Comparison
EnterpriseRAG-Bench vs rag-fusion
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.
Markdown twin · EnterpriseRAG-Bench alternatives · rag-fusion alternatives
GraphCanon updated 2d
vs
Trust & integrity
| Signal | EnterpriseRAG-Bench | rag-fusion |
|---|---|---|
| Maintenance | Steady (81d since push) As of 3w · github_public_v1 | Slowing (118d since push) As of 2d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Personal account As of 2d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- EnterpriseRAG-Bench
- Dataset and benchmark for RAG on company internal documents
- rag-fusion
- multi-query generation + Reciprocal Rank Fusion for retrieval-augmented generation
Stars
- EnterpriseRAG-Bench
- 489
- rag-fusion
- 952
Forks
- EnterpriseRAG-Bench
- 52
- rag-fusion
- 115
Open issues
- EnterpriseRAG-Bench
- 9
- rag-fusion
- 0
Language
- EnterpriseRAG-Bench
- -
- rag-fusion
- Python
Adopt for
- EnterpriseRAG-Bench
- EnterpriseRAG-Bench specializes in benchmarking RAG models on company internal documents, offering specific evaluation metrics for enterprise-level data.
- rag-fusion
- RAG-Fusion leverages multi-query generation and Reciprocal Rank Fusion for enhanced retrieval-augmented generation tasks, supporting evaluations via NFCorpus/BEIR.
Persona
- EnterpriseRAG-Bench
- -
- rag-fusion
- -
Runtime
- EnterpriseRAG-Bench
- -
- rag-fusion
- -
License
- EnterpriseRAG-Bench
- MIT license allows free usage and modification with attribution.
- rag-fusion
- MIT
Last pushed
- EnterpriseRAG-Bench
- May 8, 2026
- rag-fusion
- Apr 26, 2026
Categories
- EnterpriseRAG-Bench
- Data & Retrieval, Evaluation & Observability
- rag-fusion
- Data & Retrieval, Evaluation & Observability
Trust and health
Maintenance
- EnterpriseRAG-Bench
- Steady (60%)
- rag-fusion
- Slowing (36%)
Days since push
- EnterpriseRAG-Bench
- 81d
- rag-fusion
- 118d
Open issues (now)
- EnterpriseRAG-Bench
- 9
- rag-fusion
- 0
Stars delta
- EnterpriseRAG-Bench
- Unknown
- rag-fusion
- +6 (30d)
Open issues delta
- EnterpriseRAG-Bench
- Unknown
- rag-fusion
- 0 (30d)
Owner type
- EnterpriseRAG-Bench
- Organization
- rag-fusion
- User
Full report
- EnterpriseRAG-Bench
- Trust report
- rag-fusion
- Trust report
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).
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
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 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (onyx-dot-app/EnterpriseRAG-Bench) · observed Jul 28, 2026
- GitHub forks (onyx-dot-app/EnterpriseRAG-Bench) · observed Jul 28, 2026
- Last push (onyx-dot-app/EnterpriseRAG-Bench) · observed May 8, 2026
- License file (MIT) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Raudaschl/rag-fusion) · observed Aug 23, 2026
- GitHub forks (Raudaschl/rag-fusion) · observed Aug 23, 2026
- Last push (Raudaschl/rag-fusion) · observed Apr 26, 2026
- License file (MIT) · observed Aug 23, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: EnterpriseRAG-Bench 489 · rag-fusion 952 (synced Jul 28, 2026).
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 and rag-fusion alternatives (EnterpriseRAG-Bench markdown twin, rag-fusion markdown twin), 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 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; rag-fusion trust report.