Handle API rate limit reached

app.py

@@ -4,6 +4,7 @@ import requests
 import inspect
 import pandas as pd
 from react_agent import ReActAgent
+import time
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -70,12 +71,64 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            submitted_answer = agent(item)
+            submitted_answer = agent(str(item))
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+            print(results_log[-1])
         except Exception as e:
-             print(f"Error running agent on task {task_id}: {e}")
-             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+            print(f"Error running agent on task {task_id}: {e}")
+            # results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+
+            try:
+                # Build new client with other provider and retry
+                agent = ReActAgent(provider="Google", model="gemini-2.5-pro")
+                submitted_answer = agent(str(item))
+                answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+                results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+                print(results_log[-1])
+
+            except Exception as e:
+                print(f"Error running agent on task {task_id}: {e}")
+
+                try:
+                    agent = ReActAgent(provider="Mistral", model="mistral-large-latest")
+                    submitted_answer = agent(str(item))
+                    answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+                    results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+                    print(results_log[-1])
+
+                except Exception as e:
+                    print(f"Error running agent on task {task_id}: {e}")
+
+                    try:
+                        agent = ReActAgent(provider="Groq", model="llama-3.3-70b-versatile")
+                        submitted_answer = agent(str(item))
+                        answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+                        results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+                        print(results_log[-1])
+
+                    except Exception as e:
+                        print(f"Error running agent on task {task_id}: {e}")
+                        try:
+                            agent = ReActAgent(provider="Groq", model="deepseek-r1-distill-llama-70b")
+                            submitted_answer = agent(str(item))
+                            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+                            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+                            print(results_log[-1])
+                        except Exception as e:
+                            print(f"Error running agent on task {task_id}: {e}")
+                            try:
+                                agent = ReActAgent(provider="Groq", model="qwen-qwq-32b")
+                                submitted_answer = agent(str(item))
+                                answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+                                results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+                                print(results_log[-1])
+                            except Exception as e:
+                                print(f"Error running agent on task {task_id}: {e}")
+                                results_log.append({"Task ID": task_id, "Question": question_text, "Error": e})
+    
+    # Wait for a while to ensure the agent is not overwhelmed
+    time.sleep(10)
 
     if not answers_payload:
         print("Agent did not produce any answers to submit.")

custom_tools.py

@@ -7,10 +7,10 @@ from bs4 import BeautifulSoup
 import pandas as pd
 from dotenv import load_dotenv 
 from mistralai import Mistral
+from groq import Groq
 
 from requests.exceptions import RequestException, Timeout, TooManyRedirects
-import errno
-from typing import Optional, List, Union
+from typing import List, Union
 from youtube_transcript_api._errors import (
     TranscriptsDisabled,
     NoTranscriptFound,
@@ -20,7 +20,57 @@ from youtube_transcript_api._errors import (
 from urllib.parse import urlparse, parse_qs
 
 from langchain_core.tools import tool
-from langchain_community.tools import DuckDuckGoSearchResults
+from langchain_community.tools import BraveSearch
+
+@tool
+def web_search(query: str) -> str:
+    """
+    Search the web using Brave Search and return the top 3 results.
+    Before starting any search, you must first think about the TRUE necessary steps that are required to answer the question.
+    If you need to search for information, the query should be just a few keywords that can be used to find the desired web page.
+    If the question specifies a date, do not put the date into the query
+    Args:
+        query (str): The search query.
+        Returns:
+        str: A string containing the top 3 search results.
+    """
+    api_key = os.getenv("BRAVE")
+    tool = BraveSearch.from_api_key(api_key=api_key, search_kwargs={"count":3, "spellcheck": False})
+    results = tool.invoke(query)
+    return results
+
+@tool
+def url_search(url: str) -> str:
+    """
+    Access a specific URL provided by the web_search tool call.
+    
+    Args:
+        url (str): The URL to access.
+
+    Returns:
+        str: The HTML content of the accessed URL or an error message.
+    """
+    try:
+        response = requests.get(url, timeout=10)
+        response.raise_for_status()
+        soup = BeautifulSoup(response.text, 'html.parser')
+        
+        for tag in soup(['script']):
+            tag.decompose()
+
+        # Extract and return the body of the page
+        body_content = soup.find('body')
+        if body_content:
+            return body_content.get_text(separator='\n', strip=True)
+        else:
+            return "No body content found in the accessed URL."
+            
+    except Timeout:
+        return "Request timed out while trying to access the URL."
+    except TooManyRedirects:
+        return "Too many redirects while trying to access the URL."
+    except RequestException as e:
+        return f"Failed to access the URL. Error: {e}"
 
 
 @tool
@@ -322,7 +372,8 @@ def transcript_audio(task_id: str, file_name: str) -> str:
 
 custom_tools = [
     wiki_search,
-    DuckDuckGoSearchResults(),
+    web_search,
+    url_search,
     # add_numbers,
     sum_excel_cols,
     youtube_transcript,

react_agent.py

@@ -2,37 +2,67 @@ import os
 from dotenv import load_dotenv
 from langchain_core.messages import HumanMessage
 from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_mistralai import ChatMistralAI
+from langchain_groq import ChatGroq
 from langgraph.prebuilt import create_react_agent
 from custom_tools import custom_tools
 
 class ReActAgent:
-    def __init__(self):
+    def __init__(self, provider: str="Google", model: str="gemini-2.5-flash"):
         load_dotenv()
-        os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE")
-        # Initialize your LLM
-        llm = ChatGoogleGenerativeAI(
-            model="gemini-2.5-flash",
-            temperature=0,
-            max_retries=5
-        )
-        sys_prompt = "You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, DON'T use comma to write your number NEITHER use units such as $ or percent sign unless specified otherwise. If you are asked for a string, DON'T use articles, NEITHER abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.\n\n\n\
-        You will be provided with tools to help you answer questions. If you are asked to look for an information or make a calculation, absolutely use the tools provided to you. You should AVOID calculating by yourself and ABSOLUTELY use appropriate tools. If needed, use one tool first, then use the output of that tool as an input to another thinking then to the use of another tool."
+
+        if provider=="Google":
+            os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE")
+            # Initialize your LLM
+            llm = ChatGoogleGenerativeAI(
+                model=model,
+                temperature=0,
+                max_retries=5
+            )
+
+        if provider=="Mistral":
+            os.environ["MISTRAL_API_KEY"] = os.getenv("MISTRAL")
+            # Initialize your LLM
+            llm = ChatMistralAI(
+                model=model,
+                temperature=0,
+                max_retries=5
+            )
+
+        if provider=="Groq":
+            os.environ["GROQ_API_KEY"] = os.getenv("GROQ")
+            # Initialize your LLM
+            llm = ChatGroq(
+                model=model,
+                temperature=0,
+                max_retries=5
+            )
+
+        sys_prompt = "You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, DON'T use comma to write your number NEITHER use units such as $ or percent sign unless specified otherwise. If you are asked for a string, DON'T use articles, NEITHER abbreviations (e.g. for cities) capitalize the first letter, and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending, unless the first letter capitalization, whether the element to be put in the list is a number or a string.\n\n\n \
+        \n \
+        You will be provided with tools to help you answer questions.\n \
+        If you are asked to make a calculation, absolutely use the tools provided to you. You should AVOID calculating by yourself and ABSOLUTELY use appropriate tools.\n \
+        If you need to search for information, use the web_search tool rather than wiki_search, unless the question specifies searching on wikipedia. After using the web_search tool, look for the first URL provided with the url_search tool and ask yourself if the answer is in the tool response. If it is, answer the question. If not, search on other links.\n \
+        \n \
+        If needed, use one tool first, then use the output of that tool as an input to another thinking then to the use of another tool."
         # Build the ReAct agent
         self.agent = create_react_agent(
             model=llm,
             tools=custom_tools,
             prompt=sys_prompt  
         )
-        print("ReActAgent initialized.")
+        print(f"ReActAgent initialized with {provider} - {model}.")
 
     def __call__(self, question: str) -> str:
         # Wrap question in HumanMessage to match React expectations
         input_msg = HumanMessage(content=question)
         # Invoke the agent; returns a stream or single response
         out = self.agent.invoke({"messages": [input_msg]})
+        for o in out["messages"]:
+            print(o)
         # The last message contains the agent's reply
         reply = out["messages"][-1].content
         # Optionally, strip out “Final Answer:” headers
-        if "Final Answer:" in reply:
-            reply = reply.split("Final Answer:")[-1].strip()
+        if "FINAL ANSWER: " in reply:
+            reply = reply.split("FINAL ANSWER: ")[-1].strip()
         return reply

requirements.txt

@@ -4,6 +4,7 @@ gradio
 langchain==0.3.26 
 langchain_community==0.3.26 
 langchain-google-genai==2.1.6
+langchain-groq==0.3.5
 langchain-mistralai==0.2.10
 langgraph==0.4.10
 mistralai==1.7.0