How To Search Content Which ChatGPT Can’t Find Today — OpenAI | Python

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In this article, I’ll show you how can you get your hands dirty with Langchain agents. If you are not aware what Langchain is, I would recommend you to watch my recording here, wherein I just briefed about it.

Langchain agents are the ones who uses LLM to determine what needs to be done and in which order. You can have a quick look at the available agents in the documentation, but I’ll list them here too.

  • zero-shot-react-description
  • react-docstore
  • self-ask-with-search
  • conversational-react-description

Here agents work via tools and tools are nothing but those are functions which will be used by agents to interact with outside world.

List of tools which are available today are:

  • python_repl
  • serpapi
  • wolfram-alpha
  • requests
  • terminal
  • pal-math
  • pal-colored-objects
  • llm-math
  • open-meteo-api
  • news-api
  • tmdb-api
  • google-search
  • searx-search
  • google-serper, etc

In this article, I’m covering serpapi.

Import Required Packages

In order to get started, we need to import these below packages:

from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.llms import OpenAI
import os
Python

Set LLM And Initialize Agent

Next we need to set LLM, which is OpenAI here and then we need to initialize agent as shown below:

llm = OpenAI(temperature=0)
tools = load_tools([“serpapi”])
agent = initialize_agent(tools, llm, agent=”zero-shot-react-description”)

If this is what interests you, check out my complete article here.

Here is the video post too.



Comments

  1. AnonymousJune 10, 2026 at 11:45 AM

    great

    ReplyDelete
  2. AnonymousJune 10, 2026 at 11:45 AM

    This is a practical and informative article that explains how users can effectively search for and retrieve information generated by ChatGPT. As AI-powered tools become increasingly integrated into daily workflows, understanding how to interact with them efficiently is essential for maximizing productivity and obtaining relevant results. The article provides useful guidance for both new and experienced users of generative AI platforms.

    ReplyDelete
  3. AnonymousJune 10, 2026 at 11:46 AM

    The growing adoption of AI-driven tools is transforming how users access information, automate tasks, and improve decision-making processes. Understanding the technologies behind these systems can help learners stay aligned with emerging industry trends and innovations. Final Year Projects for IT provide practical exposure to modern software solutions and technology-driven applications.

    ReplyDelete
  4. AnonymousSeptember 18, 2026 at 9:27 AM

    The article provides a practical introduction to LangChain agents by explaining how agents use an LLM to determine the actions and sequence required to complete a task. The distinction between agents and tools is particularly useful, since tools act as functions that allow the agent to interact with external systems.

    ReplyDelete
  5. AnonymousSeptember 18, 2026 at 9:27 AM

    The OpenAI-based initialization is another useful part of the example, as it shows the basic connection between an LLM and an agent framework. Exploring this pattern further can provide a good foundation for understanding how applications built around ChatGPT Developer Training can incorporate external tools and real-time information sources.

    ReplyDelete
  6. AnonymousSeptember 18, 2026 at 9:27 AM

    I also found the list of available LangChain tools helpful because it shows how the same agent architecture can be extended beyond search to Python execution, mathematical reasoning, APIs, weather, news, and other external services. This makes LangChain agents interesting for building practical AI Tools Training projects rather than limiting LLMs to simple question-and-answer interactions.

    ReplyDelete
  7. AnonymousSeptember 18, 2026 at 9:28 AM

    The discussion of LangChain agents and their use of LLMs with external tools provides a useful foundation for understanding how generative AI applications can move beyond simple text generation. The combination of agents, search tools such as SerpAPI, and Python-based development can be especially relevant when exploring Gen Ai Projects For Final Year, where students can apply these concepts to practical AI workflows.

    ReplyDelete

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