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State management is central to building robust LangGraph applications. This guide covers advanced patterns for defining, updating, and transforming state.

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State Schemas

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TypedDict State

The most common approach is using TypedDict: 
from typing_extensions import TypedDict

class State(TypedDict):
    input: str
    results: list[str]
    final_answer: str

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Pydantic Models

For validation and complex data structures, use Pydantic: 
from pydantic import BaseModel, Field

class State(BaseModel):
    input: str
    results: list[str] = Field(default_factory=list)
    final_answer: str | None = None

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State Reducers

Reducers define how state updates are merged with existing state.

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Built-in Reducers

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add_messages

For message-based workflows: 
from typing import Annotated
from collections.abc import Sequence
from langchain_core.messages import BaseMessage
from langgraph.graph import add_messages

class ChatState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], add_messages]
The add_messages reducer:
  • Appends new messages to the list
  • Updates messages with matching IDs
  • Supports message deletion with RemoveMessage

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Operator Reducers

Use operators for simple reductions: 
from typing import Annotated
from operator import add

class State(TypedDict):
    # Accumulate values by addition
    total: Annotated[int, add]

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Custom Reducers

Define custom reducer functions: 
from typing import Annotated

def merge_lists(left: list, right: list) -> list:
    """Merge lists, removing duplicates while preserving order."""
    seen = set(left)
    result = list(left)
    for item in right:
        if item not in seen:
            result.append(item)
            seen.add(item)
    return result

class State(TypedDict):
    items: Annotated[list[str], merge_lists]

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Updating State

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Returning Updates

Nodes return dictionaries with state updates: 
def process_node(state: State) -> dict:
    # Process input
    result = process(state["input"])
    
    # Return partial update
    return {"results": [result]}

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Multiple Updates

Return multiple state fields: 
def analyze_node(state: State) -> dict:
    results = state["results"]
    analysis = analyze(results)
    
    return {
        "analysis": analysis,
        "final_answer": generate_answer(analysis),
    }

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Conditional Updates

Update state conditionally based on logic: 
def conditional_node(state: State) -> dict:
    if should_update(state):
        return {"status": "updated"}
    return {}  # No update

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State Channels

LangGraph uses channels internally to manage state.

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Channel Types

  • LastValue: Stores the most recent value (default)
  • BinaryOperatorAggregate: Applies a binary operator (e.g., add)
  • Topic: For pub/sub patterns

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Ephemeral State

Some state doesn’t need persistence: 
from langgraph.channels.ephemeral_value import EphemeralValue

# Ephemeral values are not saved to checkpoints
channels = {
    "temp_data": EphemeralValue(str),
}

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Input and Output Schemas

Define separate schemas for input and output: 
class InputState(TypedDict):
    question: str

class OutputState(TypedDict):
    answer: str

class InternalState(TypedDict):
    question: str
    context: list[str]
    answer: str

graph = StateGraph(
    InternalState,
    input_schema=InputState,
    output_schema=OutputState,
)
This provides a clean API:
  • Input: Only accepts question
  • Output: Only returns answer
  • Internal: Full state available to nodes

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Context Schema

Use context for runtime configuration: 
from typing import Literal
from langgraph.runtime import Runtime

class AgentContext(TypedDict):
    model: Literal["anthropic", "openai"]

def call_model(state, runtime: Runtime[AgentContext]):
    model_choice = runtime.context.get("model", "anthropic")
    # Use the selected model
    ...

graph = StateGraph(AgentState, context_schema=AgentContext)

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State Inspection

Inspect state during execution: 
# Get current state
state = app.get_state(config)
print(state.values)

# Get state history
for state in app.get_state_history(config):
    print(f"Step {state.metadata['step']}: {state.values}")

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Advanced Patterns

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Managed Values

LangGraph provides managed values that are automatically handled: 
from langgraph.managed import IsLastStep

class State(TypedDict):
    is_last_step: IsLastStep
Available managed values:
  • IsLastStep: Boolean indicating if this is the last step
  • RemainingSteps: Number of remaining steps

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Dynamic Send

Send dynamic updates to specific nodes: 
from langgraph.types import Send

def fan_out_node(state: State):
    return [
        Send("process_item", {"item": item})
        for item in state["items"]
    ]

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Command Pattern

Use Command for advanced control flow: 
from langgraph.types import Command

def decision_node(state: State):
    if should_interrupt():
        return Command(
            update={"status": "paused"},
            goto="human_review",
        )
    return {"status": "continuing"}

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Best Practices

  • Keep state flat: Avoid deeply nested structures
  • Use type hints: Enable better IDE support and validation
  • Choose the right reducer: Match the reducer to your data merging needs
  • Separate concerns: Use input/output schemas for clean APIs
  • Document state fields: Add docstrings to state classes
  • Validate state: Use Pydantic for runtime validation when needed

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Next Steps

  • Learn about Persistence to save state across executions
  • Explore Memory for long-term state storage
  • Add Interrupts to modify state during execution

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