test_react_agent.py — langchain Source File

Source Code

# import dataclasses
# import inspect
# from types import UnionType
# from typing import (
#     Annotated,
#     Union,
# )

# import pytest
# from langchain_core.language_models import BaseChatModel
# from langchain_core.messages import (
#     AIMessage,
#     HumanMessage,
#     MessageLikeRepresentation,
#     RemoveMessage,
#     SystemMessage,
#     ToolCall,
#     ToolMessage,
# )
# from langchain_core.runnables import RunnableConfig, RunnableLambda
# from langchain_core.tools import BaseTool, InjectedToolCallId, ToolException
# from langchain_core.tools import tool as dec_tool
# from langgraph.checkpoint.base import BaseCheckpointSaver
# from langgraph.graph import START, MessagesState, StateGraph
# from langgraph.graph.message import REMOVE_ALL_MESSAGES
# from langgraph.runtime import Runtime
# from langgraph.store.base import BaseStore
# from langgraph.store.memory import InMemoryStore
# from langgraph.types import Command, Interrupt, interrupt
# from pydantic import BaseModel, Field
# from typing_extensions import TypedDict

# from langchain.agents import (
#     AgentState,
#     create_agent,
# )
# from langchain.tools import (
#     ToolNode,
#     InjectedState,
#     InjectedStore,
# )
# from langchain.tools.tool_node import (
#     _get_state_args,
#     _infer_handled_types,
# )

# from tests.unit_tests.agents.any_str import AnyStr
# from tests.unit_tests.agents.messages import _AnyIdHumanMessage, _AnyIdToolMessage
# from tests.unit_tests.agents.model import FakeToolCallingModel

# pytestmark = pytest.mark.anyio


# def test_no_prompt(sync_checkpointer: BaseCheckpointSaver) -> None:
#     model = FakeToolCallingModel()

#     agent = create_agent(
#         model,
#         [],
#         checkpointer=sync_checkpointer,
// ... (928 more lines)