LlamaIndex vs LangChain: Which to Use for RAG

The honest answer is that this comparison stopped being a fork in the road some time ago. Both projects will load your PDFs, chunk them, embed them, retrieve, and answer. Neither will be the reason your RAG system succeeds or fails, because chunk boundaries, retrieval quality, and evaluation dominate everything a framework decides for you.

What still differs is the centre of gravity. Each project’s own documentation says what it optimises for, and reading those statements side by side is more useful than any feature checklist.

What each project says it is

LlamaIndex describes itself as the leading framework for building LLM-powered agents over your data, built around context augmentation, and it organises its RAG documentation around five stages: loading, indexing, storing, querying, and evaluation. The named use cases open with question answering and RAG, then chatbots, document understanding and data extraction, and autonomous agents. Note the order: agents appear on the list, but they arrive after the corpus.

LangChain’s current overview leads somewhere else. Its headline construct is create_agent, described as a minimal, highly configurable agent harness, and the framework’s stated model is “Agent = Model + Harness”, where the harness is the prompt, the tools, and the middleware wrapped around a model loop. Retrieval in that world is a tool the agent may call.

That is the real difference. One project treats your corpus as the primary object and the agent as something you build on top of it. The other treats the agent loop as the primary object and your corpus as one of the things it can reach.

Side by side

	LlamaIndex	LangChain
Stated centre	Context augmentation over your data	A configurable agent harness
Documentation spine	Loading, indexing, storing, querying, evaluating	Model, tools, prompt, middleware
Retrieval model	Indexes and query engines as first-class objects	Retriever as a component or agent tool
Orchestration	Workflows: event-driven steps that emit and consume events	LangGraph for low-level graph orchestration
Retrieval tuning built in	Node postprocessors: similarity cutoff, rerankers, metadata replacement, context reordering	Assembled from components and integrations
Evaluation	Retrieval and response evaluators in-framework	Handled largely through LangSmith
Packaging	llama-index-core plus per-integration packages	langchain plus provider packages, langgraph, langsmith

Read that table as a statement of emphasis, not capability. Anything in one column can be built in the other; the question is how much of it you write yourself.

Where LlamaIndex is the shorter path

The corpus is the hard part. If your problem is a few hundred thousand messy documents and the failure mode is “the answer exists but retrieval never surfaces it”, LlamaIndex gives you more that is already assembled. Index types other than plain vector similarity, a router that picks a strategy per query, and a documented ingestion pipeline are all in the box rather than in your codebase.

Retrieval tuning has named parts. The node postprocessor layer is a small but genuinely useful abstraction: SimilarityPostprocessor drops nodes below a score cutoff, SentenceTransformerRerank, LLMRerank and CohereRerank reorder candidates by relevance to the actual question, MetadataReplacementPostProcessor swaps a retrieved sentence for its surrounding window, and LongContextReorder rearranges nodes before they are packed into the prompt. Those are the interventions that usually move answer quality, and having them as a documented pipeline stage means you tune rather than rewrite.

You want measurement without a SaaS account. Retrieval metrics such as hit rate and mean reciprocal rank, response evaluators for faithfulness and relevancy, and synthetic question generation from your own text all live in the framework.

Where LangChain is the shorter path

The agent loop is the hard part. If retrieval is one capability among many, and the real work is tool selection, multi-step planning, human-in-the-loop approval, and middleware around a model call, LangChain’s abstractions are aimed directly at that. Building the same harness on top of a retrieval-first framework means writing the parts LangChain hands you.

The integrations you need are the agent-side ones. The two catalogues differ in shape more than in size. LlamaIndex’s is weighted toward readers, vector stores, and embedding providers, which is what a corpus needs. LangChain’s is weighted toward chat-model providers and callable tools, which is what an agent loop needs. If the connector that decides your project is a tool the model calls rather than a source it reads, look there first, and check the specific package in both registries before treating either catalogue as a reason.

You want tracing as a default. LangSmith is a first-party observability answer that arrives with the ecosystem rather than being wired in later.

The dichotomy is soft

Both projects have converged on event-driven or graph orchestration for anything multi-step. LlamaIndex Workflows break an application into steps that each receive an event, do work, and return another event, with the next step chosen by type annotation, explicitly to escape the branch-and-loop awkwardness of directed acyclic graphs. LangGraph exists for the same class of problem from the other direction.

They also compose. A LangChain or LangGraph agent can call a LlamaIndex query engine as a tool, which is a common and unremarkable arrangement: retrieval quality from one project, agent ergonomics from the other. If you find yourself arguing the question as either-or, check first whether the real decision is which one owns the index.

What this comparison is not about

Speed. Both frameworks are thin Python around the same network calls. Latency in a RAG request is embedding the query, the vector store round trip, an optional rerank, and the generation call. Framework overhead is noise next to those, and any benchmark claiming otherwise is measuring a configuration difference rather than a framework difference.

Answer quality. Neither project writes your chunk boundaries, chooses your embedding model, or attaches metadata to nodes. Those decide whether the right passage is retrievable at all, and they are the same decisions in both stacks.

Ecosystem size as a proxy for fit. Both have more integrations than any single project uses. Count only the connectors you actually need; a longer list is not a better match.

Which is “newer”. Both have restructured substantially, and both have deprecated abstractions that tutorials still show. LlamaIndex removed ServiceContext in favour of a global Settings object, and LangChain has moved its agent story more than once. Whichever you pick, check the publication date of any snippet before you paste it.

How to actually choose

Answer these in order and stop at the first clear signal.

1. Is the deliverable an answer over documents, or a system that takes actions? Documents point to LlamaIndex, actions point to LangChain.
2. How weird is your corpus? Heavily structured PDFs, tables, mixed formats, and multi-hop questions across documents pull toward the retrieval-first stack.
3. Who is on call? Prefer the framework whose failure modes your team can already read. Debugging an unfamiliar abstraction at 2am costs more than any feature gap.
4. What is the lock-in? Both are Python libraries around portable artifacts. Your embeddings, your chunk boundaries, and your vector store outlive either framework, which is why the migration cost is far lower than the choice feels. Keep ingestion code separate from framework code and a switch stays cheap.

If nothing above breaks the tie, pick the one whose documentation you find easier to read, and spend the saved argument on evaluation instead.

What matters more than this decision

Chunk size and overlap, metadata quality, whether retrieval is semantic-only or hybrid, and whether anyone measures hit rate before shipping. All four are framework-independent, and all four outrank this comparison.

• The pipeline stages both frameworks implement: LlamaIndex ingestion, indexing and retrieval explained.
• A running pipeline in about five lines: LlamaIndex quickstart, build a RAG pipeline in Python.
• When the framework is fine and the answers still are not: LlamaIndex retrieval troubleshooting.
• Sizing the index before you commit to hardware: chunking and vector store sizer.