Quick answer: LangChain is the everything-store with the most integrations, LlamaIndex is the most focused on the data and retrieval side, and Haystack is the most production-minded with explicit typed pipelines. For a simple RAG app, DIY with a client library and a vector store is genuinely competitive. The framework choice matters less than your ingestion quality, which is where most RAG projects quietly fail.
I have built RAG pipelines with all three and without any of them. Here is how I actually think about the choice.
The Pipeline Is the Same Everywhere
Every RAG framework is a different coat of paint on the same five steps:
- Ingest: get documents from somewhere (files, databases, the web).
- Chunk: split them into retrievable pieces.
- Embed and index: vectors into a vector store.
- Retrieve: find relevant chunks for a query.
- Generate: stuff chunks into a prompt, get an answer.
Frameworks differ in which steps they make easy, how much they hide, and how painful they are when you need to do something they did not anticipate. Judge them on that, not on GitHub stars.
LangChain: Maximum Surface Area
LangChain is the biggest ecosystem in the space. If a vector store, model provider, or document loader exists, there is probably a LangChain integration for it. That breadth is the whole value proposition: you can swap components without rewriting your app, and for agentic RAG (retrieval as one tool among several, with LangGraph handling control flow) it is the most natural fit.
The cost is abstraction depth. When something misbehaves three layers down, debugging means spelunking. And the API surface has churned enough over the years that tutorials rot quickly.
Choose LangChain when you want optionality and expect your stack to change, or when RAG is one piece of a larger agent. I covered wiring web tools into it in giving LangChain agents a web fetch tool.
LlamaIndex: Built Around the Data
LlamaIndex started life as a data framework for LLMs, and it shows. Loaders, chunking strategies, index structures, retrieval modes, response synthesis: the retrieval half of RAG is clearly where the design attention went. If your problem is "I have a lot of heterogeneous documents and want good answers over them," LlamaIndex gets you to a competent baseline fastest.
The flip side: for things beyond query-over-documents, you will feel the edges sooner than with LangChain, and some of the higher-level conveniences make it less obvious what is happening under the hood until you need to tune it.
Choose LlamaIndex when retrieval quality over your corpus is the product.
Haystack: Pipelines You Can Reason About
Haystack (from deepset) takes the most engineering-flavored approach: explicit pipelines built from typed components with declared inputs and outputs. Pipelines can branch and loop, and can be serialized to YAML. It feels less magical than the other two, which is exactly the point. When something breaks, you can see where.
The trade-off is a smaller ecosystem than LangChain and less RAG-specific sugar than LlamaIndex. You write a bit more code up front to get a bit more legibility forever.
Choose Haystack when the pipeline is going to production and someone has to maintain it.
DIY: More Viable Than Framework Fans Admit
A basic RAG pipeline is an embedding call, a vector store insert, a similarity query, and a prompt. With pgvector or a hosted vector DB, that is a few hundred lines you fully understand. No framework churn, no abstraction tax.
The real cost shows up later: reranking, hybrid search, evaluation, incremental sync. You will reinvent pieces the frameworks ship. For a focused internal tool, DIY is often right. For a product with a roadmap, a framework usually pays off.
The Comparison Table
| LangChain | LlamaIndex | Haystack | DIY | |
|---|---|---|---|---|
| Sweet spot | Agentic RAG, swappable stacks | Query over documents | Production pipelines | Small, focused apps |
| Integrations | Most | Many | Fewer | Whatever you write |
| Abstraction level | High | High for RAG | Medium, explicit | None |
| Debuggability | Hardest | Middle | Best of the three | Best overall |
| Learning curve | Steep in aggregate | Gentle start | Moderate | Depends on you |
| Biggest risk | API churn, hidden complexity | Edges outside RAG | Smaller ecosystem | Reinventing wheels |
Where Live Web Data Fits
Here is the part every framework comparison skips: all three assume you already have documents. The loaders for files and databases are fine. The web loaders are the weak spot everywhere, because fetching real websites reliably (JavaScript rendering, bot walls, layout junk) is a hard infrastructure problem, not a library feature.
That is the slot I fill with link.sc: a fetch API that turns any URL into clean markdown, and a search API that returns full-page content, so ingestion becomes a plain HTTP call that works identically with every framework:
import requests
def load_url_as_markdown(url: str) -> str:
r = requests.post(
"https://link.sc/v1/fetch",
headers={"Authorization": "Bearer lsc_your_key"},
json={"url": url, "format": "markdown"},
timeout=60,
)
r.raise_for_status()
return r.json()["markdown"]
docs = [load_url_as_markdown(u) for u in [
"https://example.com/docs/getting-started",
"https://example.com/changelog",
]]
# then chunk, embed, and index with whichever framework you chose
Markdown in means your chunker sees headings and structure instead of nav bars and cookie banners, and chunk quality is most of retrieval quality. For pipelines that need fresh content rather than a static corpus, search-driven ingestion with the same pattern works too; I walked through a full build in RAG with live web data.
How I Would Choose in 2026
My honest decision tree:
- RAG is one tool inside a bigger agent: LangChain (with LangGraph).
- The product is answers over a document corpus: LlamaIndex.
- It is going to production with an on-call rotation: Haystack, or DIY with discipline.
- Weekend project or narrow internal tool: DIY, pgvector, done.
And regardless of the branch you take: spend your effort budget on ingestion and evaluation, not framework selection. Clean documents in a mediocre framework beat garbage documents in the perfect one, every time. The link.sc docs cover the fetch and search endpoints if you want the ingestion side handled.
Feeding a RAG pipeline with web content? link.sc turns any URL into clean, chunkable markdown. Get a free key.