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 Python-based tool designed for integrating retrieval and generation processes in large language models using graph structures.
Markdown twin · Awesome-LLM-RAG alternatives · graphrag alternatives
GraphCanon updated 2d
vs
Trust & integrity
| Signal | Awesome-LLM-RAG | graphrag |
|---|---|---|
| Maintenance | Very active (0d since push) As of 3w · github_public_v1 | Very active (1d since push) As of 2d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization 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
- 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
- 88
- graphrag
- 3.7k
Open issues
- Awesome-LLM-RAG
- 9
- graphrag
- 46
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 Python-based tool designed for integrating retrieval and generation processes in large language models using graph structures.
Persona
- Awesome-LLM-RAG
- -
- graphrag
- -
Runtime
- Awesome-LLM-RAG
- -
- graphrag
- -
License
- Awesome-LLM-RAG
- -
- graphrag
- MIT
Last pushed
- Awesome-LLM-RAG
- Jul 22, 2026
- graphrag
- Aug 14, 2026
Categories
- Awesome-LLM-RAG
- Data & Retrieval, LLM Frameworks
- graphrag
- Data & Retrieval, LLM Frameworks
Trust and health
Days since push
- Awesome-LLM-RAG
- 0d
- graphrag
- 1d
Open issues (now)
- Awesome-LLM-RAG
- 9
- graphrag
- 46
Stars delta
- Awesome-LLM-RAG
- Unknown
- graphrag
- +1.0k (30d)
Open issues delta
- Awesome-LLM-RAG
- Unknown
- graphrag
- -15 (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 (9).
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…
- Tags unique to graphrag: gpt, gpt-4, graph.
- When you need to leverage graph structures to enhance the efficiency of information retrieval within a Retrieval-Augmented Generation setup.
- More GitHub stars (36k vs 1.3k) - visibility, not fit.
When NOT to use graphrag
- If your application does not require or benefit from the specific graph-based approach GraphRAG employs; traditional RAG systems might be sufficient without the added layer of complexity introduced by
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 Jul 23, 2026
- GitHub forks (jxzhangjhu/Awesome-LLM-RAG) · observed Jul 23, 2026
- Last push (jxzhangjhu/Awesome-LLM-RAG) · observed Jul 22, 2026
- License file (unknown) · observed Jul 23, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (microsoft/graphrag) · observed Aug 16, 2026
- GitHub forks (microsoft/graphrag) · observed Aug 16, 2026
- Last push (microsoft/graphrag) · observed Aug 14, 2026
- License file (MIT) · observed Aug 16, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-LLM-RAG 1.3k · graphrag 36k (synced Jul 23, 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 (9).
- When should I choose graphrag over Awesome-LLM-RAG?
- Choose graphrag over Awesome-LLM-RAG when Tags unique to graphrag: gpt, gpt-4, graph; When you need to leverage graph structures to enhance the efficiency of information retrieval within a Retrieval-Augmented Generation setup; More GitHub stars (36k vs 1.3k) - visibility, not fit.
- 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 your application does not require or benefit from the specific graph-based approach GraphRAG employs; traditional RAG systems might be sufficient without the added layer of complexity introduced by
- Is Awesome-LLM-RAG or graphrag more popular on GitHub?
- graphrag has more GitHub stars (35,519 vs 1,339). 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: Very active. 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.