Construct AI Brokers with RapidAPI for Actual-Time Information

Agent creation has turn out to be simpler than ever however have you ever ever thought – how can we make them extra highly effective than they already are? I just lately considered one attainable approach – what if they’d realtime details about particular classes like finance and films. That may be actually cool, proper? Whereas exploring this feature, I discovered RapidAPI a hub of APIs that may give entry to AI brokers to realtime info with a easy API name. I then determined to make a couple of brokers that may make use of those APIs to make higher role-playing brokers.

On this article, I share the whole course of for a similar, so you’ll be able to simply comply with it and replicate the outcomes in your personal use. Allow us to begin with some primary info –

What’s RapidAPI?

RapidAPI is definitely the older title and has just lately turn out to be Nokia API Hub after acquisition. It has a catalog of APIs the place we will use or publish APIs. It covers diverse classes from Cybersecurity, Motion pictures, Communication and extra. You possibly can discover extra about RapidAPI right here.

Tips on how to Use the APIs from RapidAPI?

1. First sign-in/sign-up to RapidAPI right here

2. Go to a developer authorisation web page and create an authorization of kind ‘RapidAPI’ by click on on the ‘Add Authorization’ on the right-top.

3. Return to the house web page to find APIs and click on on any API you want. For example, I clicked on a cryptocurrency information API right here.

4. You’d see a web page like this, additionally the API key’s already current within the take a look at code. Simply be sure that the goal is about to ‘python’:

5. Now click on on ‘Subscribe to check’ on the right-top and choose the free-tier for now. After which click on on subscribe after clicking ‘Begin Free Plan’.

6. Now you need to use the test-endpoint button on the right-top and take a look at code will likely be executed and you will get the response.

Be aware: A lot of the APIs have a beneficiant free-tier and can be utilized up-to the talked about month-to-month limits.

Making Brokers built-in with RapidAPI

On this part we’ll be making brokers utilizing the ‘create_agent’ perform from langchain.brokers and the brokers will likely be powered by OpenAI, particularly the ‘gpt-5-mini’. Be happy to experiment with completely different fashions, model-providers or agent frameworks.

Prerequisite

To keep away from repetition, we are going to use the identical set of imports and initialize the APIs to make use of it for a number of brokers. And ensure to subscribe to the APIs within the hyperlinks if you wish to take a look at together with me. Additionally I’ll be utilizing Google Colab for the demo.

Subscribe to those APIs

Configure your Google Colab Pocket book

Add your OpenAI API and RapidAPI as ‘OPENAI_API_KEY’ and ‘RAPIDAPI_KEY’ within the secrets and techniques part on the left, and don’t overlook to activate the pocket book entry.

Installations

!pip set up langchain langchain_core langchain_openai -q 

Imports

from google.colab import userdata 
import os 
import http.shopper 
import json 
from langchain_core.instruments import software 
from langchain_openai import ChatOpenAI 
from langchain.brokers import create_agent

API Keys

os.environ['OPENAI_API_KEY'] = userdata.get('OPENAI_API_KEY') 
RAPIDAPI_KEY = userdata.get('RAPIDAPI_KEY') 

Constructing a Information Agent

@software
def search_news(question: str, restrict: int = 10) -> str:
    """Seek for real-time information articles based mostly on a question. Returns newest information articles."""
    conn = http.shopper.HTTPSConnection("real-time-news-data.p.rapidapi.com")

    headers = {
        "x-rapidapi-key": RAPIDAPI_KEY,
        "x-rapidapi-host": "real-time-news-data.p.rapidapi.com",
    }

    conn.request(
        "GET",
        f"/search?question={question}&restrict={restrict}&time_published=anytime&nation=US&lang=en",
        headers=headers,
    )

    res = conn.getresponse()
    knowledge = res.learn()
    consequence = json.masses(knowledge.decode("utf-8"))

    return json.dumps(consequence, indent=2)

news_agent = create_agent(
    ChatOpenAI(temperature=0, mannequin="gpt-5-mini"),
    instruments=[search_news],
)

Be aware that we’re utilizing the API supplied by RapidAPI as a software and passing the software to the agent. The Agent will take assist from the software every time it feels a software name is critical.

# Check the agent 
consequence = news_agent.invoke({ 
 "messages": [{"role": "user", "content": "Search for latest news about Messi"}] 

}) 
print(consequence["messages"][-1].content material)

Outcome

Nice! We have now made our first agent and it’s wanting good. You possibly can experiment with new prompts if you happen to like.

Be aware: Our agent works totally on when requested one thing utilizing just one phrase (instance: “Sports activities”,”Forest”..and so on). It is because the software accepts solely a single string and never a sentence, to repair this we will configure our agent’s system immediate.

Inventory Agent

Let’s create a Inventory Agent that makes use of Yahoo’s API to fetch the inventory particulars utilizing a inventory ticker image of any explicit inventory.

Code

@software
def get_stock_history(image: str, interval: str = "1m", restrict: int = 640) -> str:
    """Get historic inventory value knowledge for an emblem."""
    conn = http.shopper.HTTPSConnection("yahoo-finance15.p.rapidapi.com")

    headers = {
        "x-rapidapi-key": RAPIDAPI_KEY,
        "x-rapidapi-host": "yahoo-finance15.p.rapidapi.com",
    }

    path = (
        f"/api/v2/markets/inventory/historical past?"
        f"image={image}&interval={interval}&restrict={restrict}"
    )

    conn.request("GET", path, headers=headers)
    res = conn.getresponse()
    knowledge = res.learn()
    consequence = json.masses(knowledge.decode("utf-8"))

    return json.dumps(consequence, indent=2)


stock_agent = create_agent(
    ChatOpenAI(temperature=0, mannequin="gpt-5-mini"),
    instruments=[get_stock_history],
)

# Instance name
consequence = stock_agent.invoke(
    {"messages": [{"role": "user", "content": "Get the last intraday price history for AAPL"}]}
)

print(consequence["messages"][-1].content material)

Outcome

Nice, we efficiently retrieved the output for AAPL (Apple Inc.), and the knowledge is totally actual time.

Properties Agent

The purpose right here is to create an agent utilizing an API that searches properties on the market/hire, the one we’re utilizing from Zoopla searches the properties particularly within the UK.

Code

@software
def search_properties(
    location_value: str,
    location_identifier: str = "metropolis",
    web page: int = 1,
) -> str:
    """
    Seek for residential properties.

    Args:
        location_value: The title of the situation
            (e.g., 'London', 'Manchester', 'E1 6AN').
        location_identifier: The class of the situation.
            - Use 'metropolis' for main cities (default).
            - Use 'postal_code' if the consumer offers a postcode (e.g., 'W1').
            - Use 'space' for smaller neighborhoods.
    """
    # URL encoding to forestall InvalidURL errors
    safe_val = location_value.exchange(" ", "%20").exchange(",", "%2C")
    safe_id = location_identifier.exchange(" ", "%20")

    conn = http.shopper.HTTPSConnection("zoopla.p.rapidapi.com")

    headers = {
        "x-rapidapi-key": RAPIDAPI_KEY,
        "x-rapidapi-host": "zoopla.p.rapidapi.com",
    }

    path = (
        f"/properties/v2/listing?locationValue={safe_val}"
        f"&locationIdentifier={safe_id}"
        f"&class=residential&furnishedState=Any"
        f"&sortOrder=newest_listings&web page={web page}"
    )

    conn.request("GET", path, headers=headers)
    res = conn.getresponse()
    knowledge = res.learn()
    consequence = json.masses(knowledge.decode("utf-8"))

    return json.dumps(consequence, indent=2)


property_agent = create_agent(
    ChatOpenAI(temperature=0, mannequin="gpt-5-mini"),
    instruments=[search_properties],
    system_prompt=(
        "You're a real-estate professional. When a consumer asks for a location, "
        "infer the 'location_identifier' your self, often 'metropolis' or "
        "'postal_code'. Don't ask the consumer for technical identifiers; "
        "name the software instantly."
    ),
)

consequence = property_agent.invoke(
    {"messages": [{"role": "user", "content": "Search for properties in London, England"}]}
)

print(consequence["messages"][-1].content material)

Outcome

We obtained the actual properties as output, however they’ve been blurred due to apparent causes.

Film Recommender Agent

This agent can have entry to each IMDB’s prime rated and worst rated API’s as instruments and we are going to configure the system immediate to choose which software to make use of based mostly on the immediate.

@software
def get_top_rated_movies() -> str:
    """
    Fetch the listing of top-rated English films on IMDb.

    Use this when the consumer needs a advice or a "good" film.
    """
    conn = http.shopper.HTTPSConnection("imdb236.p.rapidapi.com")

    headers = {
        "x-rapidapi-key": RAPIDAPI_KEY,
        "x-rapidapi-host": "imdb236.p.rapidapi.com",
    }

    conn.request("GET", "/api/imdb/top-rated-english-movies", headers=headers)
    res = conn.getresponse()

    # Decode and return uncooked JSON for the agent to course of
    return res.learn().decode("utf-8")


@software
def get_lowest_rated_movies() -> str:
    """
    Fetch the listing of lowest-rated films on IMDb.

    Use this when the consumer asks for "dangerous" films or films to keep away from.
    """
    conn = http.shopper.HTTPSConnection("imdb236.p.rapidapi.com")

    headers = {
        "x-rapidapi-key": RAPIDAPI_KEY,
        "x-rapidapi-host": "imdb236.p.rapidapi.com",
    }

    conn.request("GET", "/api/imdb/lowest-rated-movies", headers=headers)
    res = conn.getresponse()

    return res.learn().decode("utf-8")


movie_agent = create_agent(
    ChatOpenAI(temperature=0, mannequin="gpt-5-mini"),
    instruments=[get_top_rated_movies, get_lowest_rated_movies],
    system_prompt=(
        "You're an professional film critic. Your purpose is to assist customers discover films "
        "based mostly on high quality. If a consumer asks for one thing 'good', 'really helpful', "
        "or 'basic', name get_top_rated_movies. If a consumer asks for one thing "
        "'dangerous', 'horrible', or 'lowest rated', name get_lowest_rated_movies. "
        "Each instruments require no parameters. Summarize the leads to a pleasant "
        "approach in a single sentence."
    ),
)

# Instance utilization
consequence = movie_agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "I'm in the mood for a really terrible movie, what's the worst out there?",
            }
        ]
    }
)

print(consequence["messages"][-1].content material)

Outcome

In order for you really terrible, IMDb’s lowest-rated picks embrace Daniel der
 Zauberer (Daniel the Wizard) and Smolensk — each hovering round a 1.2
 common score and excellent if you happen to’re after a 
“so-bad-it’s-fascinating” watch. 

Nice! We have now efficiently created an agent which may entry a number of instruments and may counsel each extremely rated or worst rated films.

Conclusion

By integrating actual time APIs with brokers, we will transfer past static responses and construct techniques that really feel really clever. RapidAPI makes this integration easy and scalable throughout domains. Additionally it’s essential that we decide the proper instruments and in addition tune the agent to work in concord with the software. For example, many APIs may give an error whereas single quotes or areas are current within the argument. Additionally RapidAPI gives MCP assist throughout its APIs, which could be explored within the ongoing efforts of constructing higher brokers.

Incessantly Requested Questions

Captivated with expertise and innovation, a graduate of Vellore Institute of Know-how. At the moment working as a Information Science Trainee, specializing in Information Science. Deeply fascinated with Deep Studying and Generative AI, desperate to discover cutting-edge methods to resolve complicated issues and create impactful options.