Comparison
Awesome-LLM-RAG vs graphrag
Verdict
Pick Awesome-LLM-RAG if awesome-LLM-RAG is a curated list specific to advanced retrieval augmented generation (RAG) techniques for Large Language Models; pick graphrag if graphRAG is a research-oriented modular graph-based Retrieval-Augmented Generation system designed to enhance LLM outputs with structured data extracted from unstructured text.
Markdown twin · Awesome-LLM-RAG alternatives · graphrag alternatives
GraphCanon updated Sep 20, 2026
14views this month
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Trust & integrity
| Signal | Awesome-LLM-RAG | graphrag |
|---|---|---|
| Maintenance | Steady (60d since push) As of Sep 20, 2026 · github_public_v1 | Very active (1d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 2026 · 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
- Awesome-LLM-RAG
- a curated list of advanced retrieval augmented generation (RAG) in Large Language Models
- graphrag
- A modular graph-based Retrieval-Augmented Generation (RAG) system
Stars
- Awesome-LLM-RAG
- 1.3k
- graphrag
- 36k
Forks
- Awesome-LLM-RAG
- 95
- graphrag
- 3.8k
Open issues
- Awesome-LLM-RAG
- 15
- graphrag
- 38
Language
- Awesome-LLM-RAG
- -
- graphrag
- Python
Adopt for
- Awesome-LLM-RAG
- Awesome-LLM-RAG is a curated list specific to advanced retrieval augmented generation (RAG) techniques for Large Language Models.
- graphrag
- GraphRAG is a research-oriented modular graph-based Retrieval-Augmented Generation system designed to enhance LLM outputs with structured data extracted from unstructured text.
Persona
- Awesome-LLM-RAG
- -
- graphrag
- -
Runtime
- Awesome-LLM-RAG
- -
- graphrag
- -
License
- Awesome-LLM-RAG
- -
- graphrag
- MIT License
Last pushed
- Awesome-LLM-RAG
- Jul 22, 2026
- graphrag
- Sep 16, 2026
Categories
- Awesome-LLM-RAG
- Data & Retrieval, LLM Frameworks
- graphrag
- Data & Retrieval, LLM Frameworks
Trust and health
Maintenance
- Awesome-LLM-RAG
- Steady (60%)
- graphrag
- Very active (96%)
Days since push
- Awesome-LLM-RAG
- 60d
- graphrag
- 1d
Open issues (now)
- Awesome-LLM-RAG
- 15
- graphrag
- 38
Stars delta
- Awesome-LLM-RAG
- +9 (30d)
- graphrag
- +506 (30d)
Open issues delta
- Awesome-LLM-RAG
- +6 (30d)
- graphrag
- -8 (30d)
Owner type
- Awesome-LLM-RAG
- User
- graphrag
- Organization
Full report
- Awesome-LLM-RAG
- Trust report
- graphrag
- Trust report
Choose Awesome-LLM-RAG if…
- Tags unique to Awesome-LLM-RAG: embeddings, large-language-models, rag-embeddings, retrieval-augmented generation.
- When you are focusing on the detailed implementation and utilization of RAG in large language models, as Awesome-LLM-RAG provides a deep dive into advanced RAG approaches.
- Leaner open-issue backlog (15).
When NOT to use Awesome-LLM-RAG
- If you are looking for introductory material on LLM frameworks broadly; Awesome-LLM-RAG does not cover basics of large language models but rather focuses on advanced topics.
- Not recommended if your interest is in broad categories like general vector databases or data retrieval without a focus on RAG within LLMs, as the content is highly specialized.
Choose graphrag if…
- Pricing: Free to use under the MIT License, but users should be aware of potential costs associated with indexing operations..
- Requirements: Min 8 GB RAM; GraphRAG is a Python-based system and requires a Python environment.; Users should fine-tune their prompts for the best results..
- Tags unique to graphrag: gpt, gpt-4, gpt4, graphrag.
- When you need to extract meaningful, structured data from unstructured text using the power of LLMs.
When NOT to use graphrag
- If you require a tool that is actively developed and accepts new features or contributions, as GraphRAG is largely in maintenance mode.
- When cost is a significant concern, as GraphRAG indexing can be an expensive operation.
- If you need a production-ready, officially supported solution, as GraphRAG is a research project and not an officially supported Microsoft offering.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (jxzhangjhu/Awesome-LLM-RAG) · observed Sep 20, 2026
- GitHub forks (jxzhangjhu/Awesome-LLM-RAG) · observed Sep 20, 2026
- Last push (jxzhangjhu/Awesome-LLM-RAG) · observed Jul 22, 2026
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (microsoft/graphrag) · observed Sep 20, 2026
- GitHub forks (microsoft/graphrag) · observed Sep 20, 2026
- Last push (microsoft/graphrag) · observed Sep 16, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: Awesome-LLM-RAG 1.3k · graphrag 36k (synced Sep 20, 2026).
Common questions
- What is the difference between Awesome-LLM-RAG and graphrag?
- Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models. graphrag: A modular graph-based Retrieval-Augmented Generation (RAG) system. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-LLM-RAG over graphrag?
- Choose Awesome-LLM-RAG over graphrag when Tags unique to Awesome-LLM-RAG: embeddings, large-language-models, rag-embeddings, retrieval-augmented generation; When you are focusing on the detailed implementation and utilization of RAG in large language models, as Awesome-LLM-RAG provides a deep dive into advanced RAG approaches; Leaner open-issue backlog (15).
- When should I choose graphrag over Awesome-LLM-RAG?
- Choose graphrag over Awesome-LLM-RAG when Pricing: Free to use under the MIT License, but users should be aware of potential costs associated with indexing operations.; Requirements: Min 8 GB RAM; GraphRAG is a Python-based system and requires a Python environment.; Users should fine-tune their prompts for the best results.; Tags unique to graphrag: gpt, gpt-4, gpt4, graphrag; When you need to extract meaningful, structured data from unstructured text using the power of LLMs.
- When should I avoid Awesome-LLM-RAG?
- If you are looking for introductory material on LLM frameworks broadly; Awesome-LLM-RAG does not cover basics of large language models but rather focuses on advanced topics. Not recommended if your interest is in broad categories like general vector databases or data retrieval without a focus on RAG within LLMs, as the content is highly specialized.
- When should I avoid graphrag?
- If you require a tool that is actively developed and accepts new features or contributions, as GraphRAG is largely in maintenance mode. When cost is a significant concern, as GraphRAG indexing can be an expensive operation. If you need a production-ready, officially supported solution, as GraphRAG is a research project and not an officially supported Microsoft offering.
- Is Awesome-LLM-RAG or graphrag more popular on GitHub?
- graphrag has more GitHub stars (36,025 vs 1,348). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-LLM-RAG and graphrag open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to Awesome-LLM-RAG or graphrag?
- GraphCanon lists graph-backed alternatives at Awesome-LLM-RAG alternatives and graphrag alternatives (Awesome-LLM-RAG markdown twin, graphrag 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, Awesome-LLM-RAG or graphrag?
- Awesome-LLM-RAG: Steady. graphrag: Very active. 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 Awesome-LLM-RAG and graphrag?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLM-RAG trust report; graphrag trust report.