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This example demonstrates how to create a context-aware agent that can access real-time HackerNews data through dependency injection, enabling the agent to provide current information.
Code
add_dependencies_to_context.py
import json
import httpx
from agno.agent import Agent
from agno.models.openai import OpenAIChat
def get_top_hackernews_stories(num_stories: int = 5) -> str:
"""Fetch and return the top stories from HackerNews.
Args:
num_stories: Number of top stories to retrieve (default: 5)
Returns:
JSON string containing story details (title, url, score, etc.)
"""
# Get top stories
stories = [
{
k: v
for k, v in httpx.get(
f"https://hacker-news.firebaseio.com/v0/item/{id}.json"
)
.json()
.items()
if k != "kids" # Exclude discussion threads
}
for id in httpx.get(
"https://hacker-news.firebaseio.com/v0/topstories.json"
).json()[:num_stories]
]
return json.dumps(stories, indent=4)
# Create a Context-Aware Agent that can access real-time HackerNews data
agent = Agent(
model=OpenAIChat(id="gpt-5-mini"),
# Each function in the dependencies is resolved when the agent is run,
# think of it as dependency injection for Agents
dependencies={"top_hackernews_stories": get_top_hackernews_stories},
# We can add the entire dependencies dictionary to the user message
add_dependencies_to_context=True,
markdown=True,
)
# Example usage
agent.print_response(
"Summarize the top stories on HackerNews and identify any interesting trends.",
stream=True,
)
Usage
Create a virtual environment
Open the Terminal and create a python virtual environment.python3 -m venv .venv
source .venv/bin/activate
Install libraries
pip install -U agno openai httpx
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
Create a Python file
Create a Python file and add the above code.touch add_dependencies_to_context.py
Run Agent
python add_dependencies_to_context.py
Find All Cookbooks
Explore all the available cookbooks in the Agno repository. Click the link below to view the code on GitHub:Agno Cookbooks on GitHub