Do I even need a RAG framework?

Not always. A single index, a vector store, and a prompt is a few dozen lines against a vector DB SDK directly — no framework required. Reach for one when you need pluggable loaders, multiple retrieval strategies, reranking, evaluation, or agentic retrieval, which are tedious and error-prone to hand-roll and maintain.

LlamaIndex or LangChain for RAG?

Choose LlamaIndex when retrieval is the core problem — it has the richest indexing and querying primitives and takes retrieval most seriously. Choose LangChain when RAG is one part of a larger application that also needs agents, tools, and orchestration. A common production pattern is LlamaIndex for the retrieval layer and LangChain/LangGraph for orchestration.

Where do embeddings and reranking fit in?

Every framework here is a thin layer over the same retrieval primitives: a chunker, an embedding model, a vector store, and (usually) a reranker. The framework wires them together, but their quality determines your answers — so invest there first.

Best RAG Frameworks in 2026

Start with LlamaIndex if retrieval is the hard part — it takes indexing and querying most seriously. Use LangChain when RAG is one piece of broader orchestration, Haystack for explicit production pipelines, and DSPy when you want to optimize the pipeline programmatically rather than hand-tune prompts.

A RAG framework is the wiring between your documents and your model: it loads and chunks data, builds an index, retrieves the right context, and hands it to the LLM. You can hand-roll all of that against a vector database and a model SDK — and for a single index it's worth it. Frameworks earn their keep once you need multiple retrieval strategies, reranking, evaluation, and agentic retrieval. If you're new to the pattern, start with how RAG works.

The short answer

Retrieval is the hard part (rich indexing, query strategies, document processing) → LlamaIndex.
RAG is one piece of a broader app (agents, tools, orchestration) → LangChain.
You want explicit, testable production pipelines → Haystack.
You'd rather optimize the pipeline than hand-tune prompts → DSPy.

LlamaIndex — the data framework

If retrieval quality is what makes or breaks your app, LlamaIndex is the default. Born at the start of the RAG wave, it remains the toolkit that takes indexing and querying most seriously — pluggable data loaders, multiple index types, query engines, routers, and a deep bench of retrieval strategies beyond plain vector search. By 2026 it has grown past pure RAG into agentic document processing and agent building, but data-centric retrieval is still its center of gravity. If your bottleneck is "the model keeps missing the relevant context," this is where you start. Tool profile →

LangChain — orchestration with strong RAG support

Reach for LangChain when RAG is one component of a larger system, not the whole system. It hit a stable 1.0 in late 2025 and is the most widely adopted LLM application framework, with a vast ecosystem of integrations — loaders, vector stores, retrievers, and the chains that connect them. Its RAG building blocks are solid, and for anything stateful or agentic it pairs with LangGraph for explicit, durable orchestration and LangSmith for observability. The honest trade-off versus LlamaIndex is depth-of-retrieval against breadth-of-application; see LangChain vs LlamaIndex for the head-to-head. Tool profile →

Haystack — production pipelines from deepset

Haystack is the pick when you want RAG modeled as explicit, inspectable engineering. The deepset framework structures applications as pipelines: typed components (retrievers, rankers, generators) wired by explicit connections into a directed graph that also supports loops for agent-style flows. The payoff is testability — each component can be swapped, mocked, and evaluated independently, which is exactly what you want when shipping and iterating in production. deepset also runs a commercial enterprise platform on top for managed deployment and evaluation. There's no dedicated tool profile on AgentsCamp yet, but it's a first-class option for teams who value pipeline explicitness over a high-level abstraction.

DSPy — optimize the pipeline, don't tune prompts

DSPy is the answer to "I'm tired of hand-tuning prompts in my RAG pipeline." From the Stanford NLP group, it inverts the workflow: you write compositional Python modules (declarative "signatures"), define a metric, and let an optimizer compile the prompts — and optionally weights — that maximize it. The compiled program is a normal Python object you can cache and deploy, and the same approach covers classifiers, RAG pipelines, and agent loops. It's used by production teams at companies like Databricks and Cursor. DSPy composes with the others — you can optimize a retrieval-and-generation pipeline rather than replace your stack. Tool profile →

(Two honorable mentions: txtai is a lightweight all-in-one embeddings-database-plus-pipeline option for smaller, self-contained apps, and most major vector database vendors now ship managed RAG/retrieval endpoints if you'd rather not run a framework at all.)

How to choose

Match the framework to where your effort goes. If you'll spend it on retrieval, pick LlamaIndex. If RAG is a feature inside a bigger agent or app, pick LangChain (with LangGraph behind it). If you're optimizing for testable, production-grade pipelines, pick Haystack. If you want the system tuned by an optimizer instead of by hand, reach for DSPy — often layered on top of one of the others.

But the honest caveat outranks all of this: the framework matters less than your retrieval quality. Chunking strategy, your choice of embeddings, and a good reranking step — typically via hybrid search and reranking — move answer quality far more than which library wires them together. And before committing to RAG at all, weigh it against long context. Pick the framework that gets out of your way, then spend your real time on the retrieval layer and the vector database underneath it.