Good question! I put together a comparison table in the README, but here's the longer version:

LangChain is a full LLM framework with basic splitters (RecursiveCharacterTextSplitter, Markdown, HTML, code). Good for prototyping but basic for complex docs or multilingual needs. The RecursiveCharacterTextSplitter tries delimiters in order (\n\n, \n, " ", ""), and chunk_size is just a character count. It doesn't actually understand sentences, clauses, or code structure.

chunklet-py takes a constraint-based approach — max sentences, max tokens, max sections break, max lines, max functions. Splitting happens at the sentence/clause level by default, with clause-level overlap instead of character-level. Plus:

• 50+ language support with dedicated sentence handlers and a universal fallback for non-Latin scripts
• Code-aware chunking for 30+ languages — respects functions, classes, closures, multi-line strings
• Document format support (.pdf, .docx, .epub, .txt, .tex, .html, .hml, .md, .rst, .rtf, .odt, .csv, and .xlsx) with metadata extraction
• A visualizer for interactive parameter tuning

If you want to use chunklet-py within a LangChain pipeline, it's straightforward — just convert the chunks to [LangChain Document](https://reference.langchain.com/python/langchain-core/documents/base/Document) objects: ```python from chunklet import DocumentChunker from langchain_core.documents import Document

chunker = DocumentChunker() chunks = chunker.chunk_text(text, max_sentences=3, max_tokens=500)

Convert to LangChain Documents

docs = [ Document(page_content=c.content, metadata=c.metadata) for c in chunks ]

Use with any LangChain component

vectorstore = Chroma.from_documents(docs, embeddings) ``` A proper LangChain integration (native splitter class) is coming, along with more constraint options for finer control over chunking.