---
title: "all-in-rag vs agentic-rag-for-dummies"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/datawhalechina-all-in-rag-vs-giovannipasq-agentic-rag-for-dummies"
tools: ["datawhalechina-all-in-rag", "giovannipasq-agentic-rag-for-dummies"]
---

# all-in-rag vs agentic-rag-for-dummies

*GraphCanon updated Aug 18, 2026*

## Verdict

Pick all-in-rag if all-in-rag is a comprehensive guide for developers to learn about and implement RAG (Retrieval-Augmented Generation) technology, with a focus on end-to-end practical applications and multi-modal support. It provides an体系; pick agentic-rag-for-dummies if agentic RAG for Dummies simplifies the setup of retrieval-augmented generation agents using LangGraph and Ollama models.

[all-in-rag](https://datawhalechina.github.io/all-in-rag/) reports 10k GitHub stars, 5.2k forks, and 23 open issues, last pushed Jul 29, 2026. [agentic-rag-for-dummies](https://github.com/GiovanniPasq/agentic-rag-for-dummies) has 3.9k stars, 499 forks, and 0 open issues, last pushed Jul 25, 2026. Figures are from public GitHub metadata via [all-in-rag's repository](https://github.com/datawhalechina/all-in-rag) and [agentic-rag-for-dummies's repository](https://github.com/GiovanniPasq/agentic-rag-for-dummies).

| | [all-in-rag](/tools/datawhalechina-all-in-rag.md) | [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) |
| --- | --- | --- |
| Tagline | 🔍 检索增强生成 (RAG) 技术全栈指南 | A modular Agentic RAG built with LangGraph for learning Retrieval-Augmented Generation Agents |
| Stars | 10,437 | 3,893 |
| Forks | 5,170 | 499 |
| Open issues | 23 | 0 |
| Language | Python | Jupyter Notebook |
| Adopt for | all-in-rag is a comprehensive guide for developers to learn about and implement RAG (Retrieval-Augmented Generation) technology, with a focus on end-to-end practical applications and multi-modal support. It provides an体系 | Agentic RAG for Dummies simplifies the setup of retrieval-augmented generation agents using LangGraph and Ollama models. |
| Persona | - | - |
| Runtime | - | - |
| License | - | MIT |
| Categories | Data & Retrieval, LLM Frameworks | AI Agents, Data & Retrieval |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [all-in-rag](/tools/datawhalechina-all-in-rag.md) | [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) |
| --- | --- | --- |
| Days since push | 20d | 19d |
| Open issues (now) | 23 | 0 |
| Stars delta | +815 (30d) | Unknown |
| Open issues delta | +3 (30d) | Unknown |
| Owner type | Organization | User |
| Full report | [trust report](/tools/datawhalechina-all-in-rag/trust.md) | [trust report](/tools/giovannipasq-agentic-rag-for-dummies/trust.md) |

## Shared compatibility

- **Python**: [all-in-rag](/tools/datawhalechina-all-in-rag.md) - Python runtime; [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) - Python runtime

## Decision facts: all-in-rag

- **Adopt for:** all-in-rag is a comprehensive guide for developers to learn about and implement RAG (Retrieval-Augmented Generation) technology, with a focus on end-to-end practical applications and multi-modal support. It provides an体系

## 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.

## Choose when

### Choose all-in-rag if…

- all-in-rag is primarily Python; agentic-rag-for-dummies is Jupyter Notebook.
- Tags unique to all-in-rag: ai, embedding, milvus, multimodal.
- Also covers LLM Frameworks.
- - When you want a comprehensive resource that covers both the theoretical foundations and practical application of RAG.

### Choose agentic-rag-for-dummies if…

- agentic-rag-for-dummies is primarily Jupyter Notebook; all-in-rag 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 NOT to use all-in-rag

- - Avoid if you are looking for a solution that only focuses on theoretical aspects without practical implementation guidance.
- - If your project does not require multi-modal support or is solely focused on text-based applications, more specialized tools might provide better optimization.
- - Not suitable if you're seeking quick prototyping or a light-weight framework; all-in-rag emphasizes comprehensive learning and production-ready practices.

## 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.

## Common questions

### What is the difference between all-in-rag and agentic-rag-for-dummies?

all-in-rag: 🔍 检索增强生成 (RAG) 技术全栈指南. agentic-rag-for-dummies: A modular Agentic RAG built with LangGraph for learning Retrieval-Augmented Generation Agents. See the comparison table for live GitHub stats and shared categories.

### When should I choose all-in-rag over agentic-rag-for-dummies?

Choose all-in-rag over agentic-rag-for-dummies when all-in-rag is primarily Python; agentic-rag-for-dummies is Jupyter Notebook; Tags unique to all-in-rag: ai, embedding, milvus, multimodal; Also covers LLM Frameworks; - When you want a comprehensive resource that covers both the theoretical foundations and practical application of RAG.

### When should I choose agentic-rag-for-dummies over all-in-rag?

Choose agentic-rag-for-dummies over all-in-rag when agentic-rag-for-dummies is primarily Jupyter Notebook; all-in-rag 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 avoid all-in-rag?

- Avoid if you are looking for a solution that only focuses on theoretical aspects without practical implementation guidance. - If your project does not require multi-modal support or is solely focused on text-based applications, more specialized tools might provide better optimization. - Not suitable if you're seeking quick prototyping or a light-weight framework; all-in-rag emphasizes comprehensive learning and production-ready practices.

### 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.

### Is all-in-rag or agentic-rag-for-dummies more popular on GitHub?

all-in-rag has more GitHub stars (10,437 vs 3,893). Stars measure visibility, not whether either tool fits your constraints.

### Are all-in-rag and agentic-rag-for-dummies open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to all-in-rag or agentic-rag-for-dummies?

GraphCanon lists graph-backed alternatives at [all-in-rag alternatives](/tools/datawhalechina-all-in-rag/alternatives) and [agentic-rag-for-dummies alternatives](/tools/giovannipasq-agentic-rag-for-dummies/alternatives) ([all-in-rag markdown twin](/tools/datawhalechina-all-in-rag/alternatives.md), [agentic-rag-for-dummies markdown twin](/tools/giovannipasq-agentic-rag-for-dummies/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/datawhalechina-all-in-rag-vs-giovannipasq-agentic-rag-for-dummies.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, all-in-rag or agentic-rag-for-dummies?

all-in-rag: Active. agentic-rag-for-dummies: 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 all-in-rag and agentic-rag-for-dummies?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [all-in-rag trust report](/tools/datawhalechina-all-in-rag/trust); [agentic-rag-for-dummies trust report](/tools/giovannipasq-agentic-rag-for-dummies/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=datawhalechina-all-in-rag`](/api/graphcanon/graph?tool=datawhalechina-all-in-rag)
- 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/_