Revert "docs: replace deprecated initialize_agent with create_react_agent and non-deprecated functions" (#31492)

Reverts #31361

Author: eyurtsev

docs/docs/integrations/providers/google_serper.mdx

@@ -22,16 +22,16 @@ You can use it as part of a Self Ask chain:
 
 ```python
 from langchain_community.utilities import GoogleSerperAPIWrapper
-from langchain_core.tools import Tool
-from langchain_openai import ChatOpenAI
-from langgraph.prebuilt import create_react_agent
+from langchain_openai import OpenAI
+from langchain.agents import initialize_agent, Tool
+from langchain.agents import AgentType
 
 import os
 
 os.environ["SERPER_API_KEY"] = ""
 os.environ['OPENAI_API_KEY'] = ""
 
-llm = ChatOpenAI(temperature=0)
+llm = OpenAI(temperature=0)
 search = GoogleSerperAPIWrapper()
 tools = [
     Tool(
@@ -41,13 +41,8 @@ tools = [
     )
 ]
 
-agent = create_react_agent(llm, tools)
-
-result = agent.invoke({
-    "messages": [("human", "What is the hometown of the reigning men's U.S. Open champion?")]
-})
-
-print(result)
+self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)
+self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open champion?")
 ```
 
 #### Output

docs/docs/integrations/providers/searchapi.mdx

@@ -22,12 +22,13 @@ You can use it as part of a Self Ask chain:
 ```python
 from langchain_community.utilities import SearchApiAPIWrapper
 from langchain_openai import OpenAI
-from langchain.agents import Tool, create_openai_functions_agent, AgentExecutor
+from langchain.agents import initialize_agent, Tool
+from langchain.agents import AgentType
 
 import os
 
-os.environ["SEARCHAPI_API_KEY"] = "<your-searchapi-key>"
-os.environ['OPENAI_API_KEY'] = "<your-openai-key>"
+os.environ["SEARCHAPI_API_KEY"] = ""
+os.environ['OPENAI_API_KEY'] = ""
 
 llm = OpenAI(temperature=0)
 search = SearchApiAPIWrapper()
@@ -39,13 +40,8 @@ tools = [
     )
 ]
 
-# Create agent and executor
-agent = create_openai_functions_agent(llm=llm, tools=tools)
-agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
-
-# Run the agent
-response = agent_executor.invoke({"input": "Who lived longer: Plato, Socrates, or Aristotle?"})
-print(response["output"])
+self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)
+self_ask_with_search.run("Who lived longer: Plato, Socrates, or Aristotle?")
 ```
 
 #### Output

docs/docs/integrations/tools/chatgpt_plugins.ipynb

@@ -52,18 +52,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "d41405b5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import AgentExecutor, create_react_agent, load_tools\n",
+    "from langchain.agents import AgentType, initialize_agent, load_tools\n",
     "from langchain_openai import ChatOpenAI"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "d9e61df5",
    "metadata": {},
    "outputs": [],
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "edc0ea0e",
    "metadata": {},
    "outputs": [
@@ -113,15 +113,13 @@
    ],
    "source": [
     "llm = ChatOpenAI(temperature=0)\n",
-    "\n",
     "tools = load_tools([\"requests_all\"])\n",
     "tools += [tool]\n",
     "\n",
-    "agent = create_react_agent(llm=llm, tools=tools)\n",
-    "\n",
-    "agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)\n",
-    "\n",
-    "agent_executor.invoke({\"input\": \"what t shirts are available in klarna?\"})"
+    "agent_chain = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")\n",
+    "agent_chain.run(\"what t shirts are available in klarna?\")"
    ]
   },
   {
@@ -135,7 +133,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "AI_env",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -149,7 +147,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/tools/nasa.ipynb

@@ -35,29 +35,21 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cf0cb2e5",
+   "id": "648a2cb2-308e-4b2e-9b73-37109be4e258",
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.agents import AgentType, initialize_agent\n",
     "from langchain_community.agent_toolkits.nasa.toolkit import NasaToolkit\n",
     "from langchain_community.utilities.nasa import NasaAPIWrapper\n",
     "from langchain_openai import OpenAI\n",
-    "from langgraph.prebuilt import create_react_agent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "648a2cb2-308e-4b2e-9b73-37109be4e258",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "\n",
     "llm = OpenAI(temperature=0, openai_api_key=\"\")\n",
     "nasa = NasaAPIWrapper()\n",
     "toolkit = NasaToolkit.from_nasa_api_wrapper(nasa)\n",
-    "tools = toolkit.get_tools()\n",
-    "\n",
-    "agent = create_react_agent(llm, tools)"
+    "agent = initialize_agent(\n",
+    "    toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -104,7 +96,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "AI_env",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -118,7 +110,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,