Adding a loop in case rate limits are reached or exceeded in initial run

paper.py

@@ -1,6 +1,7 @@
 import os
 import json
 import logging
+import time
 from datetime import datetime, timedelta
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain.schema import SystemMessage, HumanMessage
@@ -32,7 +33,7 @@ class Task:
 google_api_key = os.getenv("GEMINI_API_KEY")  # 실제 Google API 키 사용
 if not google_api_key:
     logging.error("GEMINI_API_KEY is not set in the environment variables.")
-llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", google_api_key=google_api_key)
+llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash-lite", google_api_key=google_api_key)
 
 # -------------------------------------------------------------------------------
 # Define Academic Research Agents
@@ -169,6 +170,7 @@ def run_task(task: Task, input_text: str) -> str:
     Executes the given task using the associated agent's LLM and returns the response content.
     """
     try:
+        
         if not isinstance(task, Task):
             raise ValueError(f"Expected 'task' to be an instance of Task, got {type(task)}")
         if not hasattr(task, 'agent') or not isinstance(task.agent, Agent):
@@ -190,10 +192,26 @@ def run_task(task: Task, input_text: str) -> str:
             SystemMessage(content=system_input),
             HumanMessage(content=task_input)
         ]
+        
+        time.sleep(5) #delay inbetween requests to avoid reaching max quota and program terminating 
+        
         response = task.agent.llm.invoke(messages)
+        
         if not response or not response.content:
             raise ValueError("Empty response from LLM.")
         return response.content
     except Exception as e:
         logging.error(f"Error in task '{task.agent.role}': {e}")
+        
+        if "429" in str(e) or "quota" in str(e).lower():
+            logging.info("Rate limit hit, waiting 10 seconds and retrying...")
+            time.sleep(10)
+            try:
+                response = task.agent.llm.invoke(messages)
+                if not response or not response.content:
+                    raise ValueError("Empty response from LLM.")
+                return response.content
+            except Exception as retry_error:
+                logging.error(f"Retry failed: {retry_error}")
+                return f"Error: Rate limit exceeded. Please wait a few minutes and try again."
         return f"Error in {task.agent.role}: {e}"