Update langgraph vendor agent for new create_agent API

cookbook/langgraph-vendor-agent/README.md

@@ -9,7 +9,7 @@ START → planner → executor (react) → reviewer → (loop or compiler) → E
 ```
 
 - **Planner**: Reads the task and produces a JSON plan of 4-5 steps
-- **Executor**: A `create_react_agent` subgraph that handles multi-round tool calling per step
+- **Executor**: A LangGraph react subgraph that handles multi-round tool calling per step
 - **Reviewer**: Marks step done, routes back to executor or forward to compiler
 - **Compiler**: Combines all step results into the final formatted response
 

cookbook/langgraph-vendor-agent/src/langgraph_vendor_agent/plan_and_execute.py

@@ -8,13 +8,22 @@
 
 import json
 import operator
+from importlib import import_module
 from typing import Annotated, Any, TypedDict
 
 from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
 from langchain_core.tools import BaseTool
 from langgraph.graph import END, START, StateGraph
-from langgraph.prebuilt import create_react_agent
+
+# Prefer the new langchain.agents.create_agent; fall back to the legacy
+# langgraph.prebuilt.create_react_agent for older installs.
+_USE_NEW_CREATE_AGENT = False
+try:
+    _create_agent_fn = import_module("langchain.agents").create_agent
+    _USE_NEW_CREATE_AGENT = True
+except (ModuleNotFoundError, AttributeError):
+    _create_agent_fn = import_module("langgraph.prebuilt").create_react_agent
 
 
 class PlanStep(TypedDict):
@@ -84,17 +93,17 @@ def build_plan_and_execute_graph(
     else:
         exec_tools = list(tools)
 
-    react_executor = create_react_agent(
-        model,
-        exec_tools,
-        prompt=SystemMessage(
-            content=(
-                "You are an execution agent. Use the available tools to "
-                "complete the step you are given. Call as many tools as needed. "
-                "After gathering data, summarize your findings."
-            )
-        ),
+    executor_prompt = SystemMessage(
+        content=(
+            "You are an execution agent. Use the available tools to "
+            "complete the step you are given. Call as many tools as needed. "
+            "After gathering data, summarize your findings."
+        )
     )
+    if _USE_NEW_CREATE_AGENT:
+        react_executor = _create_agent_fn(model, tools=exec_tools, system_prompt=executor_prompt)
+    else:
+        react_executor = _create_agent_fn(model, exec_tools, prompt=executor_prompt)
 
     # ---- planner ----
     def planner(state: PlanExecState) -> dict[str, Any]:

cookbook/langgraph-vendor-agent/tests/test_create_agent_compat.py

@@ -0,0 +1,82 @@
+"""Tests for create_agent / create_react_agent compatibility in plan_and_execute."""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock, patch
+
+from langchain_core.messages import SystemMessage
+
+
+def test_build_graph_calls_create_fn_with_correct_kwarg() -> None:
+    """The executor node should pass system_prompt= or prompt= depending on the resolved API."""
+    from langgraph_vendor_agent import plan_and_execute as module
+
+    mock_fn = MagicMock(return_value=MagicMock())
+    original_fn = module._create_agent_fn
+    original_flag = module._USE_NEW_CREATE_AGENT
+
+    # --- new API path (system_prompt=) ---
+    module._create_agent_fn = mock_fn
+    module._USE_NEW_CREATE_AGENT = True
+    try:
+        model = MagicMock()
+        tool = MagicMock()
+        tool.name = "server__tool1"
+        tools = [tool]
+        module.build_plan_and_execute_graph(model, tools, safe_tool_filter=False)
+        _, kwargs = mock_fn.call_args
+        assert "system_prompt" in kwargs
+        assert isinstance(kwargs["system_prompt"], SystemMessage)
+    finally:
+        module._create_agent_fn = original_fn
+        module._USE_NEW_CREATE_AGENT = original_flag
+
+
+def test_build_graph_calls_legacy_fn_with_prompt_kwarg() -> None:
+    """When falling back to create_react_agent, prompt= should be used."""
+    from langgraph_vendor_agent import plan_and_execute as module
+
+    mock_fn = MagicMock(return_value=MagicMock())
+    original_fn = module._create_agent_fn
+    original_flag = module._USE_NEW_CREATE_AGENT
+
+    module._create_agent_fn = mock_fn
+    module._USE_NEW_CREATE_AGENT = False
+    try:
+        model = MagicMock()
+        tool = MagicMock()
+        tool.name = "server__tool1"
+        tools = [tool]
+        module.build_plan_and_execute_graph(model, tools, safe_tool_filter=False)
+        _, kwargs = mock_fn.call_args
+        assert "prompt" in kwargs
+        assert isinstance(kwargs["prompt"], SystemMessage)
+    finally:
+        module._create_agent_fn = original_fn
+        module._USE_NEW_CREATE_AGENT = original_flag
+
+
+def test_import_fallback_to_langgraph_prebuilt() -> None:
+    """When langchain.agents has no create_agent, we fall back to langgraph.prebuilt."""
+    from importlib import import_module as real_import_module
+
+    def fake_import_module(name: str) -> object:
+        if name == "langchain.agents":
+            mod = MagicMock(spec=[])  # spec=[] means no attributes
+            del mod.create_agent  # ensure AttributeError
+            return mod
+        return real_import_module(name)
+
+    with patch("langgraph_vendor_agent.plan_and_execute.import_module", fake_import_module):
+        # Re-run the import logic
+        try:
+            _create_agent_fn = getattr(fake_import_module("langchain.agents"), "create_agent")
+            use_new = True
+        except (ModuleNotFoundError, AttributeError):
+            _create_agent_fn = getattr(real_import_module("langgraph.prebuilt"), "create_react_agent")
+            use_new = False
+
+    assert not use_new
+    # The fallback should resolve to the real create_react_agent
+    from langgraph.prebuilt import create_react_agent
+    assert _create_agent_fn is create_react_agent