Let's Get the Hot Take Out of the Way
LangChain is over-engineered for 80% of use cases. There, we said it. We've used it in four production projects and in three of them, we ended up ripping out LangChain and replacing it with 200 lines of custom code. But — and this is important — for that remaining 20% of complex agentic workflows, LangChain is genuinely useful and hard to replace.
LlamaIndex is more focused and does its core thing (data ingestion and retrieval) very well. But it's less flexible when you need to build complex chains that go beyond retrieval.
Where LangChain Shines
LangChain's strength is composability for complex workflows. When you need to chain together multiple LLM calls, tool usage, conditional branching, and memory management — LangChain's abstractions save real time. We built a contract analysis agent that reads a PDF, extracts key terms, cross-references them against a compliance database, generates a risk assessment, and emails the results. That kind of multi-step orchestration is what LangChain was built for.
LangChain Expression Language (LCEL) was a rough transition — the documentation was confusing and the syntax took some getting used to. But once our team internalized it, the pipeline definitions became much more readable than our old imperative chain code. The streaming support in LCEL is also excellent, which matters for user-facing applications.
Where LlamaIndex Wins
If your primary use case is "I have documents, and I need to answer questions about them" — start with LlamaIndex. The ingestion pipeline is more mature than LangChain's document loaders. The node parsing, chunking strategies, and index types are more thoughtfully designed. We especially like the hierarchical indexing approach for large document sets — it mimics how a human would navigate a large corpus, starting broad and drilling down.
For a legal document search system we built, LlamaIndex's recursive retrieval (where it first identifies the relevant document, then the relevant section, then the relevant paragraph) improved answer quality by about 25% over flat retrieval with LangChain. The SubQuestionQueryEngine is also genuinely clever — it breaks complex questions into sub-questions and answers them independently before synthesizing.
The Abstractions Problem
Both frameworks suffer from abstraction bloat, but LangChain is worse. When something goes wrong — and it will — debugging through five layers of abstraction to find that your prompt template has a typo is not fun. We've had engineers spend hours debugging LangChain issues that turned out to be simple parameter misconfigurations buried under abstractions.
Our rule now: if you can explain what the code does in one sentence, don't use a framework for it. "Call GPT-4 with this prompt and parse the JSON output" doesn't need LangChain. "Orchestrate a multi-step agent that uses tools, maintains state, and handles failures gracefully" probably does.
What We Actually Recommend
For RAG applications: LlamaIndex. It's more focused, the retrieval strategies are better, and the ingestion pipeline saves you real work. For complex agentic workflows with tool use and multi-step reasoning: LangChain, specifically LangGraph for stateful agents. For simple LLM integrations: neither — just use the OpenAI SDK directly with some helper functions. Honestly, the Vercel AI SDK has been our go-to for simple use cases in TypeScript projects. It's lightweight, well-designed, and doesn't try to do too much.