These tools integrates with

QdrantvsLightRAG

High-performance vector DB with filtering versus Graph-based RAG with dual-level knowledge retrieval

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Choose Qdrant when…

  • •You need high-performance vector search in production
  • •You want OSS with Rust-level performance
  • •Filtering alongside vector search is important

Choose LightRAG when…

  • •Your queries require reasoning across multiple documents or topics
  • •You want graph-based retrieval instead of flat vector search
  • •You need both fact-level and concept-level retrieval in one system

Side-by-side comparison

Field
Qdrant
LightRAG
Category
LLM Infrastructure
Pipelines & RAG
Type
Open Source
Open Source
Free Tier
✓ Yes
✓ Yes
Pricing Plans
Cloud: Usage-based
—
GitHub Stars
⭐ 20,000
⭐ 15,000
Health
●95 — Active
●85 — Active

Qdrant

Rust-based vector database optimized for filtering. Supports named vectors, payloads, and hybrid search. Self-hostable or cloud.

Website ↗GitHub ↗

LightRAG

RAG framework that builds a knowledge graph from documents, enabling retrieval at both local (specific facts) and global (thematic) levels. Outperforms naive RAG on complex questions requiring reasoning across multiple document sections. EMNLP 2025.

Website ↗GitHub ↗

Shared Connections1 tools both integrate with

LlamaIndex

Only Qdrant (13)

LangGraphLangChainHaystackDifyVercel AI SDKChromaPineconepgvectorWeaviateMilvus

Only LightRAG (2)

QdrantOpenAI API

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See how Qdrant and LightRAG fit into the bigger picture — 235 tools, 543 relationships, all mapped.

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