AI Frameworks · HKUDS

LightRAG

LightRAG is a Python-based retrieval-augmented generation (RAG) framework that combines vector retrieval with knowledge graph indexing for faster, more accurate document querying. It supports multiple storage backends (Neo4j, MongoDB, PostgreSQL, OpenSearch), multimodal document processing, and offers configurable LLM roles for extraction, querying, and keyword generation.

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Source: GitHub — github.com/HKUDS/LightRAG

37.4k

GitHub stars

5.3k

Forks

Python

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	HKUDS/LightRAG
Owner	HKUDS
Primary language	Python
License	MIT — OSI-approved
Stars	37.4k
Forks	5.3k
Open issues	226
Latest release	v1.5.4 (2026-06-24)
Last updated	2026-07-07
Source	https://github.com/HKUDS/LightRAG

What LightRAG is

LightRAG implements a dual-level retrieval strategy combining dense vector search with entity-relationship knowledge graphs, optimized for large-scale datasets. It provides pluggable storage backends, role-specific LLM configuration, multiple text chunking strategies (Fix, Recursive, Vector, Paragraph), integrated reranking, and RAGAS-based evaluation with Langfuse tracing support.

Quickstart

Get the LightRAG source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/HKUDS/LightRAG.git2cd LightRAG3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Complex Multi-Document Knowledge Extraction

Extract, link, and query relationships across large document collections using knowledge graphs. Ideal for legal discovery, research synthesis, or enterprise knowledge management where entity relationships matter.

High-Performance Document Q&A Systems

Build production RAG applications requiring sub-second retrieval over millions of documents. Supports multimodal input (text, PDFs, images, tables) with configurable accuracy/speed tradeoffs via reranking.

Open-Source LLM Integration

Deploy RAG with local or self-hosted open models (Qwen, Llama, etc.). Role-specific LLM configuration allows using different model sizes for extraction vs. querying, reducing inference costs.

Implementation considerations

Storage backend selection is critical: MongoDB/PostgreSQL suit smaller deployments; Neo4j and OpenSearch better for large-scale graph traversal and full-text search.
Knowledge graph extraction quality depends on LLM capability; open-source models may require fine-tuning or larger model sizes (30B+) for accuracy on domain-specific text.
Text chunking strategy (Fix/Recursive/Vector/Paragraph) should be tuned per domain; vector chunking requires pre-trained embeddings and adds preprocessing latency.
Role-specific LLM configuration allows cost optimization (e.g., cheap models for keywords, premium for extraction) but requires separate API keys and error handling per role.
Multimodal document processing via MinerU/Docling requires external service setup; offline deployment guide exists but demands pre-staging all model weights and dependencies.

When to avoid it — and what to weigh

Real-Time Streaming Data Ingestion — No evidence of streaming ingest or event-driven architecture. Batch processing is the primary path; knowledge graph regeneration on document deletion suggests offline optimization workflows.
Proprietary Closed-Source Model Lock-In — Framework is agnostic to LLM vendor but no built-in cost controls or rate-limiting. Heavy reliance on external API costs (OpenAI, Claude, etc.) without native cost governance tooling.
Sub-Millisecond Latency Requirements — Dual-level retrieval (vector + graph) adds computational overhead. Latency depends on storage backend performance and LLM inference; not optimized for ultra-low-latency use cases.
Minimal External Dependencies Constraint — Requires Python 3.10+, vector databases, graph databases, embedding services, and reranking models. Docker-based local deployment available but adds infrastructure complexity.

License & commercial use

MIT License (MIT) — permissive open-source license allowing commercial use, modification, and distribution with minimal restrictions. Attribution and license notice required in derivative works.

MIT license clearly permits commercial use without restriction. However, review your own compliance obligations for any embedded third-party services (storage backends, embedding providers, LLM APIs). The framework itself imposes no commercial use barriers, but you assume responsibility for licensing any external inference engines or data stores you integrate.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

No explicit security posture stated. Considerations: (1) External API keys (LLM, embedding, reranking providers) must be securely managed; no built-in secret rotation evident. (2) Storage backends (Neo4j, PostgreSQL, MongoDB) assume network security at deployment level. (3) Multimodal document processing via external services (MinerU/Docling) introduces third-party data transmission risk; sanitization not detailed. (4) No input validation or prompt injection mitigation mechanisms described. (5) Offline deployment guide may mitigate some supply-chain risks but requires careful dependency vetting.

Alternatives to consider

LlamaIndex (formerly GPT Index)

Mature RAG framework with stronger documentation, broader LLM/vector store integrations, and native evaluation APIs. Better for teams requiring battle-tested abstractions; LightRAG is faster for knowledge graph use cases.

Langchain + Custom Graph Layer

Maximum flexibility via composable chains; larger ecosystem. However, requires more boilerplate; LightRAG abstracts graph/vector dual retrieval out-of-the-box, reducing complexity for knowledge-heavy applications.

GraphRAG (Microsoft)

Also uses knowledge graphs for RAG but closed-source; GraphRAG focuses on LLM-driven indexing. LightRAG is open-source, offers more storage backend options, and includes reranking/multimodal support.

Software development agency

Build on LightRAG with DEV.co software developers

LightRAG reduces RAG complexity with dual-level retrieval and pluggable storage. Evaluate fit for your use case via GitHub or join the Discord community for guidance on storage backend selection and LLM tuning.

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LightRAG FAQ

Can I use LightRAG with local, self-hosted LLMs?

Yes. LightRAG is LLM-agnostic and supports local inference via Ollama, vLLM, or other OpenAI-compatible endpoints. Role-specific configuration allows mixing model sizes (e.g., small for keywords, large for extraction).

What storage backend should I choose for production?

For <1M documents, PostgreSQL+vector extension is cost-effective. Neo4j excels at complex graph queries. OpenSearch unifies vector, graph, and full-text search. MongoDB suits rapid prototyping. Choice depends on query patterns and team ops expertise.

Does LightRAG support real-time document updates?

Document deletion with automatic KG regeneration is supported, but this is a batch operation. Real-time streaming ingest is not a documented feature; incremental KG updates would require custom implementation.

How does LightRAG compare to RAG-Anything and VideoRAG mentioned in the changelog?

RAG-Anything is a related project (now merged into LightRAG) focusing on multimodal document parsing. VideoRAG and MiniRAG are separate projects from the same team. LightRAG is the general-purpose production framework; these are specialized extensions.

Work with a software development agency

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