Small chunks give precise embeddings but lack context; big chunks have complete context but diluted embeddings. Hierarchical Chunking builds multi-level indexes (2048→512→128 tokens) with an Auto-Merge algorithm: leaf nodes match precisely, and when hit density exceeds a threshold the parent node is returned to the LLM instead. HiChunk shows a 12.7% evidence recall improvement; LlamaIndex and Haystack have it built in.
LlamaIndex and Haystack are code-first frameworks; RAGFlow and Dify are managed application platforms; R2R packages retrieval as an API service. Choose how much control your team needs over ingestion, retrieval, and operations before choosing a tool.
LlamaIndex (51,775 GitHub stars, MIT, verified 2026-08-21) has moved its center of gravity from indexing to Workflows: the standalone llama-index-workflows package pulls 2.81M weekly PyPI downloads, more than the 1.97M of the llama-index umbrella package itself. This post covers the core abstractions, the trade-off against hand-rolling a pipeline, and a hands-on test of its defaults on Traditional Chinese text — at the same chunk_size=1024, English fits 4,645 characters and Traditional Chinese only 1,332. Plus one fact you need before choosing: the TypeScript port is archived and unmaintained.
Sorted by GitHub Stars, a survey of 15 mainstream AI Agent frameworks in 2026 — their positioning, key features, and ideal use cases. Not a ranking — it's a map.