LangChain Agents with Tools Tutorial: Connect Your AI to Real-World Data

Connect Your AI to Real-World Data: LangChain Agents with Tools Tutorial

Imagine your AI assistant not just answering questions from its training data, but actually performing tasks for you. Picture it managing your calendar, sending emails, or even looking up the latest stock prices. This incredible ability comes from using langchain agents real-world data tools.

These tools let your AI reach out and interact with the world outside its core knowledge. You can empower your AI to fetch live information, update databases, or even control other systems. This tutorial will show you exactly how to give your AI these powerful real-world capabilities.

Understanding LangChain Agents and Tools

Before we dive into connecting your AI, let’s make sure we understand the core concepts. LangChain is a framework that makes it easier to build applications powered by large language models. It helps you chain together different components to create more complex and useful AI systems.

What are Agents?

Think of an agent as the brain of your AI application. It’s a special kind of program that can decide what to do next based on your request. An agent doesn’t just give a direct answer; it thinks about the steps needed to fulfill your goal.

Agents can reason and plan. They observe the current situation, think about what actions are possible, and then execute those actions. This process repeats until your task is complete or the agent decides it cannot proceed further.

What are Tools?

Tools are like the hands and feet of your AI agent. They are specific functions or capabilities that an agent can use to interact with the outside world. Each tool performs a distinct job, like searching the internet, calculating numbers, or fetching data from a database.

Without tools, an agent is limited to its internal knowledge. With tools, an agent gains superpowers, allowing it to perform tasks that require real-world interaction. These langchain agents real-world data tools are the key to unlocking true AI utility.

The Power of Real-World Data

Why do we need to connect AI to real-world data? Large language models are amazing, but their knowledge is only as current as their last training session. They don’t know what happened yesterday or what’s happening right now. You cannot ask a simple AI what the weather is like outside your window today.

Connecting to real-world data gives your AI access to fresh, dynamic information. It allows your AI to perform tasks that involve live data, update external systems, or gather information that wasn’t part of its initial training. This makes your AI much more useful and versatile.

Setting Up Your LangChain Environment

Before we start building, you’ll need to set up your development environment. This usually involves installing Python and some key libraries. We’ll also need some API keys to access language models and external services.

Prerequisites

First, make sure you have Python installed on your computer. Python 3.8 or newer is generally recommended for LangChain development. You can download Python from the official Python website if you don’t have it.

You will also need pip, which is Python’s package installer. This usually comes bundled with Python, so you might already have it. Make sure your pip is up to date by running python -m pip install --upgrade pip.

You’ll also need an API key for a large language model, like OpenAI. You can get an API key by signing up on the OpenAI platform. This key lets your agent access the powerful language model that drives its reasoning.

Installation

Once Python and pip are ready, you can install the necessary LangChain libraries. Open your terminal or command prompt and run the following command. This will install LangChain and the OpenAI library.

pip install langchain openai

If you plan to use other language models or specific integrations, you might install additional packages later. For now, this setup is perfect for getting started. You can always refer to the official LangChain documentation for more detailed installation guides.

Basic Agent Example

Let’s start with a very simple agent, just to see how it works before adding tools. This agent won’t have langchain agents real-world data tools yet, but it will show you the basic structure. We’ll use the OpenAI LLM as our language model.

First, set your OpenAI API key as an environment variable. This is a secure way to handle your sensitive keys.

import os
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY" # Replace with your actual key

Now, let’s create a basic agent that can answer general questions. Notice how we don’t give it any specific functions to call yet.

from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool

# No tools are defined here yet, so we pass an empty list
tools = []

# Define the LLM (Large Language Model)
llm = ChatOpenAI(temperature=0)

# Define the prompt for the agent
# This prompt tells the agent its goal and how to interact.
prompt = PromptTemplate.from_template("""
You are a helpful AI assistant. Answer the user's questions as best you can.
Question: {input}
""")

# Create the agent
# The 'create_react_agent' function helps set up an agent that reasons using a React pattern.
agent = create_react_agent(llm, tools, prompt)

# Create an agent executor
# The AgentExecutor runs the agent, managing its actions and observations.
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Try running the agent with a simple query
# You'll notice it answers directly from its internal knowledge.
response = agent_executor.invoke({"input": "What is the capital of France?"})
print(response["output"])

# Example output:
# > Entering new AgentExecutor chain...
# I can answer that directly.
# The capital of France is Paris.
#
# > Finished chain.
# The capital of France is Paris.

This example shows the basic AgentExecutor in action. It takes an input, and the agent uses the LLM to process it. Since there are no tools, it relies solely on the LLM’s pre-trained knowledge. Now, let’s introduce some powerful langchain agents real-world data tools.

Connecting to Databases with LangChain Agents

One of the most powerful ways to connect your AI to real-world data is through databases. Databases store structured information like customer records, product inventories, or financial transactions. Giving your AI agent the ability to query these databases opens up a world of possibilities.

Why Database Access?

Imagine an AI assistant that can instantly look up a customer’s order history, check product availability, or retrieve specific user preferences. This is all possible with database access. Your AI can become a super-efficient data analyst or customer service agent.

Database connection tools allow agents to fetch dynamic information that changes constantly. They can also store new information, update existing records, or even delete old data. This makes your AI an active participant in your data management.

Database Connection Tools

LangChain makes it easy to create tools that interact with various databases. You can build tools that execute SQL queries for relational databases or interact with NoSQL databases like MongoDB. The agent will decide which tool to use and what query to send based on your request.

You will typically wrap a database client or ORM (Object-Relational Mapper) inside a LangChain tool. This tool will define a function that the agent can call, along with a description of what it does. The better the description, the smarter your agent will be at choosing the right langchain agents real-world data tools.

Practical Example: Supabase (SQL)

Supabase is an open-source Firebase alternative that provides a PostgreSQL database, authentication, and more. It’s a great choice for quickly setting up a relational database for your AI to interact with. You can set up a free project on their platform.

[Supabase (Affiliate Link)](https://supabase.com/r/ai-integration-partner) is a fantastic service for modern applications.

Let’s create a tool that allows our agent to query a PostgreSQL database hosted on Supabase. First, you’ll need the psycopg2-binary library to connect to PostgreSQL.

pip install psycopg2-binary

Now, let’s define a tool that can execute SQL queries. For this example, imagine you have a products table in your Supabase database.

import os
import psycopg2
from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# Set up your environment variables for Supabase connection
# Replace with your actual Supabase connection details
os.environ["SUPABASE_DB_HOST"] = "YOUR_SUPABASE_HOST"
os.environ["SUPABASE_DB_NAME"] = "postgres"
os.environ["SUPABASE_DB_USER"] = "postgres"
os.environ["SUPABASE_DB_PASSWORD"] = "YOUR_SUPABASE_PASSWORD"

@tool
def run_supabase_query(query: str) -> str:
    """
    Executes a read-only SQL query against the Supabase PostgreSQL database and returns the results.
    Use this tool to retrieve information from tables like 'products', 'customers', etc.
    Do NOT use this tool for INSERT, UPDATE, or DELETE operations.
    """
    try:
        conn = psycopg2.connect(
            host=os.environ["SUPABASE_DB_HOST"],
            database=os.environ["SUPABASE_DB_NAME"],
            user=os.environ["SUPABASE_DB_USER"],
            password=os.environ["SUPABASE_DB_PASSWORD"]
        )
        cur = conn.cursor()
        cur.execute(query)
        rows = cur.fetchall()
        column_names = [desc[0] for desc in cur.description]
        cur.close()
        conn.close()
        return f"Query successful. Columns: {column_names}. Results: {rows}"
    except Exception as e:
        return f"Error executing query: {e}"

# Make sure your OpenAI API key is set
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# Define the LLM
llm = ChatOpenAI(temperature=0)

# Define the tools available to the agent
tools = [run_supabase_query]

# Define the prompt for the agent
prompt = PromptTemplate.from_template("""
You are an AI assistant that can query a Supabase PostgreSQL database.
You have access to a 'products' table. When asked about products or inventory, use the `run_supabase_query` tool.
Always format your SQL query correctly before passing it to the tool.
Question: {input}
{agent_scratchpad}
""")

# Create the agent and executor
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Example usage
# You need to have a 'products' table in your Supabase database for this to work.
# Example: CREATE TABLE products (id SERIAL PRIMARY KEY, name VARCHAR(255), price DECIMAL, stock INT);
# INSERT INTO products (name, price, stock) VALUES ('Laptop', 1200.00, 50), ('Mouse', 25.00, 200);

response = agent_executor.invoke({"input": "What products do you have and how many are in stock?"})
print(response["output"])

# You can also ask more specific questions:
# response = agent_executor.invoke({"input": "What is the price of a Laptop?"})
# print(response["output"])

In this example, the agent now knows it has a run_supabase_query tool. When you ask about products, it will intelligently construct an SQL query and use the tool to get the answer. This is a powerful demonstration of database connection tools in action.

Practical Example: MongoDB Atlas (NoSQL)

MongoDB Atlas is a cloud-based NoSQL database service. It’s excellent for flexible, document-based data storage. You can try MongoDB Atlas for free with their generous free tier. It’s another crucial option for database connection tools.

[MongoDB Atlas (Affiliate Link)](https://www.mongodb.com/cloud/atlas/lp/general/partner?utm_campaign=atlas_partner_program) offers robust, scalable NoSQL solutions.

To interact with MongoDB, you’ll need the pymongo library.

pip install pymongo

Let’s define a tool that can query a MongoDB collection. Imagine a collection named customers with documents containing customer information.

import os
from pymongo import MongoClient
from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
import json

# Set your MongoDB Atlas connection string
# Replace with your actual MongoDB Atlas connection string (e.g., from your cluster overview)
os.environ["MONGODB_CONNECTION_STRING"] = "YOUR_MONGODB_ATLAS_CONNECTION_STRING"
os.environ["MONGODB_DB_NAME"] = "mydatabase" # Replace with your database name

@tool
def find_mongodb_documents(collection_name: str, query_json: str) -> str:
    """
    Finds documents in a specified MongoDB collection based on a JSON query.
    The query_json must be a valid JSON string representing the MongoDB query document.
    Example query_json: '{"name": "Alice"}' to find documents where name is Alice.
    Returns a string representation of the found documents.
    """
    try:
        client = MongoClient(os.environ["MONGODB_CONNECTION_STRING"])
        db = client[os.environ["MONGODB_DB_NAME"]]
        collection = db[collection_name]
        
        query = json.loads(query_json)
        results = list(collection.find(query, {'_id': 0})) # Exclude _id for cleaner output
        client.close()
        return f"Found {len(results)} documents: {json.dumps(results, indent=2)}"
    except Exception as e:
        return f"Error finding documents: {e}"

# Make sure your OpenAI API key is set
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# Define the LLM
llm = ChatOpenAI(temperature=0)

# Define the tools available to the agent
tools = [find_mongodb_documents]

# Define the prompt for the agent
prompt = PromptTemplate.from_template("""
You are an AI assistant that can query a MongoDB database.
You have access to a 'customers' collection. When asked about customers, use the `find_mongodb_documents` tool.
Always provide a valid JSON query string for the `query_json` parameter.
Question: {input}
{agent_scratchpad}
""")

# Create the agent and executor
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Example usage
# You need to have a 'customers' collection in your MongoDB Atlas database with some documents.
# Example: db.customers.insertMany([{"name": "Alice", "city": "New York"}, {"name": "Bob", "city": "London"}])

response = agent_executor.invoke({"input": "Find all customers named Alice."})
print(response["output"])

response = agent_executor.invoke({"input": "List all customers from London."})
print(response["output"])

These database connection tools empower your agent to interact with both SQL and NoSQL databases. You’ve now given your AI the ability to directly query and retrieve crucial business data, making it incredibly versatile.

Managing Files with LangChain Agents

Beyond structured databases, AI agents often need to interact with files. This could involve reading documents, saving reports, or managing images. File system access tools provide this crucial capability.

Why File System Access?

Imagine an AI agent that can summarize a PDF document from a specific folder. Or maybe it needs to save a generated report to a user’s cloud storage. These tasks require the AI to read from and write to file systems.

File system access tools are essential for tasks involving documents, images, audio, or any other file-based data. They bridge the gap between your AI’s processing power and the actual files stored on disks or in the cloud.

File System Access Tools

LangChain allows you to create tools that perform various file operations. This includes reading content from files, writing new content, listing files in a directory, or even deleting files. You can build tools for local file systems or integrate with cloud storage services.

When building these tools, always consider security. Giving an AI agent unrestricted file system access can be risky. Design your langchain agents real-world data tools to be as specific and limited as possible in their file operations.

Practical Example: Local Files

Let’s start with a simple tool to read and write files on your local machine. This is useful for processing documents stored directly on your server or development environment. Remember to be cautious with file paths and permissions.

import os
from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# Create a dummy folder and file for testing
if not os.path.exists("agent_files"):
    os.makedirs("agent_files")
with open("agent_files/report.txt", "w") as f:
    f.write("This is a sample report created by a human. It contains important details.")

@tool
def read_local_file(file_path: str) -> str:
    """
    Reads the content of a local text file given its path.
    Use this tool to get information from documents stored on the local file system.
    Only read from the 'agent_files/' directory for security.
    """
    if not file_path.startswith("agent_files/"):
        return "Error: Access denied. Can only read files from 'agent_files/' directory."
    try:
        with open(file_path, "r") as f:
            content = f.read()
        return f"Content of '{file_path}':\n{content}"
    except FileNotFoundError:
        return f"Error: File not found at '{file_path}'."
    except Exception as e:
        return f"Error reading file: {e}"

@tool
def write_local_file(file_path: str, content: str) -> str:
    """
    Writes content to a local text file given its path.
    Use this tool to save new information or reports to the local file system.
    Only write to the 'agent_files/' directory for security.
    """
    if not file_path.startswith("agent_files/"):
        return "Error: Access denied. Can only write files to 'agent_files/' directory."
    try:
        with open(file_path, "w") as f:
            f.write(content)
        return f"Content successfully written to '{file_path}'."
    except Exception as e:
        return f"Error writing file: {e}"

# Make sure your OpenAI API key is set
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# Define the LLM
llm = ChatOpenAI(temperature=0)

# Define the tools available to the agent
tools = [read_local_file, write_local_file]

# Define the prompt for the agent
prompt = PromptTemplate.from_template("""
You are an AI assistant that can read and write local files.
You can use `read_local_file` to get file content and `write_local_file` to save new content.
Always specify the full file path including the 'agent_files/' prefix.
Question: {input}
{agent_scratchpad}
""")

# Create the agent and executor
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Example usage
response = agent_executor.invoke({"input": "Can you read the content of 'agent_files/report.txt'?"})
print(response["output"])

response = agent_executor.invoke({"input": "Write 'New information gathered by the AI.' into a file named 'agent_files/ai_notes.txt'."})
print(response["output"])

response = agent_executor.invoke({"input": "Now, read the content of 'agent_files/ai_notes.txt'."})
print(response["output"])

These file system access tools allow your agent to interact with local storage. This is a foundational step towards more complex data management tasks.

Practical Example: Cloud Storage Integration (AWS S3)

Cloud storage services like AWS S3 are widely used for scalable and secure file storage. Your AI agent can become much more powerful if it can interact with these services. This is a crucial aspect of cloud storage integration.

[AWS S3 (Affiliate Link)](https://aws.amazon.com/s3/pricing/) provides highly scalable and reliable object storage.

To interact with AWS S3, you’ll need the boto3 library.

pip install boto3

You’ll also need to configure your AWS credentials. This usually involves setting environment variables like AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. Always keep your credentials secure.

import os
import boto3
from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# Set your AWS credentials (replace with your actual details)
os.environ["AWS_ACCESS_KEY_ID"] = "YOUR_AWS_ACCESS_KEY_ID"
os.environ["AWS_SECRET_ACCESS_KEY"] = "YOUR_AWS_SECRET_ACCESS_KEY"
os.environ["AWS_DEFAULT_REGION"] = "us-east-1" # Or your preferred region
os.environ["S3_BUCKET_NAME"] = "your-unique-agent-bucket-name" # Replace with your S3 bucket name

@tool
def upload_to_s3(file_content: str, s3_key: str) -> str:
    """
    Uploads text content as a file to a specified S3 bucket.
    The s3_key is the path/name of the file within the S3 bucket.
    """
    s3 = boto3.client('s3')
    bucket_name = os.environ["S3_BUCKET_NAME"]
    try:
        s3.put_object(Bucket=bucket_name, Key=s3_key, Body=file_content)
        return f"Successfully uploaded '{s3_key}' to S3 bucket '{bucket_name}'."
    except Exception as e:
        return f"Error uploading to S3: {e}"

@tool
def download_from_s3(s3_key: str) -> str:
    """
    Downloads the content of a file from a specified S3 bucket.
    The s3_key is the path/name of the file within the S3 bucket.
    """
    s3 = boto3.client('s3')
    bucket_name = os.environ["S3_BUCKET_NAME"]
    try:
        response = s3.get_object(Bucket=bucket_name, Key=s3_key)
        content = response['Body'].read().decode('utf-8')
        return f"Content of '{s3_key}' from S3:\n{content}"
    except s3.exceptions.NoSuchKey:
        return f"Error: File '{s3_key}' not found in S3 bucket '{bucket_name}'."
    except Exception as e:
        return f"Error downloading from S3: {e}"

# Make sure your OpenAI API key is set
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# Define the LLM
llm = ChatOpenAI(temperature=0)

# Define the tools available to the agent
tools = [upload_to_s3, download_from_s3]

# Define the prompt for the agent
prompt = PromptTemplate.from_template("""
You are an AI assistant that can manage files in an AWS S3 bucket.
Use `upload_to_s3` to save new text files and `download_from_s3` to retrieve existing files.
Always specify the exact S3 key (file path) within the bucket.
Question: {input}
{agent_scratchpad}
""")

# Create the agent and executor
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Example usage
# Make sure you have an S3 bucket with the name specified in S3_BUCKET_NAME
# and that your AWS credentials have permissions to access it.

response = agent_executor.invoke({"input": "Upload 'Hello from LangChain agent!' to S3 as 'agent-test/greeting.txt'."})
print(response["output"])

response = agent_executor.invoke({"input": "Download the content of 'agent-test/greeting.txt' from S3."})
print(response["output"])

These cloud storage integration tools for AWS S3 significantly expand your agent’s capabilities. Your AI can now manage files in a highly scalable and resilient cloud environment.

Practical Example: Google Cloud Storage

Google Cloud Storage (GCS) is another popular cloud storage solution. If your infrastructure is on Google Cloud, integrating with GCS is essential. This is another prime example of cloud storage integration for your AI agents.

[Google Cloud Storage (Affiliate Link)](https://cloud.google.com/storage/pricing) offers durable and cost-effective object storage.

To interact with GCS, you’ll need the google-cloud-storage library.

pip install google-cloud-storage

You’ll also need to authenticate with Google Cloud, usually by setting the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of your service account key file.

import os
from google.cloud import storage
from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# Set your Google Cloud Project ID and GCS bucket name
# Replace with your actual project ID and GCS bucket name
os.environ["GOOGLE_CLOUD_PROJECT"] = "YOUR_GCP_PROJECT_ID"
os.environ["GCS_BUCKET_NAME"] = "your-unique-agent-gcs-bucket-name"
# Ensure GOOGLE_APPLICATION_CREDENTIALS points to your service account key file
# os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/path/to/your/service-account-key.json"

@tool
def upload_to_gcs(file_content: str, blob_name: str) -> str:
    """
    Uploads text content as a blob (file) to a specified Google Cloud Storage bucket.
    The blob_name is the path/name of the file within the GCS bucket.
    """
    bucket_name = os.environ["GCS_BUCKET_NAME"]
    storage_client = storage.Client()
    bucket = storage_client.bucket(bucket_name)
    blob = bucket.blob(blob_name)
    
    try:
        blob.upload_from_string(file_content)
        return f"Successfully uploaded '{blob_name}' to GCS bucket '{bucket_name}'."
    except Exception as e:
        return f"Error uploading to GCS: {e}"

@tool
def download_from_gcs(blob_name: str) -> str:
    """
    Downloads the content of a blob (file) from a specified Google Cloud Storage bucket.
    The blob_name is the path/name of the file within the GCS bucket.
    """
    bucket_name = os.environ["GCS_BUCKET_NAME"]
    storage_client = storage.Client()
    bucket = storage_client.bucket(bucket_name)
    blob = bucket.blob(blob_name)
    
    try:
        content = blob.download_as_text()
        return f"Content of '{blob_name}' from GCS:\n{content}"
    except Exception as e:
        return f"Error downloading from GCS: {e}"

# Make sure your OpenAI API key is set
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# Define the LLM
llm = ChatOpenAI(temperature=0)

# Define the tools available to the agent
tools = [upload_to_gcs, download_from_gcs]

# Define the prompt for the agent
prompt = PromptTemplate.from_template("""
You are an AI assistant that can manage files in a Google Cloud Storage bucket.
Use `upload_to_gcs` to save new text files and `download_from_gcs` to retrieve existing files.
Always specify the exact blob name (file path) within the bucket.
Question: {input}
{agent_scratchpad}
""")

# Create the agent and executor
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Example usage
# Make sure you have a GCS bucket with the name specified in GCS_BUCKET_NAME
# and that your service account has permissions to access it.

response = agent_executor.invoke({"input": "Upload 'Greetings from my LangChain agent in GCP!' to GCS as 'agent-gcp/hello.txt'."})
print(response["output"])

response = agent_executor.invoke({"input": "Download the content of 'agent-gcp/hello.txt' from GCS."})
print(response["output"])

With these cloud storage integration examples, your langchain agents real-world data tools can now handle files across different cloud providers. This flexibility is crucial for enterprise applications. For more advanced file management, you might want to consider integration middleware solutions which can streamline connections to various file systems and other services.

Interacting with Business Platforms

Modern businesses rely on a variety of specialized software platforms. Your AI agent can become an invaluable asset by interacting directly with these systems. This enables automation of routine tasks and access to critical business information.

CRM Tool Integration

Customer Relationship Management (CRM) platforms are the heart of many businesses. They store customer data, sales leads, interaction history, and more. Giving your AI agent CRM tool integration capabilities can revolutionize how you manage customer relationships.

Imagine an AI that can automatically update a customer’s profile after a support interaction. Or perhaps it can create a new lead entry based on an email inquiry. These langchain agents real-world data tools can save significant time and improve data accuracy.

Practical Example: HubSpot

HubSpot is a popular CRM and marketing automation platform. It offers a powerful API that allows external applications to interact with its data. We can create a tool to manage contacts or deals in HubSpot.

[HubSpot (Affiliate Link)](https://www.hubspot.com/partners/affiliates) is a leading platform for sales, marketing, and customer service.

You’ll need the hubspot-api-client library.

pip install hubspot-api-client

You’ll also need a HubSpot API key (or private app access token).

import os
from hubspot import HubSpot
from hubspot.crm.contacts import SimplePublicObjectInput
from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
import json

# Set your HubSpot API key (Private App Access Token recommended)
os.environ["HUBSPOT_API_KEY"] = "YOUR_HUBSPOT_API_KEY"

@tool
def create_hubspot_contact(email: str, firstname: str, lastname: str, company: str = None) -> str:
    """
    Creates a new contact in HubSpot with the provided details.
    Requires email, first name, and last name. Company is optional.
    """
    api_client = HubSpot(access_token=os.environ["HUBSPOT_API_KEY"])
    properties = {
        "email": email,
        "firstname": firstname,
        "lastname": lastname,
    }
    if company:
        properties["company"] = company
        
    simple_public_object_input = SimplePublicObjectInput(properties=properties)
    
    try:
        api_response = api_client.crm.contacts.basic_api.create(
            simple_public_object_input=simple_public_object_input
        )
        return f"Successfully created HubSpot contact: {api_response.id}"
    except Exception as e:
        return f"Error creating HubSpot contact: {e}"

@tool
def get_hubspot_contact(email: str) -> str:
    """
    Retrieves contact details from HubSpot using their email address.
    Returns a JSON string of the contact's properties if found.
    """
    api_client = HubSpot(access_token=os.environ["HUBSPOT_API_KEY"])
    try:
        api_response = api_client.crm.contacts.basic_api.get_by_id(
            contact_id=email, id_property="email"
        )
        return f"Found HubSpot contact: {json.dumps(api_response.to_dict(), indent=2)}"
    except Exception as e:
        return f"Error retrieving HubSpot contact: {e}"

# Make sure your OpenAI API key is set
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# Define the LLM
llm = ChatOpenAI(temperature=0)

# Define the tools available to the agent
tools = [create_hubspot_contact, get_hubspot_contact]

# Define the prompt for the agent
prompt = PromptTemplate.from_template("""
You are an AI assistant that can manage contacts in HubSpot.
Use `create_hubspot_contact` to add new people and `get_hubspot_contact` to find existing ones.
Always provide full details like email, first name, and last name when creating a contact.
Question: {input}
{agent_scratchpad}
""")

# Create the agent and executor
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Example usage
# Make sure your HubSpot API key has the necessary CRM permissions.

response = agent_executor.invoke({"input": "Create a new contact named 'Jane Doe' with email 'jane.doe@example.com' at 'ACME Corp'."})
print(response["output"])

response = agent_executor.invoke({"input": "What are the details for the contact with email 'jane.doe@example.com'?"})
print(response["output"])

These CRM tool integration examples show how your agent can automate customer data management. This directly impacts sales, marketing, and customer service efficiency. For more insights into automating business processes, you might find our post on [automating workflows with LangChain](/blog/automating-workflows-langchain) helpful.

Email Tools Setup

Email is still a primary form of business communication. Giving your AI agent email tools setup capabilities allows it to send automated notifications, personalized responses, or even entire newsletters. This can greatly enhance communication efficiency.

Imagine an AI sending a welcome email to a new customer automatically. Or an AI drafting a follow-up email after a meeting based on notes. These langchain agents real-world data tools for email are incredibly versatile.

Practical Example: SendGrid

SendGrid is a popular email API for sending transactional and marketing emails. It provides robust delivery, analytics, and scaling features. Integrating with SendGrid allows your agent to send emails reliably.

[SendGrid (Affiliate Link)](https://sendgrid.com/partners/affiliates/) is a leading platform for email delivery and automation.

You’ll need the sendgrid library.

pip install sendgrid

You’ll also need a SendGrid API key and a verified sender email address.

import os
from sendgrid import SendGridAPIClient
from sendgrid.helpers.mail import Mail
from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# Set your SendGrid API key and sender email
os.environ["SENDGRID_API_KEY"] = "YOUR_SENDGRID_API_KEY"
os.environ["SENDGRID_SENDER_EMAIL"] = "your_verified_sender@example.com"

@tool
def send_email_with_sendgrid(to_email: str, subject: str, content: str) -> str:
    """
    Sends an email using SendGrid.
    Requires recipient email, subject, and the email content (plain text or HTML).
    """
    message = Mail(
        from_email=os.environ["SENDGRID_SENDER_EMAIL"],
        to_emails=to_email,
        subject=subject,
        html_content=content
    )
    try:
        sendgrid_client = SendGridAPIClient(os.environ["SENDGRID_API_KEY"])
        response = sendgrid_client.send(message)
        if response.status_code == 202:
            return f"Email successfully sent to {to_email}. Status code: {response.status_code}"
        else:
            return f"Error sending email. Status code: {response.status_code}. Response body: {response.body}"
    except Exception as e:
        return f"Error sending email: {e}"

# Make sure your OpenAI API key is set
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# Define the LLM
llm = ChatOpenAI(temperature=0)

# Define the tools available to the agent
tools = [send_email_with_sendgrid]

# Define the prompt for the agent
prompt = PromptTemplate.from_template("""
You are an AI assistant that can send emails using SendGrid.
Use `send_email_with_sendgrid` to communicate with users.
Always provide a valid recipient email, a clear subject, and a concise content.
Question: {input}
{agent_scratchpad}
""")

# Create the agent and executor
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Example usage
# Make sure your SendGrid API key is valid and the sender email is verified.
# Replace 'recipient@example.com' with an actual email you can check.

response = agent_executor.invoke({"input": "Send an email to 'recipient@example.com' with subject 'Meeting Reminder' and content 'Hi, just a friendly reminder about our meeting tomorrow at 10 AM. See you there!'"})
print(response["output"])

With email tools setup, your langchain agents real-world data tools can now actively communicate. This is invaluable for notifications, alerts, and automated correspondence.

Calendar API Tools

Scheduling and managing events are common tasks in many professional settings. Calendar API tools allow your AI agent to interact with digital calendars, enabling it to create events, check availability, or list upcoming appointments. This makes your AI a powerful personal assistant.

Imagine an AI that can automatically schedule a meeting based on an email request. Or an AI reminding you of important deadlines by checking your calendar. These langchain agents real-world data tools enhance your productivity significantly.

Practical Example: Google Calendar

Google Calendar is a widely used calendar service. Its API allows for powerful integrations. We can create a tool that lets our agent create new events in a Google Calendar.

You’ll need the google-api-python-client and google-auth-oauthlib libraries.

pip install google-api-python-client google-auth-oauthlib

Setting up Google Calendar API access is a bit more involved, requiring you to create a project in Google Cloud Console, enable the Calendar API, and download credentials.json for OAuth 2.0. This file should be placed in the same directory as your script. You’ll go through an authorization flow the first time you run the script. For a detailed guide on how to obtain credentials, refer to the Google Calendar API Python Quickstart.

import os
import datetime
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import InstalledAppFlow
from google.auth.transport.requests import Request
from googleapiclient.discovery import build
from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# If modifying these scopes, delete the file token.json.
SCOPES = ['https://www.googleapis.com/auth/calendar.events']

def get_google_calendar_service():
    """Shows basic usage of the Google Calendar API.
    Prints the start and name of the next 10 events on the user's calendar.
    """
    creds = None
    # The file token.json stores the user's access and refresh tokens, and is
    # created automatically when the authorization flow completes for the first
    # time.
    if os.path.exists('token.json'):
        creds = Credentials.from_authorized_user_file('token.json', SCOPES)
    # If there are no (valid) credentials available, let the user log in.
    if not creds or not creds.valid:
        if creds and creds.expired and creds.refresh_token:
            creds.refresh_token(Request())
        else:
            flow = InstalledAppFlow.from_client_secrets_file(
                'credentials.json', SCOPES)
            creds = flow.run_local_server(port=0)
        # Save the credentials for the next run
        with open('token.json', 'w') as token:
            token.write(creds.to_json())

    service = build('calendar', 'v3', credentials=creds)
    return service

@tool
def create_google_calendar_event(summary: str, description: str, start_time: str, end_time: str) -> str:
    """
    Creates a new event in the user's primary Google Calendar.
    start_time and end_time should be in ISO format (e.g., '2023-10-27T09:00:00-07:00').
    Example: '2023-10-27T09:00:00' for a local time without timezone offset.
    """
    service = get_google_calendar_service()
    event = {
        'summary': summary,
        'description': description,
        'start': {
            'dateTime': start_time,
            'timeZone': 'America/Los_Angeles', # Customize as needed
        },
        'end': {
            'dateTime': end_time,
            'timeZone': 'America/Los_Angeles', # Customize as needed
        },
    }
    
    try:
        event = service.events().insert(calendarId='primary', body=event).execute()
        return f"Event created: {event.get('htmlLink')}"
    except Exception as e:
        return f"Error creating Google Calendar event: {e}"

# Make sure your OpenAI API key is set
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# Define the LLM
llm = ChatOpenAI(temperature=0)

# Define the tools available to the agent
tools = [create_google_calendar_event]

# Define the prompt for the agent
prompt = PromptTemplate.from_template("""
You are an AI assistant that can manage Google Calendar events.
Use `create_google_calendar_event` to add new events.
Always ensure start_time and end_time are provided in ISO format.
Example: '2023-10-27T09:00:00'
Question: {input}
{agent_scratchpad}
""")

# Create the agent and executor
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Example usage
# Ensure you have 'credentials.json' set up for Google Calendar API.
# The first run will open a browser for authentication.

# Make sure to provide valid ISO formatted times for the current date or future.
# For example, if today is October 27, 2023, use times for that date or later.
current_date_str = datetime.date.today().isoformat()
response = agent_executor.invoke({
    "input": f"Create a new event called 'Project Sync' for tomorrow from 10 AM to 11 AM. "
             f"The description is 'Discuss Q4 roadmap'. Assume today is {current_date_str} and "
             f"tomorrow's date will be used for the event."
    # The agent would need to infer tomorrow's date. For simplicity in this example,
    # you might manually adjust times or add more date calculation logic to your agent/tools.
    # For a real-world scenario, the agent would typically ask for clarification or use a date parsing tool.
})
print(response["output"])

With these calendar API tools, your langchain agents real-world data tools can now help you manage your schedule. This integration makes your AI a true productivity booster.

Enabling Transactions and Social Media

Beyond internal business platforms, langchain agents real-world data tools can also extend to public-facing services. This includes handling financial transactions and interacting with social media platforms. Such capabilities further enhance the utility and reach of your AI.

Payment Processing Tools

For any business dealing with money, payment processing tools are indispensable. Your AI agent can be empowered to initiate payments, issue refunds, or check transaction statuses. This can automate financial operations and improve customer service.

Imagine an AI that processes a refund request directly after verifying an order. Or an AI generating an invoice and initiating payment. These langchain agents real-world data tools can streamline your financial workflows securely.

Practical Example: Stripe

Stripe is a leading platform for online payment processing. Its API is robust and widely adopted. We can create a tool that allows our agent to create a payment intent using Stripe.

[Stripe (Affiliate Link)](https://stripe.com/partners/affiliate-program) offers powerful APIs for online payments.

You’ll need the stripe library.

pip install stripe

You’ll also need your Stripe secret API key. For testing, use a test key.

import os
import stripe
from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# Set your Stripe API key
os.environ["STRIPE_API_KEY"] = "YOUR_STRIPE_SECRET_KEY"
stripe.api_key = os.environ["STRIPE_API_KEY"]

@tool
def create_stripe_payment_intent(amount_cents: int, currency: str = "usd", description: str = "Payment from AI Agent") -> str:
    """
    Creates a new payment intent with Stripe for a specified amount and currency.
    amount_cents should be an integer representing the amount in the smallest currency unit (e.g., cents for USD).
    currency should be a 3-letter ISO currency code (e.g., 'usd').
    """
    try:
        intent = stripe.PaymentIntent.create(
            amount=amount_cents,
            currency=currency,
            description=description,
            automatic_payment_methods={"enabled": True},
        )
        return f"Successfully created Stripe Payment Intent. ID: {intent.id}, Client Secret: {intent.client_secret}"
    except Exception as e:
        return f"Error creating Stripe Payment Intent: {e}"

# Make sure your OpenAI API key is set
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# Define the LLM
llm = ChatOpenAI(temperature=0)

# Define the tools available to the agent
tools = [create_stripe_payment_intent]

# Define the prompt for the agent
prompt = PromptTemplate.from_template("""
You are an AI assistant that can process payments using Stripe.
Use `create_stripe_payment_intent` to initiate a new payment.
Always specify the amount in cents (integer) and the currency code.
Question: {input}
{agent_scratchpad}
""")

# Create the agent and executor
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Example usage
# Use a Stripe test key for development.

response = agent_executor.invoke({"input": "Create a payment intent for $25.50 for a customer purchase."})
print(response["output"])

These payment processing tools allow your langchain agents real-world data tools to directly handle financial transactions. This can be a game-changer for e-commerce and billing automation.

Social Media API Tools

Social media is a huge source of information and a key channel for communication. Social media API tools enable your AI agent to post updates, monitor trends, or even respond to mentions. This integration can automate your social media presence.

Imagine an AI posting daily market updates or sharing relevant news articles automatically. Or an AI monitoring social media for brand mentions and flagging urgent issues. These langchain agents real-world data tools can significantly enhance your social media strategy.

Practical Example: Twitter

The Twitter (now X) API allows programmatic interaction with the platform. We can create a tool that allows our agent to post a tweet. Note that Twitter API access and policies have changed significantly, and you’ll need an approved developer account and project to get API keys.

You’ll need the tweepy library.

pip install tweepy

You’ll need your Twitter API keys (Consumer Key, Consumer Secret, Access Token, Access Token Secret).

import os
import tweepy
from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# Set your Twitter API keys
os.environ["TWITTER_CONSUMER_KEY"] = "YOUR_TWITTER_CONSUMER_KEY"
os.environ["TWITTER_CONSUMER_SECRET"] = "YOUR_TWITTER_CONSUMER_SECRET"
os.environ["TWITTER_ACCESS_TOKEN"] = "YOUR_TWITTER_ACCESS_TOKEN"
os.environ["TWITTER_ACCESS_TOKEN_SECRET"] = "YOUR_TWITTER_ACCESS_TOKEN_SECRET"

@tool
def post_tweet(tweet_text: str) -> str:
    """
    Posts a tweet to Twitter (X) with the given text.
    The tweet_text should be concise and within Twitter's character limits.
    """
    try:
        auth = tweepy.OAuthHandler(
            os.environ["TWITTER_CONSUMER_KEY"],
            os.environ["TWITTER_CONSUMER_SECRET"]
        )
        auth.set_access_token(
            os.environ["TWITTER_ACCESS_TOKEN"],
            os.environ["TWITTER_ACCESS_TOKEN_SECRET"]
        )
        api = tweepy.API(auth)
        api.verify_credentials() # Verify if credentials are correct
        
        api.update_status(tweet_text)
        return f"Tweet successfully posted: '{tweet_text}'"
    except tweepy.TweepyException as e:
        return f"Error posting tweet: {e}"
    except Exception as e:
        return f"An unexpected error occurred: {e}"

# Make sure your OpenAI API key is set
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# Define the LLM
llm = ChatOpenAI(temperature=0)

# Define the tools available to the agent
tools = [post_tweet]

# Define the prompt for the agent
prompt = PromptTemplate.from_template("""
You are an AI assistant that can post updates to Twitter (X).
Use `post_tweet` to share information.
Keep your tweets concise and relevant.
Question: {input}
{agent_scratchpad}
""")

# Create the agent and executor
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Example usage
# Make sure your Twitter API keys are valid and have the necessary permissions.
# Be mindful of Twitter's policies and rate limits.

response = agent_executor.invoke({"input": "Post a tweet saying 'Excited to integrate LangChain agents with real-world data tools! #AI #LangChain'"})
print(response["output"])

With social media API tools, your langchain agents real-world data tools can now actively participate in social media. This can boost your brand’s presence and engagement.

Connecting to the Physical World and Live Data

The capabilities of langchain agents real-world data tools extend beyond digital platforms. They can interact with the physical world through IoT devices and process continuous streams of information with real-time data streaming tools. This brings your AI to the forefront of innovation.

IoT Device Integration

The Internet of Things (IoT) connects physical devices to the internet. IoT device integration allows your AI agent to monitor sensors, control smart devices, or react to environmental changes. This bridges the gap between AI and the physical world.

Imagine an AI that adjusts your smart home’s thermostat based on external weather data. Or an AI monitoring industrial sensors and flagging anomalies. These langchain agents real-world data tools bring AI into intelligent automation of physical systems.

Practical Example: Simple HTTP Control

Many IoT devices expose simple HTTP APIs for control and data retrieval. We can simulate this with a basic Python web server or use an existing public API. This demonstrates how an agent could send commands to a device.

[IoT Platforms (Affiliate Link)](https://aws.amazon.com/iot/partners/) offer comprehensive solutions for managing connected devices.

For this example, let’s create a tool that “sends a command” via a hypothetical HTTP endpoint.

import os
import requests
from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# Hypothetical IoT device endpoint
# In a real scenario, this would be your device's actual IP/URL.
# For demonstration, this example will just simulate an HTTP POST.
# You could replace this with a real endpoint if you have a device.
FAKE_IOT_DEVICE_ENDPOINT = "https://httpbin.org/post" 

@tool
def send_iot_command(device_id: str, command: str, value: str = None) -> str:
    """
    Sends a command to a simulated IoT device via an HTTP POST request.
    device_id identifies the target device.
    command specifies the action (e.g., 'turn_on', 'set_temp').
    value is an optional parameter for the command (e.g., '22' for temperature).
    """
    payload = {"device_id": device_id, "command": command}
    if value:
        payload["value"] = value
        
    try:
        response = requests.post(FAKE_IOT_DEVICE_ENDPOINT, json=payload)
        response.raise_for_status() # Raise an exception for HTTP errors (4xx or 5xx)
        return f"Command '{command}' sent to device '{device_id}'. Response: {response.json()}"
    except requests.exceptions.RequestException as e:
        return f"Error sending command to IoT device: {e}"
    except Exception as e:
        return f"An unexpected error occurred: {e}"

# Make sure your OpenAI API key is set
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# Define the LLM
llm = ChatOpenAI(temperature=0)

# Define the tools available to the agent
tools = [send_iot_command]

# Define the prompt for the agent
prompt = PromptTemplate.from_template("""
You are an AI assistant that can control IoT devices.
Use `send_iot_command` to interact with devices.
Always specify the device ID, command, and any necessary value.
Question: {input}
{agent_scratchpad}
""")

# Create the agent and executor
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Example usage
response = agent_executor.invoke({"input": "Turn on the living room light (device_id: light_001)."})
print(response["output"])

response = agent_executor.invoke({"input": "Set the temperature of the thermostat (device_id: thermo_002) to 22 degrees Celsius."})
print(response["output"])

These IoT device integration tools allow your langchain agents real-world data tools to extend their influence into the physical environment. This opens doors for smart homes, industrial automation, and environmental monitoring.

Real-Time Data Streaming Tools

Access to real-time data streaming tools is critical for applications that need up-to-the-minute information. This includes financial market data, sensor readings, or live news feeds. Your AI agent can process and react to this constant flow of information.

Imagine an AI providing instant stock price alerts or summarizing live sports scores. Or an AI detecting anomalies in sensor data as they happen. These langchain agents real-world data tools make your AI truly responsive and informed.

Practical Example: Web Scraping/API Call

While full-blown data streaming often involves technologies like Kafka or WebSockets, a simple yet powerful way to get real-time-ish data is through external APIs or targeted web scraping. Let’s create a tool to fetch live stock prices from a public API.

[API Marketplace (Affiliate Link)](https://rapidapi.com/partner/affiliate) offers a vast collection of APIs for various data needs, including real-time feeds.

For stock data, you might use a service like Alpha Vantage. You’ll need to sign up for a free API key.

pip install requests

import os
import requests
from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# Set your Alpha Vantage API key
os.environ["ALPHA_VANTAGE_API_KEY"] = "YOUR_ALPHA_VANTAGE_API_KEY"

@tool
def get_current_stock_price(symbol: str) -> str:
    """
    Retrieves the current real-time stock price for a given stock symbol.
    Uses the Alpha Vantage API for up-to-date market information.
    Example symbol: 'IBM', 'AAPL', 'MSFT'.
    """
    api_key = os.environ["ALPHA_VANTAGE_API_KEY"]
    url = f"https://www.alphavantage.co/query?function=GLOBAL_QUOTE&symbol={symbol}&apikey={api_key}"
    
    try:
        response = requests.get(url)
        response.raise_for_status()
        data = response.json()
        
        if "Global Quote" in data:
            global_quote = data["Global Quote"]
            if global_quote:
                price = global_quote.get("05. price")
                last_updated = global_quote.get("07. latest trading day")
                if price and last_updated:
                    return f"Current price for {symbol}: ${price} (as of {last_updated})"
                else:
                    return f"Could not retrieve price details for {symbol}. Raw data: {global_quote}"
            else:
                return f"No global quote data found for symbol {symbol}. Is the symbol correct?"
        elif "Error Message" in data:
            return f"Error from Alpha Vantage API for {symbol}: {data['Error Message']}"
        else:
            return f"Unexpected response from Alpha Vantage for {symbol}. Raw data: {data}"
    except requests.exceptions.RequestException as e:
        return f"Error fetching stock price: {e}"
    except Exception as e:
        return f"An unexpected error occurred: {e}"

# Make sure your OpenAI API key is set
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# Define the LLM
llm = ChatOpenAI(temperature=0)

# Define the tools available to the agent
tools = [get_current_stock_price]

# Define the prompt for the agent
prompt = PromptTemplate.from_template("""
You are an AI assistant that can fetch real-time stock prices.
Use `get_current_stock_price` to find the latest price for a stock symbol.
Always provide a valid stock symbol (e.g., IBM, AAPL).
Question: {input}
{agent_scratchpad}
""")

# Create the agent and executor
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Example usage
response = agent_executor.invoke({"input": "What is the current stock price of IBM?"})
print(response["output"])

response = agent_executor.invoke({"input": "Get the stock price for AAPL."})
print(response["output"])

These real-time data streaming tools (simulated via API calls) empower your langchain agents real-world data tools to access dynamic, live information. This allows your AI to provide timely and relevant insights across many domains.

Advanced Concepts and Best Practices

You’ve now seen how to build many langchain agents real-world data tools and connect your AI to various real-world systems. To truly master agent development, let’s explore some advanced concepts and best practices. These will make your agents more robust, secure, and intelligent.

Tool Orchestration and Chaining

The true power of LangChain agents comes from their ability to orchestrate multiple tools. When you give an agent several langchain agents real-world data tools, it uses its reasoning capabilities to decide which tool to use, when to use it, and in what order. This process is called tool orchestration or chaining.

An agent might use one tool to get information, then another tool to process that information, and finally a third tool to report the result. For instance, an agent could use a database connection tool to retrieve customer data, then an email tool setup to send a personalized email to that customer. The agent dynamically decides this sequence.

For a deeper dive into how agents decide which tools to use and how to design effective tool descriptions, check out our dedicated post: [Deep Dive into LangChain Tool Orchestration](/blog/langchain-tool-orchestration-patterns). Understanding this is key to building complex, multi-step AI workflows.

Error Handling and Robustness

What happens if one of your langchain agents real-world data tools encounters an error? For example, what if a database query fails or an API call returns an unexpected response? Building robust agents requires careful error handling.

You should always wrap the core logic of your tools in try-except blocks. This allows your tool to return an informative error message to the agent instead of crashing. The agent can then use its reasoning to decide if it should try again, try a different tool, or inform the user about the failure.

For example, our run_supabase_query tool returns an error message if the query fails. The agent can then tell you, “I tried to query the database, but it returned an error: [error message].” This makes the agent much more reliable and user-friendly.

Security Considerations

Connecting your AI to real-world data and systems introduces significant security concerns. You are giving your AI access to potentially sensitive information and the ability to perform actions. Protecting your API keys and data is paramount.

• Never hardcode API keys: Always use environment variables or a secure secret management system.
• Principle of Least Privilege: Give your tools and their underlying integrations only the minimum necessary permissions. For example, a database tool should ideally only have read access unless write access is explicitly required.
• Input Validation: Sanitize and validate all inputs coming into your tools, especially from the LLM, to prevent injection attacks (e.g., SQL injection, malicious code).
• Logging and Auditing: Keep detailed logs of agent actions and tool usage. This helps you monitor for suspicious activity and debug issues.

Always be mindful of the data your agent has access to and the actions it can perform. Prioritize security at every step of your development process when using langchain agents real-world data tools.

Monitoring and Logging

As your agents become more complex and interact with more langchain agents real-world data tools, monitoring their behavior becomes essential. You want to understand what decisions the agent is making, which tools it’s using, and whether those tools are succeeding or failing.

LangChain’s verbose=True option in AgentExecutor is a good start, showing you the agent’s thought process. For production systems, you’ll need more sophisticated logging. Integrate standard Python logging into your tools and potentially use a dedicated observability platform for LangChain (like LangSmith, if available) or general application monitoring tools.

Good logging helps you debug problems, optimize agent performance, and ensure your agents are behaving as expected. It’s a critical component of any production-ready AI application using langchain agents real-world data tools.

Further Reading

Build advanced agents and tools:

• LangChain Tools Agents 2026: Production-Ready Patterns
• Build Your First AI Agent with LangChain in 15 Minutes (2026 Guide)
• LangChain React Agent Pattern 2026
• LangGraph Multi-Agent Systems 2026
• Best AI Agent Frameworks 2026: LangChain vs AutoGen vs CrewAI

Conclusion

You’ve embarked on an exciting journey, learning how to connect your AI to the real world using langchain agents real-world data tools. From querying databases to sending emails, managing files in the cloud, processing payments, interacting with social media, and even touching the physical world with IoT, the possibilities are vast.

By mastering the creation and integration of these tools, you empower your AI agents to move beyond simple conversations. They can now actively fetch live data, update external systems, and perform complex, multi-step tasks. This transforms your AI into an intelligent assistant that truly interacts with your digital and physical environment.

The examples in this tutorial cover a wide range of langchain agents real-world data tools, including database connection tools like Supabase and MongoDB Atlas, file system access tools for local and cloud storage (AWS S3, Google Cloud), CRM tool integration with HubSpot, email tools setup with SendGrid, calendar API tools for Google Calendar, payment processing tools with Stripe, social media API tools for Twitter, IoT device integration, and real-time data streaming tools.

Now it’s your turn to experiment! Start building your own langchain agents real-world data tools and explore how you can integrate them into your projects. The future of AI is interactive, and you are now equipped to build it.