langgraph: allow create_react_agent to take empty tools (#2553)

libs/langgraph/langgraph/prebuilt/chat_agent_executor.py

@@ -212,6 +212,7 @@ 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.
+            If an empty list is provided, the agent will consist of a single LLM node without tool calling.
         state_schema: An optional state schema that defines graph state.
             Must have `messages` and `is_last_step` keys.
             Defaults to `AgentState` that defines those two keys.
@@ -540,19 +541,10 @@ class Agent,Tools otherClass
         # get the tool functions wrapped in a tool class from the ToolNode
         tool_classes = list(tool_node.tools_by_name.values())
 
-    if _should_bind_tools(model, tool_classes):
-        model = cast(BaseChatModel, model).bind_tools(tool_classes)
+    tool_calling_enabled = len(tool_classes) > 0
 
-    # Define the function that determines whether to continue or not
-    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__"
-        # Otherwise if there is, we continue
-        else:
-            return "tools"
+    if _should_bind_tools(model, tool_classes) and tool_calling_enabled:
+        model = cast(BaseChatModel, model).bind_tools(tool_classes)
 
     # we're passing store here for validation
     preprocessor = _get_model_preprocessing_runnable(
@@ -635,6 +627,30 @@ async def acall_model(state: AgentState, config: RunnableConfig) -> AgentState:
         # We return a list, because this will get added to the existing list
         return {"messages": [response]}
 
+    if not tool_calling_enabled:
+        # Define a new graph
+        workflow = StateGraph(state_schema or AgentState)
+        workflow.add_node("agent", RunnableCallable(call_model, acall_model))
+        workflow.set_entry_point("agent")
+        return workflow.compile(
+            checkpointer=checkpointer,
+            store=store,
+            interrupt_before=interrupt_before,
+            interrupt_after=interrupt_after,
+            debug=debug,
+        )
+
+    # Define the function that determines whether to continue or not
+    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__"
+        # Otherwise if there is, we continue
+        else:
+            return "tools"
+
     # Define a new graph
     workflow = StateGraph(state_schema or AgentState)
 

libs/langgraph/tests/test_prebuilt.py

@@ -102,6 +102,9 @@ def bind_tools(
         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):