chore: use defaults for channel values

libs/langgraph/langgraph/_internal/_fields.py

@@ -101,6 +101,21 @@ def get_field_default(name: str, type_: Any, schema: type[Any]) -> Any:
             return ...
         # Handle NotRequired[<type>] for earlier versions of python
         return None
+    if isinstance(schema, type) and issubclass(schema, BaseModel):
+        if name in schema.model_fields:
+            field = schema.model_fields[name]
+            # Check default_factory first (it takes precedence in Pydantic)
+            if field.default_factory is not None:
+                return field.default_factory()  # type: ignore[call-arg]
+            # Check if default is set (not PydanticUndefined)
+            if (
+                hasattr(field.default, "__class__")
+                and getattr(field.default.__class__, "__name__", "")
+                == "PydanticUndefinedType"
+            ):
+                pass  # No default, fall through
+            else:
+                return field.default
     if dataclasses.is_dataclass(schema):
         field_info = next(
             (f for f in dataclasses.fields(schema) if f.name == name), None

libs/langgraph/langgraph/channels/binop.py

@@ -48,11 +48,17 @@ class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
     ```
     """
 
-    __slots__ = ("value", "operator")
+    __slots__ = ("value", "operator", "default")
 
-    def __init__(self, typ: type[Value], operator: Callable[[Value, Value], Value]):
+    def __init__(
+        self,
+        typ: type[Value],
+        operator: Callable[[Value, Value], Value],
+        default: Any = MISSING,
+    ):
         super().__init__(typ)
         self.operator = operator
+        self.default = default
         # special forms from typing or collections.abc are not instantiable
         # so we need to replace them with their concrete counterparts
         typ = _strip_extras(typ)
@@ -62,10 +68,13 @@ class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
             typ = set
         if typ in (collections.abc.Mapping, collections.abc.MutableMapping):
             typ = dict
-        try:
-            self.value = typ()
-        except Exception:
-            self.value = MISSING
+        if default is not MISSING:
+            self.value = default
+        else:
+            try:
+                self.value = typ()
+            except Exception:
+                self.value = MISSING
 
     def __eq__(self, value: object) -> bool:
         return isinstance(value, BinaryOperatorAggregate) and (
@@ -87,13 +96,13 @@ class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
 
     def copy(self) -> Self:
         """Return a copy of the channel."""
-        empty = self.__class__(self.typ, self.operator)
+        empty = self.__class__(self.typ, self.operator, self.default)
         empty.key = self.key
         empty.value = self.value
         return empty
 
     def from_checkpoint(self, checkpoint: Value) -> Self:
-        empty = self.__class__(self.typ, self.operator)
+        empty = self.__class__(self.typ, self.operator, self.default)
         empty.key = self.key
         if checkpoint is not MISSING:
             empty.value = checkpoint

libs/langgraph/langgraph/graph/state.py

@@ -1567,7 +1567,7 @@ def _get_channels(
 
     type_hints = get_type_hints(schema, include_extras=True)
     all_keys = {
-        name: _get_channel(name, typ)
+        name: _get_channel(name, typ, schema=schema)
         for name, typ in type_hints.items()
         if name != "__slots__"
     }
@@ -1580,18 +1580,30 @@ def _get_channels(
 
 @overload
 def _get_channel(
-    name: str, annotation: Any, *, allow_managed: Literal[False]
+    name: str,
+    annotation: Any,
+    *,
+    allow_managed: Literal[False],
+    schema: type[Any] | None = None,
 ) -> BaseChannel: ...
 
 
 @overload
 def _get_channel(
-    name: str, annotation: Any, *, allow_managed: Literal[True] = True
+    name: str,
+    annotation: Any,
+    *,
+    allow_managed: Literal[True] = True,
+    schema: type[Any] | None = None,
 ) -> BaseChannel | ManagedValueSpec: ...
 
 
 def _get_channel(
-    name: str, annotation: Any, *, allow_managed: bool = True
+    name: str,
+    annotation: Any,
+    *,
+    allow_managed: bool = True,
+    schema: type[Any] | None = None,
 ) -> BaseChannel | ManagedValueSpec:
     # Strip out Required and NotRequired wrappers
     if hasattr(annotation, "__origin__") and annotation.__origin__ in (
@@ -1607,7 +1619,7 @@ def _get_channel(
     elif channel := _is_field_channel(annotation):
         channel.key = name
         return channel
-    elif channel := _is_field_binop(annotation):
+    elif channel := _is_field_binop(annotation, name=name, schema=schema):
         channel.key = name
         return channel
 
@@ -1630,7 +1642,12 @@ def _is_field_channel(typ: type[Any]) -> BaseChannel | None:
     return None
 
 
-def _is_field_binop(typ: type[Any]) -> BinaryOperatorAggregate | None:
+def _is_field_binop(
+    typ: type[Any],
+    *,
+    name: str | None = None,
+    schema: type[Any] | None = None,
+) -> BinaryOperatorAggregate | None:
     if hasattr(typ, "__metadata__"):
         meta = typ.__metadata__
         if len(meta) >= 1 and callable(meta[-1]):
@@ -1643,7 +1660,12 @@ def _is_field_binop(typ: type[Any]) -> BinaryOperatorAggregate | None:
                 )
                 == 2
             ):
-                return BinaryOperatorAggregate(typ, meta[-1])
+                default: Any = MISSING
+                if name is not None and schema is not None:
+                    field_default = get_field_default(name, typ, schema)
+                    if field_default is not ...:
+                        default = field_default
+                return BinaryOperatorAggregate(typ, meta[-1], default=default)
             else:
                 raise ValueError(
                     f"Invalid reducer signature. Expected (a, b) -> c. Got {sig}"

libs/langgraph/tests/test_channels.py

@@ -90,6 +90,38 @@ def test_binop() -> None:
     assert channel.get() == 10
 
 
+def test_binop_with_default() -> None:
+    # Test that a default value is used instead of typ()
+    channel = BinaryOperatorAggregate(int, operator.add, default=10).from_checkpoint(
+        MISSING
+    )
+    assert channel.get() == 10
+
+    channel.update([5])
+    assert channel.get() == 15
+
+    # Test checkpoint round-trip preserves default for new channels
+    checkpoint = channel.checkpoint()
+    restored = BinaryOperatorAggregate(int, operator.add, default=10).from_checkpoint(
+        checkpoint
+    )
+    assert restored.get() == 15
+
+    # Test from_checkpoint with MISSING uses default
+    fresh = BinaryOperatorAggregate(int, operator.add, default=10).from_checkpoint(
+        MISSING
+    )
+    assert fresh.get() == 10
+
+    # Test dict default with or_ reducer
+    channel = BinaryOperatorAggregate(
+        dict, operator.or_, default={"a": 1}
+    ).from_checkpoint(MISSING)
+    assert channel.get() == {"a": 1}
+    channel.update([{"b": 2}])
+    assert channel.get() == {"a": 1, "b": 2}
+
+
 def test_untracked_value() -> None:
     channel = UntrackedValue(dict).from_checkpoint(MISSING)
     assert channel.ValueType is dict

libs/langgraph/tests/test_pregel.py

@@ -9059,3 +9059,144 @@ def test_fork_does_not_apply_pending_writes(
 
     # Should be: 1 (input) + 20 (forked node_a) + 100 (node_b) = 121
     assert result == {"value": 121}
+
+
+def test_reducer_field_with_pydantic_default() -> None:
+    """Test that Annotated reducer fields respect Pydantic Field defaults."""
+
+    class State(BaseModel):
+        query: str
+        files: Annotated[dict[str, str], operator.or_] = Field(
+            default_factory=lambda: {"default.txt": "content"}
+        )
+
+    observed_files: list[dict] = []
+
+    def node(state: State) -> dict:
+        observed_files.append(state.files)
+        return {"files": {"new.txt": "new"}}
+
+    graph = StateGraph(State)
+    graph.add_node("node", node)
+    graph.set_entry_point("node")
+    graph.set_finish_point("node")
+    app = graph.compile()
+
+    # Invoke without providing files — should use default
+    result = app.invoke({"query": "test"})
+    assert observed_files[0] == {"default.txt": "content"}
+    assert result == {
+        "query": "test",
+        "files": {"default.txt": "content", "new.txt": "new"},
+    }
+
+
+def test_reducer_field_with_pydantic_default_explicit_value() -> None:
+    """Test that an explicit value overrides the default for reducer fields."""
+
+    class State(BaseModel):
+        files: Annotated[dict[str, str], operator.or_] = Field(
+            default_factory=lambda: {"default.txt": "content"}
+        )
+
+    observed_files: list[dict] = []
+
+    def node(state: State) -> dict:
+        observed_files.append(state.files)
+        return {}
+
+    graph = StateGraph(State)
+    graph.add_node("node", node)
+    graph.set_entry_point("node")
+    graph.set_finish_point("node")
+    app = graph.compile()
+
+    # Invoke WITH explicit files — should use provided value, not default
+    app.invoke({"files": {"custom.txt": "custom"}})
+    assert observed_files[0] == {"default.txt": "content", "custom.txt": "custom"}
+
+
+def test_reducer_field_with_dataclass_default() -> None:
+    """Test that Annotated reducer fields respect dataclass defaults."""
+
+    @dataclass
+    class State:
+        query: str
+        files: Annotated[dict[str, str], operator.or_] = field(
+            default_factory=lambda: {"default.txt": "content"}
+        )
+
+    observed_files: list[dict] = []
+
+    def node(state: State) -> dict:
+        observed_files.append(state.files)
+        return {"files": {"new.txt": "new"}}
+
+    graph = StateGraph(State)
+    graph.add_node("node", node)
+    graph.set_entry_point("node")
+    graph.set_finish_point("node")
+    app = graph.compile()
+
+    result = app.invoke({"query": "test"})
+    assert observed_files[0] == {"default.txt": "content"}
+    assert result == {
+        "query": "test",
+        "files": {"default.txt": "content", "new.txt": "new"},
+    }
+
+
+def test_reducer_field_with_pydantic_none_default() -> None:
+    """Test that None default is respected (not confused with 'no default')."""
+
+    def _reducer(a: str | None, b: str | None) -> str | None:
+        if b is not None:
+            return b
+        return a
+
+    class State(BaseModel):
+        query: str
+        data: Annotated[str | None, _reducer] = None
+
+    observed: list[Any] = []
+
+    def node(state: State) -> dict:
+        observed.append(state.data)
+        return {"data": "updated"}
+
+    graph = StateGraph(State)
+    graph.add_node("node", node)
+    graph.set_entry_point("node")
+    graph.set_finish_point("node")
+    app = graph.compile()
+
+    result = app.invoke({"query": "test"})
+    assert observed[0] is None  # default None was respected
+    assert result == {"query": "test", "data": "updated"}
+
+
+def test_reducer_field_with_default_multi_step() -> None:
+    """Test that defaults work correctly across multiple graph steps."""
+
+    class State(BaseModel):
+        items: Annotated[list[str], operator.add] = Field(
+            default_factory=lambda: ["initial"]
+        )
+
+    def step1(state: State) -> dict:
+        return {"items": ["step1"]}
+
+    def step2(state: State) -> dict:
+        return {"items": ["step2"]}
+
+    graph = StateGraph(State)
+    graph.add_node("step1", step1)
+    graph.add_node("step2", step2)
+    graph.set_entry_point("step1")
+    graph.add_edge("step1", "step2")
+    graph.set_finish_point("step2")
+    app = graph.compile()
+
+    result = app.invoke({})
+    # Default ["initial"] + ["step1"] + ["step2"]
+    assert result == {"items": ["initial", "step1", "step2"]}

libs/langgraph/tests/test_pregel_async.py

@@ -9347,3 +9347,82 @@ async def test_fork_does_not_apply_pending_writes(
 
     # 1 (input) + 20 (forked node_a) + 100 (node_b) = 121
     assert result == {"value": 121}
+
+
+async def test_reducer_field_with_pydantic_default() -> None:
+    """Test that Annotated reducer fields respect Pydantic Field defaults."""
+
+    class State(BaseModel):
+        query: str
+        files: Annotated[dict[str, str], operator.or_] = Field(
+            default_factory=lambda: {"default.txt": "content"}
+        )
+
+    observed_files: list[dict] = []
+
+    def node(state: State) -> dict:
+        observed_files.append(state.files)
+        return {"files": {"new.txt": "new"}}
+
+    graph = StateGraph(State)
+    graph.add_node("node", node)
+    graph.set_entry_point("node")
+    graph.set_finish_point("node")
+    app = graph.compile()
+
+    result = await app.ainvoke({"query": "test"})
+    assert observed_files[0] == {"default.txt": "content"}
+    assert result == {
+        "query": "test",
+        "files": {"default.txt": "content", "new.txt": "new"},
+    }
+
+
+async def test_reducer_field_with_pydantic_default_explicit_value() -> None:
+    """Test that an explicit value overrides the default for reducer fields."""
+
+    class State(BaseModel):
+        files: Annotated[dict[str, str], operator.or_] = Field(
+            default_factory=lambda: {"default.txt": "content"}
+        )
+
+    observed_files: list[dict] = []
+
+    def node(state: State) -> dict:
+        observed_files.append(state.files)
+        return {}
+
+    graph = StateGraph(State)
+    graph.add_node("node", node)
+    graph.set_entry_point("node")
+    graph.set_finish_point("node")
+    app = graph.compile()
+
+    await app.ainvoke({"files": {"custom.txt": "custom"}})
+    assert observed_files[0] == {"default.txt": "content", "custom.txt": "custom"}
+
+
+async def test_reducer_field_with_default_multi_step() -> None:
+    """Test that defaults work correctly across multiple graph steps."""
+
+    class State(BaseModel):
+        items: Annotated[list[str], operator.add] = Field(
+            default_factory=lambda: ["initial"]
+        )
+
+    def step1(state: State) -> dict:
+        return {"items": ["step1"]}
+
+    def step2(state: State) -> dict:
+        return {"items": ["step2"]}
+
+    graph = StateGraph(State)
+    graph.add_node("step1", step1)
+    graph.add_node("step2", step2)
+    graph.set_entry_point("step1")
+    graph.add_edge("step1", "step2")
+    graph.set_finish_point("step2")
+    app = graph.compile()
+
+    result = await app.ainvoke({})
+    assert result == {"items": ["initial", "step1", "step2"]}