--- title: "RAG-FiT vs aikit" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/intellabs-rag-fit-vs-kaito-project-aikit" tools: ["intellabs-rag-fit", "kaito-project-aikit"] --- # RAG-FiT vs aikit *GraphCanon updated Aug 24, 2026* ## Verdict Pick RAG-FiT if rAG-FiT is a Python framework that enables developers to fine-tune large language models specifically for Retriever-Augmented Generation (RAG) tasks, with strengths in evaluation and information retrieval; 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. [RAG-FiT](https://intellabs.github.io/RAG-FiT/) reports 769 GitHub stars, 61 forks, and 1 open issues, last pushed Jun 8, 2026. [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 [RAG-FiT's repository](https://github.com/IntelLabs/RAG-FiT) and [aikit's repository](https://github.com/kaito-project/aikit). | | [RAG-FiT](/tools/intellabs-rag-fit.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Tagline | Framework for enhancing LLMs for RAG tasks using fine-tuning | Fine-tune, build, and deploy open-source LLMs easily! | | Stars | 769 | 537 | | Forks | 61 | 57 | | Open issues | 1 | 40 | | Language | Python | Go | | Adopt for | RAG-FiT is a Python framework that enables developers to fine-tune large language models specifically for Retriever-Augmented Generation (RAG) tasks, with strengths in evaluation and information retrieval. | 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 | RAG-FiT operates under the Apache-2.0 license, providing a permissive free software license that permits reuse within proprietary software. | MIT | | Categories | Evaluation & Observability, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [RAG-FiT](/tools/intellabs-rag-fit.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 76d | 0d | | Open issues (now) | 1 | 40 | | Stars delta | +1 (30d) | +3 (30d) | | Open issues delta | 0 (30d) | -3 (30d) | | Full report | [trust report](/tools/intellabs-rag-fit/trust.md) | [trust report](/tools/kaito-project-aikit/trust.md) | ## Decision facts: RAG-FiT - **Requirements:** This framework requires proficiency in Python and an understanding of RAG tasks to be effectively utilized. - **Adopt for:** RAG-FiT is a Python framework that enables developers to fine-tune large language models specifically for Retriever-Augmented Generation (RAG) tasks, with strengths in evaluation and information retrieval. - **License detail:** RAG-FiT operates under the Apache-2.0 license, providing a permissive free software license that permits reuse within proprietary software. ## 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 RAG-FiT if… - RAG-FiT is primarily Python; aikit is Go. - License: RAG-FiT is Apache-2.0, aikit is MIT. - Requirements: This framework requires proficiency in Python and an understanding of RAG tasks to be effectively utilized.. - Tags unique to RAG-FiT: evaluation, information-retrieval, llm, nlp. - Also covers Evaluation & Observability. - When seeking to improve performance of LLMs in NLP tasks requiring RAG capabilities, like question-answering or semantic search ### Choose aikit if… - aikit is primarily Go; RAG-FiT is Python. - License: aikit is MIT, RAG-FiT is Apache-2.0. - Tags unique to aikit: ai, buildkit, chatgpt, docker. - Also covers Inference & Serving, 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 NOT to use RAG-FiT - If project needs are more aligned with traditional fine-tuning methods that do not specifically enhance RAG capabilities, another tool might be more suitable - In scenarios where the development team lacks proficiency in Python, as RAG-FiT is Python-based and may have a steeper learning curve for non-Python developers ## 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 RAG-FiT and aikit? RAG-FiT: Framework for enhancing LLMs for RAG tasks using fine-tuning. 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 RAG-FiT over aikit? Choose RAG-FiT over aikit when RAG-FiT is primarily Python; aikit is Go; License: RAG-FiT is Apache-2.0, aikit is MIT; Requirements: This framework requires proficiency in Python and an understanding of RAG tasks to be effectively utilized.; Tags unique to RAG-FiT: evaluation, information-retrieval, llm, nlp; Also covers Evaluation & Observability; When seeking to improve performance of LLMs in NLP tasks requiring RAG capabilities, like question-answering or semantic search. ### When should I choose aikit over RAG-FiT? Choose aikit over RAG-FiT when aikit is primarily Go; RAG-FiT is Python; License: aikit is MIT, RAG-FiT is Apache-2.0; Tags unique to aikit: ai, buildkit, chatgpt, docker; Also covers Inference & Serving, 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 avoid RAG-FiT? If project needs are more aligned with traditional fine-tuning methods that do not specifically enhance RAG capabilities, another tool might be more suitable In scenarios where the development team lacks proficiency in Python, as RAG-FiT is Python-based and may have a steeper learning curve for non-Python developers ### 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 RAG-FiT or aikit more popular on GitHub? RAG-FiT has more GitHub stars (769 vs 537). Stars measure visibility, not whether either tool fits your constraints. ### Are RAG-FiT and aikit open source? Yes - both are open-source projects on GitHub (RAG-FiT: Apache-2.0, aikit: MIT). ### Where can I find alternatives to RAG-FiT or aikit? GraphCanon lists graph-backed alternatives at [RAG-FiT alternatives](/tools/intellabs-rag-fit/alternatives) and [aikit alternatives](/tools/kaito-project-aikit/alternatives) ([RAG-FiT markdown twin](/tools/intellabs-rag-fit/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/intellabs-rag-fit-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, RAG-FiT or aikit? RAG-FiT: Steady. 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 RAG-FiT and aikit? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [RAG-FiT trust report](/tools/intellabs-rag-fit/trust); [aikit trust report](/tools/kaito-project-aikit/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=intellabs-rag-fit`](/api/graphcanon/graph?tool=intellabs-rag-fit) - 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/_