Introduction to LangGraph

LangGraph is an exciting new framework that allows developers to create stateful applications and workflows, particularly useful when working with language models. It provides a structured way to manage complex interactions and maintain context across multiple steps.

Let's dive into the basic syntax of LangGraph and see how we can use it in our Python projects.

Setting Up LangGraph

First, make sure you have LangGraph installed. You can do this using pip:

pip install langgraph

Once installed, you can import the necessary components:

from langgraph.graph import StateGraph
from langgraph.prebuilt import ToolExecutor

Creating a StateGraph

The core component of LangGraph is the StateGraph. This represents your workflow and contains all the nodes and edges that define your application's logic.

Here's how you create a basic StateGraph:

workflow = StateGraph()

Defining Nodes

Nodes in LangGraph represent individual steps or actions in your workflow. You can add nodes to your StateGraph using the add_node method:

def process_input(state):

# Your processing logic here
    return state

workflow.add_node("process_input", process_input)

In this example, we've added a node called "process_input" that executes the process_input function when the workflow reaches this step.

Creating Edges

Edges define the flow between nodes in your graph. You can add edges using the add_edge method:

workflow.add_edge("process_input", "next_step")

This creates a directional edge from the "process_input" node to a node called "next_step".

Conditional Flows

LangGraph allows you to create conditional flows based on the state of your application. Here's an example:

def check_condition(state):
    if state["some_value"] > 10:
        return "high_value_path"
    else:
        return "low_value_path"

workflow.add_conditional_edges(
    "check_condition",
    check_condition,
    {
        "high_value_path": "process_high_value",
        "low_value_path": "process_low_value"
    }
)

This creates a conditional branch where the next step depends on the output of the check_condition function.

Using ToolExecutor

LangGraph provides a ToolExecutor class that can be useful when working with language models. Here's a simple example:

from langchain.tools import BaseTool

class MyTool(BaseTool):
    name = "my_tool"
    description = "A custom tool"

    def _run(self, query: str) -> str:

# Tool logic here
        return f"Processed: {query}"

tool_executor = ToolExecutor([MyTool()])

workflow.add_node("execute_tool", tool_executor)

This creates a node that can execute a custom tool when reached in the workflow.

Compiling and Running the Workflow

Once you've defined your workflow, you need to compile it before you can use it:

app = workflow.compile()

Now you can run your workflow with an initial state:

initial_state = {"input": "Hello, LangGraph!"}
final_state = app.invoke(initial_state)

The final_state will contain the result of your workflow after all steps have been executed.

Conclusion

This introduction to LangGraph's basic syntax should give you a solid foundation to start building your own stateful applications. As you become more familiar with the framework, you'll discover its full potential for creating complex, context-aware workflows, especially when working with language models.