LangChain vs. LlamaIndex in RAG context

LangChain and LlamaIndex are two of the most widely used frameworks for building Retrieval-Augmented Generation (RAG) systems, but they serve different roles within the pipeline. LangChain provides a comprehensive toolkit for orchestrating the entire RAG workflow—retrieval, prompt construction, tool integrations, agents, and post-processing—while LlamaIndex focuses more deeply on data ingestion, indexing, and retrieval quality. In a typical RAG setup, LangChain functions as the “application orchestrator,” whereas LlamaIndex serves as the “data engine” responsible for building a highly optimized knowledge base.

In the data preparation and indexing stage, LlamaIndex offers advanced features for chunking, metadata extraction, document hierarchies, and hybrid or graph-based index structures. This makes it exceptionally strong when the quality of retrieved information depends on how the knowledge base is constructed. LangChain also supports document loading and embedding, but LlamaIndex is built specifically to give developers fine-grained control over how data is transformed into vector indexes. These indexing-centric capabilities make LlamaIndex especially effective for improving RAG retrieval relevance and precision.

When orchestrating the live retrieval and generation process, LangChain provides greater flexibility and modularity. It excels at building multi-step chains, coordinating multiple retrievers, calling external tools or APIs, routing queries, and composing different prompts. This makes LangChain a strong choice for complex RAG applications that require logic flows, evaluation loops, or agent-style reasoning. While LlamaIndex also supports retrieval pipelines and query engines, its primary focus is ensuring that the data is structured and accessible rather than orchestrating multi-step decision workflows.

Both frameworks integrate seamlessly with modern vector databases, including ChromaDB. ChromaDB is a popular choice for storing embeddings due to its open-source nature, high performance, and flexible metadata filtering. In LangChain, ChromaDB can be plugged in with just a few lines of code as a VectorStore, allowing LangChain chains and agents to retrieve relevant documents efficiently. LlamaIndex also supports ChromaDB as a storage backend, enabling developers to use LlamaIndex’s powerful indexing and query abstractions on top of the same vector database. This means teams can use ChromaDB as a shared, persistent vector layer regardless of whether the orchestration is done through LangChain, LlamaIndex, or a combination of both.

In production RAG deployments, LangChain and LlamaIndex often work side-by-side, and ChromaDB acts as a reliable vector storage layer for both. LlamaIndex can handle the data ingestion, embedding, and index construction, storing vectors inside ChromaDB. LangChain can then use that same ChromaDB instance to retrieve relevant chunks during runtime, build prompts, and drive multi-step reasoning flows. The result is a flexible, scalable, and high-quality RAG system: LlamaIndex optimizes the data and indexing layer, LangChain manages orchestration and logic, and ChromaDB provides a shared high-speed vector store that both can rely on.