Choosing Your RAG Framework: LangChain vs. LlamaIndex vs. Haystack

In our previous projects, we've built agents and pipelines from scratch. We learned that a RAG (Retrieval-Augmented Generation) system is the "open book" that gives our LLM access to the outside world (see our introduction to RAG).

But building RAG from scratch is hard. You have to manage data loading, chunking (see our chunking guide), embedding, indexing (see our vector databases guide), retrieval, re-ranking, and final generation.

This post is for you if you're ready to build a serious RAG application and are stuck in "analysis paralysis." You've heard of LangChain, LlamaIndex, and Haystack. They all claim to build RAG. Which one do you choose, and why?

Today, we'll demystify these three open-source frameworks from an engineer's perspective. This isn't just about features; it's about philosophy, Developer Experience (DX), and choosing the right tool for your specific job.

The core problem: the RAG Pipeline

All three frameworks are trying to solve the same problem. They all build a version of this standard RAG pipeline:

graph TD
    A[1. Load Data] --> B[2. Chunk and Embed]
    B --> C[3. Store in Vector DB]
    D[User Query] --> E[4. Retrieve Context]
    C --> E
    E --> F[5. Augment Prompt]
    D --> F
    F --> G[6. Generate Answer]
    G --> H[Final Answer]

The difference isn't what they do, but how they do it and what parts they emphasize.

1. LangChain: the "Build-it-Yourself" toolkit

LangChain is the "do-it-all" toolkit for AI. RAG is just one of the many things it does.

• Philosophy: "Here are all the Lego bricks (Loaders, Splitters, Retrievers, Prompts, LLMs). You connect them."
• Developer Experience (DX): "Code-heavy" and explicit. You are a programmer building a system. Its power lies in the LangChain Expression Language (LCEL), which uses the | (pipe) operator to chain components together.
• Best For: Engineers who want total control over every step and are likely already using LangChain for other agentic tasks (like in our previous posts).

The "How": building with LCEL

With LangChain, you manually assemble your RAG chain like a K'nex set.

graph TD
    A[Query] --> B[RunnableParallel]
    B --> C[Retriever]
    B --> D[Prompt Template]
    C --> D
    D --> E[LLM]
    E --> F[Answer]

The Code:

Your code looks like this, explicitly piping each step into the next.

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain_community.vectorstores import Chroma

# Assume 'vector_store' is already created
retriever = vector_store.as_retriever()
llm = ChatOpenAI(model="gpt-4o-mini")

prompt_template = ChatPromptTemplate.from_template(
    """Answer the question based only on this context:

    {context}

    Question: {question}
    """
)

# This is the "chain". It defines the flow of data.
rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()} 
    | prompt_template 
    | llm 
    | StrOutputParser()
)

response = rag_chain.invoke("What is our Q4 revenue?")

Observation: You have 100% control. You can see and swap every single piece. The trade-off is that you are responsible for everything, including finding the best retriever, prompt, etc. It's powerful but complex.

2. LlamaIndex: the "Data-First" RAG engine

LlamaIndex is a framework built specifically for RAG. Its core philosophy is that high-performance RAG is a data problem, not just a logic problem.

• Philosophy: "RAG is complex. We've pre-built and optimized the entire pipeline for you. Just give us your data."
• Developer Experience (DX): "High-level configuration." You're not chaining components; you're configuring a high-performance engine.
• Best For: Engineers who want the best RAG performance out-of-the-box, especially for complex Q&A over many documents.

The "How": the "Easy Button"

LlamaIndex's magic is its VectorStoreIndex. It handles loading, chunking, embedding, and storing in just two lines.

graph TD
    A[Query] --> B[LlamaIndex Query Engine]
    
    subgraph ENGINE["Inside the Engine"]
        direction LR
        B1[Retrieve] --> B2[Re-Rank] --> B3[Synthesize]
    end
    
    B --> B1
    B3 --> C[Answer]

The Code:

Your code is much more abstract. You ask the "engine" for an answer, and it handles all the internal steps for you.

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# 1. Load, chunk, embed, and index ALL data in a folder
# This one line handles the entire data pipeline.
documents = SimpleDirectoryReader("./data_folder").load_data()
index = VectorStoreIndex.from_documents(documents)

# 2. Get a query engine
# This is a high-level abstraction that contains the
# retriever, prompt, and LLM all in one.
query_engine = index.as_query_engine()

# 3. Just ask the question!
response = query_engine.query("What is our Q4 revenue?")

Observation: This is incredibly fast to get started. LlamaIndex shines by providing advanced, pre-built query engines (like the SubQuestionQueryEngine from our last post) that are highly optimized for RAG. You give up fine-grained control for world-class performance.

Think About It: LlamaIndex's as_query_engine() is a bit of a "black box". This is great for speed, but what challenges might you face when you need to debug why it retrieved a specific "bad" chunk?

3. Haystack: the "Pipeline-First" framework

Haystack (from deepset.ai) is another powerful open-source RAG framework. Its philosophy is a hybrid: it gives you the "Lego bricks" like LangChain, but it's designed for building explicit RAG pipelines, not general agents.

• Philosophy: "Production RAG is a pipeline. Let's build and visualize it explicitly, step-by-step."
• Developer Experience (DX): "Explicit & visual." You define components (Retriever, Ranker, PromptBuilder) and add them to a Pipeline object. It's very clear and debuggable.
• Best For: Engineers building production systems who need explicit control over each RAG component (like adding a Ranker) and want a clear, declarative flow.

The "How": Building a Pipeline

With Haystack, you build a pipeline that looks just like our original flowchart. It's particularly strong at showing how a Ranker (which re-sorts the retrieved documents) fits in.

graph TD
    A[Query] --> B[Retriever]
    B --> C[Ranker]
    C --> D[PromptBuilder]
    D --> E[LLM]
    E --> F[Answer]

The Code:

Your code looks like you're building a flow chart.

from haystack import Pipeline
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.rankers import SomeRanker
from haystack.components.builders import PromptBuilder
from haystack.components.generators import OpenAIGenerator

# (Assume 'document_store' is already created)

# 1. Define all your components
retriever = InMemoryBM25Retriever(document_store=document_store)
ranker = SomeRanker(top_k=3)
prompt_builder = PromptBuilder(template="Context: {documents}\nQuestion: {query}")
llm = OpenAIGenerator(model="gpt-4o-mini")

# 2. Create the pipeline
rag_pipeline = Pipeline()
rag_pipeline.add_component("retriever", retriever)
rag_pipeline.add_component("ranker", ranker)
rag_pipeline.add_component("prompt_builder", prompt_builder)
rag_pipeline.add_component("llm", llm)

# 3. Explicitly connect the components
rag_pipeline.connect("retriever.documents", "ranker.documents")
rag_pipeline.connect("ranker.documents", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder.prompt", "llm.prompt")

# 4. Run the pipeline
result = rag_pipeline.run({
    "retriever": {"query": "What is our Q4 revenue?"},
    "prompt_builder": {"query": "What is our Q4 revenue?"}
})

Observation: This is the most "engineer-friendly" in terms of explicit structure. You see every component and every connection. It makes it very easy to add, remove, or swap components (like trying a different Ranker).

The Engineer's Choice: Head-to-Head

Framework	Philosophy	Developer Experience (DX)	Best-in-Class Feature
LangChain	"The AI Toolkit"	"Build-it-Yourself" (Code-heavy, total control)	Agentic Logic (LangGraph)
LlamaIndex	"The Data Framework"	"Configure-it" (High-level, fast setup)	Data Ingestion & Query Engines
Haystack	"The Pipeline Framework"	"Connect-it" (Explicit, visual, debuggable)	Pipeline Orchestration

How to choose: Scenarios and Recommendations

Scenario 1: "I'm already using LangChain for agents and just need to add RAG."

• Choice: LangChain.
• Reason: You're already in its ecosystem. Use its RAG components (LCEL) to build your retrieval chain. It will integrate perfectly with your existing LangGraph agents.

Scenario 2: "My entire app is a Q&A bot over 1,000s of complex PDFs."

• Choice: LlamaIndex.
• Reason: This is a pure, high-stakes data problem. LlamaIndex is built for this. Its advanced ingestion and pre-built query engines (like SubQuestionQueryEngine) will give you the most accurate answers with the least amount of custom code.

Scenario 3: "I'm on a production team, and I need to A/B test different Rankers and Retrievers easily."

• Choice: Haystack.
• Reason: Haystack's explicit Pipeline structure is designed for this. Swapping out the ranker component is trivial. Its visual, clear flow is perfect for a production system that multiple engineers need to understand and maintain.

Challenge for you

1. Use Case: You need to build an "Email Assistant" that can answer questions about your company's internal knowledge base (a set of PDFs) and search the public web for new info.

2. Your Task: How would you combine frameworks?

   • Which framework would you use to build the "brain" that decides whether to search the PDFs or the web? (Hint: See our ReAct agents post and agent framework comparison)
   • Which framework would you use to build the high-performance PDF search "tool"? (See our advanced RAG with tools guide)

Key takeaways

• All frameworks solve the same RAG pipeline: The difference is in philosophy, developer experience, and what they emphasize
• LangChain excels at agentic logic: Use it when you need total control and are already building agents with LangGraph
• LlamaIndex excels at data-first problems: Use it when you need the best RAG performance out-of-the-box for complex Q&A over documents
• Haystack excels at pipeline clarity: Use it when you need explicit, debuggable, production-ready RAG systems with easy component swapping
• Choose based on your primary need: LangChain for control and agent integration, LlamaIndex for data performance, Haystack for pipeline clarity and A/B testing
• Frameworks can be combined: Use LangGraph for orchestration with LlamaIndex or Haystack as tools for specialized RAG tasks

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