Comparison
knowledge-gpt vs EnterpriseRAG-Bench
Verdict
Pick knowledge-gpt if knowledge-gpt: Python toolkit for indexing and Q&A sessions with info sources using GPT & transformers; pick EnterpriseRAG-Bench if enterpriseRAG-Bench specializes in benchmarking RAG models on company internal documents, offering specific evaluation metrics for enterprise-level data.
Markdown twin · knowledge-gpt alternatives · EnterpriseRAG-Bench alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | knowledge-gpt | EnterpriseRAG-Bench |
|---|---|---|
| Maintenance | Dormant (1216d since push) As of 1d · github_public_v1 | Steady (81d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1d · github_public_v1 | Not a fork · Organization account As of 3w · 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
- knowledge-gpt
- Extract knowledge from all information sources using GPT and other language models. Index and conduct Q&A sessions with information sources.
- EnterpriseRAG-Bench
- Dataset and benchmark for RAG on company internal documents
Stars
- knowledge-gpt
- 291
- EnterpriseRAG-Bench
- 489
Forks
- knowledge-gpt
- 52
- EnterpriseRAG-Bench
- 52
Open issues
- knowledge-gpt
- 8
- EnterpriseRAG-Bench
- 9
Language
- knowledge-gpt
- Python
- EnterpriseRAG-Bench
- -
Adopt for
- knowledge-gpt
- knowledge-gpt: Python toolkit for indexing and Q&A sessions with info sources using GPT & transformers.
- EnterpriseRAG-Bench
- EnterpriseRAG-Bench specializes in benchmarking RAG models on company internal documents, offering specific evaluation metrics for enterprise-level data.
Persona
- knowledge-gpt
- -
- EnterpriseRAG-Bench
- -
Runtime
- knowledge-gpt
- -
- EnterpriseRAG-Bench
- -
License
- knowledge-gpt
- MIT
- EnterpriseRAG-Bench
- MIT license allows free usage and modification with attribution.
Last pushed
- knowledge-gpt
- Apr 25, 2023
- EnterpriseRAG-Bench
- May 8, 2026
Categories
- knowledge-gpt
- Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training
- EnterpriseRAG-Bench
- Data & Retrieval, Evaluation & Observability
Trust and health
Maintenance
- knowledge-gpt
- Dormant (18%)
- EnterpriseRAG-Bench
- Steady (60%)
Days since push
- knowledge-gpt
- 1216d
- EnterpriseRAG-Bench
- 81d
Open issues (now)
- knowledge-gpt
- 8
- EnterpriseRAG-Bench
- 9
Stars delta
- knowledge-gpt
- 0 (30d)
- EnterpriseRAG-Bench
- Unknown
Open issues delta
- knowledge-gpt
- 0 (30d)
- EnterpriseRAG-Bench
- Unknown
Full report
- knowledge-gpt
- Trust report
- EnterpriseRAG-Bench
- Trust report
Choose knowledge-gpt if…
- Tags unique to knowledge-gpt: context, embedding-vectors, gpt, huggingface-transformers.
- Also covers LLM Frameworks, Model Training.
- knowledge-gpt ships Docker support for self-hosted deployment.
- When you need a flexible, model-agnostic approach for Q&A over diverse data sources
When NOT to use knowledge-gpt
- Avoid if strictly needing real-time response performance without indexing capabilities
- Not recommended if focusing solely on visual or multimedia content extraction
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 GitHub stars (489 vs 291) - 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (geeks-of-data/knowledge-gpt) · observed Aug 23, 2026
- GitHub forks (geeks-of-data/knowledge-gpt) · observed Aug 23, 2026
- Last push (geeks-of-data/knowledge-gpt) · observed Apr 25, 2023
- License file (MIT) · observed Aug 23, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: knowledge-gpt 291 · EnterpriseRAG-Bench 489 (synced Aug 23, 2026).
Common questions
- What is the difference between knowledge-gpt and EnterpriseRAG-Bench?
- knowledge-gpt: Extract knowledge from all information sources using GPT and other language models. Index and conduct Q&A sessions with information sources.. 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 knowledge-gpt over EnterpriseRAG-Bench?
- Choose knowledge-gpt over EnterpriseRAG-Bench when Tags unique to knowledge-gpt: context, embedding-vectors, gpt, huggingface-transformers; Also covers LLM Frameworks, Model Training; knowledge-gpt ships Docker support for self-hosted deployment; When you need a flexible, model-agnostic approach for Q&A over diverse data sources.
- When should I choose EnterpriseRAG-Bench over knowledge-gpt?
- Choose EnterpriseRAG-Bench over knowledge-gpt 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 GitHub stars (489 vs 291) - visibility, not fit.
- When should I avoid knowledge-gpt?
- Avoid if strictly needing real-time response performance without indexing capabilities Not recommended if focusing solely on visual or multimedia content extraction
- 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 knowledge-gpt or EnterpriseRAG-Bench more popular on GitHub?
- EnterpriseRAG-Bench has more GitHub stars (489 vs 291). Stars measure visibility, not whether either tool fits your constraints.
- Are knowledge-gpt and EnterpriseRAG-Bench open source?
- Yes - both are open-source projects on GitHub (knowledge-gpt: MIT, EnterpriseRAG-Bench: MIT).
- Where can I find alternatives to knowledge-gpt or EnterpriseRAG-Bench?
- GraphCanon lists graph-backed alternatives at knowledge-gpt alternatives and EnterpriseRAG-Bench alternatives (knowledge-gpt markdown twin, EnterpriseRAG-Bench 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, knowledge-gpt or EnterpriseRAG-Bench?
- knowledge-gpt: Dormant. 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 knowledge-gpt and EnterpriseRAG-Bench?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: knowledge-gpt trust report; EnterpriseRAG-Bench trust report.