--- title: "LLM-Adapters vs aikit" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agi-edgerunners-llm-adapters-vs-kaito-project-aikit" tools: ["agi-edgerunners-llm-adapters", "kaito-project-aikit"] --- # LLM-Adapters vs aikit *GraphCanon updated Aug 24, 2026* ## Verdict Pick LLM-Adapters if lLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing; 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. [LLM-Adapters](https://arxiv.org/abs/2304.01933) reports 1.2k GitHub stars, 115 forks, and 55 open issues, last pushed Mar 10, 2024. [aikit](https://kaito-project.github.io/aikit/) has 537 stars, 57 forks, and 40 open issues, last pushed Aug 24, 2026. Figures are from public GitHub metadata via [LLM-Adapters's repository](https://github.com/AGI-Edgerunners/LLM-Adapters) and [aikit's repository](https://github.com/kaito-project/aikit). | | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Tagline | Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs | Fine-tune, build, and deploy open-source LLMs easily! | | Stars | 1,233 | 537 | | Forks | 115 | 57 | | Open issues | 55 | 40 | | Language | Python | Go | | Adopt for | LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing. | 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 | Apache-2.0 | MIT | | Categories | LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 896d | 0d | | Open issues (now) | 55 | 40 | | Stars delta | -1 (30d) | +3 (30d) | | Open issues delta | 0 (30d) | -3 (30d) | | Full report | [trust report](/tools/agi-edgerunners-llm-adapters/trust.md) | [trust report](/tools/kaito-project-aikit/trust.md) | ## Decision facts: LLM-Adapters - **Adopt for:** LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing. ## 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 LLM-Adapters if… - LLM-Adapters is primarily Python; aikit is Go. - License: LLM-Adapters is Apache-2.0, aikit is MIT. - Tags unique to LLM-Adapters: adapters, large language models, parameter-efficient. - Optimizing resource usage when you need to fine-tune large language models without altering their core parameters ### Choose aikit if… - aikit is primarily Go; LLM-Adapters is Python. - License: aikit is MIT, LLM-Adapters is Apache-2.0. - Tags unique to aikit: ai, buildkit, chatgpt, docker. - Also covers Inference & Serving. - 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 LLM-Adapters - You require a full retraining approach that modifies all model weights, not just adapters - Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023 ## 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 LLM-Adapters and aikit? LLM-Adapters: Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs. 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 LLM-Adapters over aikit? Choose LLM-Adapters over aikit when LLM-Adapters is primarily Python; aikit is Go; License: LLM-Adapters is Apache-2.0, aikit is MIT; Tags unique to LLM-Adapters: adapters, large language models, parameter-efficient; Optimizing resource usage when you need to fine-tune large language models without altering their core parameters. ### When should I choose aikit over LLM-Adapters? Choose aikit over LLM-Adapters when aikit is primarily Go; LLM-Adapters is Python; License: aikit is MIT, LLM-Adapters is Apache-2.0; Tags unique to aikit: ai, buildkit, chatgpt, docker; Also covers Inference & Serving; 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 LLM-Adapters? You require a full retraining approach that modifies all model weights, not just adapters Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023 ### 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 LLM-Adapters or aikit more popular on GitHub? LLM-Adapters has more GitHub stars (1,233 vs 537). Stars measure visibility, not whether either tool fits your constraints. ### Are LLM-Adapters and aikit open source? Yes - both are open-source projects on GitHub (LLM-Adapters: Apache-2.0, aikit: MIT). ### Where can I find alternatives to LLM-Adapters or aikit? GraphCanon lists graph-backed alternatives at [LLM-Adapters alternatives](/tools/agi-edgerunners-llm-adapters/alternatives) and [aikit alternatives](/tools/kaito-project-aikit/alternatives) ([LLM-Adapters markdown twin](/tools/agi-edgerunners-llm-adapters/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/agi-edgerunners-llm-adapters-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, LLM-Adapters or aikit? LLM-Adapters: 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 LLM-Adapters and aikit? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM-Adapters trust report](/tools/agi-edgerunners-llm-adapters/trust); [aikit trust report](/tools/kaito-project-aikit/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agi-edgerunners-llm-adapters`](/api/graphcanon/graph?tool=agi-edgerunners-llm-adapters) - 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/_