Should I build retrieval with LangChain or LlamaIndex?

Short answer: if your main goal is retrieval/RAG, start with LlamaIndex.
If your main goal is general app orchestration with lots of chains/tools/agents, start with LangChain.

Rule of thumb

Choose LlamaIndex if you want:

• Faster path to a good RAG system
• Strong document ingestion/indexing/query abstractions
• Better out-of-the-box retrieval workflows
• Easier handling of chunking, parsing, metadata, and query engines
• A library that feels more purpose-built for knowledge retrieval

Choose LangChain if you want:

• Broader orchestration around LLM apps
• Agents, tools, workflows, prompts, memory, callbacks
• A larger “glue code” framework for many components
• Retrieval as one part of a bigger system

My practical recommendation

• If you’re building:

  • internal docs search
  • enterprise Q&A
  • citation-backed RAG
  • semantic search over files/databases

  → Use LlamaIndex first

• If you’re building:

  • an agent that uses retrieval plus APIs plus tools
  • multi-step workflows
  • app logic where retrieval is just one module

  → Use LangChain

Important nuance

The gap is smaller than it used to be. Both can do retrieval well.
What matters most is:

• your team’s familiarity
• how much customization you need
• whether you need retrieval-focused abstractions or app orchestration abstractions

Best “engineering” answer

If you’re unsure:

1. Prototype the retrieval pipeline in LlamaIndex
2. Keep your vector DB, embedding model, and document preprocessing modular
3. If the system grows into a broader agent/workflow app, integrate with LangChain or another orchestration layer later

Bottom line

• RAG-first project → LlamaIndex
• Agent/workflow-first project → LangChain

If you want, I can also give you a decision matrix based on your exact use case, or a side-by-side architecture example for both.