llamaindex
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill llamaindex --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LlamaIndex - Data Framework for LLM Applications The leading framework for connecting LLMs with your data. ## When to use LlamaIndex **Use LlamaIndex when:** - Building RAG (retrieval-augmented generation) applications - Need document question-answering over private data - Ingesting data from multiple sources (300+ connectors) - Creating knowledge bases for LLMs - Building chatbots with enterprise data - Need structured data extraction from documents **Metrics**: - **45,100+ GitHub stars** - **23,000+ repositories** use LlamaIndex - **300+ data connectors** (LlamaHub) - **1,715+ contributors** - **v0.14.7** (stable) **Use alternatives instead**: - **LangChain**: More general-purpose, better for agents - **Haystack**: Production search pipelines - **txtai**: Lightweight semantic search - **Chroma**: Just need vector storage ## Quick start ### Installation ```bash # Starter package (recommended) pip install llama-index # Or minimal core + specific integrations pip install llama-index-core pip install llama-index-llms-openai pip install llama-index-embeddings-openai ``` ### 5-line RAG example ```python from llama_index.core import VectorStoreIndex, SimpleDirectoryReader # Load docum
- When to use LlamaIndex
- Quick start
- Installation
- 5-line RAG example
- Core concepts
- 1. Data connectors - Load documents
- 2. Indices - Structure data
- 3. Query engines - Ask questions
- 4. Retrievers - Find relevant chunks
- Agents with tools
- Basic agent
- RAG agent (document search + tools)
- Advanced RAG patterns
- Chat engine (conversational)
Starter package (recommended) pip install llama-index Or minimal core + specific integrations pip install llama-index-core pip install llama-index-llms-openai pip install llama-index-embeddings-openai
What does the llamaindex skill do?
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill llamaindex --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.