Add return_direct support

libs/langgraph/langgraph/prebuilt/chat_agent_executor.py

@@ -1,4 +1,4 @@
 from typing import (
     Annotated,
     Callable,
     Literal,
@@ -11,17 +11,13 @@
 )
 
 from langchain_core.language_models import BaseChatModel
-from langchain_core.messages import (
-    AIMessage,
-    BaseMessage,
-    SystemMessage,
-)
+from langchain_core.messages import AIMessage, BaseMessage, SystemMessage, ToolMessage
 from langchain_core.runnables import Runnable, RunnableConfig, RunnableLambda
 from langchain_core.tools import BaseTool
 
 from langgraph._api.deprecation import deprecated_parameter
 from langgraph.checkpoint.base import BaseCheckpointSaver
-from langgraph.graph import END, StateGraph
+from langgraph.graph import StateGraph
 from langgraph.graph.graph import CompiledGraph
 from langgraph.graph.message import add_messages
 from langgraph.managed import IsLastStep
@@ -430,15 +426,15 @@
     model = model.bind_tools(tool_classes)
 
     # Define the function that determines whether to continue or not
-    def should_continue(state: AgentState) -> Literal["continue", "end"]:
+    def should_continue(state: AgentState) -> Literal["tools", "__end__"]:
         messages = state["messages"]
         last_message = messages[-1]
         # If there is no function call, then we finish
         if not isinstance(last_message, AIMessage) or not last_message.tool_calls:
-            return "end"
+            return "__end__"
         # Otherwise if there is, we continue
         else:
-            return "continue"
+            return "tools"
 
     preprocessor = _get_model_preprocessing_runnable(state_modifier, messages_modifier)
     model_runnable = preprocessor | model
@@ -498,23 +494,24 @@
         "agent",
         # Next, we pass in the function that will determine which node is called next.
         should_continue,
-        # Finally we pass in a mapping.
-        # The keys are strings, and the values are other nodes.
-        # END is a special node marking that the graph should finish.
-        # What will happen is we will call `should_continue`, and then the output of that
-        # will be matched against the keys in this mapping.
-        # Based on which one it matches, that node will then be called.
-        {
-            # If `tools`, then we call the tool node.
-            "continue": "tools",
-            # Otherwise we finish.
-            "end": END,
-        },
     )
 
-    # We now add a normal edge from `tools` to `agent`.
-    # This means that after `tools` is called, `agent` node is called next.
-    workflow.add_edge("tools", "agent")
+    # If any of the tools are configured to return_directly after running,
+    # our graph needs to check if these were called
+    should_return_direct = {t.name for t in tool_classes if t.return_direct}
+
+    def route_tool_responses(state: AgentState) -> Literal["agent", "__end__"]:
+        for m in reversed(state["messages"]):
+            if not isinstance(m, ToolMessage):
+                break
+            if m.name in should_return_direct:
+                return "__end__"
+        return "agent"
+
+    if should_return_direct:
+        workflow.add_conditional_edges("tools", route_tool_responses)
+    else:
+        workflow.add_edge("tools", "agent")
 
     # Finally, we compile it!
     # This compiles it into a LangChain Runnable,

libs/langgraph/tests/__snapshots__/test_pregel.ambr

@@ -4857,13 +4857,11 @@
       {
         "source": "agent",
         "target": "tools",
-        "data": "continue",
         "conditional": true
       },
       {
         "source": "agent",
         "target": "__end__",
-        "data": "end",
         "conditional": true
       }
     ]
@@ -4875,8 +4873,8 @@
   graph TD;
   	__start__ --> agent;
   	tools --> agent;
-  	agent -.  continue  .-> tools;
-  	agent -.  end  .-> __end__;
+  	agent -.-> tools;
+  	agent -.-> __end__;
 
   '''
 # ---

libs/langgraph/tests/test_prebuilt.py

@@ -47,6 +47,9 @@
 
 
 class FakeToolCallingModel(BaseChatModel):
+    tool_calls: Optional[list[list[ToolCall]]] = None
+    index: int = 0
+
     def _generate(
         self,
         messages: List[BaseMessage],
@@ -56,7 +59,15 @@ def _generate(
     ) -> ChatResult:
         """Top Level call"""
         messages_string = "-".join([m.content for m in messages])
-        message = AIMessage(content=messages_string, id="0")
+        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
@@ -68,8 +79,6 @@ def bind_tools(
         tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
         **kwargs: Any,
     ) -> Runnable[LanguageModelInput, BaseMessage]:
-        if len(tools) > 0:
-            raise ValueError("Not supported yet!")
         return self
 
 
@@ -144,29 +153,35 @@ def test_passing_two_modifiers():
 
 
 def test_system_message_modifier():
-    model = FakeToolCallingModel()
     messages_modifier = SystemMessage(content="Foo")
-    agent_1 = create_react_agent(model, [], messages_modifier=messages_modifier)
-    agent_2 = create_react_agent(model, [], state_modifier=messages_modifier)
+    agent_1 = create_react_agent(
+        FakeToolCallingModel(), [], messages_modifier=messages_modifier
+    )
+    agent_2 = create_react_agent(
+        FakeToolCallingModel(), [], state_modifier=messages_modifier
+    )
     for agent in [agent_1, agent_2]:
         inputs = [HumanMessage("hi?")]
         response = agent.invoke({"messages": inputs})
         expected_response = {
-            "messages": inputs + [AIMessage(content="Foo-hi?", id="0")]
+            "messages": inputs + [AIMessage(content="Foo-hi?", id="0", tool_calls=[])]
         }
         assert response == expected_response
 
 
 def test_system_message_string_modifier():
-    model = FakeToolCallingModel()
     messages_modifier = "Foo"
-    agent_1 = create_react_agent(model, [], messages_modifier=messages_modifier)
-    agent_2 = create_react_agent(model, [], state_modifier=messages_modifier)
+    agent_1 = create_react_agent(
+        FakeToolCallingModel(), [], messages_modifier=messages_modifier
+    )
+    agent_2 = create_react_agent(
+        FakeToolCallingModel(), [], state_modifier=messages_modifier
+    )
     for agent in [agent_1, agent_2]:
         inputs = [HumanMessage("hi?")]
         response = agent.invoke({"messages": inputs})
         expected_response = {
-            "messages": inputs + [AIMessage(content="Foo-hi?", id="0")]
+            "messages": inputs + [AIMessage(content="Foo-hi?", id="0", tool_calls=[])]
         }
         assert response == expected_response
 
@@ -584,3 +599,100 @@ def get_day_list(days: list[str]) -> list[str]:
         [AIMessage(content="", tool_calls=tool_calls)]
     )
     assert outputs[0].content == json.dumps(data, ensure_ascii=False)
+
+
+async def test_return_direct() -> None:
+    @dec_tool(return_direct=True)
+    def tool_return_direct(input: str) -> str:
+        """A tool that returns directly."""
+        return f"Direct result: {input}"
+
+    @dec_tool
+    def tool_normal(input: str) -> str:
+        """A normal tool."""
+        return f"Normal result: {input}"
+
+    first_tool_call = [
+        ToolCall(
+            name="tool_return_direct",
+            args={"input": "Test direct"},
+            id="1",
+        ),
+    ]
+    expected_ai = AIMessage(
+        content="Test direct",
+        id="0",
+        tool_calls=first_tool_call,
+    )
+    model = FakeToolCallingModel(tool_calls=[first_tool_call, []])
+    agent = create_react_agent(model, [tool_return_direct, tool_normal])
+
+    # Test direct return for tool_return_direct
+    result = agent.invoke(
+        {"messages": [HumanMessage(content="Test direct", id="hum0")]}
+    )
+    assert result["messages"] == [
+        HumanMessage(content="Test direct", id="hum0"),
+        expected_ai,
+        ToolMessage(
+            content="Direct result: Test direct",
+            name="tool_return_direct",
+            tool_call_id="1",
+            id=result["messages"][2].id,
+        ),
+    ]
+    second_tool_call = [
+        ToolCall(
+            name="tool_normal",
+            args={"input": "Test normal"},
+            id="2",
+        ),
+    ]
+    model = FakeToolCallingModel(tool_calls=[second_tool_call, []])
+    agent = create_react_agent(model, [tool_return_direct, tool_normal])
+    result = agent.invoke(
+        {"messages": [HumanMessage(content="Test normal", id="hum1")]}
+    )
+    assert result["messages"] == [
+        HumanMessage(content="Test normal", id="hum1"),
+        AIMessage(content="Test normal", id="0", tool_calls=second_tool_call),
+        ToolMessage(
+            content="Normal result: Test normal",
+            name="tool_normal",
+            tool_call_id="2",
+            id=result["messages"][2].id,
+        ),
+        AIMessage(content="Test normal-Test normal-Normal result: Test normal", id="1"),
+    ]
+
+    both_tool_calls = [
+        ToolCall(
+            name="tool_return_direct",
+            args={"input": "Test both direct"},
+            id="3",
+        ),
+        ToolCall(
+            name="tool_normal",
+            args={"input": "Test both normal"},
+            id="4",
+        ),
+    ]
+    model = FakeToolCallingModel(tool_calls=[both_tool_calls, []])
+    agent = create_react_agent(model, [tool_return_direct, tool_normal])
+    result = agent.invoke({"messages": [HumanMessage(content="Test both", id="hum2")]})
+    assert result["messages"] == [
+        HumanMessage(content="Test both", id="hum2"),
+        AIMessage(content="Test both", id="0", tool_calls=both_tool_calls),
+        ToolMessage(
+            content="Direct result: Test both direct",
+            name="tool_return_direct",
+            tool_call_id="3",
+            id=result["messages"][2].id,
+        ),
+        ToolMessage(
+            content="Normal result: Test both normal",
+            name="tool_normal",
+            tool_call_id="4",
+            id=result["messages"][3].id,
+        ),
+    ]