Building Your First AI Agent with LangChain

The thrill and excitement of using ChatGPT has slowly faded away as people realized that it has many limitations. Even though standard LLMs can provide grammatically correct and coherent answers to your queries, they cannot book a flight or update a database for you. This is where AI agents take the limelight with their power for learning, reasoning, and executing different actions independently. You can build AI agent with LangChain as it is the top framework to develop AI agents with an extensive collection of tools.

• 51% of respondents in the LangChain State of AI Agents survey of 2024 had stated that they use AI agents in production (Source).     
• PwC conducted a survey in May 2025 in which 79% of respondents claimed that their companies had already adopted AI agents (Source). 
• IBM asked 1000 enterprise AI developers and 99% of them stated that they are experimenting with or creating AI agents (Source). 

With everyone trying to build an AI agent, you might wonder about the possibilities of creating one on your own. LangChain is the most popular framework to create AI agents and LLM-based applications by combining interoperable components and third-party integrations. Even though you can use LangChain as a standalone framework, it also supports seamless integration with the complete LangChain ecosystem. You can build your first AI agent with LangChain by learning about what it has to offer and the simple steps to create AI agents.

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Things to Know Before You Build AI Agent with LangChain

The curiosity to build your first AI agent may lead you to think about exploring LangChain capabilities immediately. Anyone who wants to figure out how to create an AI agent must know the core components of agentic architecture and how frameworks like LangChain help in creating AI agents. Traditional language models can only respond to your queries and instructions by leveraging their training data. AI agents use LLMs as their core component for decision-making and reasoning with other components ensuring autonomy. 

• Tools

AI agents can gain access to external tools such as web scrapers, APIs, and database connectors. You can think of them as interfaces that an agent uses to interact with the external world.

• Memory

Agentic AI works because of capabilities for short-term and long-term memory retention. Short-term memory helps in tracking the flow of the current conversation and long-term memory allows retaining information across different sessions. 

• Planning or Reasoning Loop

The agent also leverages a reasoning loop as the ideal strategy to break complex goals into simpler sequences of steps. 

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Why Should You Choose LangChain to Build AI Agents?

LangChain offers an ideal solution for developers to build AI agents with diverse capabilities and features. You can think of the example of the LangChain Agent Builder or the LangSmith Agent Builder that was launched in October 2025. It offers a truly no-coding agent development experience and allows almost anyone to create agents. In addition, LangChain offers the assurance of many other benefits to make developing AI agents look a lot easier.

• LangChain allows you to connect LLMs to different data sources and external or internal systems, thereby ensuring real-time data augmentation.
• Developers can swap models in and out according to the needs of their AI agent, and LangChain introduces new abstractions to ensure adaptability. 
• You can create and iterate AI agent prototypes with the module architecture of LangChain and avoid rebuilding from scratch.
• LangChain is the best framework to deploy stable AI agents with production-ready features and scale up with confidence by leveraging tested patterns.
• The continuously growing open-source community of LangChain and its expanding ecosystem helps developers capitalize on new trends.

Unraveling the Roadmap to Build AI Agent with LangChain

The features of LangChain showcase exactly why it is the best framework to build AI agents in 2026. You can take the example of a scenario in which you have to develop an AI agent that can solve complex mathematics problems and look for information on the web.

1. Complete the Initial Environment Setup

The first thing that you need to create an AI agent is to install Python, and it is recommended to have version 3.9 or higher. You should also create a new project directory and install the essential libraries with the following command:

pip install langchain langchain-openai langchain-community google-search-results

You can see that ‘langchain-openai’ is the brain of the agent and ‘google-search-results’ empowers the agent with web browsing capabilities. The next step here focuses on creating ‘.env’ file to store API keys from OpenAI and SerpAPI. 

OPENAI_API_KEY="your-openai-api-key"

SERPAPI_API_KEY="your-serpapi-api-key"

Developers can complete the environment setup by loading the environment variables in the code.

import os

from dotenv import load_dotenv

load_dotenv() 

2. Choose the LLM or Brain of Your AI Agent

The LLM is the most crucial component of your AI agent that can follow complex instructions and generate structured outputs. Let us assume that you want to use the GPT-4o Mini as the LLM for the agent in this guide. It is a reliable choice for developers who want the perfect balance of reasoning capability, cost, and speed. Developers should also ensure that the temperature setting is zero as you want deterministic outputs, and higher temperature can lead to hallucinations.

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3. Define the Tools for Your AI Agent

The next important step in building AI agents involves empowering the agent to use external tools. You can build AI agent with LangChain for solving mathematical problems and browsing the web with two distinct tools. The SerpAPI will serve as a web search tool and pull the latest updated web data. On the other hand, the LLMMathChain will help in handling precise mathematical calculations without depending on LLMs.

from langchain_community.utilities import SerpAPIWrapper

from langchain.agents import Tool

from langchain.chains import LLMMathChain

# Initialize the search wrapper

search = SerpAPIWrapper()


# Initialize the math chain helper

math_chain = LLMMathChain.from_llm(llm=llm)


# Define the tools array

tools = [

    Tool(

        name="Search",

        func=search.run,

        description="Useful for when you need to answer questions about current events or real-time web information."

    ),

    Tool(

        name="Calculator",

        func=math_chain.run,

        description="Useful for when you need to answer mathematical questions. Input should be a math expression."

    )

]

You can notice that the ‘description’ string provides instructions for the LLM in your agent. The agent will read the descriptions and decide which tool it must use to respond to user prompts.

4. Initializing Your AI Agent

Once you have your LLM and the tools ready, it is time to bind them together with agent prompt templates. You will find many pre-built prompts with LangChain that follow the ReAct framework. Developers can pull a standard prompt from the LangChain Hub and build the executor as follows,

from langchain import hub

from langchain.agents import create_react_agent, AgentExecutor


# Pull the standard ReAct prompt template

prompt = hub.pull("hwchase17/react")


# Construct the ReAct agent

agent = create_react_agent(llm, tools, prompt)


# Create the executor by combining the agent and tools

agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True)

Agentic AI developers must know that setting “verbose=True” ensures that the agent output prints the internal monologue of the agent. As a result, you can directly watch how the AI agent thinks in real-time.

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5. Testing Your AI Agent 

The final step to complete the AI agent development process involves testing the agent’s functionalities. You have to use a multi-step prompt that asks the agent to use both tools in a sequence. The “verbose=True” setting will ensure that you can see the reasoning path of the agent. It will help you see whether your AI agent is working as expected and delivers effective results.

Final Thoughts 

LangChain is one of the leading frameworks to create an AI agent from scratch with its extensive ecosystem and tools. It helps you take isolated LLMs and transform them into dynamic assistants that can work independently on assigned goals. As the popularity of no-code agent builders like the LangSmith Agent Builder continues growing, more people will be able to develop AI agents. Learn more about AI agents and create your first one right now.

FAQs

What are the best platforms to build an AI agent integrated with language model chains?

You will find many types of platforms to build AI agents integrated with language model chains. LangFlow is one of the examples of low-code builders along with LangSmith Agent Builder. You can rely on LangChain for the power of full-code frameworks and orchestration, as it offers deep integrations for chaining prompts and agent orchestration. 

Which companies offer cloud services to deploy AI agents built with language model chaining?

LangChain is one of the popular companies offering cloud services with LangGraph Cloud that helps in hosting cyclical agentic systems. You can also rely on major cloud service providers, such as Google Cloud, Amazon Web Services, and Microsoft Azure for managed services optimized for agentic AI development and LLM chaining.  

What programming languages are recommended for developing AI agents with language chain frameworks?

Python and JavaScript are the two most recommended programming language to develop AI agents with language chain frameworks. Python is the gold standard for AI programming while TypeScript has gradually gained popularity for creating user-facing web interfaces and real-time streaming tools. You can choose the right programming language by checking your project goals, infrastructure requirements, and the language chain framework you selected.

What are the top AI agent certification programs available?   

The top AI agent certification program that you should choose now is the Certified AI Agents Manager (CAIAM)™ certification by Future Skills Academy. It is a promising certification course to learn about fundamental concepts of agentic AI and architecture of AI agents. You will also learn about single-agent and multi-agent systems in the certification program along with the best practices for performance optimization and risk management.