LlamaIndex vs LangChain chunking comparison
VERDICT
| Tool | Key strength | Pricing | API access | Best for |
|---|---|---|---|---|
| LlamaIndex | Semantic-aware chunking with knowledge graph integration | Free (open-source) | Python SDK | Knowledge ingestion and retrieval |
| LangChain | Highly customizable chunking with multiple splitter classes | Free (open-source) | Python SDK | Custom pipeline chunking and LLM orchestration |
| LlamaIndex | Automatic text splitting tuned for embeddings and indexing | Free (open-source) | Python SDK | Long document processing with minimal setup |
| LangChain | Supports various chunking strategies (character, token, recursive) | Free (open-source) | Python SDK | Flexible chunking for diverse document types |
Key differences
LlamaIndex focuses on semantic chunking optimized for knowledge graph construction and retrieval, automatically handling overlap and context preservation. LangChain provides a suite of TextSplitter classes (e.g., RecursiveCharacterTextSplitter, TokenTextSplitter) that give developers explicit control over chunk size, overlap, and splitting logic. LlamaIndex integrates chunking tightly with indexing, while LangChain offers chunking as a modular preprocessing step for broader LLM workflows.
LlamaIndex chunking example
This example shows how LlamaIndex automatically chunks a long document for indexing using its default semantic chunking strategy.
from llama_index import SimpleDirectoryReader, GPTVectorStoreIndex
import os
# Load documents from directory
documents = SimpleDirectoryReader('data').load_data()
# Create an index with automatic chunking
index = GPTVectorStoreIndex.from_documents(documents)
# Query the index
response = index.query('Explain the main topic of the documents')
print(response.response)The main topic of the documents is about AI-powered document processing and knowledge retrieval.
LangChain chunking example
This example demonstrates using LangChain's RecursiveCharacterTextSplitter to chunk text with explicit control over chunk size and overlap.
from langchain.text_splitter import RecursiveCharacterTextSplitter
text = """Very long document text goes here..."""
splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200
)
chunks = splitter.split_text(text)
print(f"Number of chunks: {len(chunks)}")
print(f"First chunk preview: {chunks[0][:200]}")Number of chunks: 5 First chunk preview: Very long document text goes here...
When to use each
Use LlamaIndex when you want an end-to-end solution for document ingestion, semantic chunking, and knowledge graph indexing with minimal manual chunking setup. Use LangChain when you need precise control over chunking parameters or want to integrate chunking as part of a larger, customizable LLM pipeline.
| Scenario | Recommended tool | Reason |
|---|---|---|
| Building a semantic search index from documents | LlamaIndex | Automatic semantic chunking and indexing integration |
| Custom chunking for diverse document formats | LangChain | Modular splitters with configurable chunk size and overlap |
| Rapid prototyping with minimal chunking config | LlamaIndex | Out-of-the-box chunking optimized for embeddings |
| Complex LLM workflows requiring chunking as a preprocessing step | LangChain | Flexible chunking integrated into pipelines |
Pricing and access
Both LlamaIndex and LangChain are open-source Python libraries with free access. They do not provide hosted APIs but integrate with various LLM providers for downstream tasks.
| Option | Free | Paid | API access |
|---|---|---|---|
| LlamaIndex | Yes | No | No (SDK only) |
| LangChain | Yes | No | No (SDK only) |
Key Takeaways
- LlamaIndex excels at semantic chunking tightly integrated with knowledge graph indexing.
- LangChain offers granular, customizable chunking tools for flexible pipeline design.
- Choose LlamaIndex for quick knowledge ingestion; choose LangChain for tailored chunking control.