Should I use LlamaIndex or LangChain for internal docs?

For internal docs / knowledge base apps, I’d usually start with LlamaIndex unless you already know you need the broader “agent/workflow” ecosystem of LangChain.

Quick rule of thumb

• Choose LlamaIndex if your main problem is:

  • ingesting PDFs, docs, wiki pages, Notion/Drive/etc.
  • chunking, indexing, retrieval, reranking
  • building a strong RAG/search/Q&A experience over internal content

• Choose LangChain if your main problem is:

  • orchestrating multiple LLM tools and steps
  • agents, tool calling, multi-step workflows
  • lots of integrations beyond retrieval

Why LlamaIndex often fits internal docs better

Internal docs apps are mostly a retrieval problem:

• load documents
• clean/extract text
• chunk intelligently
• build indexes
• retrieve relevant passages
• cite sources

That’s LlamaIndex’s sweet spot. It tends to feel more direct for RAG over enterprise content.

Where LangChain shines

LangChain is better if your app will evolve into something like:

• “Ask docs” plus actions in Jira/Slack/GitHub
• multi-agent task flows
• complex chain composition
• lots of custom tool routing

Practical recommendation

If you’re unsure:

1. Prototype with LlamaIndex
2. If you later need agents/workflows, add LangChain around it or switch parts over

They’re also not mutually exclusive. Some teams use:

• LlamaIndex for ingestion/retrieval
• LangChain for orchestration/tool use

My default recommendation for internal docs

• Simple docs Q&A / search / RAG: LlamaIndex
• Docs + workflows + agents: LangChain
• Large team, unclear future: LlamaIndex first, keep architecture modular

If you want, I can give you a decision matrix based on your stack (Python/JS, vector DB, sources like Google Drive/Confluence/Notion, need for citations, etc.).