LangChain vs LangGraph: Which Should You Learn First?

Learn LangChain first, but only after you can call an LLM API directly. LangChain gives you building blocks (prompts, chat models, retrievers, tools and output parsers) for mostly linear LLM pipelines such as RAG. LangGraph is a lower-level framework for stateful agents that loop, branch, pause for human approval and resume later. LangChain's concepts make LangGraph much easier to understand.

What LangChain is for

LangChain is a Python (and JavaScript) toolkit for building applications with large language models. Its value is a common interface: you can swap OpenAI for Claude, or one vector store for another, without rewriting your app.

The pieces you will use most:

• Chat models with one interface across providers, including tool calling and structured output
• Prompt templates and output parsers that turn model text into data your code can use
• Document loaders, text splitters and retrievers for RAG
• LCEL (LangChain Expression Language), which pipes these "runnables" together into a chain with streaming and batching built in

LangChain shines when the flow is predictable: retrieve, format, ask, parse. That covers a large share of real LLM features, from document Q&A to extraction services.

What LangGraph is for

LangGraph is a framework, from the same team, for building stateful, multi-step agents as graphs. You define a shared state, nodes (functions that read and update that state) and edges (including conditional edges that decide what runs next). Because the graph can loop, an agent can call a tool, look at the result and decide to call another.

What LangGraph adds that a simple chain does not have:

• Persistence: checkpoints save state after each step, so a conversation or task can resume later
• Human-in-the-loop: interrupts pause the graph before a risky action until a person approves it
• Control: you decide exactly which steps exist and how they connect, instead of trusting a hidden agent loop
• Multi-agent patterns such as a supervisor that routes work to specialist agents

LangGraph does not require LangChain, but most people use them together: LangChain for models, tools and retrievers, LangGraph for the control flow. Recent versions of LangChain's own agent helpers run on LangGraph underneath, and LangSmith tracing works across both.

When plain SDK calls are better than either

A framework is not always the right answer. Call the provider's SDK directly when:

• The feature is one model call, such as a summary, a classification or an extraction with a JSON schema
• You use one provider and do not expect to switch
• You need full visibility into every request for debugging, cost control or compliance
• You want fewer dependencies in a small service or a serverless function

Many production systems mix both: plain SDK calls for simple endpoints, LangGraph for the one workflow that genuinely needs state and approvals. Learning the raw API first also makes framework errors far easier to debug, because you know what the framework is doing for you.

A small LangChain example: a RAG chain

This simplified sketch shows the LCEL style. Imports are omitted, and exact names change between versions, so check the current documentation before copying it.

# Simplified LangChain RAG chain (LCEL)
prompt = ChatPromptTemplate.from_messages([
    ("system", "Answer only from the context. Cite the source of each fact."),
    ("human", "Context: {context} Question: {question}"),
])

chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

answer = chain.invoke("What is the leave policy for new joiners?")

Data flows in one direction: the question goes to the retriever, the documents fill the prompt, the model answers and the parser returns a string. There is no loop and no saved state, which is exactly why a chain is the right tool here.

A small LangGraph example: an agent with human approval

This simplified sketch shows an agent that can call tools, loops until it has an answer, and stops for approval before any tool runs.

# Simplified LangGraph agent with an approval step
class State(TypedDict):
    messages: Annotated[list, add_messages]

def call_model(state):
    return {"messages": [llm_with_tools.invoke(state["messages"])]}

def next_step(state):
    last = state["messages"][-1]
    return "tools" if last.tool_calls else END

graph = StateGraph(State)
graph.add_node("agent", call_model)
graph.add_node("tools", ToolNode(tools))
graph.add_edge(START, "agent")
graph.add_conditional_edges("agent", next_step)
graph.add_edge("tools", "agent")

app = graph.compile(checkpointer=checkpointer, interrupt_before=["tools"])
app.invoke({"messages": [("user", "Refund order 1042")]},
           config={"configurable": {"thread_id": "ticket-1042"}})

Three things here have no equivalent in the chain: the loop from tools back to the agent, the checkpointer that saves state per thread, and the interrupt that waits for a human before the refund tool runs.

LangChain vs LangGraph at a glance

Recommended learning order

1. Python fundamentals, including functions, classes, type hints and async. If you are not there yet, start with a Python course online.
2. Raw LLM API calls: messages, streaming, tool calling and structured outputs with Pydantic.
3. Embeddings and a hand-built RAG pipeline, so you understand chunking, retrieval and citations without a framework.
4. LangChain: rebuild that pipeline with LCEL and trace it in LangSmith. The difference teaches you what the framework adds.
5. LangGraph: build an agent with tools, persistence and an approval step, then try a supervisor pattern.
6. MCP and evaluations: expose tools through the Model Context Protocol and measure quality before you ship.

Common mistakes to avoid

• Starting with a multi-agent framework before you can debug a single model call
• Using an agent where a fixed chain would be cheaper, faster and more predictable
• Copying tutorial code without pinning versions; both libraries evolve quickly
• Skipping tracing, then having no idea why an agent looped ten times

How the AI Engineer Accelerator covers both

The AI Engineer Accelerator at Ezibell AI Academy follows this order across 14 weeks of live online weekend classes. Weeks 2 to 6 cover LLM foundations, prompting and structured outputs, embeddings, vector databases and production RAG, built mostly without frameworks. Week 7 is LangChain: LCEL and runnables, prompt templates and output parsers, tool calling, retrievers, memory patterns and LangSmith tracing, with a lab where you rebuild your RAG service in LangChain. Weeks 8 and 9 are LangGraph: the agent loop, state, nodes and conditional routing, checkpoints, human-in-the-loop interrupts, supervisor and swarm patterns, and evaluating agents. Week 10 adds MCP, including MCP tools inside LangGraph agents.

If you want the detail by topic, see the LangChain course, AI agent course and RAG course pages. Developers who want to run LangGraph agents on AWS should also look at the Amazon Bedrock course, which includes deploying an agent on Bedrock AgentCore.

Next step

Unsure whether you are ready for LangChain or should start with Python first? WhatsApp the academy at +91 82097 16316. Share what you have built so far, and a mentor will suggest where to begin.