--- title: "agentic-rag-for-dummies vs R2R" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/giovannipasq-agentic-rag-for-dummies-vs-sciphi-ai-r2r" tools: ["giovannipasq-agentic-rag-for-dummies", "sciphi-ai-r2r"] --- # agentic-rag-for-dummies vs R2R *GraphCanon updated Aug 17, 2026* ## Verdict Pick agentic-rag-for-dummies if agentic RAG for Dummies simplifies the setup of retrieval-augmented generation agents using LangGraph and Ollama models; pick R2R if r2R is a state-of-the-art AI retrieval system that's production-ready and can easily be integrated through its RESTful API, employing Agentic Retrieval-Augmented Generation (RAG). [agentic-rag-for-dummies](https://github.com/GiovanniPasq/agentic-rag-for-dummies) reports 3.9k GitHub stars, 499 forks, and 0 open issues, last pushed Jul 25, 2026. [R2R](https://github.com/SciPhi-AI/R2R) has 8.0k stars, 645 forks, and 122 open issues, last pushed Nov 7, 2025. Figures are from public GitHub metadata via [agentic-rag-for-dummies's repository](https://github.com/GiovanniPasq/agentic-rag-for-dummies) and [R2R's repository](https://github.com/SciPhi-AI/R2R). | | [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) | [R2R](/tools/sciphi-ai-r2r.md) | | --- | --- | --- | | Tagline | A modular Agentic RAG built with LangGraph for learning Retrieval-Augmented Generation Agents | SoTA production-ready AI retrieval system with RESTful API | | Stars | 3,893 | 7,967 | | Forks | 499 | 645 | | Open issues | 0 | 122 | | Language | Jupyter Notebook | Python | | Adopt for | Agentic RAG for Dummies simplifies the setup of retrieval-augmented generation agents using LangGraph and Ollama models. | R2R is a state-of-the-art AI retrieval system that's production-ready and can easily be integrated through its RESTful API, employing Agentic Retrieval-Augmented Generation (RAG). | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, Data & Retrieval | Data & Retrieval, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) | [R2R](/tools/sciphi-ai-r2r.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 19d | 283d | | Open issues (now) | 0 | 122 | | Stars delta | Unknown | +36 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/giovannipasq-agentic-rag-for-dummies/trust.md) | [trust report](/tools/sciphi-ai-r2r/trust.md) | ## Shared compatibility - **Python**: [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) - Python runtime; [R2R](/tools/sciphi-ai-r2r.md) - Python runtime ## Decision facts: agentic-rag-for-dummies - **Adopt for:** Agentic RAG for Dummies simplifies the setup of retrieval-augmented generation agents using LangGraph and Ollama models. ## Decision facts: R2R - **Adopt for:** R2R is a state-of-the-art AI retrieval system that's production-ready and can easily be integrated through its RESTful API, employing Agentic Retrieval-Augmented Generation (RAG). ## Choose when ### Choose agentic-rag-for-dummies if… - agentic-rag-for-dummies is primarily Jupyter Notebook; R2R is Python. - Tags unique to agentic-rag-for-dummies: agent, agentic-ai, bm25, gradio. - Also covers AI Agents. - When aiming to quickly develop a retrieval-augmented generation agent, thanks to its streamlined setup with LangGraph. ### Choose R2R if… - R2R is primarily Python; agentic-rag-for-dummies is Jupyter Notebook. - Tags unique to R2R: artificial-intelligence, large language models, python, question-answering. - Also covers Inference & Serving. - When you require top-tier accuracy in an AI-based retrieval system with the ease of integration provided by a RESTful API. ## When NOT to use agentic-rag-for-dummies - If smaller language model sizes are required as they might ignore retrieval instructions or hallucinate details. - Projects sensitive about Docker and system requirements must carefully review the outlined conditions for deployment. ## When NOT to use R2R - If the application does not benefit from or requires less sophisticated methods of data retrieval that do not include RAG. - When integrating with systems or in environments where network latency might impede performance due to its dependency on a RESTful API interface. ## Common questions ### What is the difference between agentic-rag-for-dummies and R2R? agentic-rag-for-dummies: A modular Agentic RAG built with LangGraph for learning Retrieval-Augmented Generation Agents. R2R: SoTA production-ready AI retrieval system with RESTful API. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentic-rag-for-dummies over R2R? Choose agentic-rag-for-dummies over R2R when agentic-rag-for-dummies is primarily Jupyter Notebook; R2R is Python; Tags unique to agentic-rag-for-dummies: agent, agentic-ai, bm25, gradio; Also covers AI Agents; When aiming to quickly develop a retrieval-augmented generation agent, thanks to its streamlined setup with LangGraph. ### When should I choose R2R over agentic-rag-for-dummies? Choose R2R over agentic-rag-for-dummies when R2R is primarily Python; agentic-rag-for-dummies is Jupyter Notebook; Tags unique to R2R: artificial-intelligence, large language models, python, question-answering; Also covers Inference & Serving; When you require top-tier accuracy in an AI-based retrieval system with the ease of integration provided by a RESTful API. ### When should I avoid agentic-rag-for-dummies? If smaller language model sizes are required as they might ignore retrieval instructions or hallucinate details. Projects sensitive about Docker and system requirements must carefully review the outlined conditions for deployment. ### When should I avoid R2R? If the application does not benefit from or requires less sophisticated methods of data retrieval that do not include RAG. When integrating with systems or in environments where network latency might impede performance due to its dependency on a RESTful API interface. ### Is agentic-rag-for-dummies or R2R more popular on GitHub? R2R has more GitHub stars (7,967 vs 3,893). Stars measure visibility, not whether either tool fits your constraints. ### Are agentic-rag-for-dummies and R2R open source? Yes - both are open-source projects on GitHub (agentic-rag-for-dummies: MIT, R2R: MIT). ### Where can I find alternatives to agentic-rag-for-dummies or R2R? GraphCanon lists graph-backed alternatives at [agentic-rag-for-dummies alternatives](/tools/giovannipasq-agentic-rag-for-dummies/alternatives) and [R2R alternatives](/tools/sciphi-ai-r2r/alternatives) ([agentic-rag-for-dummies markdown twin](/tools/giovannipasq-agentic-rag-for-dummies/alternatives.md), [R2R markdown twin](/tools/sciphi-ai-r2r/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/giovannipasq-agentic-rag-for-dummies-vs-sciphi-ai-r2r.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentic-rag-for-dummies or R2R? agentic-rag-for-dummies: Active. R2R: Slowing. 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 agentic-rag-for-dummies and R2R? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentic-rag-for-dummies trust report](/tools/giovannipasq-agentic-rag-for-dummies/trust); [R2R trust report](/tools/sciphi-ai-r2r/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=giovannipasq-agentic-rag-for-dummies`](/api/graphcanon/graph?tool=giovannipasq-agentic-rag-for-dummies) - 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/_