Best RAG Frameworks in 2026 — Compared

Retrieval-augmented generation is still the most reliable way to ground a model in your own data without fine-tuning. The hard part is rarely the idea. It is the plumbing: loading documents, splitting them sensibly, embedding, storing vectors, retrieving the right chunks, reranking, and stitching it into a prompt a model can use. A RAG framework takes some of that plumbing off your plate. This post looks at the options that matter in 2026 and where each earns its keep.

What a RAG framework actually provides #

Strip away the marketing and a RAG framework is a bundle of these pieces:

Loaders: connectors that pull text out of PDFs, HTML, Notion, Slack, S3, databases, and the rest.

Chunking: strategies for splitting documents into retrievable units, from fixed token windows to structure-aware and semantic splitting.

Embedding: a uniform interface over embedding providers so you can swap models without rewriting call sites.

Vector store integration: adapters for Pinecone, Weaviate, Qdrant, pgvector, Milvks, and dozens more.

Retrieval: similarity search plus hybrid (dense + keyword) and metadata filtering.

Reranking: a second-pass model that reorders candidates so the best chunks land at the top.

Orchestration: the glue that turns retrieve-then-generate into multi-step flows with tools, memory, and branching.

You do not need a framework for any single one of these. You need it when the seams between them start costing you time. For the full mechanics behind each stage, our building RAG applications: a complete guide walks through them end to end.

The frameworks worth knowing #

LlamaIndex: data and retrieval first #

LlamaIndex treats retrieval as the main event. Its strength is the ingestion and indexing layer: a large catalog of loaders, node parsers that respect document structure, and query engines that handle sub-questions, routing, and recursive retrieval out of the box. If your problem is "I have a messy pile of documents and I want good answers over them," LlamaIndex gets you there with the least ceremony. It has orchestration features too, but they are not why you reach for it.

LangChain and LangGraph: general orchestration #

LangChain is the broad toolkit. In 2026 the interesting half is LangGraph, its graph-based orchestration layer for agents and stateful workflows. RAG here is one node in a larger machine: retrieve, call a tool, loop, check a condition, retrieve again. If your application is an agent that sometimes does retrieval rather than a pure question-answering system, LangGraph is the natural home. The tradeoff is surface area. There is a lot of it, and the abstractions can hide behavior you later need to control.

Haystack: production pipelines #

Haystack takes pipelines seriously. Components are explicit, connections are typed, and the whole graph is inspectable and serializable. That makes it pleasant to test, deploy, and hand to an ops team. It is less about clever retrieval tricks and more about a pipeline you can reason about a year from now.

Lighter and adjacent options #

DSPy: not a RAG framework so much as an optimizer. You declare the behavior you want and DSPy tunes prompts and few-shot examples against a metric. Pair it with any retriever when you care about squeezing quality out of a pipeline you have already built.

txtai: an all-in-one embeddings database with retrieval baked in. Great for small to mid-sized apps where you want one dependency instead of six.

Roll-your-own: a vector DB client plus a provider SDK. Often the cleanest choice, which we come back to below.

For a deeper head-to-head on the two most common picks, see LangChain vs LlamaIndex.

When raw code is cleaner #

Frameworks earn their weight when you use many of their parts. If your pipeline is retrieve-then-answer over one vector store with one embedding model, a framework is more indirection than help. Here is a complete RAG loop with nothing but a vector client and a provider SDK:

python.python

import anthropic
from qdrant_client import QdrantClient
from fastembed import TextEmbedding

client = anthropic.Anthropic()
qdrant = QdrantClient(url="http://localhost:6333")
embedder = TextEmbedding("BAAI/bge-small-en-v1.5")

def embed(text: str) -> list[float]:
return list(embedder.embed([text]))[0].tolist()

def retrieve(question: str, k: int = 5) -> list[str]:
hits = qdrant.search(
collection_name="docs",
query_vector=embed(question),
limit=k,
)
return [h.payload["text"] for h in hits]

def answer(question: str) -> str:
chunks = retrieve(question)
context = "\n\n---\n\n".join(chunks)
msg = client.messages.create(
model="claude-sonnet-4-5",
max_tokens=1024,
messages=[{
"role": "user",
"content": (
f"Answer using only the context.\n\n"
f"Context:\n{context}\n\nQuestion: {question}"
),
}],
)
return msg.content[0].text

print(answer("What is our refund window?"))

That is the whole thing. No abstraction to learn, and every line is yours to change. The moment you need six loaders, hybrid search, reranking, and a router, a framework starts to look good.

Evaluation is the part people skip #

Whatever you build, answer quality is capped by retrieval quality. If the right chunk never reaches the prompt, no model saves you. So evaluate retrieval on its own before you judge the generated answers. Track recall at k and mean reciprocal rank against a labeled set of question-to-chunk pairs. Only once retrieval is solid should you score final answers for faithfulness and relevance. LlamaIndex, Haystack, and the eval tools around them ship components for this, and DSPy is built around optimizing exactly these metrics. It is the feedback loop that tells you whether a chunking or reranking change actually helped.

Decision guide by use case #

Q&A over a document corpus: LlamaIndex. Best-in-class ingestion and retrieval with minimal setup.

Agentic app where retrieval is one step: LangGraph. You need the orchestration more than the retrieval.

A service ops has to run and maintain: Haystack. Explicit, testable, observable pipelines.

One retriever, one model, tight scope: roll-your-own. Skip the framework tax.

Small app, one dependency: txtai.

Already built, need more accuracy: add DSPy on top.

Whichever direction you go, the retriever, embedding model, and generation model behind it matter as much as the framework. Our pillar on the best LLM APIs and AI infrastructure covers those choices.

The call we'd make #

Start with the smallest thing that could work. For most teams that is LlamaIndex if the job is retrieval-heavy, or 150 lines of your own code if the pipeline is simple and you value control. Reach for LangGraph only when the app is genuinely agentic, and Haystack when a production team has to own the result. Add DSPy once you have a working baseline and a metric worth optimizing. Do not adopt a framework because it is popular. Adopt it because you are using enough of its parts that writing them yourself would be the slower path. Ground that decision in retrieval evaluation from day one, and the framework question mostly answers itself.