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chore(prebuilt): revert tool arg injection refactor (#5842)

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Reverts https://github.com/langchain-ai/langgraph/pull/5562

I anticipate that we want to do another pass at a refactor here in the
short term, but this makes it easier to adapt to new langchain core
message types for v0.4 support in the short term.
This commit is contained in:
Sydney Runkle
2025-08-06 10:12:08 -04:00
committed by GitHub
parent 925150a35d
commit 82978a8dd8
3 changed files with 24 additions and 75 deletions
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libs/prebuilt/langgraph/prebuilt/_internal.py
-26
View File
@@ -1,26 +0,0 @@
from typing import Any, Literal, TypedDict
from langchain_core.messages import ToolCall
class ToolCallWithContext(TypedDict):
"""ToolCall with additional context for graph state.
This is an internal data-structure meant to help the ToolNode accept
tools calls with additional context (e.g. state) when dispatched using the
`Send` API.
The Send API is used in create_react_agent to be able to distribute the tool
calls in parallel and support human-in-the-loop workflows where graph execution
may be paused for an indefinite time.
"""
tool_call: ToolCall
__type: Literal["tool_call_with_context"]
"""Type to parameterize the payload.
Using "__" as a prefix to be defensive against potential name collisions with
regular user state.
"""
state: Any
"""The state is provided as additional context."""
libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py
+8 -21
View File
@@ -43,7 +43,6 @@ from langgraph.graph import END, StateGraph
from langgraph.graph.message import add_messages
from langgraph.graph.state import CompiledStateGraph
from langgraph.managed import RemainingSteps
from langgraph.prebuilt._internal import ToolCallWithContext
from langgraph.prebuilt.tool_node import ToolNode
from langgraph.runtime import Runtime
from langgraph.store.base import BaseStore
@@ -790,17 +789,11 @@ def create_react_agent(
elif version == "v2":
if post_model_hook is not None:
return "post_model_hook"
return [
Send(
"tools",
ToolCallWithContext(
__type="tool_call_with_context",
tool_call=tool_call,
state=state,
),
)
for tool_call in last_message.tool_calls
tool_calls = [
tool_node.inject_tool_args(call, state, store) # type: ignore[arg-type]
for call in last_message.tool_calls
]
return [Send("tools", [tool_call]) for tool_call in tool_calls]
# Define a new graph
workflow = StateGraph(
@@ -881,17 +874,11 @@ def create_react_agent(
]
if pending_tool_calls:
return [
Send(
"tools",
ToolCallWithContext(
__type="tool_call_with_context",
tool_call=tool_call,
state=state,
),
)
for tool_call in pending_tool_calls
pending_tool_calls = [
tool_node.inject_tool_args(call, state, store) # type: ignore[arg-type]
for call in pending_tool_calls
]
return [Send("tools", [tool_call]) for tool_call in pending_tool_calls]
elif isinstance(messages[-1], ToolMessage):
return entrypoint
elif response_format is not None:
libs/prebuilt/langgraph/prebuilt/tool_node.py
+16 -28
View File
@@ -74,7 +74,6 @@ from typing_extensions import Annotated, get_args, get_origin
from langgraph._internal._runnable import RunnableCallable
from langgraph.errors import GraphBubbleUp
from langgraph.graph.message import REMOVE_ALL_MESSAGES
from langgraph.prebuilt._internal import ToolCallWithContext
from langgraph.store.base import BaseStore
from langgraph.types import Command, Send
@@ -361,8 +360,7 @@ class ToolNode(RunnableCallable):
*,
store: Optional[BaseStore],
) -> Any:
tool_calls, input_type = self._parse_input(input)
tool_calls = [self.inject_tool_args(call, input, store) for call in tool_calls]
tool_calls, input_type = self._parse_input(input, store)
config_list = get_config_list(config, len(tool_calls))
input_types = [input_type] * len(tool_calls)
with get_executor_for_config(config) as executor:
@@ -383,8 +381,7 @@ class ToolNode(RunnableCallable):
*,
store: Optional[BaseStore],
) -> Any:
tool_calls, input_type = self._parse_input(input)
tool_calls = [self.inject_tool_args(call, input, store) for call in tool_calls]
tool_calls, input_type = self._parse_input(input, store)
outputs = await asyncio.gather(
*(self._arun_one(call, input_type, config) for call in tool_calls)
)
@@ -502,13 +499,14 @@ class ToolNode(RunnableCallable):
return invalid_tool_message
try:
input = {**call, **{"type": "tool_call"}}
response = await self.tools_by_name[call["name"]].ainvoke(input, config)
call_args = {**call, **{"type": "tool_call"}}
response = await self.tools_by_name[call["name"]].ainvoke(call_args, config)
# GraphInterrupt is a special exception that will always be raised.
# It can be triggered in the following scenarios:
# (1) a NodeInterrupt is raised inside a tool
# (2) a NodeInterrupt is raised inside a graph node for a graph called as a tool
# It can be triggered in the following scenarios,
# Where GraphInterrupt(GraphBubbleUp) is raised from an `interrupt` invocation most commonly:
# (1) a GraphInterrupt is raised inside a tool
# (2) a GraphInterrupt is raised inside a graph node for a graph called as a tool
# (3) a GraphInterrupt is raised when a subgraph is interrupted inside a graph called as a tool
# (2 and 3 can happen in a "supervisor w/ tools" multi-agent architecture)
except GraphBubbleUp as e:
@@ -555,6 +553,7 @@ class ToolNode(RunnableCallable):
dict[str, Any],
BaseModel,
],
store: Optional[BaseStore],
) -> Tuple[list[ToolCall], Literal["list", "dict", "tool_calls"]]:
input_type: Literal["list", "dict", "tool_calls"]
if isinstance(input, list):
@@ -565,15 +564,6 @@ class ToolNode(RunnableCallable):
else:
input_type = "list"
messages = input
elif (
isinstance(input, dict) and input.get("__type") == "tool_call_with_context"
):
# mypy will not be able to type narrow correctly since the signature
# for input contains dict[str, Any]. We'd need to type dict[str, Any]
# before we can apply correct typing.
input = cast(ToolCallWithContext, input) # type: ignore[assignment]
input_type = "tool_calls"
return [input["tool_call"]], input_type
elif isinstance(input, dict) and (messages := input.get(self.messages_key, [])):
input_type = "dict"
elif messages := getattr(input, self.messages_key, []):
@@ -589,7 +579,10 @@ class ToolNode(RunnableCallable):
except StopIteration:
raise ValueError("No AIMessage found in input")
tool_calls = [call for call in latest_ai_message.tool_calls]
tool_calls = [
self.inject_tool_args(call, input, store)
for call in latest_ai_message.tool_calls
]
return tool_calls, input_type
def _validate_tool_call(self, call: ToolCall) -> Optional[ToolMessage]:
@@ -632,19 +625,14 @@ class ToolNode(RunnableCallable):
err_msg += f" State should contain fields {required_fields_str}."
raise ValueError(err_msg)
if isinstance(input, dict) and input.get("__type") == "tool_call_with_context":
state = input["state"]
else:
state = input
if isinstance(state, dict):
if isinstance(input, dict):
tool_state_args = {
tool_arg: state[state_field] if state_field else state
tool_arg: input[state_field] if state_field else input
for tool_arg, state_field in state_args.items()
}
else:
tool_state_args = {
tool_arg: getattr(state, state_field) if state_field else state
tool_arg: getattr(input, state_field) if state_field else input
for tool_arg, state_field in state_args.items()
}