LangGraph vs LangChain: Beginner

This lesson focuses on LangGraph vs LangChain at the beginner level. Use it to move from definition to implementation-ready explanation.

Concept

LangChain is a framework for building LLM applications - it provides chains (linear sequences of steps), integrations with 100+ LLM providers, and tools. LangGraph is built ON TOP of LangChain and adds the graph layer: cycles, persistent state, and multi-actor coordination. You can use LangGraph without any LangChain components, but they work best together in the same stack.

Key Facts

• LangChain: linear pipelines, LCEL chains, 100+ model integrations
• LangGraph: cyclic graphs, stateful agents, multi-actor coordination
• Both from LangChain Inc. - designed to complement each other
• LangGraph works standalone with direct OpenAI/Anthropic/Gemini SDK calls
• LangSmith: observability platform working with both frameworks

Reference Implementation

# LangChain: simple linear chain - ideal for RAG and stateless pipelines
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

chain = (
    ChatPromptTemplate.from_template("Answer concisely: {question}")
    | ChatOpenAI(model="gpt-4o")
)
result = chain.invoke({"question": "What is Python?"})

# LangGraph: stateful agent with loops - ideal for complex multi-step tasks
from langgraph.prebuilt import create_react_agent
from langchain_core.tools import tool

@tool
def trend_search(query: str) -> str:
    """Search a curated trend index."""
    return f"Trend notes for {query}: agents, evals, retrieval, and deployment."

agent = create_react_agent(
    ChatOpenAI(model="gpt-4o"),
    tools=[trend_search]
)
result = agent.invoke({
    "messages": [("user", "Research 2025 AI trends and summarize")]
})
# Agent loops: search -> read -> search more -> synthesize -> done

Interview Q&A

Q1. When should you use LangChain chains vs. LangGraph?

Use LangChain chains for: simple RAG, single-step transformations, document processing pipelines, stateless operations. Use LangGraph for: multi-step agents using tools, workflows needing loops, long-running tasks needing checkpointing, systems requiring HITL, and multi-agent coordination. Rule of thumb: if you need a loop, use LangGraph.

Q2. Can you use LangGraph without LangChain?

Yes. LangGraph is a standalone library. You can use the Anthropic SDK, OpenAI SDK, or any Python HTTP client directly inside your nodes. The only LangChain dependency in LangGraph is langchain-core for message types - and even those can be replaced with dicts if needed. LangGraph is model-agnostic by design.

Q3. What is LangSmith and how does it fit in?

LangSmith is the observability and evaluation platform from LangChain Inc. It is framework-agnostic - works with LangChain chains, LangGraph agents, and even raw API calls. It provides execution traces, token-cost tracking, A/B prompt testing, and evaluation datasets. In Oct 2025, LangGraph Platform was rebranded as LangSmith Deployment.

Q4. Where does the Functional API fit in the comparison?

The Functional API sits between LCEL chains and explicit StateGraph orchestration. It keeps ordinary Python function structure while adding LangGraph runtime features such as checkpointing, streaming, retries, and interrupts.

Q5. What is the simplest migration path from a chain to a graph?

Wrap the existing chain in one node first, compile a graph around it, and add checkpointing. Then split the chain into multiple nodes only where routing, retries, human review, or observability would improve the system.

Practice Task

Explain when this LangGraph pattern is safer than a linear chain, then name one production failure it prevents.