--- title: "rag-time vs gpt4all" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/microsoft-rag-time-vs-nomic-ai-gpt4all" tools: ["microsoft-rag-time", "nomic-ai-gpt4all"] --- # rag-time vs gpt4all *GraphCanon updated Jul 12, 2026* ## Verdict Pick rag-time when rag-time is primarily Jupyter Notebook; gpt4all is C++; pick gpt4all when gpt4all is primarily C++; rag-time is Jupyter Notebook. [rag-time](https://github.com/microsoft/rag-time) reports 894 GitHub stars, 316 forks, and 4 open issues, last pushed Jun 17, 2025. [gpt4all](https://nomic.ai/gpt4all) has 77k stars, 8.3k forks, and 768 open issues, last pushed May 27, 2025. Figures are from public GitHub metadata via [rag-time's repository](https://github.com/microsoft/rag-time) and [gpt4all's repository](https://github.com/nomic-ai/gpt4all). | | [rag-time](/tools/microsoft-rag-time.md) | [gpt4all](/tools/nomic-ai-gpt4all.md) | | --- | --- | --- | | Tagline | RAG Time: A 5-week Learning Journey to Mastering RAG | Run Local LLMs on Any Device | | Stars | 894 | 77,386 | | Forks | 316 | 8,304 | | Open issues | 4 | 768 | | Language | Jupyter Notebook | C++ | | Adopt for | - | GPT4All is an open-source project designed to facilitate the local deployment of large language models (LLMs). It supports commercial usage with a permissive MIT license and is implemented in C++. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Inference & Serving, LLM Frameworks, Vector Databases | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [rag-time](/tools/microsoft-rag-time.md) | [gpt4all](/tools/nomic-ai-gpt4all.md) | | --- | --- | --- | | Days since push | 388d | 409d | | Open issues (now) | 4 | 768 | | Full report | [trust report](/tools/microsoft-rag-time/trust.md) | [trust report](/tools/nomic-ai-gpt4all/trust.md) | ## Decision facts: gpt4all - **Adopt for:** GPT4All is an open-source project designed to facilitate the local deployment of large language models (LLMs). It supports commercial usage with a permissive MIT license and is implemented in C++. ## Choose when ### Choose rag-time if… - rag-time is primarily Jupyter Notebook; gpt4all is C++. - Tags unique to rag-time: ai, azure, binary-quantization, generative-ai. - Also covers Vector Databases. ### Choose gpt4all if… - gpt4all is primarily C++; rag-time is Jupyter Notebook. - Tags unique to gpt4all: ai-chat, llm-inference. - - When you require on-device inference capabilities without reliance on cloud services. ## When NOT to use rag-time - Last GitHub push was 389 days ago (dormant maintenance, Jun 17, 2025). Validate activity before betting a new project on rag-time. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use gpt4all - - In environments strictly requiring models supported by mainstream frameworks like TensorFlow or PyTorch, as GPT4All focuses on its standalone implementation. - - When the project demands seamless integration with popular cloud infrastructures that don't align well with local deployments. ## Common questions ### What is the difference between rag-time and gpt4all? rag-time: RAG Time: A 5-week Learning Journey to Mastering RAG. gpt4all: Run Local LLMs on Any Device. See the comparison table for live GitHub stats and shared categories. ### When should I choose rag-time over gpt4all? Choose rag-time over gpt4all when rag-time is primarily Jupyter Notebook; gpt4all is C++; Tags unique to rag-time: ai, azure, binary-quantization, generative-ai; Also covers Vector Databases. ### When should I choose gpt4all over rag-time? Choose gpt4all over rag-time when gpt4all is primarily C++; rag-time is Jupyter Notebook; Tags unique to gpt4all: ai-chat, llm-inference; - When you require on-device inference capabilities without reliance on cloud services. ### When should I avoid rag-time? Last GitHub push was 389 days ago (dormant maintenance, Jun 17, 2025). Validate activity before betting a new project on rag-time. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid gpt4all? - In environments strictly requiring models supported by mainstream frameworks like TensorFlow or PyTorch, as GPT4All focuses on its standalone implementation. - When the project demands seamless integration with popular cloud infrastructures that don't align well with local deployments. ### Is rag-time or gpt4all more popular on GitHub? gpt4all has more GitHub stars (77,386 vs 894). Stars measure visibility, not whether either tool fits your constraints. ### Are rag-time and gpt4all open source? Yes - both are open-source projects on GitHub (rag-time: MIT, gpt4all: MIT). ### Where can I find alternatives to rag-time or gpt4all? GraphCanon lists graph-backed alternatives at [rag-time alternatives](/tools/microsoft-rag-time/alternatives) and [gpt4all alternatives](/tools/nomic-ai-gpt4all/alternatives) ([rag-time markdown twin](/tools/microsoft-rag-time/alternatives.md), [gpt4all markdown twin](/tools/nomic-ai-gpt4all/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/microsoft-rag-time-vs-nomic-ai-gpt4all.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, rag-time or gpt4all? rag-time: Dormant. gpt4all: 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 rag-time and gpt4all? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [rag-time trust report](/tools/microsoft-rag-time/trust); [gpt4all trust report](/tools/nomic-ai-gpt4all/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=microsoft-rag-time`](/api/graphcanon/graph?tool=microsoft-rag-time) - 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/_