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docs: replace deprecated initialize_agent with create_react_agent and non-deprecated functions (#31361)

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**Description:**  
This PR updates approximately 4' occurences of the deprecated
`initialize_agent` function in LangChain documentation and examples,
replacing it with the recommended `create_react_agent` and pattern. It
also refactors related examples to align with current best practices.

**Issue:**  
Partially Fixes #29277


**Dependencies:**  
None

**X handle:**  
@TK1475

---------

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This commit is contained in:
Ahmed Hassan 2025-06-04 19:24:30 +05:00 committed by GitHub
parent 4ec46aeb73
commit 81db124351
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4 changed files with 51 additions and 32 deletions
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17
docs/docs/integrations/providers/google_serper.mdx
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@ -22,16 +22,16 @@ You can use it as part of a Self Ask chain:
```python ```python
from langchain_community.utilities import GoogleSerperAPIWrapper from langchain_community.utilities import GoogleSerperAPIWrapper
from langchain_openai import OpenAI from langchain_core.tools import Tool
from langchain.agents import initialize_agent, Tool from langchain_openai import ChatOpenAI
from langchain.agents import AgentType from langgraph.prebuilt import create_react_agent
import os import os
os.environ["SERPER_API_KEY"] = "" os.environ["SERPER_API_KEY"] = ""
os.environ['OPENAI_API_KEY'] = "" os.environ['OPENAI_API_KEY'] = ""
llm = OpenAI(temperature=0) llm = ChatOpenAI(temperature=0)
search = GoogleSerperAPIWrapper() search = GoogleSerperAPIWrapper()
tools = [ tools = [
Tool( Tool(
@ -41,8 +41,13 @@ tools = [
) )
] ]
self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True) agent = create_react_agent(llm, tools)
self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open champion?")
result = agent.invoke({
"messages": [("human", "What is the hometown of the reigning men's U.S. Open champion?")]
})
print(result)
``` ```
#### Output #### Output

16
docs/docs/integrations/providers/searchapi.mdx
View File

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

22
docs/docs/integrations/tools/chatgpt_plugins.ipynb
View File

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

28
docs/docs/integrations/tools/nasa.ipynb
View File

@ -32,6 +32,19 @@
"%pip install -qU langchain-community" "%pip install -qU langchain-community"
] ]
}, },
{
"cell_type": "code",
"execution_count": null,
"id": "cf0cb2e5",
"metadata": {},
"outputs": [],
"source": [
"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", "cell_type": "code",
"execution_count": null, "execution_count": null,
@ -39,17 +52,12 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "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",
"\n",
"llm = OpenAI(temperature=0, openai_api_key=\"\")\n", "llm = OpenAI(temperature=0, openai_api_key=\"\")\n",
"nasa = NasaAPIWrapper()\n", "nasa = NasaAPIWrapper()\n",
"toolkit = NasaToolkit.from_nasa_api_wrapper(nasa)\n", "toolkit = NasaToolkit.from_nasa_api_wrapper(nasa)\n",
"agent = initialize_agent(\n", "tools = toolkit.get_tools()\n",
" toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n", "\n",
")" "agent = create_react_agent(llm, tools)"
] ]
}, },
{ {
@ -96,7 +104,7 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3 (ipykernel)", "display_name": "AI_env",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@ -110,7 +118,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.11.5" "version": "3.12.2"
} }
}, },
"nbformat": 4, "nbformat": 4,