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This reverts commit cff349e22e.

docs/docs/concepts/persistence.md

@@ -4,6 +4,10 @@ LangGraph has a built-in persistence layer, implemented through checkpointers. W
 
 ![Checkpoints](img/persistence/checkpoints.jpg)
 
+!!! info "LangGraph API handles checkpointing automatically"
+
+    When using the LangGraph API, you don't need to implement or configure checkpointers manually. The API handles all persistence infrastructure for you behind the scenes.
+
 ## Threads
 
 A thread is a unique ID or [thread identifier](#threads) assigned to each checkpoint saved by a checkpointer. When invoking graph with a checkpointer, you **must** specify a `thread_id` as part of the `configurable` portion of the config:
@@ -26,7 +30,7 @@ Let's see what checkpoints are saved when a simple graph is invoked as follows:
 
 ```python
 from langgraph.graph import StateGraph, START, END
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 from typing import Annotated
 from typing_extensions import TypedDict
 from operator import add
@@ -49,7 +53,7 @@ workflow.add_edge(START, "node_a")
 workflow.add_edge("node_a", "node_b")
 workflow.add_edge("node_b", END)
 
-checkpointer = MemorySaver()
+checkpointer = InMemorySaver()
 graph = workflow.compile(checkpointer=checkpointer)
 
 config = {"configurable": {"thread_id": "1"}}
@@ -223,6 +227,10 @@ But, what if we want to retain some information *across threads*? Consider the c
 
 With checkpointers alone, we cannot share information across threads. This motivates the need for the [`Store`](../reference/store.md#langgraph.store.base.BaseStore) interface. As an illustration, we can define an `InMemoryStore` to store information about a user across threads. We simply compile our graph with a checkpointer, as before, and with our new `in_memory_store` variable.
 
+!!!  info "LangGraph API handles stores automatically"
+
+    When using the LangGraph API, you don't need to implement or configure stores manually. The API handles all storage infrastructure for you behind the scenes.
+
 ### Basic Usage
 
 First, let's showcase this in isolation without using LangGraph.
@@ -324,10 +332,10 @@ store.put(
 With this all in place, we use the `in_memory_store` in LangGraph. The `in_memory_store` works hand-in-hand with the checkpointer: the checkpointer saves state to threads, as discussed above, and the `in_memory_store` allows us to store arbitrary information for access *across* threads. We compile the graph with both the checkpointer and the `in_memory_store` as follows. 
 
 ```python
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 
 # We need this because we want to enable threads (conversations)
-checkpointer = MemorySaver()
+checkpointer = InMemorySaver()
 
 # ... Define the graph ...
 
@@ -440,6 +448,7 @@ Under the hood, checkpointing is powered by checkpointer objects that conform to
 * `langgraph-checkpoint-sqlite`: An implementation of LangGraph checkpointer that uses SQLite database ([SqliteSaver][langgraph.checkpoint.sqlite.SqliteSaver] / [AsyncSqliteSaver][langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver]). Ideal for experimentation and local workflows. Needs to be installed separately.
 * `langgraph-checkpoint-postgres`: An advanced checkpointer that uses Postgres database ([PostgresSaver][langgraph.checkpoint.postgres.PostgresSaver] / [AsyncPostgresSaver][langgraph.checkpoint.postgres.aio.AsyncPostgresSaver]), used in LangGraph Cloud. Ideal for using in production. Needs to be installed separately.
 
+
 ### Checkpointer interface
 
 Each checkpointer conforms to [BaseCheckpointSaver][langgraph.checkpoint.base.BaseCheckpointSaver] interface and implements the following methods:
@@ -452,7 +461,7 @@ Each checkpointer conforms to [BaseCheckpointSaver][langgraph.checkpoint.base.Ba
 If the checkpointer is used with asynchronous graph execution (i.e. executing the graph via `.ainvoke`, `.astream`, `.abatch`), asynchronous versions of the above methods will be used (`.aput`, `.aput_writes`, `.aget_tuple`, `.alist`).
 
 !!! note Note
-    For running your graph asynchronously, you can use `MemorySaver`, or async versions of Sqlite/Postgres checkpointers -- `AsyncSqliteSaver` / `AsyncPostgresSaver` checkpointers.
+    For running your graph asynchronously, you can use `InMemorySaver`, or async versions of Sqlite/Postgres checkpointers -- `AsyncSqliteSaver` / `AsyncPostgresSaver` checkpointers.
 
 ### Serializer
 

docs/docs/how-tos/persistence-functional.ipynb

@@ -16,6 +16,10 @@
     "    - [Memory](../../concepts/memory/)\n",
     "    - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n",
     "\n",
+    "!!! info \"Not needed for LangGraph API users\"\n",
+    "\n",
+    "    If you're using the LangGraph API, you needn't manually implement a checkpointer. The API automatically handles checkpointing for you. This guide is relevant when implementing LangGraph in your own custom server.\n",
+    "\n",
     "Many AI applications need memory to share context across multiple interactions on the same [thread](../../concepts/persistence#threads) (e.g., multiple turns of a conversation). In LangGraph functional API, this kind of memory can be added to any [entrypoint()][langgraph.func.entrypoint] workflow using [thread-level persistence](https://langchain-ai.github.io/langgraph/concepts/persistence).\n",
     "\n",
     "When creating a LangGraph workflow, you can set it up to persist its results by using a [checkpointer](https://langchain-ai.github.io/langgraph/reference/checkpoints/#basecheckpointsaver):\n",

docs/docs/how-tos/persistence.ipynb

@@ -31,6 +31,10 @@
     "    </p>\n",
     "</div> \n",
     "\n",
+    "!!! info \"Not needed for LangGraph API users\"\n",
+    "\n",
+    "    If you're using the LangGraph API, you needn't manually implement a checkpointer. The API automatically handles checkpointing for you. This guide is relevant when implementing LangGraph in your own custom server.\n",
+    "\n",
     "Many AI applications need memory to share context across multiple interactions. In LangGraph, this kind of memory can be added to any [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph) using [thread-level persistence](https://langchain-ai.github.io/langgraph/concepts/persistence) .\n",
     "\n",
     "When creating any LangGraph graph, you can set it up to persist its state by adding a [checkpointer](https://langchain-ai.github.io/langgraph/reference/checkpoints/#basecheckpointsaver) when compiling the graph:\n",

docs/docs/how-tos/persistence_postgres.ipynb

@@ -26,6 +26,10 @@
     "    </p>\n",
     "</div> \n",
     "\n",
+    "!!! info \"Not needed for LangGraph API users\"\n",
+    "\n",
+    "    If you're using the LangGraph API, you needn't manually implement a checkpointer. The API automatically handles checkpointing for you. This guide is relevant when implementing LangGraph in your own custom server.\n",
+    "\n",
     "When creating LangGraph agents, you can also set them up so that they persist their state. This allows you to do things like interact with an agent multiple times and have it remember previous interactions.\n",
     "\n",
     "This how-to guide shows how to use `Postgres` as the backend for persisting checkpoint state using the [`langgraph-checkpoint-postgres`](https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint-postgres) library.\n",
@@ -44,7 +48,7 @@
     "...\n",
     "```\n",
     "\n",
-    "!!! info \"Setup\"",
+    "!!! info \"Setup\"\n",
     "\n",
     "    You need to run `.setup()` once on your checkpointer to initialize the database before you can use it."
    ]

libs/checkpoint/langgraph/checkpoint/memory/__init__.py

@@ -38,6 +38,8 @@ class InMemorySaver(
         Only use `InMemorySaver` for debugging or testing purposes.
         For production use cases we recommend installing [langgraph-checkpoint-postgres](https://pypi.org/project/langgraph-checkpoint-postgres/) and using `PostgresSaver` / `AsyncPostgresSaver`.
 
+        If you are using the LangGraph Platform, no checkpointer needs to be specified. The correct managed checkpointer will be used automatically.
+
     Args:
         serde (Optional[SerializerProtocol]): The serializer to use for serializing and deserializing checkpoints. Defaults to None.
 

libs/cli/langgraph_cli/config.py

@@ -778,7 +778,22 @@ def _update_graph_paths(
         FileNotFoundError: If the local file (module) does not actually exist on disk.
         IsADirectoryError: If `module_str` points to a directory instead of a file.
     """
-    for graph_id, import_str in config["graphs"].items():
+    for graph_id, data in config["graphs"].items():
+        if isinstance(data, dict):
+            # Then we're looking for a 'path' key
+            if "path" not in data:
+                raise ValueError(
+                    f"Graph '{graph_id}' must contain a 'path' key if "
+                    f" it is a dictionary."
+                )
+            import_str = data["path"]
+        elif isinstance(data, str):
+            import_str = data
+        else:
+            raise ValueError(
+                f"Graph '{graph_id}' must be a string or a dictionary with a 'path' key."
+            )
+
         module_str, _, attr_str = import_str.partition(":")
         if not module_str or not attr_str:
             message = (
@@ -818,7 +833,10 @@ def _update_graph_paths(
                             "Add its containing package to 'dependencies' list."
                         )
             # update the config
-            config["graphs"][graph_id] = f"{module_str}:{attr_str}"
+            if isinstance(data, dict):
+                config["graphs"][graph_id]["path"] = f"{module_str}:{attr_str}"
+            else:
+                config["graphs"][graph_id] = f"{module_str}:{attr_str}"
 
 
 def _update_auth_path(

libs/cli/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-cli"
-version = "0.1.84"
+version = "0.1.89"
 description = "CLI for interacting with LangGraph API"
 authors = []
 license = "MIT"

libs/cli/tests/unit_tests/cli/test_cli.py

@@ -196,6 +196,50 @@ def test_dockerfile_command_basic() -> None:
         assert save_path.exists()
 
 
+def test_dockerfile_command_new_style_config() -> None:
+    """Test `dockerfile` command with a new style config.
+
+    This config format allows specifying agent data as a dictionary.
+    {
+        "graphs": {
+            "agent1": {
+                "path": ... # path to graph definition,
+                ... # other fields
+            }
+        }
+    }
+    """
+    runner = CliRunner()
+    config_content = {
+        "dependencies": ["./my_agent"],
+        "graphs": {
+            "agent": {
+                "path": "./my_agent/agent.py:graph",
+                "description": "This is a test agent",
+            }
+        },
+        "env": ".env",
+    }
+    with temporary_config_folder(config_content) as temp_dir:
+        save_path = temp_dir / "Dockerfile"
+        # Add agent.py file
+        agent_path = temp_dir / "my_agent" / "agent.py"
+        agent_path.parent.mkdir(parents=True, exist_ok=True)
+        agent_path.touch()
+
+        result = runner.invoke(
+            cli,
+            ["dockerfile", str(save_path), "--config", str(temp_dir / "config.json")],
+        )
+
+        # Assert command was successful
+        assert result.exit_code == 0, result.output
+        assert "✅ Created: Dockerfile" in result.output
+
+        # Check if Dockerfile was created
+        assert save_path.exists()
+
+
 def test_dockerfile_command_with_docker_compose() -> None:
     """Test the 'dockerfile' command with Docker Compose configuration."""
     runner = CliRunner()

libs/langgraph/bench/__main__.py

@@ -9,6 +9,7 @@ from bench.fanout_to_subgraph import fanout_to_subgraph, fanout_to_subgraph_sync
 from bench.pydantic_state import pydantic_state
 from bench.react_agent import react_agent
 from bench.sequential import create_sequential
+from bench.wide_dict import wide_dict
 from bench.wide_state import wide_state
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import StateGraph
@@ -251,6 +252,102 @@ benchmarks = (
             ]
         },
     ),
+    (
+        "wide_dict_25x300",
+        wide_dict(300).compile(checkpointer=None),
+        wide_dict(300).compile(checkpointer=None),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(5)
+                    }
+                    for i in range(5)
+                }
+            ]
+        },
+    ),
+    (
+        "wide_dict_25x300_checkpoint",
+        wide_dict(300).compile(checkpointer=MemorySaver()),
+        wide_dict(300).compile(checkpointer=MemorySaver()),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(5)
+                    }
+                    for i in range(5)
+                }
+            ]
+        },
+    ),
+    (
+        "wide_dict_15x600",
+        wide_dict(600).compile(checkpointer=None),
+        wide_dict(600).compile(checkpointer=None),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(5)
+                    }
+                    for i in range(3)
+                }
+            ]
+        },
+    ),
+    (
+        "wide_dict_15x600_checkpoint",
+        wide_dict(600).compile(checkpointer=MemorySaver()),
+        wide_dict(600).compile(checkpointer=MemorySaver()),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(5)
+                    }
+                    for i in range(3)
+                }
+            ]
+        },
+    ),
+    (
+        "wide_dict_9x1200",
+        wide_dict(1200).compile(checkpointer=None),
+        wide_dict(1200).compile(checkpointer=None),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(3)
+                    }
+                    for i in range(3)
+                }
+            ]
+        },
+    ),
+    (
+        "wide_dict_9x1200_checkpoint",
+        wide_dict(1200).compile(checkpointer=MemorySaver()),
+        wide_dict(1200).compile(checkpointer=MemorySaver()),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(3)
+                    }
+                    for i in range(3)
+                }
+            ]
+        },
+    ),
     (
         "sequential_10",
         create_sequential(10).compile(),

libs/langgraph/bench/wide_dict.py

@@ -0,0 +1,153 @@
+import operator
+from functools import partial
+from random import choice
+from typing import Annotated, Optional, Sequence
+
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.graph.state import StateGraph
+
+
+def wide_dict(n: int) -> StateGraph:
+    class State(TypedDict):
+        messages: Annotated[list, operator.add]
+        trigger_events: Annotated[list, operator.add]
+        """The external events that are converted by the graph."""
+        primary_issue_medium: Annotated[str, lambda x, y: y or x]
+        autoresponse: Annotated[Optional[dict], lambda _, y: y]  # Always overwrite
+        issue: Annotated[dict | None, lambda x, y: y if y else x]
+        relevant_rules: Optional[list[dict]]
+        """SOPs fetched from the rulebook that are relevant to the current conversation."""
+        memory_docs: Optional[list[dict]]
+        """Memory docs fetched from the memory service that are relevant to the current conversation."""
+        categorizations: Annotated[list[dict], operator.add]
+        """The issue categorizations auto-generated by the AI."""
+        responses: Annotated[list[dict], operator.add]
+        """The draft responses recommended by the AI."""
+
+        user_info: Annotated[Optional[dict], lambda x, y: y if y is not None else x]
+        """The current user state (by email)."""
+        crm_info: Annotated[Optional[dict], lambda x, y: y if y is not None else x]
+        """The CRM information for organization the current user is from."""
+        email_thread_id: Annotated[
+            Optional[str], lambda x, y: y if y is not None else x
+        ]
+        """The current email thread ID."""
+        slack_participants: Annotated[dict, operator.or_]
+        """The growing list of current slack participants."""
+        bot_id: Optional[str]
+        """The ID of the bot user in the slack channel."""
+        notified_assignees: Annotated[dict, operator.or_]
+
+    list_fields = {
+        "messages",
+        "trigger_events",
+        "categorizations",
+        "responses",
+        "memory_docs",
+        "relevant_rules",
+    }
+    dict_fields = {
+        "user_info",
+        "crm_info",
+        "slack_participants",
+        "notified_assignees",
+        "autoresponse",
+        "issue",
+    }
+
+    def read_write(read: str, write: Sequence[str], input: State) -> dict:
+        val = input.get(read)
+        val = {val: val} if isinstance(val, str) else val
+        val_single = val[-1] if isinstance(val, list) else val
+        val_list = val if isinstance(val, list) else [val]
+        return {
+            k: val_list
+            if k in list_fields
+            else val_single
+            if k in dict_fields
+            else "".join(choice("abcdefghijklmnopqrstuvwxyz") for _ in range(n))
+            for k in write
+        }
+
+    builder = StateGraph(State)
+    builder.add_edge(START, "one")
+    builder.add_node(
+        "one",
+        partial(read_write, "messages", ["trigger_events", "primary_issue_medium"]),
+    )
+    builder.add_edge("one", "two")
+    builder.add_node(
+        "two",
+        partial(read_write, "trigger_events", ["autoresponse", "issue"]),
+    )
+    builder.add_edge("two", "three")
+    builder.add_edge("two", "four")
+    builder.add_node(
+        "three",
+        partial(read_write, "autoresponse", ["relevant_rules"]),
+    )
+    builder.add_node(
+        "four",
+        partial(
+            read_write,
+            "trigger_events",
+            ["categorizations", "responses", "memory_docs"],
+        ),
+    )
+    builder.add_node(
+        "five",
+        partial(
+            read_write,
+            "categorizations",
+            [
+                "user_info",
+                "crm_info",
+                "email_thread_id",
+                "slack_participants",
+                "bot_id",
+                "notified_assignees",
+            ],
+        ),
+    )
+    builder.add_edge(["three", "four"], "five")
+    builder.add_edge("five", "six")
+    builder.add_node(
+        "six",
+        partial(read_write, "responses", ["messages"]),
+    )
+    builder.add_conditional_edges(
+        "six", lambda state: END if len(state["messages"]) > n else "one"
+    )
+
+    return builder
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    import uvloop
+
+    from langgraph.checkpoint.memory import MemorySaver
+
+    graph = wide_dict(1000).compile(checkpointer=MemorySaver())
+    input = {
+        "messages": [
+            {
+                str(i) * 10: {
+                    str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                    for j in range(50)
+                }
+                for i in range(50)
+            }
+        ]
+    }
+    config = {"configurable": {"thread_id": "1"}, "recursion_limit": 20000000000}
+
+    async def run():
+        async for c in graph.astream(input, config=config):
+            print(c.keys())
+
+    uvloop.install()
+    asyncio.run(run())

libs/langgraph/bench/wide_state.py

@@ -1,6 +1,7 @@
 import operator
 from dataclasses import dataclass, field
 from functools import partial
+from random import choice
 from typing import Annotated, Optional, Sequence
 
 from langgraph.constants import END, START
@@ -49,12 +50,34 @@ def wide_state(n: int) -> StateGraph:
         """The ID of the bot user in the slack channel."""
         notified_assignees: Annotated[dict, operator.or_] = field(default_factory=dict)
 
+    list_fields = {
+        "messages",
+        "trigger_events",
+        "categorizations",
+        "responses",
+        "memory_docs",
+        "relevant_rules",
+    }
+    dict_fields = {
+        "user_info",
+        "crm_info",
+        "slack_participants",
+        "notified_assignees",
+        "autoresponse",
+        "issue",
+    }
+
     def read_write(read: str, write: Sequence[str], input: State) -> dict:
         val = getattr(input, read)
+        val = {val: val} if isinstance(val, str) else val
         val_single = val[-1] if isinstance(val, list) else val
         val_list = val if isinstance(val, list) else [val]
         return {
-            k: val_list if isinstance(getattr(input, k), list) else val_single
+            k: val_list
+            if k in list_fields
+            else val_single
+            if k in dict_fields
+            else "".join(choice("abcdefghijklmnopqrstuvwxyz") for _ in range(n))
             for k in write
         }
 

libs/langgraph/langgraph/graph/schema_utils.py

@@ -1,210 +1,55 @@
 import logging
 import weakref
-from inspect import isclass
 from typing import (
     Any,
     Callable,
     Optional,
     Type,
-    Union,
-    get_args,
-    get_origin,
-    get_type_hints,
 )
 
 from pydantic import BaseModel
 from pydantic.v1 import BaseModel as BaseModelV1
-from typing_extensions import Annotated
 
 logger = logging.getLogger(__name__)
 
 
-_cache: weakref.WeakKeyDictionary[Type[Any], dict[int, "SchemaCoercionMapper"]] = (
+_cache: weakref.WeakKeyDictionary[Type[Any], "SchemaCoercionMapper"] = (
     weakref.WeakKeyDictionary()
 )
 
 
 class SchemaCoercionMapper:
+    __slots__ = ("_inited", "schema", "_fields", "_construct", "_field_coercers")
+
     def __new__(
         cls,
         schema: Type[Any],
-        type_hints: Optional[dict[str, Any]] = None,
-        max_depth: int = 12,
+        **kwargs: Any,
     ) -> "SchemaCoercionMapper":
-        if schema not in _cache:
-            _cache[schema] = {}
-        if max_depth in _cache[schema]:
-            return _cache[schema][max_depth]
-
+        if schema in _cache:
+            return _cache[schema]
         inst = super().__new__(cls)
-        _cache[schema][max_depth] = inst
+        _cache[schema] = inst
         return inst
 
     def __init__(
         self,
         schema: Type[Any],
-        type_hints: Optional[dict[str, Any]] = None,
-        max_depth: int = 12,
+        **kwargs: Any,
     ):
         if hasattr(self, "_inited"):
             return
         self._inited = True
-        self.schema = schema
-        self.type_hints = (
-            type_hints
-            if type_hints is not None
-            else get_type_hints(schema, localns={schema.__name__: schema})
-        )
-        self.max_depth = max_depth
+        if issubclass(schema, BaseModelV1):
+            self._construct: Callable[..., Any] = schema.parse_obj
 
-        if issubclass(schema, BaseModel):
-            self._fields = {
-                n: self.type_hints.get(n, f.annotation)
-                for n, f in schema.model_fields.items()
-            }
-            self._construct: Callable[..., Any] = schema.model_construct
+        elif issubclass(schema, BaseModel):
+            self._construct = schema.model_validate
 
-        elif issubclass(schema, BaseModelV1):
-            self._fields = {
-                n: self.type_hints.get(n, f.annotation)
-                for n, f in schema.__fields__.items()
-            }
-            self._construct = schema.construct
         else:
             raise TypeError("Schema is neither valid Pydantic v1 nor v2 model.")
-        self._field_coercers: Optional[dict[str, Callable[[Any, Any], Any]]] = None
 
     def __call__(self, input_data: Any, depth: Optional[int] = None) -> Any:
-        return self.coerce(input_data, depth)
-
-    def coerce(self, input_data: Any, depth: Optional[int] = None) -> Any:
-        if depth is None:
-            depth = self.max_depth
-        if not isinstance(input_data, dict) or depth <= 0:
+        if not isinstance(input_data, dict):
             return input_data
-        processed = {}
-        if self._field_coercers is None:
-            self._field_coercers = {
-                n: self._build_coercer(t, depth - 1) for n, t in self._fields.items()
-            }
-        for k, v in input_data.items():
-            fn = self._field_coercers.get(k)
-            processed[k] = fn(v, depth - 1) if fn else v
-        return self._construct(**processed)
-
-    def _build_coercer(
-        self, field_type: Any, depth: int, throw: bool = False
-    ) -> Callable[[Any, Any], Any]:
-        if depth == 0:
-            return self._passthrough
-        origin = get_origin(field_type)
-
-        if origin is Annotated:
-            real_type, *_ = get_args(field_type)
-            sub = self._build_coercer(real_type, depth - 1)
-            return lambda v, d: sub(v, d)
-        if isclass(field_type):
-            is_class_ = True
-            try:
-                is_base_model = issubclass(field_type, BaseModel)
-            except TypeError:
-                is_class_ = False
-                is_base_model = False
-
-            if is_base_model:
-                mapper = SchemaCoercionMapper(field_type, max_depth=depth - 1)
-                return lambda v, d: mapper.coerce(v, d) if isinstance(v, dict) else v
-            if is_class_ and issubclass(field_type, BaseModelV1):
-                mapper = SchemaCoercionMapper(field_type, max_depth=depth - 1)
-                return lambda v, d: mapper.coerce(v, d) if isinstance(v, dict) else v
-        if origin is list or field_type is list:
-            args = get_args(field_type)
-            if len(args) != 1:
-                return lambda v, d: v
-            sub = self._build_coercer(args[0], depth - 1)
-
-            def list_coercer(v: Any, d: Any) -> Any:
-                if not isinstance(v, (list, tuple)):
-                    return v
-                return [sub(x, d - 1) for x in v]
-
-            return list_coercer
-        if origin is set or field_type is set:
-            args = get_args(field_type)
-            if len(args) != 1:
-                return lambda v, d: v
-            sub = self._build_coercer(args[0], depth - 1)
-
-            def set_coercer(v: Any, d: Any) -> Any:
-                if not isinstance(v, (list, tuple, set)):
-                    return v
-                return {sub(x, d - 1) for x in v}
-
-            return set_coercer
-        if origin is dict or field_type is dict:
-            args = get_args(field_type)
-            if len(args) != 2:
-
-                def dict_coercer(v: Any, d: Any) -> Any:
-                    if not isinstance(v, dict):
-                        if throw:
-                            raise TypeError("Expected dict, got %s" % type(v))
-                    return v
-
-                return dict_coercer
-            k_sub = self._build_coercer(args[0], depth - 1)
-            v_sub = self._build_coercer(args[1], depth - 1)
-
-            def dict_coercer(v: Any, d: Any) -> Any:
-                if not isinstance(v, dict):
-                    if throw:
-                        raise TypeError("Expected dict, got %s" % type(v))
-                    return v
-                return {k_sub(k, d - 1): v_sub(val, d - 1) for k, val in v.items()}
-
-            return dict_coercer
-
-        if origin is tuple:
-            targs = get_args(field_type)
-            if not targs:
-                return lambda v, d: v
-            subs = [self._build_coercer(a, depth - 1) for a in targs]
-
-            def tuple_coercer(v: Any, d: Any) -> Any:
-                if not isinstance(v, (list, tuple)):
-                    return v
-                out = []
-                for i, sp in enumerate(subs):
-                    out.append(sp(v[i] if i < len(v) else None, d - 1))
-                return tuple(out)
-
-            return tuple_coercer
-        if origin is Union:
-            uargs = get_args(field_type)
-            subs, none_in_union = [], False
-            for ix, arg in enumerate(uargs):
-                if arg is type(None):
-                    none_in_union = True
-                else:
-                    subs.append(
-                        self._build_coercer(arg, depth - 1, throw=ix < len(uargs) - 1)
-                    )
-
-            def union_coercer(v: Any, d: Any) -> Any:
-                if v is None and none_in_union:
-                    return None
-                err = None
-                for sp in subs:
-                    try:
-                        return sp(v, d - 1)
-                    except TypeError as e:
-                        err = e
-                if err:
-                    raise err
-                return v
-
-            return union_coercer
-        return self._passthrough
-
-    def _passthrough(self, v: Any, d: Any) -> Any:
-        return v
+        return self._construct(input_data)

libs/langgraph/langgraph/graph/state.py

@@ -1060,7 +1060,7 @@ def _pick_mapper(
         if issubclass(schema, dict):
             return None
         if issubclass(schema, (BaseModel, BaseModelV1)):
-            return SchemaCoercionMapper(schema, type_hints)
+            return SchemaCoercionMapper(schema)
     return partial(_coerce_state, schema)
 
 

libs/langgraph/langgraph/pregel/__init__.py

@@ -39,7 +39,6 @@ from langchain_core.runnables.utils import (
     ConfigurableFieldSpec,
     get_unique_config_specs,
 )
-from langchain_core.tracers._streaming import _StreamingCallbackHandler
 from pydantic import BaseModel
 from typing_extensions import Self
 
@@ -125,6 +124,11 @@ from langgraph.utils.fields import get_enhanced_type_hints
 from langgraph.utils.pydantic import create_model, is_supported_by_pydantic
 from langgraph.utils.queue import AsyncQueue, SyncQueue  # type: ignore[attr-defined]
 
+try:
+    from langchain_core.tracers._streaming import _StreamingCallbackHandler
+except ImportError:
+    _StreamingCallbackHandler = None  # type: ignore
+
 WriteValue = Union[Callable[[Input], Output], Any]
 
 
@@ -2529,13 +2533,17 @@ class Pregel(PregelProtocol):
             run_id=config.get("run_id"),
         )
         # if running from astream_log() run each proc with streaming
-        do_stream = next(
-            (
-                cast(_StreamingCallbackHandler, h)
-                for h in run_manager.handlers
-                if isinstance(h, _StreamingCallbackHandler)
-            ),
-            None,
+        do_stream = (
+            next(
+                (
+                    cast(_StreamingCallbackHandler, h)
+                    for h in run_manager.handlers
+                    if isinstance(h, _StreamingCallbackHandler)
+                ),
+                None,
+            )
+            if _StreamingCallbackHandler is not None
+            else False
         )
         try:
             # assign defaults

libs/langgraph/langgraph/pregel/messages.py

@@ -7,6 +7,7 @@ from typing import (
     List,
     Optional,
     Sequence,
+    TypeVar,
     Union,
     cast,
 )
@@ -15,11 +16,16 @@ from uuid import UUID, uuid4
 from langchain_core.callbacks import BaseCallbackHandler
 from langchain_core.messages import BaseMessage
 from langchain_core.outputs import ChatGenerationChunk, LLMResult
-from langchain_core.tracers._streaming import T, _StreamingCallbackHandler
 
 from langgraph.constants import NS_SEP, TAG_HIDDEN, TAG_NOSTREAM
 from langgraph.types import StreamChunk
 
+try:
+    from langchain_core.tracers._streaming import _StreamingCallbackHandler
+except ImportError:
+    _StreamingCallbackHandler = object  # type: ignore
+
+T = TypeVar("T")
 Meta = tuple[tuple[str, ...], dict[str, Any]]
 
 

libs/langgraph/langgraph/pregel/remote.py

@@ -10,7 +10,6 @@ from typing import (
     cast,
 )
 
-import orjson
 from langchain_core.runnables import RunnableConfig
 from langchain_core.runnables.graph import (
     Edge as DrawableEdge,
@@ -35,6 +34,8 @@ from typing_extensions import Self
 from langgraph.checkpoint.base import CheckpointMetadata
 from langgraph.constants import (
     CONF,
+    CONFIG_KEY_CHECKPOINT_ID,
+    CONFIG_KEY_CHECKPOINT_MAP,
     CONFIG_KEY_CHECKPOINT_NS,
     CONFIG_KEY_STREAM,
     INTERRUPT,
@@ -46,6 +47,14 @@ from langgraph.pregel.types import All, PregelTask, StateSnapshot, StreamMode
 from langgraph.types import Command, Interrupt, StreamProtocol
 from langgraph.utils.config import merge_configs
 
+CONF_DROPLIST = frozenset(
+    (
+        CONFIG_KEY_CHECKPOINT_MAP,
+        CONFIG_KEY_CHECKPOINT_ID,
+        CONFIG_KEY_CHECKPOINT_NS,
+    ),
+)
+
 
 class RemoteException(Exception):
     """Exception raised when an error occurs in the remote graph."""
@@ -290,47 +299,26 @@ class RemoteGraph(PregelProtocol):
         }
 
     def _sanitize_config(self, config: RunnableConfig) -> RunnableConfig:
-        reserved_configurable_keys = frozenset(
-            [
-                "callbacks",
-                "checkpoint_map",
-                "checkpoint_id",
-                "checkpoint_ns",
-            ]
-        )
-
-        def _sanitize_obj(obj: Any) -> Any:
-            """Remove non-JSON serializable fields from the given object."""
-            if isinstance(obj, dict):
-                return {k: _sanitize_obj(v) for k, v in obj.items()}
-            elif isinstance(obj, list):
-                return [_sanitize_obj(v) for v in obj]
-            else:
-                try:
-                    orjson.dumps(obj)
-                    return obj
-                except orjson.JSONEncodeError:
-                    return None
-
-        # Remove non-JSON serializable fields from the config.
-        config = _sanitize_obj(config)
-
-        # Only include configurable keys that are not reserved and
-        # not starting with "__pregel_" prefix.
-        new_configurable = {
-            k: v
-            for k, v in config["configurable"].items()
-            if k not in reserved_configurable_keys and not k.startswith("__pregel_")
-        }
-
-        sanitized: RunnableConfig = {
-            "tags": config.get("tags") or [],
-            "metadata": config.get("metadata") or {},
-            "configurable": new_configurable,
-        }
+        """Sanitize the config to remove non-serializable fields."""
+        sanitized: RunnableConfig = {}
         if "recursion_limit" in config:
             sanitized["recursion_limit"] = config["recursion_limit"]
-
+        if "tags" in config:
+            sanitized["tags"] = [tag for tag in config["tags"] if isinstance(tag, str)]
+        if "metadata" in config:
+            sanitized["metadata"] = {}
+            for k, v in config["metadata"].items():
+                if isinstance(k, str) and isinstance(v, (str, int, float, bool)):
+                    sanitized["metadata"][k] = v
+        if "configurable" in config:
+            sanitized["configurable"] = {}
+            for k, v in config["configurable"].items():
+                if (
+                    isinstance(k, str)
+                    and k not in CONF_DROPLIST
+                    and isinstance(v, (str, int, float, bool))
+                ):
+                    sanitized["configurable"][k] = v
         return sanitized
 
     def get_state(
@@ -654,9 +642,10 @@ class RemoteGraph(PregelProtocol):
             # raise interrupt or errors
             if chunk.event.startswith("updates"):
                 if isinstance(chunk.data, dict) and INTERRUPT in chunk.data:
-                    raise GraphInterrupt(
-                        [Interrupt(**i) for i in chunk.data[INTERRUPT]]
-                    )
+                    if caller_ns:
+                        raise GraphInterrupt(
+                            [Interrupt(**i) for i in chunk.data[INTERRUPT]]
+                        )
             elif chunk.event.startswith("error"):
                 raise RemoteException(chunk.data)
             # filter for what was actually requested
@@ -748,9 +737,10 @@ class RemoteGraph(PregelProtocol):
             # raise interrupt or errors
             if chunk.event.startswith("updates"):
                 if isinstance(chunk.data, dict) and INTERRUPT in chunk.data:
-                    raise GraphInterrupt(
-                        [Interrupt(**i) for i in chunk.data[INTERRUPT]]
-                    )
+                    if caller_ns:
+                        raise GraphInterrupt(
+                            [Interrupt(**i) for i in chunk.data[INTERRUPT]]
+                        )
             elif chunk.event.startswith("error"):
                 raise RemoteException(chunk.data)
             # filter for what was actually requested

libs/langgraph/langgraph/utils/runnable.py

@@ -36,7 +36,6 @@ from langchain_core.runnables.config import (
     var_child_runnable_config,
 )
 from langchain_core.runnables.utils import Input, Output
-from langchain_core.tracers._streaming import _StreamingCallbackHandler
 from typing_extensions import TypeGuard
 
 from langgraph.constants import (
@@ -54,6 +53,11 @@ from langgraph.utils.config import (
     patch_config,
 )
 
+try:
+    from langchain_core.tracers._streaming import _StreamingCallbackHandler
+except ImportError:
+    _StreamingCallbackHandler = None  # type: ignore
+
 
 def _set_config_context(
     config: RunnableConfig,
@@ -683,13 +687,15 @@ class RunnableSeq(Runnable):
                     iterator = step.stream(input, config, **kwargs)
                 else:
                     iterator = step.transform(iterator, config)
-            if stream_handler := next(
-                (
-                    cast(_StreamingCallbackHandler, h)
-                    for h in run_manager.handlers
-                    if isinstance(h, _StreamingCallbackHandler)
-                ),
-                None,
+            if _StreamingCallbackHandler is not None and (
+                stream_handler := next(
+                    (
+                        cast(_StreamingCallbackHandler, h)
+                        for h in run_manager.handlers
+                        if isinstance(h, _StreamingCallbackHandler)
+                    ),
+                    None,
+                )
             ):
                 # populates streamed_output in astream_log() output if needed
                 iterator = stream_handler.tap_output_iter(run_manager.run_id, iterator)
@@ -749,13 +755,15 @@ class RunnableSeq(Runnable):
                         aiterator = step.atransform(aiterator, config)
                     if hasattr(aiterator, "aclose"):
                         stack.push_async_callback(aiterator.aclose)
-                if stream_handler := next(
-                    (
-                        cast(_StreamingCallbackHandler, h)
-                        for h in run_manager.handlers
-                        if isinstance(h, _StreamingCallbackHandler)
-                    ),
-                    None,
+                if _StreamingCallbackHandler is not None and (
+                    stream_handler := next(
+                        (
+                            cast(_StreamingCallbackHandler, h)
+                            for h in run_manager.handlers
+                            if isinstance(h, _StreamingCallbackHandler)
+                        ),
+                        None,
+                    )
                 ):
                     # populates streamed_output in astream_log() output if needed
                     aiterator = stream_handler.tap_output_aiter(

libs/langgraph/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph"
-version = "0.3.25"
+version = "0.3.26"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 license = "MIT"

libs/langgraph/tests/test_remote_graph.py

@@ -437,15 +437,17 @@ def test_stream():
         sync_client=mock_sync_client,
     )
 
-    # stream modes doesn't include 'updates'
-    stream_parts = []
+    # test raising graph interrupt if invoked as a subgraph
     with pytest.raises(GraphInterrupt) as exc:
         for stream_part in remote_pregel.stream(
             {"input": "data"},
-            config={"configurable": {"thread_id": "thread_1"}},
+            # pretend we invoked this as a subgraph
+            config={
+                "configurable": {"thread_id": "thread_1", "checkpoint_ns": "some_ns"}
+            },
             stream_mode="values",
         ):
-            stream_parts.append(stream_part)
+            pass
 
     assert exc.value.args[0] == [
         Interrupt(
@@ -456,6 +458,15 @@ def test_stream():
         )
     ]
 
+    # stream modes doesn't include 'updates'
+    stream_parts = []
+    for stream_part in remote_pregel.stream(
+        {"input": "data"},
+        config={"configurable": {"thread_id": "thread_1"}},
+        stream_mode="values",
+    ):
+        stream_parts.append(stream_part)
+
     assert stream_parts == [
         {"chunk": "data1"},
         {"chunk": "data2"},
@@ -470,62 +481,62 @@ def test_stream():
 
     # default stream_mode is updates
     stream_parts = []
-    with pytest.raises(GraphInterrupt):
-        for stream_part in remote_pregel.stream(
-            {"input": "data"},
-            config={"configurable": {"thread_id": "thread_1"}},
-        ):
-            stream_parts.append(stream_part)
+    for stream_part in remote_pregel.stream(
+        {"input": "data"},
+        config={"configurable": {"thread_id": "thread_1"}},
+    ):
+        stream_parts.append(stream_part)
 
     assert stream_parts == [
         {"chunk": "data3"},
         {"chunk": "data4"},
+        {"__interrupt__": ()},
     ]
 
     # list stream_mode includes mode names
     stream_parts = []
-    with pytest.raises(GraphInterrupt):
-        for stream_part in remote_pregel.stream(
-            {"input": "data"},
-            config={"configurable": {"thread_id": "thread_1"}},
-            stream_mode=["updates"],
-        ):
-            stream_parts.append(stream_part)
+    for stream_part in remote_pregel.stream(
+        {"input": "data"},
+        config={"configurable": {"thread_id": "thread_1"}},
+        stream_mode=["updates"],
+    ):
+        stream_parts.append(stream_part)
 
     assert stream_parts == [
         ("updates", {"chunk": "data3"}),
         ("updates", {"chunk": "data4"}),
+        ("updates", {"__interrupt__": ()}),
     ]
 
     # subgraphs + list modes
     stream_parts = []
-    with pytest.raises(GraphInterrupt):
-        for stream_part in remote_pregel.stream(
-            {"input": "data"},
-            config={"configurable": {"thread_id": "thread_1"}},
-            stream_mode=["updates"],
-            subgraphs=True,
-        ):
-            stream_parts.append(stream_part)
+    for stream_part in remote_pregel.stream(
+        {"input": "data"},
+        config={"configurable": {"thread_id": "thread_1"}},
+        stream_mode=["updates"],
+        subgraphs=True,
+    ):
+        stream_parts.append(stream_part)
 
     assert stream_parts == [
         ((), "updates", {"chunk": "data3"}),
         ((), "updates", {"chunk": "data4"}),
+        ((), "updates", {"__interrupt__": ()}),
     ]
 
     # subgraphs + single mode
     stream_parts = []
-    with pytest.raises(GraphInterrupt):
-        for stream_part in remote_pregel.stream(
-            {"input": "data"},
-            config={"configurable": {"thread_id": "thread_1"}},
-            subgraphs=True,
-        ):
-            stream_parts.append(stream_part)
+    for stream_part in remote_pregel.stream(
+        {"input": "data"},
+        config={"configurable": {"thread_id": "thread_1"}},
+        subgraphs=True,
+    ):
+        stream_parts.append(stream_part)
 
     assert stream_parts == [
         ((), {"chunk": "data3"}),
         ((), {"chunk": "data4"}),
+        ((), {"__interrupt__": ()}),
     ]
 
 
@@ -561,15 +572,17 @@ async def test_astream():
         client=mock_async_client,
     )
 
-    # stream modes doesn't include 'updates'
-    stream_parts = []
+    # test raising graph interrupt if invoked as a subgraph
     with pytest.raises(GraphInterrupt) as exc:
         async for stream_part in remote_pregel.astream(
             {"input": "data"},
-            config={"configurable": {"thread_id": "thread_1"}},
+            # pretend we invoked this as a subgraph
+            config={
+                "configurable": {"thread_id": "thread_1", "checkpoint_ns": "some_ns"}
+            },
             stream_mode="values",
         ):
-            stream_parts.append(stream_part)
+            pass
 
     assert exc.value.args[0] == [
         Interrupt(
@@ -580,6 +593,15 @@ async def test_astream():
         )
     ]
 
+    # stream modes doesn't include 'updates'
+    stream_parts = []
+    async for stream_part in remote_pregel.astream(
+        {"input": "data"},
+        config={"configurable": {"thread_id": "thread_1"}},
+        stream_mode="values",
+    ):
+        stream_parts.append(stream_part)
+
     assert stream_parts == [
         {"chunk": "data1"},
         {"chunk": "data2"},
@@ -596,62 +618,62 @@ async def test_astream():
 
     # default stream_mode is updates
     stream_parts = []
-    with pytest.raises(GraphInterrupt):
-        async for stream_part in remote_pregel.astream(
-            {"input": "data"},
-            config={"configurable": {"thread_id": "thread_1"}},
-        ):
-            stream_parts.append(stream_part)
+    async for stream_part in remote_pregel.astream(
+        {"input": "data"},
+        config={"configurable": {"thread_id": "thread_1"}},
+    ):
+        stream_parts.append(stream_part)
 
     assert stream_parts == [
         {"chunk": "data3"},
         {"chunk": "data4"},
+        {"__interrupt__": ()},
     ]
 
     # list stream_mode includes mode names
     stream_parts = []
-    with pytest.raises(GraphInterrupt):
-        async for stream_part in remote_pregel.astream(
-            {"input": "data"},
-            config={"configurable": {"thread_id": "thread_1"}},
-            stream_mode=["updates"],
-        ):
-            stream_parts.append(stream_part)
+    async for stream_part in remote_pregel.astream(
+        {"input": "data"},
+        config={"configurable": {"thread_id": "thread_1"}},
+        stream_mode=["updates"],
+    ):
+        stream_parts.append(stream_part)
 
     assert stream_parts == [
         ("updates", {"chunk": "data3"}),
         ("updates", {"chunk": "data4"}),
+        ("updates", {"__interrupt__": ()}),
     ]
 
     # subgraphs + list modes
     stream_parts = []
-    with pytest.raises(GraphInterrupt):
-        async for stream_part in remote_pregel.astream(
-            {"input": "data"},
-            config={"configurable": {"thread_id": "thread_1"}},
-            stream_mode=["updates"],
-            subgraphs=True,
-        ):
-            stream_parts.append(stream_part)
+    async for stream_part in remote_pregel.astream(
+        {"input": "data"},
+        config={"configurable": {"thread_id": "thread_1"}},
+        stream_mode=["updates"],
+        subgraphs=True,
+    ):
+        stream_parts.append(stream_part)
 
     assert stream_parts == [
         ((), "updates", {"chunk": "data3"}),
         ((), "updates", {"chunk": "data4"}),
+        ((), "updates", {"__interrupt__": ()}),
     ]
 
     # subgraphs + single mode
     stream_parts = []
-    with pytest.raises(GraphInterrupt):
-        async for stream_part in remote_pregel.astream(
-            {"input": "data"},
-            config={"configurable": {"thread_id": "thread_1"}},
-            subgraphs=True,
-        ):
-            stream_parts.append(stream_part)
+    async for stream_part in remote_pregel.astream(
+        {"input": "data"},
+        config={"configurable": {"thread_id": "thread_1"}},
+        subgraphs=True,
+    ):
+        stream_parts.append(stream_part)
 
     assert stream_parts == [
         ((), {"chunk": "data3"}),
         ((), {"chunk": "data4"}),
+        ((), {"__interrupt__": ()}),
     ]
 
     async_iter = MagicMock()
@@ -664,33 +686,33 @@ async def test_astream():
 
     # subgraphs + list modes
     stream_parts = []
-    with pytest.raises(GraphInterrupt):
-        async for stream_part in remote_pregel.astream(
-            {"input": "data"},
-            config={"configurable": {"thread_id": "thread_1"}},
-            stream_mode=["updates"],
-            subgraphs=True,
-        ):
-            stream_parts.append(stream_part)
+    async for stream_part in remote_pregel.astream(
+        {"input": "data"},
+        config={"configurable": {"thread_id": "thread_1"}},
+        stream_mode=["updates"],
+        subgraphs=True,
+    ):
+        stream_parts.append(stream_part)
 
     assert stream_parts == [
         (("my", "subgraph"), "updates", {"chunk": "data3"}),
         (("hello", "subgraph"), "updates", {"chunk": "data4"}),
+        (("bye", "subgraph"), "updates", {"__interrupt__": ()}),
     ]
 
     # subgraphs + single mode
     stream_parts = []
-    with pytest.raises(GraphInterrupt):
-        async for stream_part in remote_pregel.astream(
-            {"input": "data"},
-            config={"configurable": {"thread_id": "thread_1"}},
-            subgraphs=True,
-        ):
-            stream_parts.append(stream_part)
+    async for stream_part in remote_pregel.astream(
+        {"input": "data"},
+        config={"configurable": {"thread_id": "thread_1"}},
+        subgraphs=True,
+    ):
+        stream_parts.append(stream_part)
 
     assert stream_parts == [
         (("my", "subgraph"), {"chunk": "data3"}),
         (("hello", "subgraph"), {"chunk": "data4"}),
+        (("bye", "subgraph"), {"__interrupt__": ()}),
     ]
 
 

libs/sdk-js/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@langchain/langgraph-sdk",
-  "version": "0.0.62",
+  "version": "0.0.63",
   "description": "Client library for interacting with the LangGraph API",
   "type": "module",
   "packageManager": "yarn@1.22.19",

libs/sdk-js/src/client.ts

@@ -340,6 +340,7 @@ export class AssistantsClient extends BaseClient {
     assistantId?: string;
     ifExists?: OnConflictBehavior;
     name?: string;
+    description?: string;
   }): Promise<Assistant> {
     return this.fetch<Assistant>("/assistants", {
       method: "POST",
@@ -350,6 +351,7 @@ export class AssistantsClient extends BaseClient {
         assistant_id: payload.assistantId,
         if_exists: payload.ifExists,
         name: payload.name,
+        description: payload.description,
       },
     });
   }
@@ -367,6 +369,7 @@ export class AssistantsClient extends BaseClient {
       config?: Config;
       metadata?: Metadata;
       name?: string;
+      description?: string;
     },
   ): Promise<Assistant> {
     return this.fetch<Assistant>(`/assistants/${assistantId}`, {
@@ -376,6 +379,7 @@ export class AssistantsClient extends BaseClient {
         config: payload.config,
         metadata: payload.metadata,
         name: payload.name,
+        description: payload.description,
       },
     });
   }

libs/sdk-js/src/schema.ts

@@ -113,6 +113,9 @@ export interface AssistantBase {
 
   /** The name of the assistant */
   name: string;
+
+  /** The description of the assistant */
+  description?: string;
 }
 
 export interface AssistantVersion extends AssistantBase {}