Comparison
knowledge_gpt vs EnterpriseRAG-Bench
Verdict
Pick knowledge_gpt if knowledge_gpt is a Python-based tool with Streamlit UI, MIT licensed, for generating accurate document responses with citations; 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 1w
vs
Trust & integrity
| Signal | knowledge_gpt | EnterpriseRAG-Bench |
|---|---|---|
| Maintenance | Archived (807d since push) As of 1w · github_public_v1 | Steady (81d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | Published findings 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
- Accurate answers and instant citations for your documents.
- EnterpriseRAG-Bench
- Dataset and benchmark for RAG on company internal documents
Stars
- knowledge_gpt
- 1.6k
- EnterpriseRAG-Bench
- 489
Forks
- knowledge_gpt
- 787
- EnterpriseRAG-Bench
- 52
Open issues
- knowledge_gpt
- 16
- EnterpriseRAG-Bench
- 9
Language
- knowledge_gpt
- Python
- EnterpriseRAG-Bench
- -
Adopt for
- knowledge_gpt
- knowledge_gpt is a Python-based tool with Streamlit UI, MIT licensed, for generating accurate document responses with citations.
- 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
- May 29, 2024
- EnterpriseRAG-Bench
- May 8, 2026
Categories
- knowledge_gpt
- Data & Retrieval, Evaluation & Observability
- EnterpriseRAG-Bench
- Data & Retrieval, Evaluation & Observability
Trust and health
Maintenance
- knowledge_gpt
- Archived (8%)
- EnterpriseRAG-Bench
- Steady (60%)
Days since push
- knowledge_gpt
- 807d
- EnterpriseRAG-Bench
- 81d
Archived on GitHub
- knowledge_gpt
- Yes
- EnterpriseRAG-Bench
- No
Open issues (now)
- knowledge_gpt
- 16
- EnterpriseRAG-Bench
- 9
Stars delta
- knowledge_gpt
- -3 (30d)
- EnterpriseRAG-Bench
- Unknown
Open issues delta
- knowledge_gpt
- 0 (30d)
- EnterpriseRAG-Bench
- Unknown
Owner type
- knowledge_gpt
- User
- EnterpriseRAG-Bench
- Organization
OSV dependency advisories
- knowledge_gpt
- Published findings
- EnterpriseRAG-Bench
- No lockfile (source not queried)
Full report
- knowledge_gpt
- Trust report
- EnterpriseRAG-Bench
- Trust report
Choose knowledge_gpt if…
- Tags unique to knowledge_gpt: docker, document-analysis, python, streamlit.
- knowledge_gpt ships Docker support for self-hosted deployment.
- When you need to generate answers from documents alongside instant citations
When NOT to use knowledge_gpt
- If your project exclusively requires web-based services without local deployments
- In scenarios where real-time citation generation is not necessary
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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (mmz-001/knowledge_gpt) · observed Aug 15, 2026
- GitHub forks (mmz-001/knowledge_gpt) · observed Aug 15, 2026
- Last push (mmz-001/knowledge_gpt) · observed May 29, 2024
- License file (MIT) · observed Aug 15, 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 1.6k · EnterpriseRAG-Bench 489 (synced Aug 15, 2026).
Common questions
- What is the difference between knowledge_gpt and EnterpriseRAG-Bench?
- knowledge_gpt: Accurate answers and instant citations for your documents.. 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: docker, document-analysis, python, streamlit; knowledge_gpt ships Docker support for self-hosted deployment; When you need to generate answers from documents alongside instant citations.
- 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 recently updated (last pushed May 8, 2026).
- When should I avoid knowledge_gpt?
- If your project exclusively requires web-based services without local deployments In scenarios where real-time citation generation is not necessary
- 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?
- knowledge_gpt has more GitHub stars (1,634 vs 489). 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: Archived. 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.