🎉 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:

1. Semantic Aggregation

   • Group low-level entities into clusters; generate summary nodes and build adjacency relations among them for efficient navigation.

2. Knowledge Graph Construction

   • Construct a multi-layer graph where nodes represent entities and aggregated summaries, with explicit inter-node relations for graph-based traversal.

3. 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.

4. Redundancy-Aware Synthesis

   • Streamline retrieval paths and avoid overlapping content, ensuring concise evidence aggregation before generating responses.

5. 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

1. Clone the repository:

   git clone https://github.com/RaZzzyz/LeanRAG.git
   cd LeanRAG

2. Create a virtual environment:

   conda install -n leanrag python=3.11
   conda activate leanrag

3. 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 the text content for traceability
• text: 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:

1. Edit the configuration file:
   CommonKG/config/create_kg_conf_test.yaml
   Fill in the model's url and name, and the path to the chunk file.
2. Run extraction:

   python CommonKG/create_kg.py

   The extraction result will be saved in output_dir.
3. Process 6-tuples with descriptions:

   python CommonKG/deal_triple.py

   Outputs include:
   • entity.jsonl
   • relation.jsonl

Relies on LLM capability to perform few-shot extraction with given examples in the prompt.

Usage:

1. Edit GraphExtraction/chunk.py to fill in url and model. The chunk_file is the same as in CommonKG, generated from Step 1.
2. Deduplicate extraction results:

   python GraphExtraction/deal_triple.py

   Outputs include:
   • 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

1. Select the correct chunks_file.

2. Query the graph for Top-K entities based on query.

3. Generate paths between nodes according to the tree structure.

4. 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:

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:

@inproceedings{zhang2026leanrag,
  title={Leanrag: Knowledge-graph-based generation with semantic aggregation and hierarchical retrieval},
  author={Zhang, Yaoze and Wu, Rong and Cai, Pinlong and Wang, Xiaoman and Yan, Guohang and Mao, Song and Wang, Ding and Shi, Botian},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={40},
  number={41},
  pages={34862--34869},
  year={2026}
}