chore: import upstream snapshot with attribution

libs/langgraph/tests/example_app/example_graph.py

@@ -0,0 +1,89 @@
+from typing import Annotated
+
+from langchain_core.messages import AIMessage, BaseMessage, ToolMessage
+from langchain_core.tools import tool
+from typing_extensions import TypedDict
+
+from langgraph.func import entrypoint, task
+from langgraph.graph.message import add_messages
+from tests.fake_chat import FakeChatModel
+
+
+class AgentState(TypedDict):
+    messages: Annotated[list[BaseMessage], add_messages]
+
+
+@tool
+def search_api(query: str) -> str:
+    """Searches the API for the query."""
+    return f"result for {query}"
+
+
+tools = [search_api]
+tools_by_name = {t.name: t for t in tools}
+
+
+def get_model():
+    model = FakeChatModel(
+        messages=[
+            AIMessage(
+                id="ai1",
+                content="",
+                tool_calls=[
+                    {
+                        "id": "tool_call123",
+                        "name": "search_api",
+                        "args": {"query": "query"},
+                    },
+                ],
+            ),
+            AIMessage(
+                id="ai2",
+                content="",
+                tool_calls=[
+                    {
+                        "id": "tool_call234",
+                        "name": "search_api",
+                        "args": {"query": "another", "idx": 0},
+                    },
+                    {
+                        "id": "tool_call567",
+                        "name": "search_api",
+                        "args": {"query": "a third one", "idx": 1},
+                    },
+                ],
+            ),
+            AIMessage(id="ai3", content="answer"),
+        ]
+    )
+    return model
+
+
+@task
+def foo():
+    return "foo"
+
+
+@entrypoint()
+async def app(state: AgentState) -> AgentState:
+    model = get_model()
+    max_steps = 100
+    messages = state["messages"][:]
+    await foo()  # Very useful call here ya know.
+    for _ in range(max_steps):
+        message = await model.ainvoke(messages)
+        messages.append(message)
+        if not message.tool_calls:
+            break
+        # Assume it's the search tool
+        tool_results = await search_api.abatch(
+            [t["args"]["query"] for t in message.tool_calls]
+        )
+        messages.extend(
+            [
+                ToolMessage(content=tool_res, tool_call_id=tc["id"])
+                for tc, tool_res in zip(message.tool_calls, tool_results)
+            ]
+        )
+
+    return entrypoint.final(value=messages[-1], save={"messages": messages})