Update agent.py

agent.py

@@ -1,15 +1,13 @@
 """LangGraph Agent"""
 import os
 from dotenv import load_dotenv
-from langchain.agents import create_agent
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_groq import ChatGroq
 from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
 from langchain_core.messages import SystemMessage
-
+from langgraph.prebuilt import create_react_agent  # Importação corrigida para o LangGraph
 from tools import multiply, wiki_search, web_search, arvix_search, execute_python_code, YouTubeVideoAnalysisTool, read_excel_format, transcribe_mp3
 
-
 load_dotenv()
 
 _AGENT_DIR = os.path.dirname(os.path.abspath(__file__))
@@ -32,29 +30,27 @@ tools = [
     transcribe_mp3,
 ]
 
-
 # Build graph function
 def build_graph(provider: str | None = None):
-
     if provider is None:
         provider = os.getenv("LLM_PROVIDER", "groq").strip().lower()
 
     if provider == "google":
         llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature=0)
-
     elif provider == "groq":
         model = os.getenv("GROQ_MODEL")
         seed = int(os.getenv("GROQ_SEED", "42"))
-        llm = ChatGroq(model=model, temperature=0, model_kwargs={"seed": seed})
-        
+        llm = ChatGroq(model=model, temperature=0, model_kwargs={"seed": seed})            
     elif provider == "huggingface":
         # TODO: Add huggingface endpoint. crédits tres limités...
         llm = ChatHuggingFace(
-            llm=HuggingFaceEndpoint(
+            llm=HuggingFaceEndpoint(                
                 url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
                 temperature=0,
             ),
         )
     else:
         raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
-    return create_agent(llm, tools, system_prompt=sys_msg)
+
+    # Retorno corrigido usando a estrutura exata exigida pelo LangGraph
+    return create_react_agent(llm, tools, state_modifier=[sys_msg])