LlamaIndex
Connect your own data to LLMs for retrieval-augmented generation — LlamaParse (formerly LlamaCloud) automates document parsing, extraction, and indexing for agentic workflows.
Alternatives
Overview
LlamaIndex is a data framework for building retrieval-augmented generation (RAG) and agentic AI applications that need to query private or enterprise data, document collections, databases, APIs, and knowledge bases, rather than relying solely on the LLM's training knowledge. Its core abstraction is the Index: ingest your data (PDFs, Notion pages, Confluence wikis, SQL databases, Slack exports, website content), build a structured index that enables semantic search, and query it through LLMs that answer from your actual data with source citations. LlamaIndex handles the pipeline from raw data ingestion through chunking, embedding, indexing, retrieval, and response generation, providing production-ready implementations of each step that teams can configure rather than build. The framework's Query Engines cover simple retrieval and generation, multi-document comparison, knowledge graph traversal, and structured SQL query generation from natural language.
Agents built on LlamaIndex can plan and execute multi-step research tasks across a connected set of data sources, querying multiple indexes, combining results, and generating synthesized responses. LlamaIndex is available in Python and TypeScript. The open-source framework is free; LlamaCloud provides managed ingestion pipelines and hosted indexes for production deployments. LlamaIndex is most commonly chosen over LangChain specifically for data-heavy RAG applications where the data ingestion pipeline, indexing strategies, and retrieval precision are the primary engineering concerns.
Key Features
- Data ingestion and indexing
- Advanced retrieval and query engines
- Agents and workflows
- LlamaCloud and LlamaParse
- • Best-in-class for RAG
- • Many data connectors
- • Good query abstractions
- • Overlaps with LangChain
- • Learning curve for advanced features
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