--- title: "RAG_Techniques vs HippoRAG" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nirdiamant-rag-techniques-vs-osu-nlp-group-hipporag" tools: ["nirdiamant-rag-techniques", "osu-nlp-group-hipporag"] --- # RAG_Techniques vs HippoRAG *GraphCanon updated Sep 20, 2026* ## Verdict Pick RAG_Techniques if rAG_Techniques offers detailed Jupyter Notebook tutorials on advanced Retrieval-Augmented Generation techniques, focusing on embeddings, semantic search, and integration with LLMs like GPT and LLaMA; pick HippoRAG if hippoRAG is a RAG framework that integrates knowledge from external documents into LLMs, inspired by human long-term memory, and supports personalized page rank and knowledge graphs. [RAG_Techniques](https://diamant-ai.com) reports 30k GitHub stars, 3.6k forks, and 5 open issues, last pushed Sep 15, 2026. [HippoRAG](https://arxiv.org/abs/2405.14831) has 4.0k stars, 428 forks, and 9 open issues, last pushed Sep 3, 2026. Figures are from public GitHub metadata via [RAG_Techniques's repository](https://github.com/NirDiamant/RAG_Techniques) and [HippoRAG's repository](https://github.com/OSU-NLP-Group/HippoRAG). | | [RAG_Techniques](/tools/nirdiamant-rag-techniques.md) | [HippoRAG](/tools/osu-nlp-group-hipporag.md) | | --- | --- | --- | | Tagline | Showcases advanced Retrieval-Augmented Generation (RAG) techniques with detailed notebook tutorials. | A RAG framework enabling LLMs to integrate knowledge from external documents | | Stars | 29,525 | 4,012 | | Forks | 3,611 | 428 | | Open issues | 5 | 9 | | Language | Jupyter Notebook | Python | | Adopt for | RAG_Techniques offers detailed Jupyter Notebook tutorials on advanced Retrieval-Augmented Generation techniques, focusing on embeddings, semantic search, and integration with LLMs like GPT and LLaMA. | HippoRAG is a RAG framework that integrates knowledge from external documents into LLMs, inspired by human long-term memory, and supports personalized page rank and knowledge graphs. | | Persona | - | - | | Runtime | - | - | | License | Licensed under a custom non-commercial license. | MIT | | Categories | Data & Retrieval, LLM Frameworks, Model Training | Data & Retrieval, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [RAG_Techniques](/tools/nirdiamant-rag-techniques.md) | [HippoRAG](/tools/osu-nlp-group-hipporag.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 2d | 14d | | Open issues (now) | 5 | 9 | | Stars delta | +449 (30d) | +110 (30d) | | Open issues delta | -9 (30d) | +2 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/nirdiamant-rag-techniques/trust.md) | [trust report](/tools/osu-nlp-group-hipporag/trust.md) | ## Decision facts: RAG_Techniques - **Pricing:** freemium - Free to use for non-commercial purposes. - **Requirements:** Min 8 GB RAM; Requires a Python environment and Jupyter Notebook for running the tutorials. - **Adopt for:** RAG_Techniques offers detailed Jupyter Notebook tutorials on advanced Retrieval-Augmented Generation techniques, focusing on embeddings, semantic search, and integration with LLMs like GPT and LLaMA. - **License detail:** Licensed under a custom non-commercial license. ## Decision facts: HippoRAG - **Requirements:** Ensure you have Python 3.10 installed.; Set environment variables such as `CUDA_VISIBLE_DEVICES`, `HF_HOME`, and `OPENAI_API_KEY` as needed.; Install HippoRAG using Conda or `uv` for a project-local environment. - **Adopt for:** HippoRAG is a RAG framework that integrates knowledge from external documents into LLMs, inspired by human long-term memory, and supports personalized page rank and knowledge graphs. ## Choose when ### Choose RAG_Techniques if… - RAG_Techniques is primarily Jupyter Notebook; HippoRAG is Python. - License: RAG_Techniques is Other, HippoRAG is MIT. - Pricing: Free to use for non-commercial purposes.. - Requirements: Min 8 GB RAM; Requires a Python environment and Jupyter Notebook for running the tutorials.. - Tags unique to RAG_Techniques: ai, embeddings, information retrieval, llm. - Also covers Model Training. - When you need comprehensive tutorials on RAG techniques, including embeddings and semantic search, with practical examples in Jupyter Notebooks. ### Choose HippoRAG if… - HippoRAG is primarily Python; RAG_Techniques is Jupyter Notebook. - License: HippoRAG is MIT, RAG_Techniques is Other. - Requirements: Ensure you have Python 3.10 installed.; Set environment variables such as `CUDA_VISIBLE_DEVICES`, `HF_HOME`, and `OPENAI_API_KEY` as needed.; Install HippoRAG using Conda or `uv` for a project-local environment.. - Tags unique to HippoRAG: external documents, knowledge graphs, knowledge integration, llms. - When you need a framework that integrates knowledge from external documents into LLMs in a manner inspired by human long-term memory. ## When NOT to use RAG_Techniques - If you are looking for a repository that focuses on a broader range of AI techniques beyond RAG, as RAG_Techniques is specialized in Retrieval-Augmented Generation. - If you require a repository that supports commercial use, as RAG_Techniques is licensed under a custom non-commercial license. ## When NOT to use HippoRAG - If your project does not require the integration of knowledge from external documents into LLMs in a manner inspired by human long-term memory. - When personalized page rank and knowledge graphs are not necessary for your project's knowledge retrieval and integration needs. - If you are not using Python 3.10 or do not wish to manage a project-local environment with Conda or `uv`. ## Common questions ### What is the difference between RAG_Techniques and HippoRAG? RAG_Techniques: Showcases advanced Retrieval-Augmented Generation (RAG) techniques with detailed notebook tutorials.. HippoRAG: A RAG framework enabling LLMs to integrate knowledge from external documents. See the comparison table for live GitHub stats and shared categories. ### When should I choose RAG_Techniques over HippoRAG? Choose RAG_Techniques over HippoRAG when RAG_Techniques is primarily Jupyter Notebook; HippoRAG is Python; License: RAG_Techniques is Other, HippoRAG is MIT; Pricing: Free to use for non-commercial purposes.; Requirements: Min 8 GB RAM; Requires a Python environment and Jupyter Notebook for running the tutorials.; Tags unique to RAG_Techniques: ai, embeddings, information retrieval, llm; Also covers Model Training; When you need comprehensive tutorials on RAG techniques, including embeddings and semantic search, with practical examples in Jupyter Notebooks. ### When should I choose HippoRAG over RAG_Techniques? Choose HippoRAG over RAG_Techniques when HippoRAG is primarily Python; RAG_Techniques is Jupyter Notebook; License: HippoRAG is MIT, RAG_Techniques is Other; Requirements: Ensure you have Python 3.10 installed.; Set environment variables such as `CUDA_VISIBLE_DEVICES`, `HF_HOME`, and `OPENAI_API_KEY` as needed.; Install HippoRAG using Conda or `uv` for a project-local environment.; Tags unique to HippoRAG: external documents, knowledge graphs, knowledge integration, llms; When you need a framework that integrates knowledge from external documents into LLMs in a manner inspired by human long-term memory. ### When should I avoid RAG_Techniques? If you are looking for a repository that focuses on a broader range of AI techniques beyond RAG, as RAG_Techniques is specialized in Retrieval-Augmented Generation. If you require a repository that supports commercial use, as RAG_Techniques is licensed under a custom non-commercial license. ### When should I avoid HippoRAG? If your project does not require the integration of knowledge from external documents into LLMs in a manner inspired by human long-term memory. When personalized page rank and knowledge graphs are not necessary for your project's knowledge retrieval and integration needs. If you are not using Python 3.10 or do not wish to manage a project-local environment with Conda or `uv`. ### Is RAG_Techniques or HippoRAG more popular on GitHub? RAG_Techniques has more GitHub stars (29,525 vs 4,012). Stars measure visibility, not whether either tool fits your constraints. ### Are RAG_Techniques and HippoRAG open source? Yes - both are open-source projects on GitHub (RAG_Techniques: Other, HippoRAG: MIT). ### Where can I find alternatives to RAG_Techniques or HippoRAG? GraphCanon lists graph-backed alternatives at [RAG_Techniques alternatives](/tools/nirdiamant-rag-techniques/alternatives) and [HippoRAG alternatives](/tools/osu-nlp-group-hipporag/alternatives) ([RAG_Techniques markdown twin](/tools/nirdiamant-rag-techniques/alternatives.md), [HippoRAG markdown twin](/tools/osu-nlp-group-hipporag/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/nirdiamant-rag-techniques-vs-osu-nlp-group-hipporag.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, RAG_Techniques or HippoRAG? RAG_Techniques: Very active. HippoRAG: 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_Techniques and HippoRAG? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [RAG_Techniques trust report](/tools/nirdiamant-rag-techniques/trust); [HippoRAG trust report](/tools/osu-nlp-group-hipporag/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nirdiamant-rag-techniques`](/api/graphcanon/graph?tool=nirdiamant-rag-techniques) - 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/_