LightRAG
https://github.com/HKUDS/LightRAG
Entity-relationship graph + broader-theme retrieval. Incremental update support. Integrates with RAG-Anything for multimodal docs.
At a glance
- Type
- Dual-level graph retrieval
- Tier
- T3
- Section
- Retrieval-as-memory hybrids
- Created
- 2024-10
- Latest release
- v1.4.15 2026-04-19
- License
- MIT
- GitHub
- 34.8k★ +196/mo Python
- Pricing
- searched not found
- Funding
- not applicable — not commercial
Taxonomy
- storage
- graph
- retrieval
- graph-traversal
- persistence
- long-term
- update
- read-only
- unit
- chunk
- governance
- inspectable
- conflict
- n/a
When to use
Optimised for: retrieval quality / accuracy on long-context QA
Anti-fit: most are research papers; production fitness varies by maintainer
Pros & cons
Pros
Simpler than GraphRAG with reasonable quality — good middle-ground for shops avoiding heavy GraphRAG indexing.
Cons
Less rich retrieval than GraphRAG; quality on community-summary queries is weaker.
Claims & capabilities
EMNLP 2025 Findings. Recent updates added OpenSearch unified storage backend and reranker support.
Technical surface
- API surface
- not applicable — research paper
- Backend storage
- not applicable — research paper
- Deployment
- searched not found
- Embedding model
- not applicable — research paper
- Multi-tenancy
- not applicable — research paper
- MCP
- not applicable — research paper, no deployed product
- A2A
- not applicable — research paper, no deployed product
- OpenTelemetry
- not applicable — research paper, no deployed product
Similar systems
Other retrieval-as-memory hybrids in the catalog, ranked by inbound references.
- GraphRAG (Microsoft) T1
Modular graph-based RAG. Leiden community detection over knowledge graph; community summaries at multiple levels. DRIFT Search hybrid (Oct 2024).
- RETRO T3
DeepMind. Retrieval-Enhanced Transformer with chunked cross-attention over a 2T-token retrieval database. Frozen BERT retriever + differentiable encoder.
- Atlas T3
Meta AI. Jointly fine-tunes retriever + LM for few-shot tasks.
- RAPTOR T3
Stanford recursive-summarisation tree over the corpus — clusters and summarises documents into a hierarchical index; retrieves at multiple levels. ICLR 2024.
- Self-RAG T4
Trains an LLM end-to-end to emit reflection tokens that decide whether to retrieve, assess passage relevance, and critique output quality — all within a single model.
- HippoRAG / HippoRAG2 T3
Hippocampus-inspired RAG. LLMs + knowledge graphs + Personalized PageRank to mimic neocortex/hippocampus roles. NeurIPS 2024.
Related systems
References (1)
- GraphRAG (Microsoft) cites — S2 isInfluential citation