Awesome-RAG and Awesome-GraphRAG
These are complementary resources, as the second tool specifically curates resources for graph-based RAG, which is a specialized subfield of the broader RAG applications covered by the first tool, allowing a user to first explore general RAG applications and then deep-dive into graph-based approaches if relevant.
About Awesome-RAG
Danielskry/Awesome-RAG
😎 Awesome list of Retrieval-Augmented Generation (RAG) applications in Generative AI.
Compiles tools, frameworks, and architecture patterns for building RAG systems—covering everything from naive retrieval pipelines to advanced approaches like agentic RAG, graph-based retrieval, and multimodal generation. Organizes resources across Python ecosystems (LangChain, LlamaIndex, Haystack), vector databases, evaluation metrics, and production deployment strategies. Serves as a structured knowledge map connecting RAG theory, implementation tutorials, and best practices for reducing hallucinations through grounded context retrieval.
About Awesome-GraphRAG
DEEP-PolyU/Awesome-GraphRAG
Awesome-GraphRAG: A curated list of resources (surveys, papers, benchmarks, and opensource projects) on graph-based retrieval-augmented generation.
Organizes GraphRAG research into three core dimensions—knowledge organization (graph construction via entity extraction or hierarchical indexing), retrieval mechanisms (semantic similarity, logical reasoning, GNN-based, and LLM-based approaches), and knowledge integration (fine-tuning vs. in-context learning)—with accompanying benchmarks and open-source implementations. The repository maps distinct GraphRAG paradigms including knowledge-based approaches that extract entity-relation graphs from raw text and index-based approaches that build topic hierarchies, contrasting both against traditional chunk-based RAG. Covers integration patterns across LLM frameworks and provides curated links to peer-reviewed papers, established benchmarks like GraphRAG-Bench, and reference implementations including LinearRAG and LogicRAG.
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