InjectedState

Example:

from typing import List
from typing_extensions import Annotated, TypedDict

from langchain_core.messages import BaseMessage, AIMessage
from langchain.tools import InjectedState, ToolNode, tool

class AgentState(TypedDict):
    messages: List[BaseMessage]
    foo: str

@tool
def state_tool(x: int, state: Annotated[dict, InjectedState]) -> str:
    '''Do something with state.'''
    if len(state["messages"]) > 2:
        return state["foo"] + str(x)
    else:
        return "not enough messages"

@tool
def foo_tool(x: int, foo: Annotated[str, InjectedState("foo")]) -> str:
    '''Do something else with state.'''
    return foo + str(x + 1)

node = ToolNode([state_tool, foo_tool])

tool_call1 = {"name": "state_tool", "args": {"x": 1}, "id": "1", "type": "tool_call"}
tool_call2 = {"name": "foo_tool", "args": {"x": 1}, "id": "2", "type": "tool_call"}
state = {
    "messages": [AIMessage("", tool_calls=[tool_call1, tool_call2])],
    "foo": "bar",
}
node.invoke(state)

[
    ToolMessage(content="not enough messages", name="state_tool", tool_call_id="1"),
    ToolMessage(content="bar2", name="foo_tool", tool_call_id="2"),
]

• InjectedState arguments are automatically excluded from tool schemas presented to language models
• ToolNode handles the injection process during execution
• Tools can mix regular arguments (controlled by the model) with injected arguments (controlled by the system)
• State injection occurs after the model generates tool calls but before tool execution