HKUDS/MiniRAG

Health v2 · maintenance only

Adoption not part of the score

2008 stars · 257 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MiniRAG is an extremely simple retrieval-augmented generation framework designed to work with small, open-source language models. It uses semantic-aware heterogeneous graph indexing and lightweight topology-enhanced retrieval to achieve LLM-level RAG performance with small models while using only 25% of the storage.

Use cases

• build a RAG pipeline with small open-source language models
• run retrieval-augmented generation on resource-constrained or on-device hardware
• index documents into a heterogeneous knowledge graph for retrieval
• reduce RAG storage requirements compared to LLM-based systems
• evaluate lightweight RAG systems with the LiHua-World benchmark
• query documents with graph-based topology-enhanced retrieval

When to choose

• you want RAG performance without relying on large proprietary LLMs
• you are deploying on-device or in resource-constrained environments
• you need lower storage footprint for your RAG system
• you prefer graph-based retrieval over pure vector search
• you want to benchmark lightweight RAG approaches

When to avoid

• you already have access to strong large language models and want maximum retrieval quality
• you need a turnkey managed RAG service rather than a Python framework
• your data does not benefit from entity/graph-based indexing
• you require extensive production tooling and enterprise support

Facets

framework · maturity active

rag search-engine nlp llm-inference machine-learning large-language-models artificial-intelligence python self-hosted cross-platform small-language-models graph-indexing knowledge-graph lightweight-rag on-device heterogeneous-graph benchmark-dataset retrieval-augmented-generation natural-language-processing search docker

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markdown · JSON · MCP: product_card(name="HKUDS/MiniRAG")