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

# EnterpriseRAG-Bench vs rag-fusion

*GraphCanon updated Aug 23, 2026*

## Verdict

Pick EnterpriseRAG-Bench if enterpriseRAG-Bench specializes in benchmarking RAG models on company internal documents, offering specific evaluation metrics for enterprise-level data; pick rag-fusion if rAG-Fusion leverages multi-query generation and Reciprocal Rank Fusion for enhanced retrieval-augmented generation tasks, supporting evaluations via NFCorpus/BEIR.

[EnterpriseRAG-Bench](https://www.onyx.app/) reports 489 GitHub stars, 52 forks, and 9 open issues, last pushed May 8, 2026. [rag-fusion](https://github.com/Raudaschl/rag-fusion) has 952 stars, 115 forks, and 0 open issues, last pushed Apr 26, 2026. Figures are from public GitHub metadata via [EnterpriseRAG-Bench's repository](https://github.com/onyx-dot-app/EnterpriseRAG-Bench) and [rag-fusion's repository](https://github.com/Raudaschl/rag-fusion).

| | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | [rag-fusion](/tools/raudaschl-rag-fusion.md) |
| --- | --- | --- |
| Tagline | Dataset and benchmark for RAG on company internal documents | multi-query generation + Reciprocal Rank Fusion for retrieval-augmented generation |
| Stars | 489 | 952 |
| Forks | 52 | 115 |
| Open issues | 9 | 0 |
| Language | - | Python |
| Adopt for | EnterpriseRAG-Bench specializes in benchmarking RAG models on company internal documents, offering specific evaluation metrics for enterprise-level data. | RAG-Fusion leverages multi-query generation and Reciprocal Rank Fusion for enhanced retrieval-augmented generation tasks, supporting evaluations via NFCorpus/BEIR. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT license allows free usage and modification with attribution. | MIT |
| Categories | Data & Retrieval, Evaluation & Observability | Data & Retrieval, Evaluation & Observability |

## Trust and health

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

| | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | [rag-fusion](/tools/raudaschl-rag-fusion.md) |
| --- | --- | --- |
| Maintenance | Steady (60%) | Slowing (36%) |
| Days since push | 81d | 118d |
| Open issues (now) | 9 | 0 |
| Stars delta | Unknown | +6 (30d) |
| Open issues delta | Unknown | 0 (30d) |
| Owner type | Organization | User |
| Full report | [trust report](/tools/onyx-dot-app-enterpriserag-bench/trust.md) | [trust report](/tools/raudaschl-rag-fusion/trust.md) |

## 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.

## Decision facts: rag-fusion

- **Adopt for:** RAG-Fusion leverages multi-query generation and Reciprocal Rank Fusion for enhanced retrieval-augmented generation tasks, supporting evaluations via NFCorpus/BEIR.

## Choose when

### Choose EnterpriseRAG-Bench if…

- Tags unique to EnterpriseRAG-Bench: benchmark, dataset, enterprise-search, evaluation.
- When you need to evaluate retrieval-augmented generation models specifically for processing extensive and complex enterprise documentation
- More recently updated (last pushed May 8, 2026).

### Choose rag-fusion if…

- Tags unique to rag-fusion: chromadb, openai, python, rag-fusion.
- For enhancing precision in retrieval-augmented generation tasks needing complex query processing
- More GitHub stars (952 vs 489) - visibility, not fit.

## 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

## When NOT to use rag-fusion

- If you require real-time performance, as multi-query generation may introduce latency
- In scenarios where only simple keyword-based search suffices without the need for advanced fusion techniques

## Common questions

### What is the difference between EnterpriseRAG-Bench and rag-fusion?

EnterpriseRAG-Bench: Dataset and benchmark for RAG on company internal documents. rag-fusion: multi-query generation + Reciprocal Rank Fusion for retrieval-augmented generation. See the comparison table for live GitHub stats and shared categories.

### When should I choose EnterpriseRAG-Bench over rag-fusion?

Choose EnterpriseRAG-Bench over rag-fusion when Tags unique to EnterpriseRAG-Bench: benchmark, dataset, enterprise-search, evaluation; When you need to evaluate retrieval-augmented generation models specifically for processing extensive and complex enterprise documentation; More recently updated (last pushed May 8, 2026).

### When should I choose rag-fusion over EnterpriseRAG-Bench?

Choose rag-fusion over EnterpriseRAG-Bench when Tags unique to rag-fusion: chromadb, openai, python, rag-fusion; For enhancing precision in retrieval-augmented generation tasks needing complex query processing; More GitHub stars (952 vs 489) - visibility, not fit.

### 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

### When should I avoid rag-fusion?

If you require real-time performance, as multi-query generation may introduce latency In scenarios where only simple keyword-based search suffices without the need for advanced fusion techniques

### Is EnterpriseRAG-Bench or rag-fusion more popular on GitHub?

rag-fusion has more GitHub stars (952 vs 489). Stars measure visibility, not whether either tool fits your constraints.

### Are EnterpriseRAG-Bench and rag-fusion open source?

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

### Where can I find alternatives to EnterpriseRAG-Bench or rag-fusion?

GraphCanon lists graph-backed alternatives at [EnterpriseRAG-Bench alternatives](/tools/onyx-dot-app-enterpriserag-bench/alternatives) and [rag-fusion alternatives](/tools/raudaschl-rag-fusion/alternatives) ([EnterpriseRAG-Bench markdown twin](/tools/onyx-dot-app-enterpriserag-bench/alternatives.md), [rag-fusion markdown twin](/tools/raudaschl-rag-fusion/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/onyx-dot-app-enterpriserag-bench-vs-raudaschl-rag-fusion.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, EnterpriseRAG-Bench or rag-fusion?

EnterpriseRAG-Bench: Steady. rag-fusion: 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 EnterpriseRAG-Bench and rag-fusion?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [EnterpriseRAG-Bench trust report](/tools/onyx-dot-app-enterpriserag-bench/trust); [rag-fusion trust report](/tools/raudaschl-rag-fusion/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=onyx-dot-app-enterpriserag-bench`](/api/graphcanon/graph?tool=onyx-dot-app-enterpriserag-bench)
- 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/_