LangChain vs LlamaIndex vs Building It Yourself
What each framework decides for you, what that costs later, and when no framework is the right answer.
The short answer
They solve different bottlenecks. If you need orchestration for agents and workflows, use LangChain (and LangGraph for stateful agent loops). If you need indexing and retrieval over a document corpus, use LlamaIndex. If you need to learn the stack or avoid abstraction churn, build your pipeline yourself. Hybrid is common. Framework choice stays neutral because this site does not crown a single framework for every architecture. As-of July 2026.
When should you use LangChain vs LlamaIndex?
The deciding question is whether your bottleneck is orchestration or indexing and retrieval. Orchestration means branching tool and agent logic over durable state, which points to LangChain and LangGraph. Indexing and retrieval quality over documents points to LlamaIndex. Ask that first; most cases become obvious before you reach the trade-off table.
What’s the difference between LangChain and LlamaIndex?
LangChain and LlamaIndex both help you assemble RAG, but they tilt toward different responsibilities. The table below is a directional summary of where the work usually sits.
| Factor | LangChain | LlamaIndex | Build yourself |
|---|---|---|---|
| Primary bottleneck | Orchestration and agent/workflow control | Indexing and retrieval over documents | Complete control of both |
| Document indexing depth | Customisable but not the core focus | Core focus | You own the implementation |
| Agent and tool orchestration | Core strength | Possible via wrappers but not the primary tilt | You own the loop and stops |
| Debugging and abstraction cost | Higher when the abstraction hides failures | Higher when you customise retrieval details | Lower abstraction surprise but higher engineering load |
| Version churn | Framework APIs can change | Framework APIs can change | Fewer external knobs but more internal maintenance |
| Time to first RAG | Often faster to assemble | Often faster to assemble | Slower but more learnable |
Pick the framework that matches the stage you want to control. For running code paths, different frameworks often converge on the same underlying RAG pattern: chunking or indexing, retrieval, and generation.
Can you use LangChain and LlamaIndex together?
Yes. Many production stacks use both because indexing and orchestration are independent layers. A common pattern is to use LlamaIndex for ingest and retrieval, then use LangChain or LangGraph for the agent loop and workflow control.
Should you build RAG without a framework?
Yes when your priority is to learn or to control every stage of the pipeline rather than rely on abstractions. A framework can be optional scaffolding, not a requirement for “what RAG is.” If you want to go deeper into implementation, start with the runnable build path at RAG pipeline build.
LangChain or LlamaIndex?
Use LangChain when your bottleneck is orchestration for agents and workflows. Use LlamaIndex when your bottleneck is indexing and retrieval quality over a document corpus. Many stacks combine both.
Can I use both?
Yes. A common pattern is LlamaIndex for indexing and retrieval and LangChain or LangGraph for the agent loop and workflow orchestration.
Should I build without a framework?
Yes when you want full control and you are learning how each pipeline stage works. A framework is an assembly option, not the definition of RAG.
Is LangChain the same as RAG?
No. LangChain is a way to assemble components; RAG is the retrieve-and-generate pattern. LangChain can help you build RAG, but it is not itself the definition.
Where does Haystack fit?
Haystack is another RAG assembly option. It fits when you prefer a pipeline-first framework model, but it is not automatically a better choice for every RAG architecture.