---
title: "agentic-rag-for-dummies vs Awesome-LLM-RAG"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/giovannipasq-agentic-rag-for-dummies-vs-jxzhangjhu-awesome-llm-rag"
tools: ["giovannipasq-agentic-rag-for-dummies", "jxzhangjhu-awesome-llm-rag"]
---

# agentic-rag-for-dummies vs Awesome-LLM-RAG

*GraphCanon updated Aug 22, 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 Awesome-LLM-RAG if awesome-LLM-RAG is a curated list specific to advanced retrieval augmented generation (RAG) techniques for Large Language Models.

[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. [Awesome-LLM-RAG](https://github.com/jxzhangjhu/Awesome-LLM-RAG) has 1.3k stars, 94 forks, and 13 open issues, last pushed Jul 22, 2026. Figures are from public GitHub metadata via [agentic-rag-for-dummies's repository](https://github.com/GiovanniPasq/agentic-rag-for-dummies) and [Awesome-LLM-RAG's repository](https://github.com/jxzhangjhu/Awesome-LLM-RAG).

| | [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) | [Awesome-LLM-RAG](/tools/jxzhangjhu-awesome-llm-rag.md) |
| --- | --- | --- |
| Tagline | A modular Agentic RAG built with LangGraph for learning Retrieval-Augmented Generation Agents | a curated list of advanced retrieval augmented generation (RAG) in Large Language Models |
| Stars | 3,893 | 1,343 |
| Forks | 499 | 94 |
| Open issues | 0 | 13 |
| Language | Jupyter Notebook | - |
| Adopt for | Agentic RAG for Dummies simplifies the setup of retrieval-augmented generation agents using LangGraph and Ollama models. | Awesome-LLM-RAG is a curated list specific to advanced retrieval augmented generation (RAG) techniques for Large Language Models. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | - |
| Categories | AI Agents, Data & Retrieval | Data & Retrieval, LLM Frameworks |

## Trust and health

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

| | [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) | [Awesome-LLM-RAG](/tools/jxzhangjhu-awesome-llm-rag.md) |
| --- | --- | --- |
| Maintenance | Active (82%) | Steady (60%) |
| Days since push | 19d | 31d |
| Open issues (now) | 0 | 13 |
| Stars delta | Unknown | +4 (30d) |
| Open issues delta | Unknown | +4 (30d) |
| Full report | [trust report](/tools/giovannipasq-agentic-rag-for-dummies/trust.md) | [trust report](/tools/jxzhangjhu-awesome-llm-rag/trust.md) |

## Shared compatibility

- **Python**: [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) - Python runtime; [Awesome-LLM-RAG](/tools/jxzhangjhu-awesome-llm-rag.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: Awesome-LLM-RAG

- **Adopt for:** Awesome-LLM-RAG is a curated list specific to advanced retrieval augmented generation (RAG) techniques for Large Language Models.

## Choose when

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

- 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 Awesome-LLM-RAG if…

- Tags unique to Awesome-LLM-RAG: embeddings, large language models, rag-embeddings, retrieval-augmented-generation.
- Also covers LLM Frameworks.
- When you are focusing on the detailed implementation and utilization of RAG in large language models, as Awesome-LLM-RAG provides a deep dive into advanced RAG approaches.

## 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 Awesome-LLM-RAG

- If you are looking for introductory material on LLM frameworks broadly; Awesome-LLM-RAG does not cover basics of large language models but rather focuses on advanced topics.
- Not recommended if your interest is in broad categories like general vector databases or data retrieval without a focus on RAG within LLMs, as the content is highly specialized.

## Common questions

### What is the difference between agentic-rag-for-dummies and Awesome-LLM-RAG?

agentic-rag-for-dummies: A modular Agentic RAG built with LangGraph for learning Retrieval-Augmented Generation Agents. Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models. See the comparison table for live GitHub stats and shared categories.

### When should I choose agentic-rag-for-dummies over Awesome-LLM-RAG?

Choose agentic-rag-for-dummies over Awesome-LLM-RAG when 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 Awesome-LLM-RAG over agentic-rag-for-dummies?

Choose Awesome-LLM-RAG over agentic-rag-for-dummies when Tags unique to Awesome-LLM-RAG: embeddings, large language models, rag-embeddings, retrieval-augmented-generation; Also covers LLM Frameworks; When you are focusing on the detailed implementation and utilization of RAG in large language models, as Awesome-LLM-RAG provides a deep dive into advanced RAG approaches.

### 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 Awesome-LLM-RAG?

If you are looking for introductory material on LLM frameworks broadly; Awesome-LLM-RAG does not cover basics of large language models but rather focuses on advanced topics. Not recommended if your interest is in broad categories like general vector databases or data retrieval without a focus on RAG within LLMs, as the content is highly specialized.

### Is agentic-rag-for-dummies or Awesome-LLM-RAG more popular on GitHub?

agentic-rag-for-dummies has more GitHub stars (3,893 vs 1,343). Stars measure visibility, not whether either tool fits your constraints.

### Are agentic-rag-for-dummies and Awesome-LLM-RAG open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to agentic-rag-for-dummies or Awesome-LLM-RAG?

GraphCanon lists graph-backed alternatives at [agentic-rag-for-dummies alternatives](/tools/giovannipasq-agentic-rag-for-dummies/alternatives) and [Awesome-LLM-RAG alternatives](/tools/jxzhangjhu-awesome-llm-rag/alternatives) ([agentic-rag-for-dummies markdown twin](/tools/giovannipasq-agentic-rag-for-dummies/alternatives.md), [Awesome-LLM-RAG markdown twin](/tools/jxzhangjhu-awesome-llm-rag/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-jxzhangjhu-awesome-llm-rag.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 Awesome-LLM-RAG?

agentic-rag-for-dummies: Active. Awesome-LLM-RAG: Steady. 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 Awesome-LLM-RAG?

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); [Awesome-LLM-RAG trust report](/tools/jxzhangjhu-awesome-llm-rag/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/_