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

# agentic-rag-for-dummies vs rags

*GraphCanon updated Aug 18, 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 rags if decision-critical facts for 'rags':.

[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. [rags](https://github.com/run-llama/rags) has 6.5k stars, 656 forks, and 37 open issues, last pushed Apr 5, 2024. Figures are from public GitHub metadata via [agentic-rag-for-dummies's repository](https://github.com/GiovanniPasq/agentic-rag-for-dummies) and [rags's repository](https://github.com/run-llama/rags).

| | [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) | [rags](/tools/run-llama-rags.md) |
| --- | --- | --- |
| Tagline | A modular Agentic RAG built with LangGraph for learning Retrieval-Augmented Generation Agents | Build ChatGPT over your data with natural language |
| Stars | 3,893 | 6,549 |
| Forks | 499 | 656 |
| Open issues | 0 | 37 |
| Language | Jupyter Notebook | Python |
| Adopt for | Agentic RAG for Dummies simplifies the setup of retrieval-augmented generation agents using LangGraph and Ollama models. | Decision-critical facts for 'rags': |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | MIT License |
| Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval |

## Trust and health

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

| | [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) | [rags](/tools/run-llama-rags.md) |
| --- | --- | --- |
| Maintenance | Active (82%) | Dormant (18%) |
| Days since push | 19d | 865d |
| Open issues (now) | 0 | 37 |
| Stars delta | Unknown | +6 (30d) |
| Open issues delta | Unknown | -1 (30d) |
| Owner type | User | Organization |
| Full report | [trust report](/tools/giovannipasq-agentic-rag-for-dummies/trust.md) | [trust report](/tools/run-llama-rags/trust.md) |

## Shared compatibility

- **Python**: [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) - Python runtime; [rags](/tools/run-llama-rags.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: rags

- **Requirements:** Installation leverages poetry for dependency management.; Setup requires configuration with OpenAI key and potentially creating a virtual environment.
- **Adopt for:** Decision-critical facts for 'rags':
- **License detail:** MIT License

## Choose when

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

- agentic-rag-for-dummies is primarily Jupyter Notebook; rags is Python.
- Tags unique to agentic-rag-for-dummies: agentic-ai, bm25, gradio, langchain.
- When aiming to quickly develop a retrieval-augmented generation agent, thanks to its streamlined setup with LangGraph.

### Choose rags if…

- rags is primarily Python; agentic-rag-for-dummies is Jupyter Notebook.
- Requirements: Installation leverages poetry for dependency management.; Setup requires configuration with OpenAI key and potentially creating a virtual environment..
- Tags unique to rags: chatbot, chatgpt, openai, streamlit.
- When leveraging natural language queries over proprietary user data using OpenAI services.

## 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 rags

- Not suitable if you seek solutions not dependent on OpenAI's services as the underlying framework is tightly coupled with OpenAI APIs.
- Avoid using rags if your project involves sensitive or highly confidential data since it requires integrating API keys, potentially posing security concerns.
- If your team does not have familiarity or access to Streamlit for app development, you might find setting up and deploying a conversational agent more challenging.

## Common questions

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

agentic-rag-for-dummies: A modular Agentic RAG built with LangGraph for learning Retrieval-Augmented Generation Agents. rags: Build ChatGPT over your data with natural language. See the comparison table for live GitHub stats and shared categories.

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

Choose agentic-rag-for-dummies over rags when agentic-rag-for-dummies is primarily Jupyter Notebook; rags is Python; Tags unique to agentic-rag-for-dummies: agentic-ai, bm25, gradio, langchain; When aiming to quickly develop a retrieval-augmented generation agent, thanks to its streamlined setup with LangGraph.

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

Choose rags over agentic-rag-for-dummies when rags is primarily Python; agentic-rag-for-dummies is Jupyter Notebook; Requirements: Installation leverages poetry for dependency management.; Setup requires configuration with OpenAI key and potentially creating a virtual environment.; Tags unique to rags: chatbot, chatgpt, openai, streamlit; When leveraging natural language queries over proprietary user data using OpenAI services.

### 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 rags?

Not suitable if you seek solutions not dependent on OpenAI's services as the underlying framework is tightly coupled with OpenAI APIs. Avoid using rags if your project involves sensitive or highly confidential data since it requires integrating API keys, potentially posing security concerns. If your team does not have familiarity or access to Streamlit for app development, you might find setting up and deploying a conversational agent more challenging.

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

rags has more GitHub stars (6,549 vs 3,893). Stars measure visibility, not whether either tool fits your constraints.

### Are agentic-rag-for-dummies and rags open source?

Yes - both are open-source projects on GitHub (agentic-rag-for-dummies: MIT, rags: MIT).

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

GraphCanon lists graph-backed alternatives at [agentic-rag-for-dummies alternatives](/tools/giovannipasq-agentic-rag-for-dummies/alternatives) and [rags alternatives](/tools/run-llama-rags/alternatives) ([agentic-rag-for-dummies markdown twin](/tools/giovannipasq-agentic-rag-for-dummies/alternatives.md), [rags markdown twin](/tools/run-llama-rags/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-run-llama-rags.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 rags?

agentic-rag-for-dummies: Active. rags: 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 agentic-rag-for-dummies and rags?

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); [rags trust report](/tools/run-llama-rags/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/_