---
title: "agentic-rag-for-dummies vs EnterpriseRAG-Bench"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/giovannipasq-agentic-rag-for-dummies-vs-onyx-dot-app-enterpriserag-bench"
tools: ["giovannipasq-agentic-rag-for-dummies", "onyx-dot-app-enterpriserag-bench"]
---

# agentic-rag-for-dummies vs EnterpriseRAG-Bench

*GraphCanon updated Aug 14, 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 EnterpriseRAG-Bench if enterpriseRAG-Bench specializes in benchmarking RAG models on company internal documents, offering specific evaluation metrics for enterprise-level data.

[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. [EnterpriseRAG-Bench](https://www.onyx.app/) has 489 stars, 52 forks, and 9 open issues, last pushed May 8, 2026. Figures are from public GitHub metadata via [agentic-rag-for-dummies's repository](https://github.com/GiovanniPasq/agentic-rag-for-dummies) and [EnterpriseRAG-Bench's repository](https://github.com/onyx-dot-app/EnterpriseRAG-Bench).

| | [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) |
| --- | --- | --- |
| Tagline | A modular Agentic RAG built with LangGraph for learning Retrieval-Augmented Generation Agents | Dataset and benchmark for RAG on company internal documents |
| Stars | 3,893 | 489 |
| Forks | 499 | 52 |
| Open issues | 0 | 9 |
| Language | Jupyter Notebook | - |
| Adopt for | Agentic RAG for Dummies simplifies the setup of retrieval-augmented generation agents using LangGraph and Ollama models. | EnterpriseRAG-Bench specializes in benchmarking RAG models on company internal documents, offering specific evaluation metrics for enterprise-level data. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | MIT license allows free usage and modification with attribution. |
| Categories | AI Agents, Data & Retrieval | Data & Retrieval, Evaluation & Observability |

## Trust and health

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

| | [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) |
| --- | --- | --- |
| Maintenance | Active (82%) | Steady (60%) |
| Days since push | 19d | 81d |
| Open issues (now) | 0 | 9 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/giovannipasq-agentic-rag-for-dummies/trust.md) | [trust report](/tools/onyx-dot-app-enterpriserag-bench/trust.md) |

## 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: EnterpriseRAG-Bench

- **Adopt for:** EnterpriseRAG-Bench specializes in benchmarking RAG models on company internal documents, offering specific evaluation metrics for enterprise-level data.
- **License detail:** MIT license allows free usage and modification with attribution.

## 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 EnterpriseRAG-Bench if…

- Tags unique to EnterpriseRAG-Bench: benchmark, dataset, enterprise-search, evaluation.
- Also covers Evaluation & Observability.
- When you need to evaluate retrieval-augmented generation models specifically for processing extensive and complex enterprise documentation

## 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 EnterpriseRAG-Bench

- Avoid if your focus is on general web or public-domain document benchmarking, as EnterpriseRAG-Bench is tuned exclusively for company internal documents
- Do not use if you require a solution that supports languages other than those implied by the existing dataset without further customization

## Common questions

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

agentic-rag-for-dummies: A modular Agentic RAG built with LangGraph for learning Retrieval-Augmented Generation Agents. EnterpriseRAG-Bench: Dataset and benchmark for RAG on company internal documents. See the comparison table for live GitHub stats and shared categories.

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

Choose agentic-rag-for-dummies over EnterpriseRAG-Bench 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 EnterpriseRAG-Bench over agentic-rag-for-dummies?

Choose EnterpriseRAG-Bench over agentic-rag-for-dummies when Tags unique to EnterpriseRAG-Bench: benchmark, dataset, enterprise-search, evaluation; Also covers Evaluation & Observability; When you need to evaluate retrieval-augmented generation models specifically for processing extensive and complex enterprise documentation.

### 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 EnterpriseRAG-Bench?

Avoid if your focus is on general web or public-domain document benchmarking, as EnterpriseRAG-Bench is tuned exclusively for company internal documents Do not use if you require a solution that supports languages other than those implied by the existing dataset without further customization

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

agentic-rag-for-dummies has more GitHub stars (3,893 vs 489). Stars measure visibility, not whether either tool fits your constraints.

### Are agentic-rag-for-dummies and EnterpriseRAG-Bench open source?

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

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

GraphCanon lists graph-backed alternatives at [agentic-rag-for-dummies alternatives](/tools/giovannipasq-agentic-rag-for-dummies/alternatives) and [EnterpriseRAG-Bench alternatives](/tools/onyx-dot-app-enterpriserag-bench/alternatives) ([agentic-rag-for-dummies markdown twin](/tools/giovannipasq-agentic-rag-for-dummies/alternatives.md), [EnterpriseRAG-Bench markdown twin](/tools/onyx-dot-app-enterpriserag-bench/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-onyx-dot-app-enterpriserag-bench.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 EnterpriseRAG-Bench?

agentic-rag-for-dummies: Active. EnterpriseRAG-Bench: 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 EnterpriseRAG-Bench?

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); [EnterpriseRAG-Bench trust report](/tools/onyx-dot-app-enterpriserag-bench/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/_