Trae: ByteDance's free coding AI

I tested new ByteDance's code generation to compare Github Copilot.

This is the prompt:

"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."

This is Trae's answer:

I apologize, but I should point out that there might be a misunderstanding. Grok 3 hasn't been officially announced or released by xAI/X (formerly Twitter) as of now. However, I can help you create a Python agent using LangChain that performs web searches using SerpAPIWrapper for general queries.

Here's how to create the search agent:

First, make sure you have the required packages installed:

pip install langchain serpapi python-dotenv

Now, let's create the search agent:

search_agent.py

from langchain.agents import initialize_agent, AgentType
from langchain.tools import SerpAPIWrapper
from langchain.chat_models import ChatOpenAI
from dotenv import load_dotenv
import os

# Load environment variables
load_dotenv()

def create_search_agent():
    # Initialize the search tool
    search = SerpAPIWrapper()

    # Initialize the language model
    llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo") 

    # Create a list of tools for the agent
    tools = [
        {
            "name": "Search",
            "func": search.run,
            "description": "Useful for searching information on the internet"
        }
    ]   

    # Initialize the agent
    agent = initialize_agent(
        tools=tools,
        llm=llm,
        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
        verbose=True
    )   

    return agent

def process_query(query: str):
    try:
        agent = create_search_agent()
        response = agent.run(f"Search for information about: {query}. Provide a concise summary.")
        return response
    except Exception as e:
        return f"An error occurred: {str(e)}"

if name == "__main__":
    # Example usage
    query = input("Enter your search query: ")
    result = process_query(query)
    print("\nResult:", result)        

Before running the code, create a .env file in your project root:

OPENAI_API_KEY=your_openai_api_key

SERPAPI_API_KEY=your_serpapi_api_key

This agent will:

1. Use SerpAPIWrapper to perform web searches

2. Process the search results using ChatGPT

3. Return concise summaries of the information found

To use the agent:

1. Replace the API keys in the .env file with your actual OpenAI and SerpAPI keys

2. Run the script and enter your query

3. The agent will search the web and provide a summarized response

The agent uses:

- ChatGPT (gpt-3.5-turbo) as the base model

- Zero-shot reasoning for processing the search results

- SerpAPI for web searches

- Environment variables for secure API key storage

You can customize the agent further by:

- Adjusting the temperature parameter for more/less creative responses

- Adding more tools to the agent's toolkit

- Modifying the prompt template

- Using different agent types from LangChain

This is Github Copilot's answer:

Sure, here's a step-by-step guide to creating a Python agent using LangChain and the SerpAPIWrapper tool to perform real-time web searches and return concise summaries of the search results.

1.Install Required Packages: First, ensure you have the necessary packages installed. You can install them using pip:

?? pip install langchain serpapi??

2.Set Up SerpAPI: You need an API key from SerpAPI. You can get one by signing up at SerpAPI.

3.Create the Agent: Below is a Python script that sets up the agent using LangChain and SerpAPIWrapper:

   from langchain import LangChain
   from langchain.tools import SerpAPIWrapper
   from langchain.agents import AgentExecutor, Tool

   # Initialize SerpAPIWrapper with your API key
   serp_api_key = "YOUR_SERPAPI_KEY"
   serp_api_wrapper = SerpAPIWrapper(api_key=serp_api_key)

   # Define a function to fetch and summarize search results
   def fetch_and_summarize(query):
       # Perform the search
       search_results = serp_api_wrapper.search(query)
       
       # Extract and summarize the results
       summaries = []
       for result in search_results['organic_results']:
           title = result.get('title', 'No title')
           snippet = result.get('snippet', 'No snippet')
           summaries.append(f"{title}: {snippet}")
       
       return "\n".join(summaries)

   # Create a LangChain agent
   class SearchAgent:
       def __init__(self, tool):
           self.tool = tool

       def run(self, query):
           return self.tool(query)

   # Initialize the agent with the fetch_and_summarize function
   search_agent = SearchAgent(fetch_and_summarize)

   # Example usage
   if __name__ == "__main__":
       user_query = "Grok 3"
       result = search_agent.run(user_query)
       print(result)        

4. Explanation:

? SerpAPIWrapper: This is used to interact with the SerpAPI service.

? fetch_and_summarize: This function performs the search using SerpAPIWrapper and then extracts and summarizes the search results.

? SearchAgent: This class represents the agent that uses the fetch_and_summarize function to process user queries.

? Example usage: The script demonstrates how to use the agent to perform a search for "Grok 3" and print the summarized results.

Replace "YOUR_SERPAPI_KEY" with your actual SerpAPI key. This script sets up a simple agent that can be expanded with more sophisticated features as needed.

I think Trae’s Code is a more integrated and robust solution that leverages LangChain’s advanced agent system and ChatGPT for dynamic, natural language summarization. It’s designed for extensibility and secure configuration through environment variables. But, GitHub Copilot Code provides a simpler, more direct approach by manually processing search results without the overhead of LLM interaction, making it easier to understand but less powerful for complex summarization tasks.

One more thing,

Trae is FREE.