LangChain vs Ragas for RAG: Which Should You Use?

LangChain and Ragas solve different problems in the RAG stack. LangChain is the orchestration layer for building retrieval pipelines, tools, chains, and agents; Ragas is the evaluation layer for measuring whether your RAG system actually works. For RAG, use LangChain to build it and Ragas to validate it.

Quick Comparison

When LangChain Wins

• •

  You are building the actual RAG application.

  • •If you need RecursiveCharacterTextSplitter, Chroma, FAISS, Pinecone, BM25Retriever, or custom retrievers wrapped into one pipeline, LangChain is the obvious choice.
  • •A typical path looks like: load docs with a loader, split them, embed them with OpenAIEmbeddings or another embedding model, then wire retrieval into a chain.
• •

  You need orchestration beyond retrieval.

  • •LangChain gives you RunnableSequence, RunnableParallel, tool calling, memory patterns, and agent workflows.
  • •If your “RAG” app also needs SQL lookup, policy lookup, ticket creation, or human handoff logic, LangChain handles that better than a pure eval library.
• •

  You want production-grade composability.

  • •The newer runnable API is cleaner than the old monolithic chain style.
  • •You can compose retrievers with prompt templates and models without locking yourself into one opinionated pattern.
• •

  You need control over retrieval plumbing.

  • •LangChain lets you swap retrievers quickly: vector similarity search today, hybrid search tomorrow.
  • •If you care about metadata filtering by customer segment, product line, or jurisdictional rules in banking/insurance, this matters.

Example:

from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_chroma import Chroma

retriever = Chroma(persist_directory="./db").as_retriever(search_kwargs={"k": 4})

prompt = ChatPromptTemplate.from_template(
    "Answer using only this context:\n\n{context}\n\nQuestion: {question}"
)

llm = ChatOpenAI(model="gpt-4o-mini")

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

rag_chain = (
    {"context": retriever | format_docs, "question": lambda x: x["question"]}
    | prompt
    | llm
)

That is the job LangChain was built for: connect retrieval to generation.

When Ragas Wins

• •

  You already have a RAG system and need to know if it’s any good.

  • •Ragas is built for evaluation metrics like faithfulness, answer_relevancy, context_precision, and context_recall.
  • •If your stakeholders ask whether hallucinations dropped after a retriever change, LangChain will not answer that for you.
• •

  You need regression testing across releases.

  • •When you change chunk size from 500 to 1,000 tokens or switch embeddings models, you want before/after scores on the same dataset.
  • •Ragas makes this practical by evaluating against a prepared dataset rather than relying on gut feel.
• •

  You care about retrieval quality more than app wiring.

  • •In regulated domains like insurance claims or banking support bots, “seems fine” is not acceptable.
  • •Use Ragas to detect whether retrieved context actually supports the answer instead of just looking relevant.
• •

  You want synthetic test data generation for eval loops.

  • •Ragas can help generate question-answer pairs from documents so you don’t have to handcraft every test case.
  • •That’s useful when you have hundreds of policy docs or product manuals and need coverage fast.

Example:

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy
from datasets import Dataset

data = Dataset.from_dict({
    "question": ["What is the claim filing deadline?"],
    "answer": ["The deadline is 30 days."],
    "contexts": [["Claims must be filed within 30 days of the incident."]],
    "ground_truths": [["The claim filing deadline is 30 days from the incident."]]
})

result = evaluate(data, metrics=[faithfulness, answer_relevancy])
print(result)

That’s what Ragas is for: scoring whether your system behaves like a reliable retrieval product.

For RAG Specifically

Use LangChain if you are building the pipeline. Use Ragas if you are proving it works. If I had to pick one for a serious RAG project in banking or insurance: start with LangChain for implementation and add Ragas immediately for evaluation gates before every release.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.