LlamaIndex
used in
0
stacks
Data framework for connecting LLMs to private data, with RAG, agents, and event-driven workflows.
Provides data connectors, indexing, and retrieval primitives for building RAG and knowledge-assistant applications over your own data, available in Python and TypeScript. It adds agents with ReAct and function calling plus an event-driven Workflows API for multi-step processes. The open-source library is free; the vendor also offers LlamaCloud with managed parsing (LlamaParse) on credit-based pricing.
Strengths
- Extensive data connectors and RAG tooling
- Strong document parsing and ingestion (LlamaParse/LlamaCloud)
- Agents and event-driven workflows built in
- Large ecosystem and integrations
Tradeoffs
- Broad API surface can be complex to navigate
- Heritage is RAG-centric rather than agent-first
- Frequent API changes across versions