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Pipelines & RAGOSS

LlamaIndex

⭐ 37,000 GitHub stars
Last commit: —· synced today
Health:100/100 — Active
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Framework specialized in data ingestion, indexing, and retrieval for LLM applications. The go-to for complex RAG pipelines.

90-day trajectory · health score + stars

Pricing

Free tier
Website ↗GitHub ↗

Connections

Cursor
often used withCompare →
LangGraph
often used withCompare →
LangChain
often used withCompare →
Qdrant
integrates withCompare →
Chroma
integrates withCompare →
pgvector
integrates withCompare →
Weaviate
integrates withCompare →
Langfuse
integrates withCompare →

Featured in stacks

Zero-Budget OSS Stack
Data + AI Pipeline
Multi-Modal RAG Stack
OSS Self-Hosted AI Stack
Edge / On-Device AI Stack
Document Intelligence Stack

Ruled out by 6 stacks

TypeScript-Only AI Stack
“TypeScript version is a second-class citizen compared to the Python library”
Browser AI / Web Agent Stack
“RAG pipeline framework; the data source here is live web pages, not a document corpus”
LLM Production Infra Stack
“Full RAG framework; Qdrant's native clients handle retrieval without the extra abstraction”
Evaluation & Quality Stack
“Retrieval pipeline framework — evaluation here is model-and-prompt focused, not retrieval-focused”
Memory-Augmented Agent Stack
“RAG over documents; Mem0 handles episodic memory — what users said and decided, not what documents contain”
Graph RAG Stack
“LightRAG handles its own document ingestion and graph construction — adding LlamaIndex creates a redundant ingestion layer”