LangGraph State Management

Design and manage state schemas for LangGraph workflows.

When to Use

• Designing workflow state schemas
• Choosing TypedDict vs Pydantic
• Multi-agent state accumulation
• State validation and typing

TypedDict Approach (Simple)

from typing import TypedDict, Annotated
from operator import add

class WorkflowState(TypedDict):
    input: str
    output: str
    agent_responses: Annotated[list[dict], add]  # Accumulates
    metadata: dict

MessagesState Pattern (2026 Best Practice)

from langgraph.graph import MessagesState
from langgraph.graph.message import add_messages
from typing import Annotated

# Option 1: Use built-in MessagesState (recommended)
class AgentState(MessagesState):
    """Extends MessagesState with custom fields."""
    user_id: str
    context: dict

# Option 2: Define messages manually with add_messages reducer
class CustomState(TypedDict):
    messages: Annotated[list, add_messages]  # Smart append/update by ID
    metadata: dict

Why add_messages matters:

• Appends new messages (doesn't overwrite)
• Updates existing messages by ID
• Handles message deduplication automatically

Note: MessageGraph is deprecated in LangGraph v1.0.0. Use StateGraph with a messages key instead.

Pydantic Approach (Validation)

from pydantic import BaseModel, Field

class WorkflowState(BaseModel):
    input: str = Field(description="User input")
    output: str = ""
    agent_responses: list[dict] = Field(default_factory=list)

    def add_response(self, agent: str, result: str):
        self.agent_responses.append({"agent": agent, "result": result})

Accumulating State Pattern

from typing import Annotated
from operator import add

class AnalysisState(TypedDict):
    url: str
    raw_content: str

    # Accumulate agent outputs
    findings: Annotated[list[Finding], add]
    embeddings: Annotated[list[Embedding], add]

    # Control flow
    current_agent: str
    agents_completed: list[str]
    quality_passed: bool

Key Pattern: Annotated[list[T], add]

• Without add: Each node replaces the list
• With add: Each node appends to the list
• Critical for multi-agent workflows

Custom Reducers

from typing import Annotated

def merge_dicts(a: dict, b: dict) -> dict:
    """Custom reducer that merges dictionaries."""
    return {**a, **b}

class State(TypedDict):
    config: Annotated[dict, merge_dicts]  # Merges updates

def last_value(a, b):
    """Keep only the latest value."""
    return b

class State(TypedDict):
    status: Annotated[str, last_value]  # Overwrites

State Immutability

def node(state: WorkflowState) -> WorkflowState:
    """Return new state, don't mutate in place."""
    # Wrong: state["output"] = "result"
    # Right:
    return {
        **state,
        "output": "result"
    }

Key Decisions

2026 Guidance: Use TypedDict inside the graph (lightweight, no runtime overhead). Use Pydantic at boundaries (inputs/outputs, user-facing data) for validation.

Common Mistakes

• Forgetting add reducer (overwrites instead of accumulates)
• Mutating state in place (breaks checkpointing)
• Deeply nested state (hard to debug)
• No type hints (lose IDE support)

Related Skills

• langgraph-routing - Using state for routing decisions
• langgraph-checkpoints - State persistence
• type-safety-validation - Pydantic patterns