HackerNewsTools

Overview

HackerNewsTools provides access to HackerNews stories, comments, and user data. This tool enables agents to fetch trending tech news, analyze discussions, and track community engagement on HackerNews.

Installation

pip install agno

Basic Usage

from agno.agent import Agent
from agno.tools.hackernews import HackerNewsTools
from agno.models.nebius import Nebius
import os

hackernews_tools = HackerNewsTools()

agent = Agent(
    name="Tech News Analyst",
    tools=[hackernews_tools],
    model=Nebius(
        id="Qwen/Qwen3-30B-A3B",
        api_key=os.getenv("NEBIUS_API_KEY")
    ),
    markdown=True,
    show_tool_calls=True
)

agent.print_response("What are the top stories on HackerNews today?")

API Reference

Provides methods to interact with the HackerNews API.

__init__

constructor

Creates a new instance of HackerNewsTools. No parameters required.

Available Methods

The tool automatically provides the following capabilities when used with an agent:

Fetch top stories: Get current top-ranked stories
Get story details: Retrieve full story information including title, URL, score, and comments
Read comments: Access comment threads and discussions
User profiles: Look up HackerNews user information
Track trends: Identify trending topics and patterns

Agent Configuration

Recommended Instructions

INSTRUCTIONS = """You are an intelligent HackerNews analyst and tech news curator. Your capabilities include:

1. Analyzing HackerNews content:
   - Track trending topics and patterns
   - Analyze user engagement and comments
   - Identify interesting discussions and debates
   - Provide insights about tech trends
   - Compare stories across different time periods

2. When analyzing stories:
   - Look for patterns in user engagement
   - Identify common themes and topics
   - Highlight particularly insightful comments
   - Note any controversial or highly debated points
   - Consider the broader tech industry context

3. When providing summaries:
   - Be engaging and conversational
   - Include relevant context and background
   - Highlight the most interesting aspects
   - Make connections between related stories
   - Suggest why the content matters
"""

agent = Agent(
    name="Tech News Analyst",
    instructions=[INSTRUCTIONS],
    tools=[hackernews_tools],
    model=Nebius(id="Qwen/Qwen3-30B-A3B", api_key=os.getenv("NEBIUS_API_KEY")),
    markdown=True
)

Example Use Cases

response = agent.run("What are the most discussed topics on HackerNews today?")
print(response.content)

Find Popular Stories

response = agent.run("Show me the top 5 stories about AI and machine learning")
print(response.content)

Track User Engagement

response = agent.run("Which stories have the most comments and why?")
print(response.content)

Response Format

The agent will automatically format responses based on the data retrieved from HackerNews:

Story titles with links
Scores and point counts
Comment counts and engagement metrics
Authors and submission times
Discussion summaries and key insights

Best Practices

Enable markdown: Set markdown=True for better formatted responses
Show tool calls: Set show_tool_calls=True during development to see API interactions
Use specific queries: More specific questions yield better results
Consider rate limits: HackerNews API has rate limits, avoid excessive requests
Cache results: For repeated queries, consider implementing caching

Integration with Other Tools

from agno.tools.hackernews import HackerNewsTools
from agno.tools.duckduckgo import DuckDuckGoTools

agent = Agent(
    name="Comprehensive Tech Analyst",
    tools=[
        HackerNewsTools(),
        DuckDuckGoTools(search=True, news=True)
    ],
    model=Nebius(id="Qwen/Qwen3-30B-A3B", api_key=os.getenv("NEBIUS_API_KEY"))
)