about various LLMs that may have differing licensing conditions including but not limited to the 

## 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 open-llms if…

- License: open-llms is Apache-2.0, aikit is MIT.
- Pricing: Free and open-source models for commercial use are listed here. Usage of the models themselves varies by license, with some possibly requiring contributions or acknowledgements..
- Tags unique to open-llms: commercial, large language models, llm, llms.
- When you need a curated list of open-source large language models (LLMs) that are specifically licensed for commercial use.

### Choose aikit if…

- License: aikit is MIT, open-llms 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 NOT to use open-llms

- If you require proprietary or closed-source LLMs as this repository exclusively lists open-source models that are available for commercial use under permissive licenses such as Apache 2.0.
- For projects needing a detailed technical implementation guide of each listed LLM, since 'open-llms' acts primarily as an index rather than providing in-depth tutorials on implementing and training L4

## 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 open-llms and aikit?

open-llms: A list of open LLMs available for commercial use.. 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 open-llms over aikit?

Choose open-llms over aikit when License: open-llms is Apache-2.0, aikit is MIT; Pricing: Free and open-source models for commercial use are listed here. Usage of the models themselves varies by license, with some possibly requiring contributions or acknowledgements.; Tags unique to open-llms: commercial, large language models, llm, llms; When you need a curated list of open-source large language models (LLMs) that are specifically licensed for commercial use.

### When should I choose aikit over open-llms?

Choose aikit over open-llms when License: aikit is MIT, open-llms 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 avoid open-llms?

If you require proprietary or closed-source LLMs as this repository exclusively lists open-source models that are available for commercial use under permissive licenses such as Apache 2.0. For projects needing a detailed technical implementation guide of each listed LLM, since 'open-llms' acts primarily as an index rather than providing in-depth tutorials on implementing and training L4

### 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 open-llms or aikit more popular on GitHub?

open-llms has more GitHub stars (12,849 vs 534). Stars measure visibility, not whether either tool fits your constraints.

### Are open-llms and aikit open source?

Yes - both are open-source projects on GitHub (open-llms: Apache-2.0, aikit: MIT).

### Where can I find alternatives to open-llms or aikit?

GraphCanon lists graph-backed alternatives at [open-llms alternatives](/tools/eugeneyan-open-llms/alternatives) and [aikit alternatives](/tools/kaito-project-aikit/alternatives) ([open-llms markdown twin](/tools/eugeneyan-open-llms/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/eugeneyan-open-llms-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, open-llms or aikit?

open-llms: 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 open-llms and aikit?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [open-llms trust report](/tools/eugeneyan-open-llms/trust); [aikit trust report](/tools/kaito-project-aikit/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=eugeneyan-open-llms`](/api/graphcanon/graph?tool=eugeneyan-open-llms)
- 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/_