✍️ By Subodh Sir | Updated: October 2026 | ⏱️ 8 min read
What Is LangGraph? LangGraph vs LangChain Explained for Beginners
Introduction: Why Everyone Is Talking About LangGraph
If you build AI apps, you have probably hit this wall: a simple chatbot works fine, but the moment you need an AI that thinks, uses tools, checks its own work and tries again, a straight line of steps is not enough. That is exactly the problem LangGraph solves.
In this guide, I (Subodh Sir) will explain what is LangGraph, how lang graph works with a simple Python example, and the real difference in LangGraph vs LangChain, in plain language so any beginner can follow.
What Is LangGraph?
LangGraph is a library from the LangChain team for building stateful, multi-step AI agents. Instead of running steps one after another in a fixed chain, you describe your app as a graph:
- State: the shared memory that every step can read and update (messages, results, counters).
- Nodes: the steps. A node is usually a Python function: call an LLM, run a tool, validate an answer.
- Edges: the arrows that decide which node runs next.
- Conditional edges: "if-else" decisions, such as "if the answer is weak, go back and retry".
Simple analogy: think of a Google Maps route. A normal chain is one fixed road. LangGraph is a full road map with junctions, U-turns and checkpoints, so your AI can choose the next road based on what just happened.
📊 Graphical View: How a LangGraph Agent Flows
⬇️
Node 1: Understand the question (LLM)
⬇️
Node 2: Use a tool (search / database / API)
⬇️
Decision: Is the answer good enough?
✅ Yes → go to END | 🔁 No → loop back to Node 2
⬇️
END (final answer)
That "loop back" arrow is the superpower. Classic chains cannot loop easily; LangGraph is designed for it.
Key Features of LangGraph
| Feature | What It Means for You |
|---|---|
| Loops & branching | Agent can retry, reflect and choose different paths |
| Persistent state (checkpointing) | Save progress, resume later, keep chat memory |
| Human-in-the-loop | Pause for approval before a risky action (like sending an email) |
| Streaming | Show tokens and step-by-step progress live to users |
| Multi-agent support | Several specialist agents (researcher, writer, reviewer) working together |
LangGraph Example in Python (Beginner Friendly)
Install it first:
pip install langgraph
Here is a tiny graph with two nodes: one writes a draft, one reviews it.
from typing import TypedDict
from langgraph.graph import StateGraph, START, END
# 1. Define the shared state
class State(TypedDict):
topic: str
draft: str
approved: bool
# 2. Define nodes (just Python functions)
def write_draft(state: State):
return {"draft": f"A short note about {state['topic']}"}
def review(state: State):
ok = len(state["draft"]) > 10
return {"approved": ok}
# 3. Decide where to go next
def route(state: State):
return END if state["approved"] else "write_draft"
# 4. Build the graph
builder = StateGraph(State)
builder.add_node("write_draft", write_draft)
builder.add_node("review", review)
builder.add_edge(START, "write_draft")
builder.add_edge("write_draft", "review")
builder.add_conditional_edges("review", route)
graph = builder.compile()
# 5. Run it
result = graph.invoke({"topic": "LangGraph", "draft": "", "approved": False})
print(result)
How this example works
- State holds the topic, the draft and the approval flag.
- write_draft creates text and saves it into state.
- review checks it. If it fails, the conditional edge sends the flow back to write_draft (a loop).
- When approved, the graph reaches END and returns the final state.
In a real app, write_draft would call an LLM and review would be another LLM check or a rule-based validator.
LangGraph vs LangChain: What Is the Difference?
This is the most searched question, so let me make it simple. They are not rivals. LangChain is the toolbox; LangGraph is the workflow engine for complex agents. They work together.
| Point | LangChain | LangGraph |
|---|---|---|
| Core idea | Components and integrations (models, prompts, tools, retrievers) | Orchestration of steps as a graph |
| Flow style | Mostly linear (step 1 → 2 → 3) | Loops, branches, cycles |
| State / memory | Basic | First-class shared state with checkpoints |
| Human approval | Manual work needed | Built-in interrupt and resume |
| Best for | Quick prototypes, RAG, simple chains | Production agents, multi-agent systems, long-running tasks |
| Learning curve | Easier to start | Slightly steeper, more control |
📊 Graphical View: LangChain vs LangGraph Flow
Prompt ➡️ LLM ➡️ Parser ➡️ Output
LangGraph (graph):
Plan ➡️ Tool ➡️ Check? ➡️ Done 🔁 (if check fails, loop back to Tool or Plan)
When Should You Use LangGraph?
- ✅ Customer support agents that look up orders, escalate to a human and remember the conversation.
- ✅ Research assistants that search, summarize, critique themselves and refine.
- ✅ Coding agents that write code, run tests and fix errors in a loop.
- ✅ Approval workflows where a person must confirm before money or emails are sent.
- ❌ Skip it for a one-shot "summarize this text" call. A single LLM call is enough.
Advantages and Limitations
✅ Advantages
- Full control over agent behavior (no hidden magic).
- Reliable long-running workflows with saved state.
- Easy debugging because every step is a visible node.
- Works with any LLM provider.
⚠️ Limitations
- More setup than a simple chain.
- You must design the graph carefully, or loops can run too long (set a recursion limit).
- Beginners need to learn state, nodes and edges first.
Conclusion
So, what is LangGraph? It is a graph-based framework that lets your AI agents loop, branch, remember and wait for humans, all in a clear, debuggable structure. In the LangGraph vs LangChain debate, the best answer is: use LangChain for building blocks and LangGraph for orchestrating complex, stateful agents.
Start small: build the two-node example above, add an LLM call, then add a loop. Within a day you will understand why lang graph has become a go-to choice for production AI agents.
👉 Found this helpful? Share it with a friend who is learning AI, and leave a comment with what you want to build with LangGraph. I will cover it in the next post!
Frequently Asked Questions (FAQ)
Q1. What is LangGraph in simple words?
LangGraph is a Python (and JavaScript) framework to build AI agents as a graph of steps with shared memory, so the agent can loop, branch and retry.
Q2. What is the main difference between LangGraph and LangChain?
LangChain provides components and integrations, mostly for linear chains. LangGraph adds graph-based orchestration with loops, state, persistence and human approval for complex agents.
Q3. Do I need LangChain to use LangGraph?
No. LangGraph can be used on its own, but many developers use LangChain models and tools inside LangGraph nodes because they work well together.
Q4. Is LangGraph free and open source?
Yes, the core library is open source and free to use. Optional hosted services and monitoring tools from the LangChain ecosystem may have their own pricing.
Q5. Is LangGraph good for beginners?
Yes, if you know basic Python. Start with a two-node graph, then add LLM calls and conditional edges step by step.
Q6. Can LangGraph build multi-agent systems?
Yes. You can create separate nodes or sub-graphs for different agents (researcher, writer, reviewer) and connect them in one graph.
Q7. When should I NOT use LangGraph?
For simple one-step tasks like a single prompt-and-answer call, plain LLM code or a basic chain is faster and easier.
About the author: Subodh Sir writes beginner-friendly guides on AI, Angular and modern web development. Read more on AngularThink.
