Elon not disappointing us

It shows better performance than any current top rankers.

This is Chatbot Arena scores.

Most of all, they made it in such short period of time.

I want to test code generation with yesterday's article.

"Using LangChain, create a Python agent that employs the SerpAPIWrapper tool to perform real-time web searches for Grok 3. The agent should accept user queries, utilize the SerpAPIWrapper to fetch relevant information from the web, and return concise summaries of the search results."

Grok-3 generated like this:

import os
from langchain.agents import AgentExecutor, create_react_agent
from langchain import hub
from langchain.tools import Tool
from langchain_community.utilities import SerpAPIWrapper
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
from langchain.chains import LLMChain
from langchain_openai import OpenAI
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

class Grok3SearchAgent:
    def __init__(self):
        # Initialize the LLM (OpenAI in this case)
        self.llm = OpenAI(temperature=0.5)
        
        # Configure SerpAPI wrapper
        self.search = SerpAPIWrapper(
            serpapi_api_key=os.getenv("SERPAPI_API_KEY"),
            params={
                "engine": "google",
                "google_domain": "google.com",
                "gl": "us",
                "hl": "en"
            }
        )
        
        # Create custom prompt template
        self.prompt_template = PromptTemplate(
            input_variables=["query", "results"],
            template="""
            User query: {query}
            
            Search results: {results}
            
            Please provide a concise summary of the relevant information about Grok 3 based on the search results. Focus on key details and recent developments. Limit the response to 150 words or less.
            """
        )
        
        # Create summary chain
        self.summary_chain = LLMChain(
            llm=self.llm,
            prompt=self.prompt_template
        )
        
        # Define tools
        self.tools = [
            Tool(
                name="Search",
                func=self.search.run,
                description="Useful for searching current information about Grok 3 on the web."
            )
        ]
        
        # Initialize memory
        self.memory = ConversationBufferMemory(
            memory_key="chat_history",
            return_messages=True
        )
        
        # Pull the ReAct prompt template from LangChain Hub
        prompt = hub.pull("hwchase17/react")
        
        # Create the ReAct agent
        self.agent = create_react_agent(
            llm=self.llm,
            tools=self.tools,
            prompt=prompt
        )
        
        # Create agent executor
        self.agent_executor = AgentExecutor(
            agent=self.agent,
            tools=self.tools,
            memory=self.memory,
            verbose=True,
            handle_parsing_errors=True
        )

    def search_and_summarize(self, query: str) -> str:
        """
        Perform a search for Grok 3 information and return a summarized response.
        """
        try:
            # Run the agent to get search results
            search_results = self.agent_executor.invoke({
                "input": f"Search for current information about Grok 3: {query}"
            })
            
            # Extract the search results from the agent's output
            results = search_results.get('output', '')
            
            # Generate summary using the summary chain
            summary = self.summary_chain.run(
                query=query,
                results=results
            )
            
            return summary.strip()
            
        except Exception as e:
            return f"Error processing request: {str(e)}"

def main():
    # Initialize the agent
    agent = Grok3SearchAgent()
    
    print("Welcome to the Grok 3 Information Search Agent!")
    print("Enter your query about Grok 3 (or 'quit' to exit)")
    
    while True:
        query = input("\nQuery: ").strip()
        
        if query.lower() == 'quit':
            print("Goodbye!")
            break
            
        if not query:
            print("Please enter a valid query.")
            continue
            
        print("\nSearching...")
        result = agent.search_and_summarize(query)
        print("\nResult:")
        print(result)

if __name__ == "__main__":
    main()        

The code is more detailed like adding ConversationBufferMemory and prebuilt ReAct Agent and instantiating latest class Tool() that Trae and Copilot couldn't generate.

So this is best coding AI that I have ever tested but need more broad R&D.

Grok-3 is available in Grok app for X Premium Plus, which now starts at $40 per month

But if you want to try it you can use Chatbot Arena