🎉 This paper has been accepted by AAAI-26! 🎉
LeanRAG is an efficient, open-source framework for Retrieval-Augmented Generation, leveraging knowledge graph structures with semantic aggregation and hierarchical retrieval to generate context-aware, concise, and high-fidelity responses.
- Semantic Aggregation: Clusters entities into semantically coherent summaries and constructs explicit relations to form a navigable aggregation-level knowledge network.
- Hierarchical, Structure-Guided Retrieval: Initiates retrieval from fine-grained entities and traverses up the knowledge graph to gather rich, highly relevant evidence efficiently.
- Reduced Redundancy: Optimizes retrieval paths to significantly reduce redundant information—LeanRAG achieves ~46% lower retrieval redundancy compared to flat retrieval baselines (based on benchmark evaluations).
- Benchmark Performance: Demonstrates superior performance across multiple QA benchmarks with improved response quality and retrieval efficiency.
LeanRAG’s processing pipeline follows these core stages:
-
Semantic Aggregation
- Group low-level entities into clusters; generate summary nodes and build adjacency relations among them for efficient navigation.
-
Knowledge Graph Construction
- Construct a multi-layer graph where nodes represent entities and aggregated summaries, with explicit inter-node relations for graph-based traversal.
-
Query Processing & Hierarchical Retrieval
- Anchor queries at the most relevant detailed entities ("bottom-up"), then traverse upward through the semantic aggregation graph to collect evidence spans.
-
Redundancy-Aware Synthesis
- Streamline retrieval paths and avoid overlapping content, ensuring concise evidence aggregation before generating responses.
-
Generation
- Use retrieved, well-structured evidence as input to an LLM to produce coherent, accurate, and contextually grounded answers.
- Python 3.10+
- Conda for environment management
-
Clone the repository:
git clone https://github.com/RaZzzyz/LeanRAG.git cd LeanRAG -
Create a virtual environment:
conda install -n leanrag python=3.11 conda activate leanrag
-
Install the required dependencies:
pip install -r requirements.txt
Here’s a typical pipeline flow:
In file_chunk.py, split the document into chunks:
- Chunk size:
1024 - Sliding step:
128(i.e., use a sliding window with step 128)
Each dictionary in the resulting chunk file contains two attributes:
hash_code: hash calculated from thetextcontent for traceabilitytext: the chunk text content
Two knowledge graph extraction methods are currently provided:
Based on Wikipedia entities. First, define a head entity list, then extract triples from the document.
Usage:
- Edit the configuration file:
CommonKG/config/create_kg_conf_test.yaml
Fill in the model'surlandname, and the path to the chunk file. - Run extraction:
The extraction result will be saved in output_dir.
python CommonKG/create_kg.py
- Process 6-tuples with descriptions:
Outputs include:
python CommonKG/deal_triple.py
- entity.jsonl
- relation.jsonl
Relies on LLM capability to perform few-shot extraction with given examples in the prompt.
Usage:
- Edit
GraphExtraction/chunk.pyto fill in url and model. The chunk_file is the same as in CommonKG, generated from Step 1. - Deduplicate extraction results:
Outputs include:
python GraphExtraction/deal_triple.py
- entity.jsonl
- relation.jsonl
python build_graph.py-
Cluster extracted entity and relation descriptions and generate relationships.
-
Construct a tree-structured knowledge graph, supporting retrieval and Q&A.
python query_graph.py-
Select the correct chunks_file.
-
Query the graph for Top-K entities based on query.
-
Generate paths between nodes according to the tree structure.
-
Return same-level relationships and aggregated entity information along the paths to the LLM for final answer generation.
On four challenging QA benchmarks spanning diverse domains, LeanRAG consistently delivers:
| Metric | LeanRAG | HiRAG | Naive | GraphRAG | LightRAG | FastGraphRAG | KAG |
|---|---|---|---|---|---|---|---|
| Comprehensiveness | 8.89±0.01 | 8.72±0.02 | 8.20±0.01 | 8.52±0.01 | 8.19±0.02 | 6.56±0.02 | 7.90±0.03 |
| Empowerment | 8.16±0.02 | 7.86±0.03 | 7.52±0.03 | 7.73±0.02 | 7.56±0.03 | 5.82±0.03 | 7.41±0.04 |
| Diversity | 7.73±0.01 | 7.21±0.02 | 6.65±0.03 | 7.04±0.02 | 6.69±0.04 | 4.88±0.03 | 6.42±0.04 |
| Overall | 8.59±0.01 | 8.08±0.02 | 7.47±0.02 | 7.87±0.01 | 7.61±0.04 | 5.76±0.02 | 7.25±0.03 |
| Metric | LeanRAG | HiRAG | Naive | GraphRAG | LightRAG | FastGraphRAG | KAG |
|---|---|---|---|---|---|---|---|
| Comprehensiveness | 8.92±0.01 | 8.92±0.01 | 8.94±0.01 | 8.55±0.02 | 8.76±0.02 | 6.79±0.01 | 8.22±0.02 |
| Empowerment | 8.68±0.02 | 8.66±0.02 | 8.69±0.04 | 8.28±0.04 | 8.50±0.04 | 6.67±0.04 | 8.52±0.05 |
| Diversity | 7.87±0.02 | 7.84±0.02 | 7.79±0.02 | 7.42±0.02 | 7.63±0.04 | 5.45±0.04 | 7.03±0.02 |
| Overall | 8.82±0.02 | 8.77±0.02 | 8.77±0.03 | 8.37±0.04 | 8.59±0.04 | 6.31±0.03 | 7.99±0.03 |
| Metric | LeanRAG | HiRAG | Naive | GraphRAG | LightRAG | FastGraphRAG | KAG |
|---|---|---|---|---|---|---|---|
| Comprehensiveness | 8.88±0.02 | 8.68±0.02 | 8.85±0.01 | 8.95±0.01 | 8.24±0.02 | 3.87±0.02 | 8.41±0.02 |
| Empowerment | 8.42±0.03 | 8.18±0.06 | 8.28±0.03 | 8.33±0.02 | 7.83±0.05 | 3.53±0.03 | 8.20±0.03 |
| Diversity | 7.49±0.03 | 7.00±0.03 | 7.10±0.04 | 7.47±0.03 | 6.87±0.01 | 2.87±0.02 | 6.71±0.01 |
| Overall | 8.49±0.04 | 8.00±0.04 | 8.21±0.03 | 8.44±0.01 | 7.74±0.03 | 3.43±0.02 | 7.83±0.03 |
| Metric | LeanRAG | HiRAG | Naive | GraphRAG | LightRAG | FastGraphRAG | KAG |
|---|---|---|---|---|---|---|---|
| Comprehensiveness | 8.94±0.06 | 8.99±0.00 | 8.85±0.01 | 8.97±0.01 | 8.71±0.01 | 3.28±0.01 | 8.22±0.01 |
| Empowerment | 8.66±0.02 | 8.52±0.02 | 8.51±0.03 | 8.52±0.02 | 8.23±0.02 | 3.29±0.05 | 8.33±0.06 |
| Diversity | 8.06±0.03 | 7.98±0.02 | 7.76±0.06 | 7.95±0.02 | 7.68±0.03 | 3.01±0.03 | 7.07±0.02 |
| Overall | 8.87±0.02 | 8.87±0.03 | 8.69±0.03 | 8.85±0.01 | 8.56±0.02 | 3.17±0.02 | 7.95±0.03 |
| Metric | Mix (NaiveRAG) | Mix (LeanRAG) | CS (NaiveRAG) | CS (LeanRAG) | Legal (NaiveRAG) | Legal (LeanRAG) | Agriculture (NaiveRAG) | Agriculture (LeanRAG) |
|---|---|---|---|---|---|---|---|---|
| Comprehensiveness | 11.9% | 88.1% | 41.0% | 59.0% | 30.0% | 70.0% | 37.7% | 62.3% |
| Empowerment | 1.5% | 98.5% | 40.5% | 59.5% | 24.5% | 75.5% | 19.8% | 80.2% |
| Diversity | 3.1% | 96.9% | 28.0% | 72.0% | 9.0% | 91.0% | 10.0% | 90.0% |
| Overall | 2.7% | 97.3% | 39.5% | 60.5% | 23.5% | 76.5% | 19.3% | 80.7% |
| Metric | Mix (GraphRAG) | Mix (LeanRAG) | CS (GraphRAG) | CS (LeanRAG) | Legal (GraphRAG) | Legal (LeanRAG) | Agriculture (GraphRAG) | Agriculture (LeanRAG) |
|---|---|---|---|---|---|---|---|---|
| Comprehensiveness | 35.0% | 65.0% | 41.0% | 59.0% | 49.0% | 51.0% | 45.5% | 54.5% |
| Empowerment | 20.0% | 80.0% | 33.5% | 66.5% | 44.0% | 56.0% | 27.0% | 73.0% |
| Diversity | 16.5% | 83.5% | 34.0% | 66.0% | 44.0% | 56.0% | 22.0% | 78.0% |
| Overall | 21.9% | 78.1% | 37.5% | 62.5% | 47.0% | 53.0% | 28.5% | 71.5% |
| Metric | Mix (LightRAG) | Mix (LeanRAG) | CS (LightRAG) | CS (LeanRAG) | Legal (LightRAG) | Legal (LeanRAG) | Agriculture (LightRAG) | Agriculture (LeanRAG) |
|---|---|---|---|---|---|---|---|---|
| Comprehensiveness | 28.8% | 71.2% | 44.5% | 55.5% | 25.0% | 75.0% | 38.0% | 62.0% |
| Empowerment | 16.5% | 83.5% | 35.5% | 64.5% | 12.0% | 88.0% | 17.0% | 83.0% |
| Diversity | 13.1% | 86.9% | 34.0% | 66.0% | 40.5% | 59.5% | 16.5% | 83.5% |
| Overall | 18.8% | 81.2% | 38.5% | 61.5% | 21.0% | 79.0% | 18.5% | 81.5% |
| Metric | Mix (FastGraphRAG) | Mix (LeanRAG) | CS (FastGraphRAG) | CS (LeanRAG) | Legal (FastGraphRAG) | Legal (LeanRAG) | Agriculture (FastGraphRAG) | Agriculture (LeanRAG) |
|---|---|---|---|---|---|---|---|---|
| Comprehensiveness | 0.0% | 100.0% | 0.5% | 99.5% | 1.0% | 99.0% | 0.5% | 99.5% |
| Empowerment | 0.0% | 100.0% | 0.0% | 100.0% | 0.5% | 99.5% | 0.0% | 100.0% |
| Diversity | 0.0% | 100.0% | 0.8% | 99.2% | 2.5% | 97.5% | 0.0% | 100.0% |
| Overall | 0.0% | 100.0% | 0.0% | 100.0% | 4.5% | 95.5% | 0.0% | 100.0% |
| Metric | Mix (KAG) | Mix (LeanRAG) | CS (KAG) | CS (LeanRAG) | Legal (KAG) | Legal (LeanRAG) | Agriculture (KAG) | Agriculture (LeanRAG) |
|---|---|---|---|---|---|---|---|---|
| Comprehensiveness | 1.5% | 98.5% | 5.0% | 95.0% | 5.0% | 95.0% | 2.5% | 97.5% |
| Empowerment | 1.9% | 98.1% | 3.0% | 97.0% | 4.5% | 95.5% | 2.5% | 97.5% |
| Diversity | 1.2% | 98.8% | 4.0% | 96.0% | 2.5% | 97.5% | 1.0% | 99.0% |
| Overall | 1.2% | 98.8% | 3.5% | 96.5% | 4.5% | 95.5% | 1.0% | 99.0% |
| Metric | Mix (HiRAG) | Mix (LeanRAG) | CS (HiRAG) | CS (LeanRAG) | Legal (HiRAG) | Legal (LeanRAG) | Agriculture (HiRAG) | Agriculture (LeanRAG) |
|---|---|---|---|---|---|---|---|---|
| Comprehensiveness | 43.8% | 56.2% | 46.5% | 53.5% | 29.5% | 70.5% | 49.5% | 50.5% |
| Empowerment | 26.5% | 73.5% | 43.5% | 56.5% | 16.5% | 83.5% | 26.5% | 73.5% |
| Diversity | 20.4% | 79.6% | 44.5% | 55.5% | 23.5% | 76.5% | 23.5% | 76.5% |
| Overall | 28.1% | 71.9% | 45.0% | 55.0% | 21.5% | 78.5% | 28.0% | 72.0% |
We gratefully acknowledge the use of the following open-source projects in our work:
-
nano-graphrag: a simple, easy-to-hack GraphRAG implementation
-
HiRAG: a novel hierarchy entity aggregation and optimized retrieval RAG method
If you find LeanRAG useful, please cite our paper:
@misc{zhang2025leanragknowledgegraphbasedgenerationsemantic,
title={LeanRAG: Knowledge-Graph-Based Generation with Semantic Aggregation and Hierarchical Retrieval},
author={Yaoze Zhang and Rong Wu and Pinlong Cai and Xiaoman Wang and Guohang Yan and Song Mao and Ding Wang and Botian Shi},
year={2025},
eprint={2508.10391},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2508.10391},
}