langgraph: separate prebuilt into a standalone package (#3589)

.github/workflows/ci.yml

@@ -39,6 +39,7 @@ jobs:
               - 'libs/checkpoint-sqlite/**'
               - 'libs/checkpoint-postgres/**'
               - 'libs/scheduler-kafka/**'
+              - 'libs/prebuilt/**'
             sdk-js:
               - 'libs/sdk-js/**'
 
@@ -56,6 +57,7 @@ jobs:
             "libs/checkpoint-sqlite",
             "libs/checkpoint-postgres",
             "libs/scheduler-kafka",
+            "libs/prebuilt",
           ]
     if: needs.changes.outputs.python == 'true'
     uses: ./.github/workflows/_lint.yml
@@ -74,6 +76,7 @@ jobs:
             "libs/checkpoint",
             "libs/checkpoint-sqlite",
             "libs/checkpoint-postgres",
+            "libs/prebuilt",
           ]
     if: needs.changes.outputs.python == 'true'
     uses: ./.github/workflows/_test.yml

.github/workflows/release.yml

@@ -195,7 +195,7 @@ jobs:
               "$PKG_NAME==$VERSION" \
           )
 
-          if [[ "$PKG_NAME" == *checkpoint* ]]; then
+          if [[ "$PKG_NAME" == *checkpoint* || "$PKG_NAME" == *prebuilt* ]]; then
             # since checkpoint packages are namespace packages, import them with . convention
             # i.e. import langgraph.checkpoint or langgraph.checkpoint.sqlite
             IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/./g)"

.github/workflows/run_notebooks.yml

@@ -22,7 +22,7 @@ jobs:
       matrix:
         lib-version:
           - "development"
-          - "latest"
+          # - "latest"
 
     steps:
       - uses: actions/checkout@v4

README.md

@@ -70,7 +70,7 @@ pip install -U langgraph
 Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!
 
 ```shell
-pip install langchain-anthropic
+pip install langgraph-prebuilt langchain-anthropic
 ```
 
 ```shell

docs/docs/how-tos/agent-handoffs.ipynb

@@ -78,7 +78,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-anthropic"
+    "%pip install -U langgraph langgraph-prebuilt langchain-anthropic"
    ]
   },
   {

docs/docs/how-tos/async.ipynb

@@ -67,7 +67,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain_anthropic"
+    "%pip install --quiet -U langgraph langgraph-prebuilt langchain_anthropic"
    ]
   },
   {

docs/docs/how-tos/create-react-agent-hitl.ipynb

@@ -59,7 +59,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai"
    ]
   },
   {

docs/docs/how-tos/create-react-agent-memory.ipynb

@@ -64,7 +64,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai"
    ]
   },
   {

docs/docs/how-tos/create-react-agent-structured-output.ipynb

@@ -52,7 +52,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai"
    ]
   },
   {

docs/docs/how-tos/create-react-agent-system-prompt.ipynb

@@ -60,7 +60,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai"
    ]
   },
   {

docs/docs/how-tos/create-react-agent.ipynb

@@ -68,7 +68,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai"
    ]
   },
   {

docs/docs/how-tos/memory/semantic-search.ipynb

@@ -21,7 +21,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai langchain"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai langchain"
    ]
   },
   {

docs/docs/how-tos/multi-agent-multi-turn-convo-functional.ipynb

@@ -89,7 +89,7 @@
    "outputs": [],
    "source": [
     "# %%capture --no-stderr\n",
-    "# %pip install -U langgraph langchain-anthropic"
+    "# %pip install -U langgraph langgraph-prebuilt langchain-anthropic"
    ]
   },
   {

docs/docs/how-tos/multi-agent-multi-turn-convo.ipynb

@@ -72,7 +72,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-anthropic"
+    "%pip install -U langgraph langgraph-prebuilt langchain-anthropic"
    ]
   },
   {

docs/docs/how-tos/multi-agent-network-functional.ipynb

@@ -79,7 +79,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-anthropic"
+    "%pip install -U langgraph langgraph-prebuilt langchain-anthropic"
    ]
   },
   {

docs/docs/how-tos/multi-agent-network.ipynb

@@ -57,7 +57,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-anthropic"
+    "%pip install -U langgraph langgraph-prebuilt langchain-anthropic"
    ]
   },
   {

docs/docs/how-tos/pass-config-to-tools.ipynb

@@ -70,7 +70,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain_anthropic"
+    "%pip install --quiet -U langgraph langgraph-prebuilt langchain_anthropic"
    ]
   },
   {

docs/docs/how-tos/pass-run-time-values-to-tools.ipynb

@@ -110,7 +110,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain-openai"
+    "%pip install --quiet -U langgraph langgraph-prebuilt langchain-openai"
    ]
   },
   {

docs/docs/how-tos/persistence_mongodb.ipynb

@@ -65,7 +65,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U pymongo langgraph langgraph-checkpoint-mongodb"
+    "%pip install -U pymongo langgraph langgraph-prebuilt langgraph-checkpoint-mongodb"
    ]
   },
   {

docs/docs/how-tos/persistence_postgres.ipynb

@@ -44,8 +44,7 @@
     "...\n",
     "```\n",
     "\n",
-    "!!! info \"Setup\"",
-    "\n",
+    "!!! info \"Setup\"\n",
     "    You need to run `.setup()` once on your checkpointer to initialize the database before you can use it."
    ]
   },
@@ -70,7 +69,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U psycopg psycopg-pool langgraph langgraph-checkpoint-postgres"
+    "%pip install -U psycopg psycopg-pool langgraph langgraph-prebuilt langgraph-checkpoint-postgres"
    ]
   },
   {

docs/docs/how-tos/persistence_redis.ipynb

@@ -70,7 +70,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U redis langgraph langchain_openai"
+    "%pip install -U redis langgraph langgraph-prebuilt langchain_openai"
    ]
   },
   {

docs/docs/how-tos/react-agent-from-scratch.ipynb

@@ -28,7 +28,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai"
    ]
   },
   {

docs/docs/how-tos/run-id-langsmith.ipynb

@@ -81,7 +81,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain_openai"
+    "%pip install --quiet -U langgraph langgraph-prebuilt langchain_openai"
    ]
   },
   {

docs/docs/how-tos/stream-multiple.ipynb

@@ -34,7 +34,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai langchain-community"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai langchain-community"
    ]
   },
   {

docs/docs/how-tos/streaming-events-from-within-tools.ipynb

@@ -93,7 +93,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai"
    ]
   },
   {

docs/docs/how-tos/update-state-from-tools.ipynb

@@ -66,7 +66,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai"
    ]
   },
   {

docs/docs/tutorials/multi_agent/agent_supervisor.ipynb

@@ -35,7 +35,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain_community langchain_anthropic langchain_experimental"
+    "%pip install -U langgraph langgraph-prebuilt langchain_community langchain_anthropic langchain_experimental"
    ]
   },
   {

docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb

@@ -49,7 +49,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain_community langchain_anthropic langchain_experimental"
+    "%pip install -U langgraph langgraph-prebuilt langchain_community langchain_anthropic langchain_experimental"
    ]
   },
   {

docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb

@@ -37,7 +37,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langchain_community langchain_anthropic langchain_experimental matplotlib langgraph"
+    "%pip install -U langchain_community langchain_anthropic langchain_experimental matplotlib langgraph langgraph-prebuilt"
    ]
   },
   {

docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb

@@ -51,7 +51,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain-community langchain-openai tavily-python"
+    "%pip install --quiet -U langgraph langgraph-prebuilt langchain-community langchain-openai tavily-python"
    ]
   },
   {

docs/poetry.lock

@@ -3507,17 +3507,17 @@ langchain-core = ">=0.3.34,<1.0.0"
 
 [[package]]
 name = "langgraph"
-version = "0.2.74"
+version = "0.3.0"
 description = "Building stateful, multi-actor applications with LLMs"
 optional = false
 python-versions = ">=3.9.0,<4.0"
-groups = ["docs", "test"]
+groups = ["docs"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = []
 develop = true
 
 [package.dependencies]
-langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.20,!=0.3.21,!=0.3.22"
+langchain-core = ">=0.1,<0.4"
 langgraph-checkpoint = "^2.0.10"
 langgraph-sdk = "^0.1.42"
 
@@ -3546,22 +3546,21 @@ url = "../libs/checkpoint"
 
 [[package]]
 name = "langgraph-checkpoint-mongodb"
-version = "0.1.0"
+version = "0.1.1"
 description = "Library with a MongoDB implementation of LangGraph checkpoint saver."
 optional = false
-python-versions = "<4.0.0,>=3.9.0"
+python-versions = ">=3.9"
 groups = ["test"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "langgraph_checkpoint_mongodb-0.1.0-py3-none-any.whl", hash = "sha256:52f20956b36e0275ff805a1eea1db4c1a7e5e0ffe0a1ade65969004fa1654703"},
-    {file = "langgraph_checkpoint_mongodb-0.1.0.tar.gz", hash = "sha256:3165c134ad5c82a3fe02fef04c81dcd48a3f5d031e07a9d1cb84457241f76793"},
+    {file = "langgraph_checkpoint_mongodb-0.1.1-py3-none-any.whl", hash = "sha256:1ff2c3cb2a9139c38ea9cf398659b8b32d6bbfcc4999713b62014431477c5ac5"},
+    {file = "langgraph_checkpoint_mongodb-0.1.1.tar.gz", hash = "sha256:350d347b0458fb7977231ac1295095bef512458ee0debe09fd394d913b8d89d3"},
 ]
 
 [package.dependencies]
-langgraph = ">=0.2.38,<0.3.0"
-langgraph-checkpoint = ">=2.0.0,<3.0.0"
+langgraph-checkpoint = ">=2.0.0"
 motor = ">3.5.0"
-pymongo = ">=4.9.0,<4.10.0"
+pymongo = ">=4.9,<4.12"
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
@@ -3603,13 +3602,33 @@ langgraph-checkpoint = "^2.0.15"
 type = "directory"
 url = "../libs/checkpoint-sqlite"
 
+[[package]]
+name = "langgraph-prebuilt"
+version = "1.0.0"
+description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
+optional = false
+python-versions = "^3.9.0,<4.0"
+groups = ["docs"]
+markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
+files = []
+develop = true
+
+[package.dependencies]
+langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.20,!=0.3.21,!=0.3.22"
+langgraph = ">=0.3,<0.4"
+langgraph-checkpoint = "^2.0.10"
+
+[package.source]
+type = "directory"
+url = "../libs/prebuilt"
+
 [[package]]
 name = "langgraph-sdk"
 version = "0.1.53"
 description = "SDK for interacting with LangGraph API"
 optional = false
 python-versions = "^3.9.0,<4.0"
-groups = ["docs", "test"]
+groups = ["docs"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = []
 develop = true
@@ -8631,4 +8650,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = "^3.10"
-content-hash = "6dce741bb0e3d73af45d234fb1605d97f1bbf23c2e71e0a456cc6987d67554e7"
+content-hash = "ac9af57c6abaddd1f181551a7bb8194ef3e4491391a0f2dc71417d68e85cb5b3"

docs/pyproject.toml

@@ -13,6 +13,7 @@ hub = "^3.0.1"
 
 [tool.poetry.group.docs.dependencies]
 langgraph = { path = "../libs/langgraph/", develop = true }
+langgraph-prebuilt = {path = "../libs/prebuilt", develop = true}
 langgraph-checkpoint = { path = "../libs/checkpoint/", develop = true }
 langgraph-checkpoint-sqlite = { path = "../libs/checkpoint-sqlite", develop = true }
 langgraph-checkpoint-postgres = { path = "../libs/checkpoint-postgres", develop = true }

libs/langgraph/README.md

@@ -70,7 +70,7 @@ pip install -U langgraph
 Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!
 
 ```shell
-pip install langchain-anthropic
+pip install langgraph-prebuilt langchain-anthropic
 ```
 
 ```shell

libs/langgraph/langgraph/prebuilt/tool_executor.py

@@ -1,134 +0,0 @@
-from typing import Any, Callable, Sequence, Union
-
-from langchain_core.load.serializable import Serializable
-from langchain_core.runnables import RunnableConfig
-from langchain_core.tools import BaseTool
-from langchain_core.tools import tool as create_tool
-
-from langgraph._api.deprecation import deprecated
-from langgraph.utils.runnable import RunnableCallable
-
-INVALID_TOOL_MSG_TEMPLATE = (
-    "{requested_tool_name} is not a valid tool, "
-    "try one of [{available_tool_names_str}]."
-)
-
-
-@deprecated("0.2.0", "langgraph.prebuilt.ToolNode", removal="0.3.0")
-class ToolInvocationInterface:
-    """Interface for invoking a tool.
-
-    Attributes:
-        tool (str): The name of the tool to invoke.
-        tool_input (Union[str, dict]): The input to pass to the tool.
-
-    """
-
-    tool: str
-    tool_input: Union[str, dict]
-
-
-@deprecated("0.2.0", "langgraph.prebuilt.ToolNode", removal="0.3.0")
-class ToolInvocation(Serializable):
-    """Information about how to invoke a tool.
-
-    Attributes:
-        tool (str): The name of the Tool to execute.
-        tool_input (Union[str, dict]): The input to pass in to the Tool.
-
-    Examples:
-        Basic usage:
-        ```pycon
-        >>> invocation = ToolInvocation(
-        ...    tool="search",
-        ...     tool_input="What is the capital of France?"
-        ... )
-        ```
-    """
-
-    tool: str
-    tool_input: Union[str, dict]
-
-
-@deprecated("0.2.0", "langgraph.prebuilt.ToolNode", removal="0.3.0")
-class ToolExecutor(RunnableCallable):
-    """Executes a tool invocation.
-
-    Args:
-        tools (Sequence[BaseTool]): A sequence of tools that can be invoked.
-        invalid_tool_msg_template (str, optional): The template for the error message
-            when an invalid tool is requested. Defaults to INVALID_TOOL_MSG_TEMPLATE.
-
-    Examples:
-        Basic usage:
-
-        ```pycon
-        >>> from langchain_core.tools import tool
-        >>> from langgraph.prebuilt.tool_executor import ToolExecutor, ToolInvocation
-        ...
-        ...
-        >>> @tool
-        ... def search(query: str) -> str:
-        ...     \"\"\"Search engine.\"\"\"
-        ...     return f"Searching for: {query}"
-        ...
-        ...
-        >>> tools = [search]
-        >>> executor = ToolExecutor(tools)
-        ...
-        >>> invocation = ToolInvocation(tool="search", tool_input="What is the capital of France?")
-        >>> result = executor.invoke(invocation)
-        >>> print(result)
-        "Searching for: What is the capital of France?"
-        ```
-        Handling invalid tool:
-
-        ```pycon
-        >>> invocation = ToolInvocation(
-        ...     tool="nonexistent", tool_input="What is the capital of France?"
-        ... )
-        >>> result = executor.invoke(invocation)
-        >>> print(result)
-        "nonexistent is not a valid tool, try one of [search]."
-        ```
-    """
-
-    def __init__(
-        self,
-        tools: Sequence[Union[BaseTool, Callable]],
-        *,
-        invalid_tool_msg_template: str = INVALID_TOOL_MSG_TEMPLATE,
-    ) -> None:
-        super().__init__(self._execute, afunc=self._aexecute, trace=False)
-        tools_ = [
-            tool if isinstance(tool, BaseTool) else create_tool(tool) for tool in tools
-        ]
-        self.tools = tools_
-        self.tool_map = {t.name: t for t in tools_}
-        self.invalid_tool_msg_template = invalid_tool_msg_template
-
-    def _execute(
-        self, tool_invocation: ToolInvocationInterface, config: RunnableConfig
-    ) -> Any:
-        if tool_invocation.tool not in self.tool_map:
-            return self.invalid_tool_msg_template.format(
-                requested_tool_name=tool_invocation.tool,
-                available_tool_names_str=", ".join([t.name for t in self.tools]),
-            )
-        else:
-            tool = self.tool_map[tool_invocation.tool]
-            output = tool.invoke(tool_invocation.tool_input, config)
-            return output
-
-    async def _aexecute(
-        self, tool_invocation: ToolInvocationInterface, config: RunnableConfig
-    ) -> Any:
-        if tool_invocation.tool not in self.tool_map:
-            return self.invalid_tool_msg_template.format(
-                requested_tool_name=tool_invocation.tool,
-                available_tool_names_str=", ".join([t.name for t in self.tools]),
-            )
-        else:
-            tool = self.tool_map[tool_invocation.tool]
-            output = await tool.ainvoke(tool_invocation.tool_input, config)
-            return output

libs/langgraph/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.0.0 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.
 
 [[package]]
 name = "aiosqlite"
@@ -1348,7 +1348,7 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.10"
+version = "2.0.16"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -1366,7 +1366,7 @@ url = "../checkpoint"
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.12"
+version = "2.0.15"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -1375,7 +1375,7 @@ files = []
 develop = true
 
 [package.dependencies]
-langgraph-checkpoint = "^2.0.10"
+langgraph-checkpoint = "^2.0.15"
 orjson = ">=3.10.1"
 psycopg = "^3.2.0"
 psycopg-pool = "^3.2.0"
@@ -1386,7 +1386,7 @@ url = "../checkpoint-postgres"
 
 [[package]]
 name = "langgraph-checkpoint-sqlite"
-version = "2.0.3"
+version = "2.0.5"
 description = "Library with a SQLite implementation of LangGraph checkpoint saver."
 optional = false
 python-versions = "^3.9.0"
@@ -1396,15 +1396,34 @@ develop = true
 
 [package.dependencies]
 aiosqlite = "^0.20.0"
-langgraph-checkpoint = "^2.0.10"
+langgraph-checkpoint = "^2.0.15"
 
 [package.source]
 type = "directory"
 url = "../checkpoint-sqlite"
 
+[[package]]
+name = "langgraph-prebuilt"
+version = "1.0.0"
+description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
+optional = false
+python-versions = "^3.9.0,<4.0"
+groups = ["dev"]
+files = []
+develop = true
+
+[package.dependencies]
+langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.20,!=0.3.21,!=0.3.22"
+langgraph = ">=0.3,<0.4"
+langgraph-checkpoint = "^2.0.10"
+
+[package.source]
+type = "directory"
+url = "../prebuilt"
+
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.51"
+version = "0.1.53"
 description = "SDK for interacting with LangGraph API"
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -3491,4 +3510,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9.0,<4.0"
-content-hash = "caf943b02b6913c05d15c37fda6d216669f789e2a059b7e8e2490b2bdcd23e0e"
+content-hash = "967adb1fe0f48b76a53b79c8604768b2ce74042f88d1791f57ea1ed0a0ede0ad"

libs/langgraph/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph"
-version = "0.2.76"
+version = "0.3.0"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 license = "MIT"
@@ -9,7 +9,7 @@ repository = "https://www.github.com/langchain-ai/langgraph"
 
 [tool.poetry.dependencies]
 python = ">=3.9.0,<4.0"
-langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.20,!=0.3.21,!=0.3.22"
+langchain-core = ">=0.1,<0.4"
 langgraph-checkpoint = "^2.0.10"
 langgraph-sdk = "^0.1.42"
 
@@ -26,6 +26,7 @@ ruff = "^0.6.2"
 jupyter = "^1.0.0"
 pytest-xdist = {extras = ["psutil"], version = "^3.6.1"}
 pytest-repeat = "^0.9.3"
+langgraph-prebuilt = {path = "../prebuilt", develop = true}
 langgraph-checkpoint = {path = "../checkpoint", develop = true}
 langgraph-checkpoint-sqlite = {path = "../checkpoint-sqlite", develop = true}
 langgraph-checkpoint-postgres = {path = "../checkpoint-postgres", develop = true}

libs/prebuilt/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

libs/prebuilt/Makefile

@@ -0,0 +1,78 @@
+.PHONY: all format lint test test_watch integration_tests spell_check spell_fix benchmark profile
+
+# Default target executed when no arguments are given to make.
+all: help
+
+######################
+# TESTING AND COVERAGE
+######################
+
+start-postgres:
+	docker compose -f tests/compose-postgres.yml up -V --force-recreate --wait --remove-orphans
+
+stop-postgres:
+	docker compose -f tests/compose-postgres.yml down -v
+
+TEST ?= .
+
+test:
+	make start-postgres && poetry run pytest $(TEST); \
+	EXIT_CODE=$$?; \
+	make stop-postgres; \
+	exit $$EXIT_CODE
+
+test_watch:
+	make start-postgres && poetry run ptw $(TEST); \
+	EXIT_CODE=$$?; \
+	make stop-postgres; \
+	exit $$EXIT_CODE
+
+######################
+# LINTING AND FORMATTING
+######################
+
+# Define a variable for Python and notebook files.
+PYTHON_FILES=.
+MYPY_CACHE=.mypy_cache
+lint format: PYTHON_FILES=.
+lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --relative --diff-filter=d main . | grep -E '\.py$$|\.ipynb$$')
+lint_package: PYTHON_FILES=langgraph
+lint_tests: PYTHON_FILES=tests
+lint_tests: MYPY_CACHE=.mypy_cache_test
+
+lint lint_diff lint_package lint_tests:
+	poetry run ruff check .
+	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
+	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE)
+	[ "$(PYTHON_FILES)" = "" ] || poetry run mypy langgraph --cache-dir $(MYPY_CACHE)
+
+format format_diff:
+	poetry run ruff format $(PYTHON_FILES)
+	poetry run ruff check --select I --fix $(PYTHON_FILES)
+
+spell_check:
+	poetry run codespell --toml pyproject.toml
+
+spell_fix:
+	poetry run codespell --toml pyproject.toml -w
+
+
+######################
+# HELP
+######################
+
+help:
+	@echo '===================='
+	@echo '-- DOCUMENTATION --'
+	
+	@echo '-- LINTING --'
+	@echo 'format                       - run code formatters'
+	@echo 'lint                         - run linters'
+	@echo 'spell_check               	- run codespell on the project'
+	@echo 'spell_fix               		- run codespell on the project and fix the errors'
+	@echo '-- TESTS --'
+	@echo 'coverage                     - run unit tests and generate coverage report'
+	@echo 'test                         - run unit tests'
+	@echo 'test TEST_FILE=<test_file>   - run all tests in file'
+	@echo 'test_watch                   - run unit tests in watch mode'

libs/prebuilt/README.md

@@ -0,0 +1,120 @@
+# LangGraph Prebuilt
+
+This library defines high-level APIs for creating and executing LangGraph agents and tools.
+
+## Installation
+
+```bash
+pip install langgraph-prebuilt
+```
+
+## Agents
+
+`langgraph-prebuilt` provides an [implementation](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent) of a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent - `create_react_agent`:
+
+```bash
+pip install langchain-anthropic
+```
+
+```python
+from langchain_anthropic import ChatAnthropic
+from langgraph.prebuilt import create_react_agent
+
+# Define the tools for the agent to use
+def search(query: str):
+    """Call to surf the web."""
+    # This is a placeholder, but don't tell the LLM that...
+    if "sf" in query.lower() or "san francisco" in query.lower():
+        return "It's 60 degrees and foggy."
+    return "It's 90 degrees and sunny."
+
+tools = [search]
+model = ChatAnthropic(model="claude-3-7-sonnet-latest")
+
+app = create_react_agent(model, tools)
+# run the agent
+app.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+)
+```
+
+## Tools
+
+### ToolNode
+
+`langgraph-prebuilt` provides an [implementation](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_node.ToolNode) of a node that executes tool calls - `ToolNode`:
+
+```python
+from langgraph.prebuilt import ToolNode
+from langchain_core.messages import AIMessage
+
+def search(query: str):
+    """Call to surf the web."""
+    # This is a placeholder, but don't tell the LLM that...
+    if "sf" in query.lower() or "san francisco" in query.lower():
+        return "It's 60 degrees and foggy."
+    return "It's 90 degrees and sunny."
+
+tool_node = ToolNode([search])
+tool_calls = [{"name": "search", "args": {"query": "what is the weather in sf"}, "id": "1"}]
+ai_message = AIMessage(content="", tool_calls=tool_calls)
+# execute tool call
+tool_node.invoke({"messages": [ai_message]})
+```
+
+### ValidationNode
+
+`langgraph-prebuilt` provides an [implementation](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_validator.ValidationNode) of a node that validates tool calls against a pydantic schema - `ValidationNode`:
+
+```python
+from pydantic import BaseModel, field_validator
+from langgraph.prebuilt import ValidationNode
+from langchain_core.messages import AIMessage
+
+
+class SelectNumber(BaseModel):
+    a: int
+
+    @field_validator("a")
+    def a_must_be_meaningful(cls, v):
+        if v != 37:
+            raise ValueError("Only 37 is allowed")
+        return v
+
+validation_node = ValidationNode([SelectNumber])
+validation_node.invoke({
+    "messages": [AIMessage("", tool_calls=[{"name": "SelectNumber", "args": {"a": 42}, "id": "1"}])]
+})
+```
+
+## Agent Inbox
+
+The library contains schemas for using the [Agent Inbox](https://github.com/langchain-ai/agent-inbox) with LangGraph agents. Learn more about how to use Agent Inbox [here](https://github.com/langchain-ai/agent-inbox#interrupts).
+
+```python
+from langgraph.types import interrupt
+from langgraph.prebuilt.interrupt import HumanInterrupt, HumanResponse
+
+def my_graph_function():
+    # Extract the last tool call from the `messages` field in the state
+    tool_call = state["messages"][-1].tool_calls[0]
+    # Create an interrupt
+    request: HumanInterrupt = {
+        "action_request": {
+            "action": tool_call['name'],
+            "args": tool_call['args']
+        },
+        "config": {
+            "allow_ignore": True,
+            "allow_respond": True,
+            "allow_edit": False,
+            "allow_accept": False
+        },
+        "description": _generate_email_markdown(state) # Generate a detailed markdown description.
+    }
+    # Send the interrupt request inside a list, and extract the first response
+    response = interrupt([request])[0]
+    if response['type'] == "response":
+        # Do something with the response
+    ...
+```

libs/prebuilt/langgraph/prebuilt/__init__.py

@@ -1,7 +1,6 @@
 """langgraph.prebuilt exposes a higher-level API for creating and executing agents and tools."""
 
 from langgraph.prebuilt.chat_agent_executor import create_react_agent
-from langgraph.prebuilt.tool_executor import ToolExecutor, ToolInvocation
 from langgraph.prebuilt.tool_node import (
     InjectedState,
     InjectedStore,
@@ -12,8 +11,6 @@ from langgraph.prebuilt.tool_validator import ValidationNode
 
 __all__ = [
     "create_react_agent",
-    "ToolExecutor",
-    "ToolInvocation",
     "ToolNode",
     "tools_condition",
     "ValidationNode",

libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py

@@ -32,7 +32,6 @@ from langgraph.graph import END, StateGraph
 from langgraph.graph.graph import CompiledGraph
 from langgraph.graph.message import add_messages
 from langgraph.managed import IsLastStep, RemainingSteps
-from langgraph.prebuilt.tool_executor import ToolExecutor
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.store.base import BaseStore
 from langgraph.types import Checkpointer, Send
@@ -68,13 +67,6 @@ StateSchemaType = Type[StateSchema]
 
 PROMPT_RUNNABLE_NAME = "Prompt"
 
-MessagesModifier = Union[
-    SystemMessage,
-    str,
-    Callable[[Sequence[BaseMessage]], LanguageModelInput],
-    Runnable[Sequence[BaseMessage], LanguageModelInput],
-]
-
 Prompt = Union[
     SystemMessage,
     str,
@@ -119,43 +111,20 @@ def _get_prompt_runnable(prompt: Optional[Prompt]) -> Runnable:
     return prompt_runnable
 
 
-def _convert_messages_modifier_to_prompt(
-    messages_modifier: MessagesModifier,
-) -> Prompt:
-    prompt: Prompt
-    if isinstance(messages_modifier, (str, SystemMessage)):
-        return messages_modifier
-    elif callable(messages_modifier):
-
-        def prompt(state: AgentState) -> Sequence[BaseMessage]:
-            return messages_modifier(state["messages"])
-
-        return prompt
-    elif isinstance(messages_modifier, Runnable):
-        prompt = (lambda state: state["messages"]) | messages_modifier
-        return prompt
-    raise ValueError(
-        f"Got unexpected type for `messages_modifier`: {type(messages_modifier)}"
-    )
-
-
 def _convert_modifier_to_prompt(func: F) -> F:
-    """Decorator that converts state_modifier/messages_modifier kwargs to prompt kwarg."""
+    """Decorator that converts state_modifier kwarg to prompt kwarg."""
 
     @functools.wraps(func)
     def wrapper(*args: Any, **kwargs: Any) -> Any:
         prompt = kwargs.get("prompt")
         state_modifier = kwargs.pop("state_modifier", None)
-        messages_modifier = kwargs.pop("messages_modifier", None)
-        if sum(p is not None for p in (prompt, state_modifier, messages_modifier)) > 1:
+        if sum(p is not None for p in (prompt, state_modifier)) > 1:
             raise ValueError(
-                "Expected only one of prompt, state_modifier, or messages_modifier, got multiple values"
+                "Expected only one of (prompt, state_modifier), got multiple values"
             )
 
         if state_modifier is not None:
             prompt = state_modifier
-        elif messages_modifier is not None:
-            prompt = _convert_messages_modifier_to_prompt(messages_modifier)
 
         kwargs["prompt"] = prompt
         return func(*args, **kwargs)
@@ -244,7 +213,7 @@ def _validate_chat_history(
 @_convert_modifier_to_prompt
 def create_react_agent(
     model: Union[str, LanguageModelLike],
-    tools: Union[ToolExecutor, Sequence[BaseTool], ToolNode],
+    tools: Union[Sequence[BaseTool], ToolNode],
     *,
     prompt: Optional[Prompt] = None,
     response_format: Optional[
@@ -264,7 +233,7 @@ def create_react_agent(
 
     Args:
         model: The `LangChain` chat model that supports tool calling.
-        tools: A list of tools, a ToolExecutor, or a ToolNode instance.
+        tools: A list of tools or a ToolNode instance.
             If an empty list is provided, the agent will consist of a single LLM node without tool calling.
         prompt: An optional prompt for the LLM. Can take a few different forms:
 
@@ -273,8 +242,6 @@ def create_react_agent(
             - Callable: This function should take in full graph state and the output is then passed to the language model.
             - Runnable: This runnable should take in full graph state and the output is then passed to the language model.
 
-            !!! Note
-                Prior to `v0.2.68`, the prompt was set using `state_modifier` / `messages_modifier` parameters.
         response_format: An optional schema for the final agent output.
 
             If provided, output will be formatted to match the given schema and returned in the 'structured_response' state key.
@@ -617,10 +584,7 @@ def create_react_agent(
             else AgentState
         )
 
-    if isinstance(tools, ToolExecutor):
-        tool_classes: Sequence[BaseTool] = tools.tools
-        tool_node = ToolNode(tool_classes)
-    elif isinstance(tools, ToolNode):
+    if isinstance(tools, ToolNode):
         tool_classes = list(tools.tools_by_name.values())
         tool_node = tools
     else:

libs/prebuilt/langgraph/prebuilt/tool_validator.py

@@ -29,6 +29,7 @@ from langchain_core.runnables import (
 )
 from langchain_core.runnables.config import get_executor_for_config
 from langchain_core.tools import BaseTool, create_schema_from_function
+from langchain_core.utils.pydantic import is_basemodel_subclass
 from pydantic import BaseModel, ValidationError
 from pydantic.v1 import BaseModel as BaseModelV1
 from pydantic.v1 import ValidationError as ValidationErrorV1
@@ -78,7 +79,7 @@ class ValidationNode(RunnableCallable):
         >>> from typing_extensions import TypedDict
         ...
         >>> from langchain_anthropic import ChatAnthropic
-        >>> from pydantic import BaseModel, validator
+        >>> from pydantic import BaseModel, field_validator
         ...
         >>> from langgraph.graph import END, START, StateGraph
         >>> from langgraph.prebuilt import ValidationNode
@@ -88,18 +89,15 @@ class ValidationNode(RunnableCallable):
         >>> class SelectNumber(BaseModel):
         ...     a: int
         ...
-        ...     @validator("a")
+        ...     @field_validator("a")
         ...     def a_must_be_meaningful(cls, v):
         ...         if v != 37:
         ...             raise ValueError("Only 37 is allowed")
         ...         return v
         ...
         ...
-        >>> class State(TypedDict):
-        ...     messages: Annotated[list, add_messages]
-        ...
-        >>> builder = StateGraph(State)
-        >>> llm = ChatAnthropic(model="claude-3-haiku-20240307").bind_tools([SelectNumber])
+        >>> builder = StateGraph(Annotated[list, add_messages])
+        >>> llm = ChatAnthropic(model="claude-3-5-haiku-latest").bind_tools([SelectNumber])
         >>> builder.add_node("model", llm)
         >>> builder.add_node("validation", ValidationNode([SelectNumber]))
         >>> builder.add_edge(START, "model")
@@ -177,6 +175,13 @@ class ValidationNode(RunnableCallable):
                     raise ValueError(
                         f"Tool {schema.name} does not have an args_schema defined."
                     )
+                elif not isinstance(
+                    schema.args_schema, type
+                ) or not is_basemodel_subclass(schema.args_schema):
+                    raise ValueError(
+                        "Validation node only works with tools that have a pydantic BaseModel args_schema. "
+                        f"Got {schema.name} with args_schema: {schema.args_schema}."
+                    )
                 self.schemas_by_name[schema.name] = schema.args_schema
             elif isinstance(schema, type) and issubclass(
                 schema, (BaseModel, BaseModelV1)

libs/prebuilt/pyproject.toml

@@ -0,0 +1,63 @@
+[tool.poetry]
+name = "langgraph-prebuilt"
+version = "1.0.0"
+description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
+authors = []
+license = "MIT"
+readme = "README.md"
+repository = "https://www.github.com/langchain-ai/langgraph"
+packages = [{ include = "langgraph" }]
+
+[tool.poetry.dependencies]
+python = "^3.9.0,<4.0"
+langgraph = ">=0.3,<0.4"
+langgraph-checkpoint = "^2.0.10"
+langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.20,!=0.3.21,!=0.3.22"
+
+[tool.poetry.group.dev.dependencies]
+ruff = "^0.6.2"
+codespell = "^2.2.0"
+pytest = "^7.2.1"
+pytest-asyncio = "^0.21.1"
+pytest-mock = "^3.11.1"
+pytest-watcher = "^0.4.1"
+mypy = "^1.10.0"
+langgraph = {path = "../langgraph", develop = true}
+langgraph-checkpoint = {path = "../checkpoint", develop = true}
+langgraph-checkpoint-sqlite = {path = "../checkpoint-sqlite", develop = true}
+langgraph-checkpoint-postgres = {path = "../checkpoint-postgres", develop = true}
+
+[tool.pytest.ini_options]
+# --strict-markers will raise errors on unknown marks.
+# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks
+#
+# https://docs.pytest.org/en/7.1.x/reference/reference.html
+# --strict-config       any warnings encountered while parsing the `pytest`
+#                       section of the configuration file raise errors.
+addopts = "--strict-markers --strict-config --durations=5 -vv"
+asyncio_mode = "auto"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.ruff]
+lint.select = [ "E", "F", "I", "TID251" ]
+lint.ignore = [ "E501" ]
+
+[tool.pytest-watcher]
+now = true
+delay = 0.1
+runner_args = ["--ff", "-v", "--tb", "short"]
+patterns = ["*.py"]
+
+[tool.mypy]
+# https://mypy.readthedocs.io/en/stable/config_file.html
+disallow_untyped_defs = "True"
+explicit_package_bases = "True"
+warn_no_return = "False"
+warn_unused_ignores = "True"
+warn_redundant_casts = "True"
+allow_redefinition = "True"
+disable_error_code = "typeddict-item, return-value"

libs/prebuilt/tests/any_str.py

@@ -0,0 +1,86 @@
+import re
+from typing import Any, Sequence, Union
+
+from typing_extensions import Self
+
+
+class FloatBetween(float):
+    def __new__(cls, min_value: float, max_value: float) -> Self:
+        return super().__new__(cls, min_value)
+
+    def __init__(self, min_value: float, max_value: float) -> None:
+        super().__init__()
+        self.min_value = min_value
+        self.max_value = max_value
+
+    def __eq__(self, other: object) -> bool:
+        return (
+            isinstance(other, float)
+            and other >= self.min_value
+            and other <= self.max_value
+        )
+
+    def __hash__(self) -> int:
+        return hash((float(self), self.min_value, self.max_value))
+
+
+class AnyStr(str):
+    def __init__(self, prefix: Union[str, re.Pattern] = "") -> None:
+        super().__init__()
+        self.prefix = prefix
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, str) and (
+            other.startswith(self.prefix)
+            if isinstance(self.prefix, str)
+            else self.prefix.match(other)
+        )
+
+    def __hash__(self) -> int:
+        return hash((str(self), self.prefix))
+
+
+class AnyDict(dict):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, dict) or len(self) != len(other):
+            return False
+        for k, v in self.items():
+            if kk := next((kk for kk in other if kk == k), None):
+                if v == other[kk]:
+                    continue
+                else:
+                    return False
+        else:
+            return True
+
+
+class AnyVersion:
+    def __init__(self) -> None:
+        super().__init__()
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, (str, int, float))
+
+    def __hash__(self) -> int:
+        return hash(str(self))
+
+
+class UnsortedSequence:
+    def __init__(self, *values: Any) -> None:
+        self.seq = values
+
+    def __eq__(self, value: object) -> bool:
+        return (
+            isinstance(value, Sequence)
+            and len(self.seq) == len(value)
+            and all(a in value for a in self.seq)
+        )
+
+    def __hash__(self) -> int:
+        return hash(frozenset(self.seq))
+
+    def __repr__(self) -> str:
+        return repr(self.seq)

libs/prebuilt/tests/compose-postgres.yml

@@ -0,0 +1,17 @@
+name: langgraph-tests
+services:
+  postgres-test:
+    image: postgres:16
+    ports:
+      - "5442:5432"
+    environment:
+      POSTGRES_DB: postgres
+      POSTGRES_USER: postgres
+      POSTGRES_PASSWORD: postgres
+    healthcheck:
+      test: pg_isready -U postgres
+      start_period: 10s
+      timeout: 1s
+      retries: 5
+      interval: 60s
+      start_interval: 1s

libs/prebuilt/tests/conftest.py

@@ -0,0 +1,448 @@
+import sys
+from contextlib import asynccontextmanager
+from typing import AsyncIterator, Optional
+from uuid import UUID, uuid4
+
+import pytest
+from langchain_core import __version__ as core_version
+from packaging import version
+from psycopg import AsyncConnection, Connection
+from psycopg_pool import AsyncConnectionPool, ConnectionPool
+from pytest_mock import MockerFixture
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+from langgraph.checkpoint.postgres import PostgresSaver, ShallowPostgresSaver
+from langgraph.checkpoint.postgres.aio import (
+    AsyncPostgresSaver,
+    AsyncShallowPostgresSaver,
+)
+from langgraph.checkpoint.sqlite import SqliteSaver
+from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
+from langgraph.store.base import BaseStore
+from langgraph.store.memory import InMemoryStore
+from langgraph.store.postgres import AsyncPostgresStore, PostgresStore
+
+pytest.register_assert_rewrite("tests.memory_assert")
+
+DEFAULT_POSTGRES_URI = "postgres://postgres:postgres@localhost:5442/"
+# TODO: fix this once core is released
+IS_LANGCHAIN_CORE_030_OR_GREATER = version.parse(core_version) >= version.parse(
+    "0.3.0.dev0"
+)
+
+
+@pytest.fixture
+def anyio_backend():
+    return "asyncio"
+
+
+@pytest.fixture()
+def deterministic_uuids(mocker: MockerFixture) -> MockerFixture:
+    side_effect = (
+        UUID(f"00000000-0000-4000-8000-{i:012}", version=4) for i in range(10000)
+    )
+    return mocker.patch("uuid.uuid4", side_effect=side_effect)
+
+
+# checkpointer fixtures
+
+
+@pytest.fixture(scope="function")
+def checkpointer_memory():
+    from tests.memory_assert import MemorySaverAssertImmutable
+
+    yield MemorySaverAssertImmutable()
+
+
+@pytest.fixture(scope="function")
+def checkpointer_sqlite():
+    with SqliteSaver.from_conn_string(":memory:") as checkpointer:
+        yield checkpointer
+
+
+@asynccontextmanager
+async def _checkpointer_sqlite_aio():
+    async with AsyncSqliteSaver.from_conn_string(":memory:") as checkpointer:
+        yield checkpointer
+
+
+@pytest.fixture(scope="function")
+def checkpointer_postgres():
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        with PostgresSaver.from_conn_string(
+            DEFAULT_POSTGRES_URI + database
+        ) as checkpointer:
+            checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@pytest.fixture(scope="function")
+def checkpointer_postgres_shallow():
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        with ShallowPostgresSaver.from_conn_string(
+            DEFAULT_POSTGRES_URI + database
+        ) as checkpointer:
+            checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@pytest.fixture(scope="function")
+def checkpointer_postgres_pipe():
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        with PostgresSaver.from_conn_string(
+            DEFAULT_POSTGRES_URI + database
+        ) as checkpointer:
+            checkpointer.setup()
+            # setup can't run inside pipeline because of implicit transaction
+            with checkpointer.conn.pipeline() as pipe:
+                checkpointer.pipe = pipe
+                yield checkpointer
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@pytest.fixture(scope="function")
+def checkpointer_postgres_pool():
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        with ConnectionPool(
+            DEFAULT_POSTGRES_URI + database, max_size=10, kwargs={"autocommit": True}
+        ) as pool:
+            checkpointer = PostgresSaver(pool)
+            checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _checkpointer_postgres_aio():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        async with AsyncPostgresSaver.from_conn_string(
+            DEFAULT_POSTGRES_URI + database
+        ) as checkpointer:
+            await checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _checkpointer_postgres_aio_shallow():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        async with AsyncShallowPostgresSaver.from_conn_string(
+            DEFAULT_POSTGRES_URI + database
+        ) as checkpointer:
+            await checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _checkpointer_postgres_aio_pipe():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        async with AsyncPostgresSaver.from_conn_string(
+            DEFAULT_POSTGRES_URI + database
+        ) as checkpointer:
+            await checkpointer.setup()
+            # setup can't run inside pipeline because of implicit transaction
+            async with checkpointer.conn.pipeline() as pipe:
+                checkpointer.pipe = pipe
+                yield checkpointer
+    finally:
+        # drop unique db
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _checkpointer_postgres_aio_pool():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        async with AsyncConnectionPool(
+            DEFAULT_POSTGRES_URI + database, max_size=10, kwargs={"autocommit": True}
+        ) as pool:
+            checkpointer = AsyncPostgresSaver(pool)
+            await checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def awith_checkpointer(
+    checkpointer_name: Optional[str],
+) -> AsyncIterator[BaseCheckpointSaver]:
+    if checkpointer_name is None:
+        yield None
+    elif checkpointer_name == "memory":
+        from tests.memory_assert import MemorySaverAssertImmutable
+
+        yield MemorySaverAssertImmutable()
+    elif checkpointer_name == "sqlite_aio":
+        async with _checkpointer_sqlite_aio() as checkpointer:
+            yield checkpointer
+    elif checkpointer_name == "postgres_aio":
+        async with _checkpointer_postgres_aio() as checkpointer:
+            yield checkpointer
+    elif checkpointer_name == "postgres_aio_shallow":
+        async with _checkpointer_postgres_aio_shallow() as checkpointer:
+            yield checkpointer
+    elif checkpointer_name == "postgres_aio_pipe":
+        async with _checkpointer_postgres_aio_pipe() as checkpointer:
+            yield checkpointer
+    elif checkpointer_name == "postgres_aio_pool":
+        async with _checkpointer_postgres_aio_pool() as checkpointer:
+            yield checkpointer
+    else:
+        raise NotImplementedError(f"Unknown checkpointer: {checkpointer_name}")
+
+
+@asynccontextmanager
+async def _store_postgres_aio():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
+    database = f"test_{uuid4().hex[:16]}"
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with AsyncPostgresStore.from_conn_string(
+            DEFAULT_POSTGRES_URI + database
+        ) as store:
+            await store.setup()
+            yield store
+    finally:
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _store_postgres_aio_pipe():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
+    database = f"test_{uuid4().hex[:16]}"
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with AsyncPostgresStore.from_conn_string(
+            DEFAULT_POSTGRES_URI + database
+        ) as store:
+            await store.setup()  # Run in its own transaction
+        async with AsyncPostgresStore.from_conn_string(
+            DEFAULT_POSTGRES_URI + database, pipeline=True
+        ) as store:
+            yield store
+    finally:
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _store_postgres_aio_pool():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
+    database = f"test_{uuid4().hex[:16]}"
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with AsyncPostgresStore.from_conn_string(
+            DEFAULT_POSTGRES_URI + database,
+            pool_config={"max_size": 10},
+        ) as store:
+            await store.setup()
+            yield store
+    finally:
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@pytest.fixture(scope="function")
+def store_postgres():
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield store
+        with PostgresStore.from_conn_string(DEFAULT_POSTGRES_URI + database) as store:
+            store.setup()
+            yield store
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@pytest.fixture(scope="function")
+def store_postgres_pipe():
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield store
+        with PostgresStore.from_conn_string(DEFAULT_POSTGRES_URI + database) as store:
+            store.setup()  # Run in its own transaction
+        with PostgresStore.from_conn_string(
+            DEFAULT_POSTGRES_URI + database, pipeline=True
+        ) as store:
+            yield store
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@pytest.fixture(scope="function")
+def store_postgres_pool():
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield store
+        with PostgresStore.from_conn_string(
+            DEFAULT_POSTGRES_URI + database, pool_config={"max_size": 10}
+        ) as store:
+            store.setup()
+            yield store
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@pytest.fixture(scope="function")
+def store_in_memory():
+    yield InMemoryStore()
+
+
+@asynccontextmanager
+async def awith_store(store_name: Optional[str]) -> AsyncIterator[BaseStore]:
+    if store_name is None:
+        yield None
+    elif store_name == "in_memory":
+        yield InMemoryStore()
+    elif store_name == "postgres_aio":
+        async with _store_postgres_aio() as store:
+            yield store
+    elif store_name == "postgres_aio_pipe":
+        async with _store_postgres_aio_pipe() as store:
+            yield store
+    elif store_name == "postgres_aio_pool":
+        async with _store_postgres_aio_pool() as store:
+            yield store
+    else:
+        raise NotImplementedError(f"Unknown store {store_name}")
+
+
+ALL_CHECKPOINTERS_SYNC = [
+    "memory",
+    "sqlite",
+    "postgres",
+    "postgres_pipe",
+    "postgres_pool",
+    "postgres_shallow",
+]
+
+ALL_CHECKPOINTERS_ASYNC = [
+    "memory",
+    "sqlite_aio",
+    "postgres_aio",
+    "postgres_aio_pipe",
+    "postgres_aio_pool",
+    "postgres_aio_shallow",
+]

libs/prebuilt/tests/memory_assert.py

@@ -0,0 +1,134 @@
+import asyncio
+import os
+import tempfile
+from collections import defaultdict
+from functools import partial
+from typing import Any, Optional
+
+from langchain_core.runnables import RunnableConfig
+
+from langgraph.checkpoint.base import (
+    ChannelVersions,
+    Checkpoint,
+    CheckpointMetadata,
+    CheckpointTuple,
+    SerializerProtocol,
+    copy_checkpoint,
+)
+from langgraph.checkpoint.memory import InMemorySaver, PersistentDict
+
+
+class NoopSerializer(SerializerProtocol):
+    def loads_typed(self, data: tuple[str, bytes]) -> Any:
+        return data[1]
+
+    def dumps_typed(self, obj: Any) -> tuple[str, bytes]:
+        return "type", obj
+
+
+class MemorySaverAssertImmutable(InMemorySaver):
+    storage_for_copies: defaultdict[str, dict[str, dict[str, Checkpoint]]]
+
+    def __init__(
+        self,
+        *,
+        serde: Optional[SerializerProtocol] = None,
+        put_sleep: Optional[float] = None,
+    ) -> None:
+        _, filename = tempfile.mkstemp()
+        super().__init__(
+            serde=serde, factory=partial(PersistentDict, filename=filename)
+        )
+        self.storage_for_copies = defaultdict(lambda: defaultdict(dict))
+        self.put_sleep = put_sleep
+        self.stack.callback(os.remove, filename)
+
+    def put(
+        self,
+        config: dict,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> None:
+        if self.put_sleep:
+            import time
+
+            time.sleep(self.put_sleep)
+        # assert checkpoint hasn't been modified since last written
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"]["checkpoint_ns"]
+        if saved := super().get(config):
+            assert (
+                self.serde.loads_typed(
+                    self.storage_for_copies[thread_id][checkpoint_ns][saved["id"]]
+                )
+                == saved
+            )
+        self.storage_for_copies[thread_id][checkpoint_ns][checkpoint["id"]] = (
+            self.serde.dumps_typed(copy_checkpoint(checkpoint))
+        )
+        # call super to write checkpoint
+        return super().put(config, checkpoint, metadata, new_versions)
+
+
+class MemorySaverAssertCheckpointMetadata(InMemorySaver):
+    """This custom checkpointer is for verifying that a run's configurable
+    fields are merged with the previous checkpoint config for each step in
+    the run. This is the desired behavior. Because the checkpointer's (a)put()
+    method is called for each step, the implementation of this checkpointer
+    should produce a side effect that can be asserted.
+    """
+
+    def put(
+        self,
+        config: RunnableConfig,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> None:
+        """The implementation of put() merges config["configurable"] (a run's
+        configurable fields) with the metadata field. The state of the
+        checkpoint metadata can be asserted to confirm that the run's
+        configurable fields were merged with the previous checkpoint config.
+        """
+        configurable = config["configurable"].copy()
+
+        # remove checkpoint_id to make testing simpler
+        checkpoint_id = configurable.pop("checkpoint_id", None)
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"]["checkpoint_ns"]
+        self.storage[thread_id][checkpoint_ns].update(
+            {
+                checkpoint["id"]: (
+                    self.serde.dumps_typed(checkpoint),
+                    # merge configurable fields and metadata
+                    self.serde.dumps_typed({**configurable, **metadata}),
+                    checkpoint_id,
+                )
+            }
+        )
+        return {
+            "configurable": {
+                "thread_id": config["configurable"]["thread_id"],
+                "checkpoint_id": checkpoint["id"],
+            }
+        }
+
+    async def aput(
+        self,
+        config: RunnableConfig,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> RunnableConfig:
+        return await asyncio.get_running_loop().run_in_executor(
+            None, self.put, config, checkpoint, metadata, new_versions
+        )
+
+
+class MemorySaverNoPending(InMemorySaver):
+    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+        result = super().get_tuple(config)
+        if result:
+            return CheckpointTuple(result.config, result.checkpoint, result.metadata)
+        return result

libs/prebuilt/tests/messages.py

@@ -0,0 +1,50 @@
+"""Redefined messages as a work-around for pydantic issue with AnyStr.
+
+The code below creates version of pydantic models
+that will work in unit tests with AnyStr as id field
+Please note that the `id` field is assigned AFTER the model is created
+to workaround an issue with pydantic ignoring the __eq__ method on
+subclassed strings.
+"""
+
+from typing import Any
+
+from langchain_core.documents import Document
+from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage, ToolMessage
+
+from tests.any_str import AnyStr
+
+
+def _AnyIdDocument(**kwargs: Any) -> Document:
+    """Create a document with an id field."""
+    message = Document(**kwargs)
+    message.id = AnyStr()
+    return message
+
+
+def _AnyIdAIMessage(**kwargs: Any) -> AIMessage:
+    """Create ai message with an any id field."""
+    message = AIMessage(**kwargs)
+    message.id = AnyStr()
+    return message
+
+
+def _AnyIdAIMessageChunk(**kwargs: Any) -> AIMessageChunk:
+    """Create ai message with an any id field."""
+    message = AIMessageChunk(**kwargs)
+    message.id = AnyStr()
+    return message
+
+
+def _AnyIdHumanMessage(**kwargs: Any) -> HumanMessage:
+    """Create a human message with an any id field."""
+    message = HumanMessage(**kwargs)
+    message.id = AnyStr()
+    return message
+
+
+def _AnyIdToolMessage(**kwargs: Any) -> ToolMessage:
+    """Create a tool message with an any id field."""
+    message = ToolMessage(**kwargs)
+    message.id = AnyStr()
+    return message

libs/prebuilt/tests/model.py

@@ -0,0 +1,98 @@
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+    Type,
+    Union,
+)
+
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models import BaseChatModel, LanguageModelInput
+from langchain_core.messages import (
+    AIMessage,
+    BaseMessage,
+    ToolCall,
+)
+from langchain_core.outputs import ChatGeneration, ChatResult
+from langchain_core.runnables import Runnable, RunnableLambda
+from langchain_core.tools import BaseTool
+from pydantic import BaseModel
+
+from langgraph.prebuilt.chat_agent_executor import StructuredResponse
+
+
+class FakeToolCallingModel(BaseChatModel):
+    tool_calls: Optional[list[list[ToolCall]]] = None
+    structured_response: Optional[StructuredResponse] = None
+    index: int = 0
+    tool_style: Literal["openai", "anthropic"] = "openai"
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        """Top Level call"""
+        messages_string = "-".join([m.content for m in messages])
+        tool_calls = (
+            self.tool_calls[self.index % len(self.tool_calls)]
+            if self.tool_calls
+            else []
+        )
+        message = AIMessage(
+            content=messages_string, id=str(self.index), tool_calls=tool_calls.copy()
+        )
+        self.index += 1
+        return ChatResult(generations=[ChatGeneration(message=message)])
+
+    @property
+    def _llm_type(self) -> str:
+        return "fake-tool-call-model"
+
+    def with_structured_output(
+        self, schema: Type[BaseModel]
+    ) -> Runnable[LanguageModelInput, StructuredResponse]:
+        if self.structured_response is None:
+            raise ValueError("Structured response is not set")
+
+        return RunnableLambda(lambda x: self.structured_response)
+
+    def bind_tools(
+        self,
+        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, BaseMessage]:
+        if len(tools) == 0:
+            raise ValueError("Must provide at least one tool")
+
+        tool_dicts = []
+        for tool in tools:
+            if not isinstance(tool, BaseTool):
+                raise TypeError(
+                    "Only BaseTool is supported by FakeToolCallingModel.bind_tools"
+                )
+
+            # NOTE: this is a simplified tool spec for testing purposes only
+            if self.tool_style == "openai":
+                tool_dicts.append(
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": tool.name,
+                        },
+                    }
+                )
+            elif self.tool_style == "anthropic":
+                tool_dicts.append(
+                    {
+                        "name": tool.name,
+                    }
+                )
+
+        return self.bind(tools=tool_dicts)

libs/prebuilt/tests/test_validation_node.py

@@ -0,0 +1,81 @@
+from typing import Any
+
+import pytest
+from langchain_core.messages import AIMessage
+from langchain_core.tools import tool as dec_tool
+from pydantic import BaseModel
+from pydantic.v1 import BaseModel as BaseModelV1
+
+from langgraph.prebuilt import ValidationNode
+
+pytestmark = pytest.mark.anyio
+
+
+def my_function(some_val: int, some_other_val: str) -> str:
+    return f"{some_val} - {some_other_val}"
+
+
+class MyModel(BaseModel):
+    some_val: int
+    some_other_val: str
+
+
+class MyModelV1(BaseModelV1):
+    some_val: int
+    some_other_val: str
+
+
+@dec_tool
+def my_tool(some_val: int, some_other_val: str) -> str:
+    """Cool."""
+    return f"{some_val} - {some_other_val}"
+
+
+@pytest.mark.parametrize(
+    "tool_schema",
+    [
+        my_function,
+        MyModel,
+        MyModelV1,
+        my_tool,
+    ],
+)
+@pytest.mark.parametrize("use_message_key", [True, False])
+async def test_validation_node(tool_schema: Any, use_message_key: bool):
+    validation_node = ValidationNode([tool_schema])
+    tool_name = getattr(tool_schema, "name", getattr(tool_schema, "__name__", None))
+    inputs = [
+        AIMessage(
+            "hi?",
+            tool_calls=[
+                {
+                    "name": tool_name,
+                    "args": {"some_val": 1, "some_other_val": "foo"},
+                    "id": "some 0",
+                },
+                {
+                    "name": tool_name,
+                    # Wrong type for some_val
+                    "args": {"some_val": "bar", "some_other_val": "foo"},
+                    "id": "some 1",
+                },
+            ],
+        ),
+    ]
+    if use_message_key:
+        inputs = {"messages": inputs}
+    result = await validation_node.ainvoke(inputs)
+    if use_message_key:
+        result = result["messages"]
+
+    def check_results(messages: list):
+        assert len(messages) == 2
+        assert all(m.type == "tool" for m in messages)
+        assert not messages[0].additional_kwargs.get("is_error")
+        assert messages[1].additional_kwargs.get("is_error")
+
+    check_results(result)
+    result_sync = validation_node.invoke(inputs)
+    if use_message_key:
+        result_sync = result_sync["messages"]
+    check_results(result_sync)