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LangGraph Platform explained

Q: LangGraph Platform explained

The LangGraph platform enables building stateful, graph-based AI agents using Python by defining nodes as functions and connecting them with edges. Use StateGraph to create, compile, and invoke these graphs for complex workflows.

Quick answer

The LangGraph platform enables building stateful, graph-based AI agents using Python by defining nodes as functions and connecting them with edges. Use StateGraph to create, compile, and invoke these graphs for complex workflows.

PREREQUISITES

Python 3.8+
pip install langgraph
Basic Python programming knowledge

Setup

Install the langgraph package via pip and prepare your Python environment. No API keys are required as LangGraph runs locally.

bash

pip install langgraph

output

Collecting langgraph
  Downloading langgraph-1.0.0-py3-none-any.whl (15 kB)
Installing collected packages: langgraph
Successfully installed langgraph-1.0.0

Step by step

Create a state type using TypedDict, define node functions that transform state, build a StateGraph, add nodes and edges, compile the graph, and invoke it with initial state.

python

from langgraph.graph import StateGraph, END
from typing import TypedDict

class State(TypedDict):
    messages: list[str]

def greet_node(state: State) -> State:
    return {"messages": state["messages"] + ["Hello from LangGraph!"]}

graph = StateGraph(State)
graph.add_node("greet", greet_node)
graph.set_entry_point("greet")
graph.add_edge("greet", END)

app = graph.compile()
result = app.invoke({"messages": []})
print(result["messages"])

output

['Hello from LangGraph!']

Common variations

You can create more complex graphs with multiple nodes and branching edges, use async node functions for asynchronous workflows, and persist graph state using custom checkpointers.

python

import asyncio
from langgraph.graph import StateGraph, END
from typing import TypedDict

class AsyncState(TypedDict):
    count: int

async def increment_node(state: AsyncState) -> AsyncState:
    await asyncio.sleep(0.1)  # simulate async work
    return {"count": state["count"] + 1}

graph = StateGraph(AsyncState)
graph.add_node("increment", increment_node)
graph.set_entry_point("increment")
graph.add_edge("increment", END)

app = graph.compile()

async def main():
    result = await app.invoke({"count": 0})
    print(result["count"])

asyncio.run(main())

output

Troubleshooting

If you see TypeError about state mismatch, ensure your TypedDict matches the node function signatures.
If nodes do not execute, verify you set the entry point with set_entry_point().
For async nodes, confirm you use await when invoking the compiled graph.

✅

Key Takeaways

Use StateGraph to build modular, stateful AI workflows with Python functions as nodes.
Define your state with TypedDict for type safety and clarity.
Compile the graph before invoking it to run your AI agent logic.
Async node functions enable integration with asynchronous APIs or I/O.
Set an entry point node to start graph execution correctly.

Verified 2026-04

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