--- title: "graphrag-rs vs rag-time" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automataia-graphrag-rs-vs-microsoft-rag-time" tools: ["automataia-graphrag-rs", "microsoft-rag-time"] --- # graphrag-rs vs rag-time *GraphCanon updated Aug 23, 2026* ## Verdict Pick graphrag-rs if graphRAG-rs creates knowledge graphs from documents, enables natural language querying with customizable entity extraction and local LLM support, written in Rust; 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. [graphrag-rs](https://automataia.github.io/graphrag-rs/) reports 526 GitHub stars, 50 forks, and 0 open issues, last pushed Jun 2, 2026. [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 [graphrag-rs's repository](https://github.com/automataIA/graphrag-rs) and [rag-time's repository](https://github.com/microsoft/rag-time). | | [graphrag-rs](/tools/automataia-graphrag-rs.md) | [rag-time](/tools/microsoft-rag-time.md) | | --- | --- | --- | | Tagline | GraphRAG-rs implements Graph-based Retrieval Augmented Generation for knowledge graph creation and natural language querying with entity extraction and LLM integration. | RAG Time: A 5-week Learning Journey to Mastering RAG | | Stars | 526 | 898 | | Forks | 50 | 320 | | Open issues | 0 | 4 | | Language | Rust | Jupyter Notebook | | Adopt for | GraphRAG-rs creates knowledge graphs from documents, enables natural language querying with customizable entity extraction and local LLM support, written in Rust. | 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 | MIT | 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 | Data & Retrieval, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [graphrag-rs](/tools/automataia-graphrag-rs.md) | [rag-time](/tools/microsoft-rag-time.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 81d | 431d | | Open issues (now) | 0 | 4 | | Stars delta | +4 (30d) | +5 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/automataia-graphrag-rs/trust.md) | [trust report](/tools/microsoft-rag-time/trust.md) | ## Decision facts: graphrag-rs - **Adopt for:** GraphRAG-rs creates knowledge graphs from documents, enables natural language querying with customizable entity extraction and local LLM support, written in Rust. ## 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 graphrag-rs if… - graphrag-rs is primarily Rust; rag-time is Jupyter Notebook. - Tags unique to graphrag-rs: embeddings, entity-extraction, graphrag, knowledge-graph. - Need Rust-based implementation for integration into existing Rust projects ### Choose rag-time if… - rag-time is primarily Jupyter Notebook; graphrag-rs is Rust. - Requirements: Min 8 GB RAM. - Tags unique to rag-time: generative-ai, hybrid-search, indexing, language-model. - 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 graphrag-rs - Seeking solutions that offer cloud-hosted machine learning services directly - Projects that demand Python libraries due to ecosystem dependencies ## 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 graphrag-rs and rag-time? graphrag-rs: GraphRAG-rs implements Graph-based Retrieval Augmented Generation for knowledge graph creation and natural language querying with entity extraction and LLM integration.. 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 graphrag-rs over rag-time? Choose graphrag-rs over rag-time when graphrag-rs is primarily Rust; rag-time is Jupyter Notebook; Tags unique to graphrag-rs: embeddings, entity-extraction, graphrag, knowledge-graph; Need Rust-based implementation for integration into existing Rust projects. ### When should I choose rag-time over graphrag-rs? Choose rag-time over graphrag-rs when rag-time is primarily Jupyter Notebook; graphrag-rs is Rust; Requirements: Min 8 GB RAM; Tags unique to rag-time: generative-ai, hybrid-search, indexing, language-model; 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 graphrag-rs? Seeking solutions that offer cloud-hosted machine learning services directly Projects that demand Python libraries due to ecosystem dependencies ### 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 graphrag-rs or rag-time more popular on GitHub? rag-time has more GitHub stars (898 vs 526). Stars measure visibility, not whether either tool fits your constraints. ### Are graphrag-rs and rag-time open source? Yes - both are open-source projects on GitHub (graphrag-rs: MIT, rag-time: MIT). ### Where can I find alternatives to graphrag-rs or rag-time? GraphCanon lists graph-backed alternatives at [graphrag-rs alternatives](/tools/automataia-graphrag-rs/alternatives) and [rag-time alternatives](/tools/microsoft-rag-time/alternatives) ([graphrag-rs markdown twin](/tools/automataia-graphrag-rs/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/automataia-graphrag-rs-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, graphrag-rs or rag-time? graphrag-rs: Steady. 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 graphrag-rs and rag-time? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [graphrag-rs trust report](/tools/automataia-graphrag-rs/trust); [rag-time trust report](/tools/microsoft-rag-time/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automataia-graphrag-rs`](/api/graphcanon/graph?tool=automataia-graphrag-rs) - 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/_