--- title: "Dot vs rag-time" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alexpinel-dot-vs-microsoft-rag-time" tools: ["alexpinel-dot", "microsoft-rag-time"] --- # Dot vs rag-time *GraphCanon updated Aug 23, 2026* ## Verdict Pick Dot if local, JavaScript-based all-in-one solution for Text-To-Speech, RAG models, and working with LLMs; pick rag-time if rAG Time is tailored for those looking to systematically learn and apply Retrieval-Augmented Generation techniques in a structured 5-week program. [Dot](https://dotapp.uk/) reports 1.9k GitHub stars, 110 forks, and 14 open issues, last pushed Dec 9, 2024. [rag-time](https://github.com/microsoft/rag-time) has 898 stars, 320 forks, and 4 open issues, last pushed Jun 17, 2025. Figures are from public GitHub metadata via [Dot's repository](https://github.com/alexpinel/Dot) and [rag-time's repository](https://github.com/microsoft/rag-time). | | [Dot](/tools/alexpinel-dot.md) | [rag-time](/tools/microsoft-rag-time.md) | | --- | --- | --- | | Tagline | Text-To-Speech, RAG, and LLMs. All local! | RAG Time: A 5-week Learning Journey to Mastering RAG | | Stars | 1,911 | 898 | | Forks | 110 | 320 | | Open issues | 14 | 4 | | Language | JavaScript | Jupyter Notebook | | Adopt for | Local, JavaScript-based all-in-one solution for Text-To-Speech, RAG models, and working with LLMs | RAG Time is tailored for those looking to systematically learn and apply Retrieval-Augmented Generation techniques in a structured 5-week program. | | Persona | - | - | | Runtime | - | - | | License | GPL-3.0 | The MIT License provides freedom to use, copy, modify and distribute the software provided that copyright and license information are retained. | | Categories | Data & Retrieval, LLM Frameworks, Speech & Audio | Data & Retrieval, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Dot](/tools/alexpinel-dot.md) | [rag-time](/tools/microsoft-rag-time.md) | | --- | --- | --- | | Days since push | 620d | 431d | | Open issues (now) | 14 | 4 | | Stars delta | +1 (30d) | +5 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/alexpinel-dot/trust.md) | [trust report](/tools/microsoft-rag-time/trust.md) | ## Decision facts: Dot - **Adopt for:** Local, JavaScript-based all-in-one solution for Text-To-Speech, RAG models, and working with LLMs ## Decision facts: rag-time - **Requirements:** Min 8 GB RAM - **Adopt for:** RAG Time is tailored for those looking to systematically learn and apply Retrieval-Augmented Generation techniques in a structured 5-week program. - **License detail:** The MIT License provides freedom to use, copy, modify and distribute the software provided that copyright and license information are retained. ## Choose when ### Choose Dot if… - Dot is primarily JavaScript; rag-time is Jupyter Notebook. - License: Dot is GPL-3.0, rag-time is MIT. - Tags unique to Dot: document-chat, embeddings, faiss, langchain. - Also covers Speech & Audio. - When you are working in a local environment and have projects that require Text-To-Speech capabilities along with RAG and LLM functionalities. ### Choose rag-time if… - rag-time is primarily Jupyter Notebook; Dot is JavaScript. - License: rag-time is MIT, Dot is GPL-3.0. - Requirements: Min 8 GB RAM. - Tags unique to rag-time: ai, generative-ai, hybrid-search, indexing. - Also covers Model Training. - When you need a detailed, week-by-week learning path specifically focused on the nuances of RAG techniques, from basics to advanced applications. ## When NOT to use Dot - If your team does not have proficiency in JavaScript or the requirement is to use a language other than JavaScript for development purposes. - When the need arises for cloud-based services that provide more scalable and maintainable infrastructure, as Dot works strictly in a local environment. - For organizations that require real-time speech processing at scale without self-hosting capabilities where reliability and continuous availability are paramount. ## When NOT to use rag-time - If you prefer ad-hoc or self-directed learning without a structured timeline. Other tools may offer more flexible formats, which can be preferable if adhering to strict schedules is not ideal. - When your focus is solely on either indexing or generation models and not the integration of both for RAG. In this case, specialized resources for just indexing or model training might suffice. ## Common questions ### What is the difference between Dot and rag-time? Dot: Text-To-Speech, RAG, and LLMs. All local!. rag-time: RAG Time: A 5-week Learning Journey to Mastering RAG. See the comparison table for live GitHub stats and shared categories. ### When should I choose Dot over rag-time? Choose Dot over rag-time when Dot is primarily JavaScript; rag-time is Jupyter Notebook; License: Dot is GPL-3.0, rag-time is MIT; Tags unique to Dot: document-chat, embeddings, faiss, langchain; Also covers Speech & Audio; When you are working in a local environment and have projects that require Text-To-Speech capabilities along with RAG and LLM functionalities. ### When should I choose rag-time over Dot? Choose rag-time over Dot when rag-time is primarily Jupyter Notebook; Dot is JavaScript; License: rag-time is MIT, Dot is GPL-3.0; Requirements: Min 8 GB RAM; Tags unique to rag-time: ai, generative-ai, hybrid-search, indexing; Also covers Model Training; When you need a detailed, week-by-week learning path specifically focused on the nuances of RAG techniques, from basics to advanced applications. ### When should I avoid Dot? If your team does not have proficiency in JavaScript or the requirement is to use a language other than JavaScript for development purposes. When the need arises for cloud-based services that provide more scalable and maintainable infrastructure, as Dot works strictly in a local environment. For organizations that require real-time speech processing at scale without self-hosting capabilities where reliability and continuous availability are paramount. ### When should I avoid rag-time? If you prefer ad-hoc or self-directed learning without a structured timeline. Other tools may offer more flexible formats, which can be preferable if adhering to strict schedules is not ideal. When your focus is solely on either indexing or generation models and not the integration of both for RAG. In this case, specialized resources for just indexing or model training might suffice. ### Is Dot or rag-time more popular on GitHub? Dot has more GitHub stars (1,911 vs 898). Stars measure visibility, not whether either tool fits your constraints. ### Are Dot and rag-time open source? Yes - both are open-source projects on GitHub (Dot: GPL-3.0, rag-time: MIT). ### Where can I find alternatives to Dot or rag-time? GraphCanon lists graph-backed alternatives at [Dot alternatives](/tools/alexpinel-dot/alternatives) and [rag-time alternatives](/tools/microsoft-rag-time/alternatives) ([Dot markdown twin](/tools/alexpinel-dot/alternatives.md), [rag-time markdown twin](/tools/microsoft-rag-time/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/alexpinel-dot-vs-microsoft-rag-time.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Dot or rag-time? Dot: Dormant. rag-time: 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 Dot and rag-time? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Dot trust report](/tools/alexpinel-dot/trust); [rag-time trust report](/tools/microsoft-rag-time/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alexpinel-dot`](/api/graphcanon/graph?tool=alexpinel-dot) - 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/_