LangGraph Architecture Decisions

When to Use LangGraph

Use LangGraph When You Need:

• Stateful conversations - Multi-turn interactions with memory
• Human-in-the-loop - Approval gates, corrections, interventions
• Complex control flow - Loops, branches, conditional routing
• Multi-agent coordination - Multiple LLMs working together
• Persistence - Resume from checkpoints, time travel debugging
• Streaming - Real-time token streaming, progress updates
• Reliability - Retries, error recovery, durability guarantees

Consider Alternatives When:

State Schema Decisions

TypedDict vs Pydantic

Recommendation: Use TypedDict for most cases. Use Pydantic when you need validation or complex nested structures.

Reducer Selection

State Size Considerations

# SMALL STATE (< 1MB) - Put in state
class State(TypedDict):
    messages: Annotated[list, add_messages]
    context: str

# LARGE DATA - Use Store
class State(TypedDict):
    messages: Annotated[list, add_messages]
    document_ref: str  # Reference to store

def node(state, *, store: BaseStore):
    doc = store.get(namespace, state["document_ref"])
    # Process without bloating checkpoints

Graph Structure Decisions

Single Graph vs Subgraphs

Single Graph when:

• All nodes share the same state schema
• Simple linear or branching flow
• < 10 nodes

Subgraphs when:

• Different state schemas needed
• Reusable components across graphs
• Team separation of concerns
• Complex hierarchical workflows

Conditional Edges vs Command

# Conditional Edge - when routing is the focus
def router(state) -> Literal["a", "b"]:
    return "a" if condition else "b"
builder.add_conditional_edges("node", router)