Merge branch 'main' into brace/agent-survey-docs

docs/docs/cloud/deployment/setup.md

@@ -5,10 +5,10 @@ A LangGraph application must be configured with a [LangGraph API configuration f
 This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraph-example), which you can play around with to learn more about how to setup your LangGraph application for deployment.
 
 !!! tip "Setup with pyproject.toml"
-If you prefer using poetry for dependency management, check out [this how-to guide](./setup_pyproject.md) on using `pyproject.toml` for LangGraph Cloud.
+    If you prefer using poetry for dependency management, check out [this how-to guide](./setup_pyproject.md) on using `pyproject.toml` for LangGraph Cloud.
 
 !!! tip "Setup with a Monorepo"
-If you are interested in deploying a graph located inside a monorepo, take a look at [this](https://github.com/langchain-ai/langgraph-example-monorepo) repository for an example of how to do so.
+    If you are interested in deploying a graph located inside a monorepo, take a look at [this](https://github.com/langchain-ai/langgraph-example-monorepo) repository for an example of how to do so.
 
 The final repo structure will look something like this:
 
@@ -129,7 +129,7 @@ graph = workflow.compile()
 ```
 
 !!! warning "Assign `CompiledGraph` to Variable"
-The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module (alternatively, you can provide [a function that creates a graph](./graph_rebuild.md)).
+    The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module (alternatively, you can provide [a function that creates a graph](./graph_rebuild.md)).
 
 Example file directory:
 
@@ -166,7 +166,7 @@ Example `langgraph.json` file:
 Note that the variable name of the `CompiledGraph` appears at the end of the value of each subkey in the top-level `graphs` key (i.e. `:<variable_name>`).
 
 !!! warning "Configuration Location"
-The LangGraph API configuration file must be placed in a directory that is at the same level or higher than the Python files that contain compiled graphs and associated dependencies.
+    The LangGraph API configuration file must be placed in a directory that is at the same level or higher than the Python files that contain compiled graphs and associated dependencies.
 
 Example file directory:
 

docs/docs/cloud/deployment/setup_pyproject.md

@@ -5,7 +5,7 @@ A LangGraph application must be configured with a [LangGraph API configuration f
 This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraph-example-pyproject), which you can play around with to learn more about how to setup your LangGraph application for deployment.
 
 !!! tip "Setup with requirements.txt"
-  If you prefer using `requirements.txt` for dependency management, check out [this how-to guide](./setup.md).
+    If you prefer using `requirements.txt` for dependency management, check out [this how-to guide](./setup.md).
 
 !!! tip "Setup with a Monorepo"
     If you are interested in deploying a graph located inside a monorepo, take a look at [this](https://github.com/langchain-ai/langgraph-example-monorepo) repository for an example of how to do so.

libs/langgraph/langgraph/graph/state.py

@@ -151,8 +151,8 @@ def __init__(
         self.input = input
         self.output = output
         self._add_schema(state_schema)
-        self._add_schema(input)
-        self._add_schema(output)
+        self._add_schema(input, allow_managed=False)
+        self._add_schema(output, allow_managed=False)
         self.config_schema = config_schema
         self.waiting_edges: set[tuple[tuple[str, ...], str]] = set()
 
@@ -162,10 +162,17 @@ def _all_edges(self) -> set[tuple[str, str]]:
             (start, end) for starts, end in self.waiting_edges for start in starts
         }
 
-    def _add_schema(self, schema: Type[Any]) -> None:
+    def _add_schema(self, schema: Type[Any], /, allow_managed: bool = True) -> None:
         if schema not in self.schemas:
             _warn_invalid_state_schema(schema)
             channels, managed = _get_channels(schema)
+            if managed and not allow_managed:
+                names = ", ".join(managed)
+                schema_name = getattr(schema, "__name__", "")
+                raise ValueError(
+                    f"Invalid managed channels detected in {schema_name}: {names}."
+                    " Managed channels are not permitted in Input/Output schema."
+                )
             self.schemas[schema] = {**channels, **managed}
             for key, channel in channels.items():
                 if key in self.channels:
@@ -474,45 +481,22 @@ class CompiledStateGraph(CompiledGraph):
     def get_input_schema(
         self, config: Optional[RunnableConfig] = None
     ) -> type[BaseModel]:
-        if isclass(self.builder.input) and issubclass(
-            self.builder.input, (BaseModel, BaseModelV1)
-        ):
-            return self.builder.input
-        else:
-            keys = list(self.builder.schemas[self.builder.input].keys())
-            if len(keys) == 1 and keys[0] == "__root__":
-                return create_model(  # type: ignore[call-overload]
-                    self.get_name("Input"),
-                    __root__=(self.channels[keys[0]].UpdateType, None),
-                )
-            else:
-                return create_model(  # type: ignore[call-overload]
-                    self.get_name("Input"),
-                    **{
-                        k: (
-                            self.channels[k].UpdateType,
-                            (
-                                get_field_default(
-                                    k,
-                                    self.channels[k].UpdateType,
-                                    self.builder.input,
-                                )
-                            ),
-                        )
-                        for k in self.builder.schemas[self.builder.input]
-                        if isinstance(self.channels[k], BaseChannel)
-                    },
-                )
+        return _get_schema(
+            typ=self.builder.input,
+            schemas=self.builder.schemas,
+            channels=self.builder.channels,
+            name=self.get_name("Input"),
+        )
 
     def get_output_schema(
         self, config: Optional[RunnableConfig] = None
     ) -> type[BaseModel]:
-        if isclass(self.builder.output) and issubclass(
-            self.builder.output, (BaseModel, BaseModelV1)
-        ):
-            return self.builder.output
-
-        return super().get_output_schema(config)
+        return _get_schema(
+            typ=self.builder.output,
+            schemas=self.builder.schemas,
+            channels=self.builder.channels,
+            name=self.get_name("Output"),
+        )
 
     def attach_node(self, key: str, node: Optional[StateNodeSpec]) -> None:
         if key == START:
@@ -772,3 +756,38 @@ def _is_field_managed_value(name: str, typ: Type[Any]) -> Optional[ManagedValueS
                 return decoration
 
     return None
+
+
+def _get_schema(
+    typ: Type,
+    schemas: dict,
+    channels: dict,
+    name: str,
+) -> type[BaseModel]:
+    if isclass(typ) and issubclass(typ, (BaseModel, BaseModelV1)):
+        return typ
+    else:
+        keys = list(schemas[typ].keys())
+        if len(keys) == 1 and keys[0] == "__root__":
+            return create_model(  # type: ignore[call-overload]
+                name,
+                __root__=(channels[keys[0]].UpdateType, None),
+            )
+        else:
+            return create_model(  # type: ignore[call-overload]
+                name,
+                **{
+                    k: (
+                        channels[k].UpdateType,
+                        (
+                            get_field_default(
+                                k,
+                                channels[k].UpdateType,
+                                typ,
+                            )
+                        ),
+                    )
+                    for k in schemas[typ]
+                    if k in channels and isinstance(channels[k], BaseChannel)
+                },
+            )

libs/langgraph/langgraph/utils/fields.py

@@ -1,12 +1,7 @@
+import dataclasses
 from typing import Any, Optional, Type, Union
 
-from typing_extensions import (
-    Annotated,
-    NotRequired,
-    ReadOnly,
-    Required,
-    get_origin,
-)
+from typing_extensions import Annotated, NotRequired, ReadOnly, Required, get_origin
 
 
 def _is_optional_type(type_: Any) -> bool:
@@ -92,6 +87,18 @@ def get_field_default(name: str, type_: Any, schema: Type[Any]) -> Any:
             return ...
         # Handle NotRequired[<type>] for earlier versions of python
         return None
+    if dataclasses.is_dataclass(schema):
+        field_info = next(
+            (f for f in dataclasses.fields(schema) if f.name == name), None
+        )
+        if field_info:
+            if (
+                field_info.default is not dataclasses.MISSING
+                and field_info.default is not ...
+            ):
+                return field_info.default
+            elif field_info.default_factory is not dataclasses.MISSING:
+                return field_info.default_factory()
     # Note, we ignore ReadOnly attributes,
     # as they don't make much sense. (we don't care if you mutate the state in your node)
     # and mutating state in your node has no effect on our graph state.

libs/langgraph/tests/test_state.py

@@ -1,4 +1,6 @@
+import inspect
 import warnings
+from dataclasses import dataclass, field
 from typing import Annotated as Annotated2
 from typing import Any, Optional
 
@@ -8,6 +10,7 @@
 from typing_extensions import Annotated, NotRequired, Required, TypedDict
 
 from langgraph.graph.state import StateGraph, _warn_invalid_state_schema
+from langgraph.managed.shared_value import SharedValue
 
 
 class State(BaseModel):
@@ -104,14 +107,25 @@ class InputState(SomeParentState, total=total_):  # type: ignore
         val5: Annotated[Required[str], "foo"]
         val6: Annotated[NotRequired[str], "bar"]
 
+    class OutputState(SomeParentState, total=total_):  # type: ignore
+        out_val1: str
+        out_val2: Optional[str]
+        out_val3: Required[str]
+        out_val4: NotRequired[dict]
+        out_val5: Annotated[Required[str], "foo"]
+        out_val6: Annotated[NotRequired[str], "bar"]
+
     class State(InputState):  # this would be ignored
         val4: dict
+        some_shared_channel: Annotated[str, SharedValue.on("assistant_id")] = field(
+            default="foo"
+        )
 
-    builder = StateGraph(State, input=InputState)
+    builder = StateGraph(State, input=InputState, output=OutputState)
     builder.add_node("n", lambda x: x)
     builder.add_edge("__start__", "n")
     graph = builder.compile()
-    model = graph.input_schema
+    model = graph.get_input_schema()
     json_schema = model.schema()
 
     if total_ is False:
@@ -130,3 +144,125 @@ class State(InputState):  # this would be ignored
     assert (
         set(json_schema["properties"].keys()) == expected_required | expected_optional
     )
+
+    # Check output schema. Should be the same process
+    output_schema = graph.get_output_schema().schema()
+    if total_ is False:
+        expected_required = set()
+        expected_optional = {"out_val2", "out_val1"}
+    else:
+        expected_required = {"out_val1"}
+        expected_optional = {"out_val2"}
+
+    expected_required |= {"val0a", "out_val3", "out_val5"}
+    expected_optional |= {"val0b", "out_val4", "out_val6"}
+
+    assert set(output_schema.get("required", set())) == expected_required
+    assert (
+        set(output_schema["properties"].keys()) == expected_required | expected_optional
+    )
+
+
+@pytest.mark.parametrize("kw_only_", [False, True])
+def test_state_schema_default_values(kw_only_: bool):
+    kwargs = {}
+    if "kw_only" in inspect.signature(dataclass).parameters:
+        kwargs = {"kw_only": kw_only_}
+
+    @dataclass(**kwargs)
+    class InputState:
+        val1: str
+        val2: Optional[int]
+        val3: Annotated[Optional[float], "optional annotated"]
+        val4: Optional[str] = None
+        val5: list[int] = field(default_factory=lambda: [1, 2, 3])
+        val6: dict[str, int] = field(default_factory=lambda: {"a": 1})
+        val7: str = field(default=...)
+        val8: Annotated[int, "some metadata"] = 42
+        val9: Annotated[str, "more metadata"] = field(default="some foo")
+        val10: str = "default"
+        val11: Annotated[list[str], "annotated list"] = field(
+            default_factory=lambda: ["a", "b"]
+        )
+        some_shared_channel: Annotated[str, SharedValue.on("assistant_id")] = field(
+            default="foo"
+        )
+
+    builder = StateGraph(InputState)
+    builder.add_node("n", lambda x: x)
+    builder.add_edge("__start__", "n")
+    graph = builder.compile()
+    for model in [graph.get_input_schema(), graph.get_output_schema()]:
+        json_schema = model.schema()
+
+        expected_required = {"val1", "val7"}
+        expected_optional = {
+            "val2",
+            "val3",
+            "val4",
+            "val5",
+            "val6",
+            "val8",
+            "val9",
+            "val10",
+            "val11",
+        }
+
+    assert set(json_schema.get("required", set())) == expected_required
+    assert (
+        set(json_schema["properties"].keys()) == expected_required | expected_optional
+    )
+
+
+def test_raises_invalid_managed():
+    class BadInputState(TypedDict):
+        some_thing: str
+        some_input_channel: Annotated[str, SharedValue.on("assistant_id")]
+
+    class InputState(TypedDict):
+        some_thing: str
+        some_input_channel: str
+
+    class BadOutputState(TypedDict):
+        some_thing: str
+        some_output_channel: Annotated[str, SharedValue.on("assistant_id")]
+
+    class OutputState(TypedDict):
+        some_thing: str
+        some_output_channel: str
+
+    class State(TypedDict):
+        some_thing: str
+        some_channel: Annotated[str, SharedValue.on("assistant_id")]
+
+    # All OK
+    StateGraph(State, input=InputState, output=OutputState)
+    StateGraph(State)
+    StateGraph(State, input=State, output=State)
+    StateGraph(State, input=InputState)
+    StateGraph(State, input=InputState)
+
+    bad_input_examples = [
+        (State, BadInputState, OutputState),
+        (State, BadInputState, BadOutputState),
+        (State, BadInputState, State),
+        (State, BadInputState, None),
+    ]
+    for _state, _inp, _outp in bad_input_examples:
+        with pytest.raises(
+            ValueError,
+            match="Invalid managed channels detected in BadInputState: some_input_channel. Managed channels are not permitted in Input/Output schema.",
+        ):
+            StateGraph(_state, input=_inp, output=_outp)
+    bad_output_examples = [
+        (State, InputState, BadOutputState),
+        (None, InputState, BadOutputState),
+        (None, State, BadOutputState),
+        (State, None, BadOutputState),
+    ]
+    for _state, _inp, _outp in bad_output_examples:
+        with pytest.raises(
+            ValueError,
+            match="Invalid managed channels detected in BadOutputState: some_output_channel. Managed channels are not permitted in Input/Output schema.",
+        ):
+            StateGraph(_state, input=_inp, output=_outp)