RAG and Data Integration

How each AI framework handles retrieval-augmented generation and connecting models to your own data.

Vercel AI SDK

Provides low-level primitives for streaming and embeddings but leaves retrieval and indexing — vector stores, chunking strategy — to the developer or a paired library.

TanStack AI

Focuses primarily on model interaction, streaming, and tool calling rather than shipping its own retrieval or indexing layer, expecting RAG to be composed from separate data libraries.

LangChain

Has broad built-in support for document loaders, text splitters, and integrations with most popular vector databases, making it a common default choice for building RAG pipelines.

LangGraph

Doesn’t add its own RAG primitives, but is commonly used to orchestrate multi-step retrieval-and-generation workflows built from LangChain’s retrieval components.

LlamaIndex

RAG and data indexing is its core focus — it’s built around ingesting, chunking, indexing, and querying your own documents, generally considered to have the deepest RAG-specific tooling in this category.

Mastra

Supports RAG through its own composable primitives for chunking, embedding, and vector store integration, alongside its agent and workflow features.

CrewAI

Supports RAG through tool integrations, such as connecting an agent to a retriever tool, rather than shipping first-party indexing infrastructure.

Semantic Kernel (Microsoft Agent Framework)

Supports retrieval through connectors to vector stores, including Azure AI Search, as one of many available plugins and connectors.

Haystack

RAG pipelines are Haystack’s original and primary use case, with mature, well-documented components for chunking, embedding, retrieval, and ranking.