Fix langchain import by using langgraph prebuilt agent

src/agent.py

@@ -4,8 +4,7 @@ import threading
 import time
 from langchain_core.tools import tool
 from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.agents import create_tool_calling_agent, AgentExecutor
-from langchain_core.prompts import ChatPromptTemplate
+from langgraph.prebuilt import create_react_agent
 from temporalio import activity
 
 os.environ["GOOGLE_API_KEY"] = "AIzaSyDKYcgVPN2oJirwzf_td3sBYMnXHWfVphU"
@@ -55,23 +54,14 @@ def run_agent_activity(payload: dict) -> str:
 
     llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro")
     
-    prompt_text = f"""You are a highly capable AI assistant operating in a WhatsApp group.
+    system_prompt = f"""You are a highly capable AI assistant operating in a WhatsApp group.
     
 The user ({sender}) has sent a message.
 Their WhatsApp Group/Chat ID (target_jid) is: {target_jid}.
 Whenever you need to send a message back, use the `send_whatsapp` tool and pass {target_jid} as the target_jid!
 If they ask for a reminder, use the `set_reminder` tool and pass {target_jid} as the target_jid!
-
-User Request: {{input}}
 """
-
-    prompt = ChatPromptTemplate.from_messages([
-        ("system", prompt_text),
-        ("placeholder", "{agent_scratchpad}"),
-    ])
     
-    agent = create_tool_calling_agent(llm, tools, prompt)
-    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
-    
-    result = agent_executor.invoke({"input": user_request})
-    return result["output"]
+    agent = create_react_agent(llm, tools, state_modifier=system_prompt)
+    result = agent.invoke({"messages": [("user", user_request)]})
+    return result["messages"][-1].content