Runtime

Convenience class that bundles run-scoped context and other runtime utilities.

This class is injected into graph nodes and middleware. It provides access to context, store, stream_writer, previous, and execution_info.

Example:

from typing import TypedDict
from langgraph.graph import StateGraph
from dataclasses import dataclass
from langgraph.runtime import Runtime
from langgraph.store.memory import InMemoryStore

@dataclass
class Context:  # (1)!
    user_id: str

class State(TypedDict, total=False):
    response: str

store = InMemoryStore()  # (2)!
store.put(("users",), "user_123", {"name": "Alice"})

def personalized_greeting(state: State, runtime: Runtime[Context]) -> State:
    '''Generate personalized greeting using runtime context and store.'''
    user_id = runtime.context.user_id  # (3)!
    name = "unknown_user"
    if runtime.store:
        if memory := runtime.store.get(("users",), user_id):
            name = memory.value["name"]

    response = f"Hello {name}! Nice to see you again."
    return {"response": response}

graph = (
    StateGraph(state_schema=State, context_schema=Context)
    .add_node("personalized_greeting", personalized_greeting)
    .set_entry_point("personalized_greeting")
    .set_finish_point("personalized_greeting")
    .compile(store=store)
)

result = graph.invoke({}, context=Context(user_id="user_123"))
print(result)
# > {'response': 'Hello Alice! Nice to see you again.'}

1. Define a schema for the runtime context.
2. Create a store to persist memories and other information.
3. Use the runtime context to access the user_id.