Comparison
Awesome-LLM-RAG vs HippoRAG
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 HippoRAG if hippoRAG is a RAG framework that integrates knowledge from external documents into LLMs, inspired by human long-term memory, and supports personalized page rank and knowledge graphs.
Markdown twin · Awesome-LLM-RAG alternatives · HippoRAG alternatives
GraphCanon updated Sep 20, 2026
8views this month
vs
Trust & integrity
| Signal | Awesome-LLM-RAG | HippoRAG |
|---|---|---|
| Maintenance | Steady (60d since push) As of Sep 20, 2026 · github_public_v1 | Active (14d 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 | Published findings 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
- HippoRAG
- A RAG framework enabling LLMs to integrate knowledge from external documents
Stars
- Awesome-LLM-RAG
- 1.3k
- HippoRAG
- 4.0k
Forks
- Awesome-LLM-RAG
- 95
- HippoRAG
- 428
Open issues
- Awesome-LLM-RAG
- 15
- HippoRAG
- 9
Language
- Awesome-LLM-RAG
- -
- HippoRAG
- 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.
- HippoRAG
- HippoRAG is a RAG framework that integrates knowledge from external documents into LLMs, inspired by human long-term memory, and supports personalized page rank and knowledge graphs.
Persona
- Awesome-LLM-RAG
- -
- HippoRAG
- -
Runtime
- Awesome-LLM-RAG
- -
- HippoRAG
- -
License
- Awesome-LLM-RAG
- -
- HippoRAG
- MIT
Last pushed
- Awesome-LLM-RAG
- Jul 22, 2026
- HippoRAG
- Sep 3, 2026
Categories
- Awesome-LLM-RAG
- Data & Retrieval, LLM Frameworks
- HippoRAG
- Data & Retrieval, LLM Frameworks
Trust and health
Maintenance
- Awesome-LLM-RAG
- Steady (60%)
- HippoRAG
- Active (82%)
Days since push
- Awesome-LLM-RAG
- 60d
- HippoRAG
- 14d
Open issues (now)
- Awesome-LLM-RAG
- 15
- HippoRAG
- 9
Stars delta
- Awesome-LLM-RAG
- +9 (30d)
- HippoRAG
- +110 (30d)
Open issues delta
- Awesome-LLM-RAG
- +6 (30d)
- HippoRAG
- +2 (30d)
Owner type
- Awesome-LLM-RAG
- User
- HippoRAG
- Organization
OSV dependency advisories
- Awesome-LLM-RAG
- No lockfile (source not queried)
- HippoRAG
- Published findings
Full report
- Awesome-LLM-RAG
- Trust report
- HippoRAG
- Trust report
Shared compatibility
- Python · Awesome-LLM-RAG: Python runtime · HippoRAG: Python runtime
Choose Awesome-LLM-RAG if…
- Tags unique to Awesome-LLM-RAG: embeddings, large-language-models, llm, rag-embeddings.
- 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.
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 HippoRAG if…
- Requirements: Ensure you have Python 3.10 installed.; Set environment variables such as `CUDA_VISIBLE_DEVICES`, `HF_HOME`, and `OPENAI_API_KEY` as needed.; Install HippoRAG using Conda or `uv` for a project-local environment..
- Tags unique to HippoRAG: external documents, knowledge graphs, knowledge integration, llms.
- When you need a framework that integrates knowledge from external documents into LLMs in a manner inspired by human long-term memory.
When NOT to use HippoRAG
- If your project does not require the integration of knowledge from external documents into LLMs in a manner inspired by human long-term memory.
- When personalized page rank and knowledge graphs are not necessary for your project's knowledge retrieval and integration needs.
- If you are not using Python 3.10 or do not wish to manage a project-local environment with Conda or `uv`.
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 (OSU-NLP-Group/HippoRAG) · observed Sep 20, 2026
- GitHub forks (OSU-NLP-Group/HippoRAG) · observed Sep 20, 2026
- Last push (OSU-NLP-Group/HippoRAG) · observed Sep 3, 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 · HippoRAG 4.0k (synced Sep 20, 2026).
Common questions
- What is the difference between Awesome-LLM-RAG and HippoRAG?
- Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models. HippoRAG: A RAG framework enabling LLMs to integrate knowledge from external documents. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-LLM-RAG over HippoRAG?
- Choose Awesome-LLM-RAG over HippoRAG when Tags unique to Awesome-LLM-RAG: embeddings, large-language-models, llm, rag-embeddings; 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.
- When should I choose HippoRAG over Awesome-LLM-RAG?
- Choose HippoRAG over Awesome-LLM-RAG when Requirements: Ensure you have Python 3.10 installed.; Set environment variables such as
CUDA_VISIBLE_DEVICES,HF_HOME, andOPENAI_API_KEYas needed.; Install HippoRAG using Conda oruvfor a project-local environment.; Tags unique to HippoRAG: external documents, knowledge graphs, knowledge integration, llms; When you need a framework that integrates knowledge from external documents into LLMs in a manner inspired by human long-term memory. - 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 HippoRAG?
- If your project does not require the integration of knowledge from external documents into LLMs in a manner inspired by human long-term memory. When personalized page rank and knowledge graphs are not necessary for your project's knowledge retrieval and integration needs. If you are not using Python 3.10 or do not wish to manage a project-local environment with Conda or
uv. - Is Awesome-LLM-RAG or HippoRAG more popular on GitHub?
- HippoRAG has more GitHub stars (4,012 vs 1,348). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-LLM-RAG and HippoRAG open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to Awesome-LLM-RAG or HippoRAG?
- GraphCanon lists graph-backed alternatives at Awesome-LLM-RAG alternatives and HippoRAG alternatives (Awesome-LLM-RAG markdown twin, HippoRAG 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 HippoRAG?
- Awesome-LLM-RAG: Steady. HippoRAG: 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 HippoRAG?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLM-RAG trust report; HippoRAG trust report.