Building Complex LLM Workflows with LangGraph

For language models (LLMs), managing complex workflows, conditional logic, and stateful execution is challenging. That’s where LangGraph comes in. It enables you to build sophisticated, stateful, and modular applications using a graph-based approach, making it ideal for multi-agent systems, conditional flows, and more.

What is LangGraph?

LangGraph is a Python framework designed to build stateful, multi-agent applications. It structures your LLM workflow as a directed acyclic graph (DAG) where each node represents a function, model call, or tool. These nodes are connected with edges that define how data flows from one node to another, allowing you to create complex, conditional workflows.

Key Features

Graph-based execution: Unlike a linear chain of tasks (like in LangChain), LangGraph allows for explicit, flexible, graph-based routing. You can branch logic or parallelize tasks.
State management: LangGraph uses a centralized, mutable state that flows through the graph, enabling easy tracking of evolving information.
Concurrency: The framework supports parallel execution of branches, making it great for handling multiple agents or parallel tasks.

Core Components

LangGraph is centered around a few key objects:

StateGraph: This is the core class used to build and compile the graph.
END: A special constant to signal graph termination.
State: The state that flows through the graph. It’s typically a dictionary or a Pydantic model.
ToolNode, FunctionNode, LLMNode: These represent various types of nodes, which can be LLM calls, tools, or functions.

Creating a Graph

Here’s a simple example to get started:

from langgraph import StateGraph, State, FunctionNode, END

# Define a state type
class MyState(State):
    count: int

# Create a graph
sg = StateGraph(MyState)

# Add nodes (functions in this case)
def increment(state: MyState) -> MyState:
    state.count += 1
    return state

# Add the increment node
sg.add_node("increment", increment)

# Set the entry point
sg.set_entry_point("increment")

# Compile the graph into an executable
graph = sg.compile()

Run the graph

result = graph.invoke(MyState(count=0))
print(result.count)  # Output should be 1

In this simple example: • We define a state (MyState) that holds a counter. • We create a node (increment) that increments this counter. • We compile and run the graph, outputting the incremented count.

Advanced Features

Conditional Logic and Branching

You can add conditional logic to decide the next node based on the state or results from previous nodes. For example:

def check_even(state: MyState) -> State:
    if state.count % 2 == 0:
        state.count += 2  # Even, increment by 2
    else:
        state.count -= 1  # Odd, decrement by 1
    return state

# Modify the graph with conditional logic
sg.add_node("check_even", check_even)
sg.add_edge("increment", "check_even")
sg.add_edge("check_even", END)

graph = sg.compile()
result = graph.invoke(MyState(count=0))  # Starts with 0, will increment by 2 to 2
print(result.count)  # Output: 2

Here, after incrementing the count, the state is checked for evenness. If even, it increments by 2, otherwise decrements by 1.

When to Use LangGraph

LangGraph shines in applications that require: • Multi-agent systems: Where different agents interact with each other and make decisions based on shared state. • Stateful workflows: When you need to maintain and pass state across multiple steps (e.g., conversational agents). • Conditional and dynamic logic: For workflows where the path changes depending on previous outputs. • Complex LLM pipelines: Combining multiple LLMs, tools, and decision-making logic into a single process.

Comparison with LangChain

Although LangChain is also a popular framework for building LLM applications, it works on a more linear or recursive basis, whereas LangGraph uses a graph-based approach that offers greater flexibility and modularity. Here’s a quick comparison:

Feature	LangGraph	LangChain
Core abstraction	Directed graph of nodes	Linear or recursive chain of steps
Execution flow	Explicit, flexible, graph-based routing	Mostly linear or tree-like flow
State management	Centralized mutable state passed through nodes	Local inputs/outputs passed between chains
Concurrency	Supports parallel branches	Limited concurrency support
Use case focus	Multi-agent systems, conditional flows	LLM wrappers, tools, agents, retrievers

Conclusion

LangGraph brings a powerful way to structure complex workflows in LLM-based systems. It excels in scenarios with branching logic, multi-agent interactions, and long-lived stateful applications. If you’re building systems that require more than just simple chains of LLM calls, LangGraph can provide the flexibility and control you need.