OSU-NLP-Group/HippoRAG

Health v2 · maintenance only

Adoption not part of the score

3967 stars · 419 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

HippoRAG is a Python RAG framework inspired by human long-term memory that combines LLMs, knowledge graphs, and Personalized PageRank to integrate knowledge across documents. It improves multi-hop retrieval and sense-making while remaining cheaper and faster than iterative or other graph-based RAG approaches.

Use cases

• answer multi-hop questions across documents
• build a knowledge-graph-based RAG pipeline
• give LLMs long-term memory over a document corpus
• cheaper alternative to GraphRAG for offline indexing
• improve retrieval accuracy for complex question answering
• continually integrate new knowledge without retraining

When to choose

• you need multi-hop or associative retrieval over a large corpus
• you want graph-based RAG with lower indexing cost than GraphRAG or LightRAG
• you're doing research on memory-augmented LLMs and want a published, benchmarked method

When to avoid

• you need a simple plug-and-play vector RAG with minimal setup
• your queries are single-hop factual lookups where standard RAG suffices
• you can't run LLM calls or GPU inference for offline indexing

Facets

library · maturity active

rag search-engine machine-learning llm-inference nlp large-language-models artificial-intelligence machine-learning python knowledge-graph personalized-pagerank multi-hop-qa long-term-memory graphrag neurips retrieval-augmented-generation natural-language-processing linux macos gpu

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markdown · JSON · MCP: product_card(name="OSU-NLP-Group/HippoRAG")