Open Source RAG Frameworks 2026: Build Free RAG Pipelines

You've probably heard the concept of RAG (Retrieval-Augmented Generation) countless times by now—letting AI query a database before answering questions to significantly reduce hallucinations. But when it actually comes to building one, most people get stuck at the same spot: too many tools, no idea which to pick. LangChain? LlamaIndex? Haystack? Chroma or Qdrant? Every one of them claims to be an all-in-one RAG solution, yet after installing a pile of libraries, you still can't get a complete Q&A pipeline running. This article helps you sort through the most mainstream open-source RAG frameworks in 2026, covering everything from tech selection to actual code in one go. Repo links are at the end.

💡 What You Will Learn

You've probably heard the concept of RAG (Retrieval-Augmented Generation) countless times by now—letting AI query a database before answering questions to significantly reduce hallucinations. But when

📜 Table of Contents

• LangChain⭐115k
• LlamaIndex⭐38k
• Haystack by deepset⭐18k
• Chroma⭐17k
• Qdrant⭐23k
• Weaviate⭐12k
• RAGFlow⭐22k
• Dify⭐60k
• |:----|:--------|:----|
• ---

LangChain⭐115k

from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_community.vectorstores import Chroma

retriever = vectorstore.as_retriever()
chain = create_stuff_documents_chain(llm, prompt)
rag_chain = create_retrieval_chain(retriever, chain)

LlamaIndex⭐38k

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What is RAG?")

Haystack by deepset⭐18k

from haystack import Pipeline
from haystack.components.retrievers import InMemoryBM25Retriever
from haystack.components.generators import OpenAIGenerator

pipe = Pipeline()
pipe.add_component("retriever", InMemoryBM25Retriever(document_store))
pipe.add_component("generator", OpenAIGenerator(model="gpt-4"))
pipe.connect("retriever.documents", "generator.documents")

Chroma⭐17k

pip install chromadb

import chromadb
from chromadb.utils import embedding_functions

client = chromadb.Client()
collection = client.create_collection("my_docs")
collection.add(documents=["RAG stands for Retrieval-Augmented Generation"], ids=["1"])
results = collection.query(query_texts=["What is RAG"], n_results=3)

Qdrant⭐23k

from qdrant_client import QdrantClient

client = QdrantClient(":memory:")
client.create_collection(collection_name="test", vectors_config=...)

Weaviate⭐12k

{
  "modules": {
    "generative-openai": {},
    "text2vec-transformers": {}
  }
}

{
  Get {
    Documents(ask: { question: "What is RAG?" }) {
      title
      _additional { answer { result } }
    }
  }
}

RAGFlow⭐22k

Dify⭐60k

|:----|:--------|:----|

# 1. Load documents → 2. Chunk/Split → 3. Embedding → 4. Store → 5. Retrieve → 6. Generate
# Example using LangChain + Chroma

from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.chains import RetrievalQA

# 
loader = TextLoader("knowledge.txt")
docs = loader.load()

# Chunk/Split
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
splits = text_splitter.split_documents(docs)

# Embedding+Store
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)

# Retrieve+GenerateAPI
qa = RetrievalQA.from_chain_type(llm=llm, retriever=vectorstore.as_retriever())
answer = qa.invoke("")

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