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  4. Elevating LLMs With Tool Use: A Simple Agentic Framework Using LangChain

Elevating LLMs With Tool Use: A Simple Agentic Framework Using LangChain

Build a smart agent with LangChain that allows LLMs to look for the latest trends, search the web, and summarize results using real-time tool calling.

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Arjun Bali user avatar
Arjun Bali
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Jun. 17, 25 · Tutorial
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Large Language Models (LLMs) are significantly changing the way we interact with data and generate insights. But their real superpower lies in the ability to connect with external tools. Tool calling turns LLMs into agents capable of browsing the web, querying databases, and generating content — all from a simple natural language prompt.

In this article, we go one step beyond single-tool agents and show how to build a multi-tool LangChain agent. We’ll walk through a use case where the agent:

  1. Picks up the latest trending topics in an industry (via a Google Trends-like tool),
  2. Searches for up-to-date articles on those topics
  3. Writes a concise, informed digest for internal use

This architecture is flexible, extensible, and relevant for any team involved in market research, content strategy, or competitive intelligence.

Why Tool Use Matters

LLMs are trained on static data. They can't fetch live information or interact with external APIs unless we give them tools. Think of tool use like giving your AI a "superpower" — the ability to Google something, call a database, or access a calendar.

LangChain provides a flexible framework to enable these capabilities via tools and agents.

Use Case: From Trend to Insight

Imagine you're part of a product or marketing team, and you want to stay updated on developments in the electric vehicle (EV) industry. Every week, you’d like a short, insightful write-up on the top trends, based on the latest data and news.

With a multi-tool LLM agent, this task can be automated. The agent performs three tasks:

  • Discover: Identify top-trending EV-related topics (e.g., "solid-state batteries," "charging infrastructure").
  • Research: Search the web for recent news on those topics.
  • Synthesize: Summarize the findings in an internal digest.

Architecture Overview

Here’s how the system is structured:

Agent Process Flowchart

The flow starts when a user asks a question or gives a topic/industry. The agent first invokes the GoogleTrendsFetcher to identify emerging keywords, then uses the Search tool to retrieve relevant articles, and finally synthesizes a concise summary using LLM. Each tool acts like a specialized worker, and the agent orchestrates their actions based on the task at hand. This modular approach allows for easy integration, customization, and scaling of the system for broader enterprise use cases.

Tools Used

  • LLM (ChatOpenAI): The reasoning engine that decides what to do and synthesizes the final output.
  • Tool 1 (GoogleTrendsFetcher): A wrapper around a trends API (real or mocked) to return current hot topics for a domain.
  • Tool 2 (DuckDuckGoSearch or TavilySearch): A tool that returns web search results for a given query.

Code Overview:

Python
 
from langchain.agents import initialize_agent, Tool
from langchain.chat_models import ChatOpenAI
from custom_tools import GoogleTrendsFetcher, DuckDuckGoSearchRun

# Initialize LLM
llm = ChatOpenAI(temperature=0, model="gpt-4")

# Define tools
google_trends = Tool(
    name="GoogleTrends",
    func=GoogleTrendsFetcher().run,
    description="Fetch trending topics for a specific industry"
)

search_tool = Tool(
    name="Search",
    func=DuckDuckGoSearchRun().run,
    description="Search web for news or updates on any topic"
)

# Agent with multi-tool capabilities
tools = [google_trends, search_tool]
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)

# Ask agent to perform the end-to-end task
question = "Give me a digest of the top 3 emerging trends in the EV industry this week."
response = agent.run(question)
print(response)

One major benefit of using LangChain’s agent architecture is interpretability. Developers and analysts can trace each tool invocation, see intermediate decisions, and validate results step by step. This not only builds trust in outputs but also helps debug failures or hallucinations — an essential feature when deploying such agents in business-critical workflows.

Prompting Tips for Multi-Step Reasoning

To make full use of multi-tool capabilities, your prompt should:

  • Specify a goal that involves multiple steps
  • Clarify the domain (e.g., electric vehicles, fintech)
  • Ask for structured output (e.g., bullet points, digest format)

Example Prompt:

"Act as a market intelligence agent. First, fetch the top 3 trending topics in the electric vehicle industry. Then, search for recent news on each topic. Finally, write a short digest summarizing the findings."

Benefits of Multi-Tool Agents

  • Automation of Research Pipelines: Saves hours of manual work
  • Cross-Domain Application: Replace EVs with any industry — AI, finance, real estate
  • Real-Time Awareness: Leverages current data rather than static knowledge
  • High-Quality Summarization: Converts raw data into valuable narratives

Conclusion: From A Few Tools to Autonomous Workflows

In this walkthrough, we've explored how combining multiple tools within LangChain unlocks true agentic power. Instead of just fetching search results, our agent plans a multi-step workflow: trend detection, article discovery, and insight generation.

This is a general pattern you can adapt:

  • Swap GoogleTrendsFetcher with Twitter trends, internal dashboards, or RSS feeds
  • Replace the search tool with a database query tool
  • Use the final output in newsletters, Slack updates, or dashboards

Some potential use cases for the multi-agentic framework

  • Skill gap analyzer: Reads performance reviews, looks at feedback, and goes through the list of available courses and matches the one that suits the user best in terms of upskilling.
  • Automating IT Ticket resolution: One agent could summarize the tickets, followed by another agent looking at the past resolutions for similar ones, and then a third agent implementing the potential fix.

Outcome: Personalized employee learning plans based on performance and business goals.

As LLMs evolve into core infrastructure, the next frontier will be defined by intelligent agents that can plan, act, and learn from their actions, mimicking real-world decision-making and enabling deeper automation across industries.

Tool Framework large language model

Opinions expressed by DZone contributors are their own.

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