LangChain vs RAG: using LangChain for RAG, GraphRAG, LlamaIndex, RAGFlow and MCP

LangChain and RAG are not alternatives: RAG, retrieval-augmented generation, is a technique for grounding a language model's answers in retrieved documents, while LangChain is a framework for building LLM applications, including RAG systems. You can build RAG with LangChain, with other frameworks, or with plain code. I built RAG.NextUpgrad with hybrid retrieval, fusion and reranking, so this page explains the difference, building RAG with LangChain, graph RAG, and comparisons with LlamaIndex, RAGFlow, Agno and MCP.

What is the difference between LangChain and RAG?

RAG is an approach: retrieve relevant information, then generate an answer from it. LangChain is a toolkit: components and abstractions for building LLM apps, such as chat models, prompts, retrievers, tools and agents. LangChain is one way to implement RAG.

Think of RAG as a recipe and LangChain as a kitchen with ready-made tools. You can follow the recipe with LangChain, LlamaIndex, Haystack, a cloud service, or your own code using an embedding model, a vector database and a model API. What determines RAG quality is the design, chunking, retrieval, reranking and prompts, not which framework you use. In interviews and design reviews, being able to explain RAG without naming any framework shows you understand the technique itself.

Can I use LangChain for RAG?

Yes. RAG is one of LangChain's most common uses. It provides document loaders, text splitters, embedding wrappers, vector store integrations, retrievers and chains that combine retrieval with generation, plus LangGraph for more complex, agentic RAG.

A LangChain RAG app typically loads documents, splits them, embeds and stores them in a vector store, wraps the store as a retriever, and builds a chain that formats retrieved chunks into a prompt for the chat model. Use LangSmith or logging to trace what was retrieved. Keep evaluation separate from the framework: a test set of questions with expected answers shows whether changes help. Pin LangChain package versions, since interfaces have changed across releases.

Does LangChain have retrievers for RAG?

Yes. LangChain's retriever interface returns relevant documents for a query, and implementations include vector store retrievers, BM25 keyword retrievers, ensemble retrievers that combine several, multi-query retrievers, contextual compression with rerankers, and parent-document retrievers.

Also asked as: does langchain have retriever for rag

For strong RAG, combine retrievers: an ensemble of BM25 and vector search approximates hybrid search, and a compression retriever with a cross-encoder reranks results. Parent-document retrieval returns larger context around small matched chunks. Many vector database integrations also support metadata filters through the retriever. Measure retrieval recall to choose among these options. Start with a simple vector retriever, then add BM25 and reranking only when your evaluation shows misses.

How do I build a RAG chatbot using LangChain?

Build single-turn RAG first, then add conversation: load and split documents, create a vector store and retriever, make a chain that rewrites follow-up questions into standalone queries using chat history, retrieves, and answers with citations.

Also asked as: how to build a rag chatbot using langchain

Steps:

• Index documents with metadata such as source and page.
• Create a retriever, ideally hybrid with reranking.
• Add a question-rewriting step that uses recent chat history.
• Prompt the model to answer only from retrieved context and cite sources.
• Store chat history per session in a database.
• Stream answers to the user interface.

Use LangGraph if the chatbot needs tools, branching or human approval steps.

What is graph RAG in LangChain?

Graph RAG retrieves from a knowledge graph, entities and their relationships, instead of, or alongside, text chunks. In LangChain, you can build it with graph database integrations such as Neo4j: extract entities and relations from documents with an LLM, store them, then query the graph to answer relationship questions.

Also asked as: what is graph rag langchain

Graph RAG helps with questions like "Which projects did this supplier affect?" where answers span many documents through shared entities. LangChain offers graph transformers to extract graph structure and chains that convert questions into graph queries such as Cypher. Combine graph retrieval with vector search for both relationships and passages, and validate generated queries. Generated graph queries can be wrong or unsafe, so restrict them to read-only access.

Is there a graph RAG tutorial with LangChain?

Yes. LangChain's documentation and Neo4j's resources include tutorials that extract a knowledge graph from documents with an LLM, store it in Neo4j, and answer questions by generating Cypher queries or combining graph and vector retrieval.

Also asked as: graph rag tutorial langchain

Tutorial outline:

• Load documents and extract entities and relationships with an LLM graph transformer.
• Store nodes and edges in a graph database.
• Add vector indexes on entity descriptions or text chunks.
• Build a chain that turns questions into graph queries.
• Combine graph results with retrieved passages in the final prompt.
• Test with relationship questions and check generated queries.

Start small, since extraction quality determines the graph's usefulness. Review a sample of extracted entities by hand before trusting the graph.

LlamaIndex vs LangChain: which is better for RAG?

LlamaIndex is focused on data ingestion, indexing and retrieval, with many advanced RAG features out of the box, so it is often quicker for document-heavy RAG. LangChain is broader, covering agents, tools and integrations, with LangGraph for complex workflows.

Also asked as: llamaindex vs langchain for rag

Choose LlamaIndex when your core problem is connecting documents to an LLM with sophisticated retrieval; choose LangChain when RAG is one part of a larger agentic application. Many teams use both, or neither: production systems sometimes use direct SDKs for control. The quality difference comes from your chunking, retrieval and evaluation choices, not the library.

LangChain vs RAGFlow: what is the difference?

RAGFlow is an open-source RAG engine with a web interface, deep document parsing for complex layouts like tables and scanned files, and ready-made retrieval and chat features. LangChain is a code framework you assemble yourself.

Choose RAGFlow when you want a working RAG system quickly, especially for complex documents, without writing much code. Choose LangChain when you need custom logic, integration into your own application, or unusual workflows. RAGFlow's strength in document understanding addresses a common RAG failure point: poor parsing. You can also use RAGFlow's parsing ideas within a custom LangChain pipeline. Check RAGFlow's licence and resource requirements, since its document parsing models need more compute than a simple pipeline.

LangChain vs Agno: what is the difference?

Agno, formerly known as Phidata, is a Python framework focused on building agents and multi-agent systems with memory, knowledge and tools, emphasising simplicity and performance. LangChain covers a wider range of LLM application patterns, with LangGraph for agent workflows.

Choose Agno for quickly building agents with a clean, lightweight API; choose LangChain and LangGraph for a large integration ecosystem and fine-grained control over agent graphs. Both can implement RAG as a knowledge source for agents. Evaluate with a small prototype in each, judging code clarity, debugging tools and performance on your task.

MCP vs RAG vs LangChain: how do they fit together?

They solve different problems. MCP, the Model Context Protocol, standardises how AI applications connect to tools and data sources. RAG is a technique for retrieving knowledge to ground answers. LangChain is a framework for building the application that uses both.

In practice, an agent built with LangChain can call an MCP server that exposes a document search tool, and that tool performs RAG retrieval, returning passages the model uses to answer. MCP makes tools reusable across clients such as Claude Desktop and IDEs; RAG makes answers accurate and cited; LangChain wires the logic together. You can use any one without the others.

Pranjul Rathour

GenAI Engineer · India

GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).

Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.

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