Artificial intelligence is moving from single-purpose chatbots to full-blown AI systems that collaborate, remember context, and adapt as tasks unfold. But building those kinds of apps is tricky. Most frameworks today work like a straight line: you give an AI a task, it responds, then you pass the output to the next step. Useful, but limited.
Enter LangGraph, an open-source framework built on top of LangChain, designed to build stateful, multi-agent workflows. Instead of chaining steps together in a rigid sequence, LangGraph lets you design your AI app as a graph, where different agents, tools, and decision points can interact flexibly.
Think of LangChain as the toolkit that gave developers the first building blocks for LLM-powered apps. LangGraph takes it to the next level: it’s the control centre for orchestrating a team of AIs that can share information, retry tasks, and keep track of state over time.
What is LangGraph?
At its core, LangGraph is a framework that helps developers build AI applications where:
- Multiple agents (AI models, tools, or humans) can work together.
- The system remembers what’s happening at each step.
- The workflow can branch, loop, or recover from errors instead of breaking when one piece fails.
In plain terms:
- LangChain is about building one AI helper.
- LangGraph is about managing a team of helpers that collaborate.
Here’s a quick snapshot:
| Feature | LangChain | LangGraph |
|---|---|---|
| Main focus | Build simple LLM apps with prompts, memory, and tools | Build complex, multi-agent workflows with state tracking |
| Structure | Chains (linear or tree-like) | Graphs (flexible, interconnected nodes) |
| Memory | Basic conversation memory | Full stateful workflows |
| Use cases | Chatbots, doc Q&A, API calls | AI research assistants, multi-agent collaborations, long-running tasks |
Graphs Vs Chains
Imagine you’re managing a project:
- In a chain, you’d assign a task to one person, who passes it on to the next, and so on. If one person messes up, the whole project stops.
- In a graph, you have a network of people working together. Tasks can flow back and forth, branches can run in parallel, and you can reassign if something goes wrong.
That’s the difference:
- Chains are rigid. Good for simple Q&A or summarization.
- Graphs are dynamic. Perfect for AI systems that need collaboration, retries, or long-term state.
Real-world example
- Chain approach: Ask an AI to “research climate change impact on agriculture” → it gives you one long response. Done.
- Graph approach: One agent gathers scientific papers, another summarizes them, another checks for bias or errors, and a final agent organizes the findings into a clear report. If one step fails, the system can retry or adjust.
Bottom line: LangGraph uses graphs because real-world AI apps behave less like a straight line and more like a living, adapting network.
Core Concepts of LangGraph
To understand LangGraph, you don’t need to be a graph theory expert. At a high level, everything boils down to nodes, edges, and state. Let’s break it down.
Nodes: The Brains and Hands of the Graph
- A node is simply a unit of work in your AI system.
- It could be:
- An agent (like a GPT model that reasons and answers questions).
- A tool (like a calculator, search API, or database lookup).
- A logic block (like “if the result is too long, summarize it”).
Analogy: Imagine nodes as people on your team. Each has a role: one researches, one writes, one checks facts.
Edges: The Pathways of Communication
- An edge connects two nodes and defines how data flows between them.
- After one node finishes its job, the edge decides where to send the output next.
Analogy: If nodes are team members, edges are the email threads or Slack channels that connect them. They make sure information gets from the right person to the next.
State: The Shared Memory
- State is what makes LangGraph powerful. It’s the memory of the system that persists across the whole workflow.
- Without state, each agent would be working in isolation. With state, they can:
- Remember what’s already been done.
- Avoid repeating tasks.
- Retry when something fails, without losing progress.
Analogy: If nodes are teammates and edges are their communication channels, state is the shared project tracker (like Notion, Trello, or Jira) where everything is logged.
Key Features at a Glance
By now, you’ve seen that LangGraph is about more than just connecting LLMs. Its real strength comes from a set of features that make complex AI systems reliable and adaptive. Here are the essentials:
Stateful Workflows
LangGraph keeps track of everything that happens during a workflow. This means agents don’t lose context midway, and tasks can be retried without starting from scratch. It’s like having a project logbook that remembers every step.
Multi-Agent Orchestration
Instead of one model trying to do everything, you can coordinate multiple agents, each with a role. One agent might research, another might summarize, and a third might fact-check. LangGraph ensures they talk to each other smoothly.
Error Handling and Retries
In a simple chain, if a step fails, the whole process collapses. In LangGraph, you can design fallback paths, retries, or alternative branches. This makes your app resilient, even when LLMs occasionally produce unexpected results.
Human-in-the-Loop Integration
LangGraph isn’t only for fully automated systems. You can insert human checkpoints, where a person reviews or approves outputs before the process continues. This is especially valuable for high-stakes use cases like legal, medical, or financial workflows.
Getting Started with LangGraph
LangGraph builds on LangChain, so if you’ve used LangChain before, the setup will feel familiar. The difference comes when you design your workflow as a graph instead of a straight chain.
Installation
You’ll need Python 3.9 or higher. Install LangGraph with pip:
pip install langgraph
Since LangGraph works alongside LangChain, it’s also common to install both:
pip install langchain langgraph
Hello World Example
Let’s create the simplest possible LangGraph: one node that echoes back a response.
from langgraph.graph import StateGraph
# Step 1: Define a simple function (this is our "node")
def greet(state):
return {"message": "Hello, LangGraph!"}
# Step 2: Create a graph and add the node
graph = StateGraph()
graph.add_node("Greeter", greet)
# Step 3: Set start and end points
graph.set_entry_point("Greeter")
graph.set_finish_point("Greeter")
# Step 4: Compile and run
app = graph.compile()
result = app.invoke({})
print(result)
What’s happening here?
- We defined a node called Greeter.
- The graph starts and finishes at this node.
- When we run it, the graph executes the node and returns a response.
Why Start Simple?
Even though this is just a single node, it shows the basic pattern:
- Define nodes (units of work).
- Connect them in a graph.
- Run the graph with state.
From here, you can add more nodes, maybe a summarizer, a translator, or a reviewer, and connect them with edges. That’s when LangGraph starts to shine.
Best Practices and Design Tips
LangGraph opens up a lot of possibilities, but like any framework, a few habits make your workflows easier to build and maintain.
Keep Graphs Modular and Reusable
Don’t cram too much logic into one node. Instead, break your graph into smaller, reusable components. For example, a “Summarizer Agent” can be reused in a research workflow, a tutoring workflow, or even a meeting-notes app. Treat nodes like Lego blocks you can snap together in different projects.
Manage State Effectively
State is powerful, but it can also get messy if you throw everything into it. Keep your state structure clean and predictable — only store what the next node really needs. Think of state like a shared whiteboard: too much clutter and no one can read it.
Debugging Strategies
Graphs can get complex, especially with loops and retries. Use logging and visualization to see how data moves between nodes. A simple practice is to log inputs and outputs at each node so you can trace where things go wrong. If something feels off, try running the graph with mock inputs first before adding the full complexity of agents and tools.
Conclusion
LangGraph takes the core ideas of LangChain and pushes them further: from linear chains to flexible, stateful graphs. With nodes, edges, and state, you can design workflows that resemble real teamwork — adaptive, collaborative, and resilient.
Whether you’re building a research assistant, a multi-agent project manager, or a human-in-the-loop decision system, LangGraph provides the scaffolding to make it work reliably.
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