Build a News Agent with Google ADK + wpnews (Python, 2025)

Why Google ADK + wpnews?

Google Agent Development Kit (ADK) is Google's official framework for building production multi-agent systems. Unlike LangChain or CrewAI, ADK is designed ground-up for hierarchical agent orchestration — agents compose other agents as sub-agents, passing typed state through structured flows.

wpnews provides 71 live AI news tools that plug directly into ADK's FunctionTool system — giving every agent in your hierarchy real-time access to structured news data without building your own news pipeline.

What you'll build

• A NewsMonitorAgent — registers all wpnews endpoints as ADK FunctionTools
• A BreakingNewsAgent sub-agent — polls hot articles and triggers on bursts
• A DailyBriefingAgent — orchestrates sub-agents to compile morning intelligence

Step 1 — Install

pip install google-adk wpnews

Get a free wpnews API key at wpnews.pro/api#get-key.

Step 2 — Register wpnews tools as ADK FunctionTools

ADK's FunctionTool wraps any Python callable. The simplest approach is to create thin wrapper functions that call the wpnews SDK:

import os
from google.adk.tools import FunctionTool
from wpnews import WPNews

_news = WPNews(api_key=os.environ.get("WPNEWS_API_KEY", ""))


def search_news(query: str, limit: int = 10, lang: str = "en") -> dict:
    """Search AI news by keyword or phrase. Returns articles with title, summary, topics, entities."""
    return {"articles": _news.search(q=query, lang=lang, limit=limit)}


def get_morning_briefing(lang: str = "en", hours: int = 6) -> dict:
    """Get agent-ready morning briefing: velocity status, hot articles, trending entities.
    Returns velocity_status (burst/normal/quiet), hot_articles[], trending_entities[]."""
    return _news.get_morning_briefing(lang=lang, hours=hours)


def get_hot_articles(hours: int = 6, limit: int = 10, lang: str = "en") -> dict:
    """Fetch breaking news ranked by freshness x quality. Best for detecting bursts."""
    return {"articles": _news.get_hot_articles(hours=hours, limit=limit, lang=lang)}


def get_topic_news(topic: str, limit: int = 10, lang: str = "en") -> dict:
    """Get articles for a specific topic slug (e.g. 'llm', 'robotics', 'open-source')."""
    return {"articles": _news.get_news(topic=topic, lang=lang, limit=limit)}


# Wrap in ADK FunctionTool
search_tool       = FunctionTool(func=search_news)
briefing_tool     = FunctionTool(func=get_morning_briefing)
hot_articles_tool = FunctionTool(func=get_hot_articles)
topic_tool        = FunctionTool(func=get_topic_news)

Step 3 — Build the NewsMonitorAgent

from google.adk.agents import Agent
from google.adk.models.lite_llm import LiteLlm

news_agent = Agent(
    name="news_monitor",
    model=LiteLlm(model="openai/gpt-4o"),
    description="Monitors live AI news and answers questions about current events.",
    instruction="""You are an AI news analyst with access to live wpnews data.
Use search_news to find articles by keyword.
Use get_morning_briefing to get the current news status and hot articles.
Use get_hot_articles to detect breaking news (burst events).
Use get_topic_news to dive deep on a specific topic.
Always cite article titles and publication times in your answers.""",
    tools=[search_tool, briefing_tool, hot_articles_tool, topic_tool],
)

Step 4 — Build a BreakingNewsAgent sub-agent

ADK agents can orchestrate other agents as sub-agents. The BreakingNewsAgent focuses solely on burst detection:

def check_news_burst(threshold: float = 1.5, hours: int = 6) -> dict:
    """Check if AI news is in burst mode. threshold: ratio above baseline to consider burst."""
    briefing = _news.get_morning_briefing(hours=hours)
    vel = briefing.get("velocity", {})
    ratio = vel.get("ratio", 1.0)
    status = vel.get("status", "normal")
    is_burst = status == "burst" or ratio >= threshold
    return {
        "is_burst": is_burst,
        "velocity_status": status,
        "ratio": ratio,
        "hot_articles": briefing.get("hot_articles", [])[:3],
    }

burst_tool = FunctionTool(func=check_news_burst)

breaking_news_agent = Agent(
    name="breaking_news_detector",
    model=LiteLlm(model="openai/gpt-4o-mini"),
    description="Detects breaking news bursts and surfaces the top 3 stories.",
    instruction="""Check if there's a breaking news burst using check_news_burst.
If is_burst is True, list the hot_articles with titles and why they matter.
If no burst, report that news activity is normal.""",
    tools=[burst_tool],
)

Step 5 — Orchestrate with DailyBriefingAgent

The orchestrator delegates to sub-agents and compiles the final briefing:

from google.adk.agents import Agent
from google.adk.models.lite_llm import LiteLlm

daily_briefing_agent = Agent(
    name="daily_briefing",
    model=LiteLlm(model="openai/gpt-4o"),
    description="Orchestrates sub-agents to compile a daily AI news briefing.",
    instruction="""Compile a comprehensive daily AI news briefing:

1. Ask breaking_news_detector if there are any burst events
2. Ask news_monitor for the morning briefing summary
3. Ask news_monitor to search for the top 3 trending topics from the briefing
4. Compile everything into a structured markdown briefing with:
   - Velocity status (burst/normal/quiet) with ratio
   - Breaking stories (if any)
   - Top trending topics with representative articles
   - Key entities making news today""",
    sub_agents=[breaking_news_agent, news_agent],
)

Step 6 — Run with ADK Runner

import asyncio
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types

async def main():
    session_service = InMemorySessionService()
    runner = Runner(
        agent=daily_briefing_agent,
        app_name="wpnews_adk",
        session_service=session_service,
    )
    session = await session_service.create_session(
        app_name="wpnews_adk",
        user_id="analyst_1",
    )
    message = types.Content(
        role="user",
        parts=[types.Part(text="Give me today's AI news briefing")]
    )
    async for event in runner.run_async(
        user_id="analyst_1",
        session_id=session.id,
        new_message=message,
    ):
        if event.is_final_response():
            print(event.content.parts[0].text)

asyncio.run(main())

Full working example (under 80 lines)

"""
Google ADK + wpnews daily briefing agent
Run: WPNEWS_API_KEY=... OPENAI_API_KEY=... python adk_news.py
"""
import asyncio, os
from google.adk.agents import Agent
from google.adk.models.lite_llm import LiteLlm
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.adk.tools import FunctionTool
from google.genai import types
from wpnews import WPNews

_news = WPNews(api_key=os.environ.get("WPNEWS_API_KEY", ""))


def get_briefing(lang: str = "en", hours: int = 6) -> dict:
    """Get AI news situational awareness: velocity status + hot articles + entities."""
    return _news.get_morning_briefing(lang=lang, hours=hours)


def search_news(query: str, limit: int = 8, lang: str = "en") -> dict:
    """Search live AI news by keyword. Returns articles with title, summary, topics."""
    return {"articles": _news.search(q=query, lang=lang, limit=limit)}


agent = Agent(
    name="ai_news_analyst",
    model=LiteLlm(model="openai/gpt-4o"),
    description="Live AI news analyst powered by wpnews",
    instruction="""Use get_briefing first to understand current AI news status.
Use search_news to find articles on specific topics.
Always include velocity_status and cite article titles with dates.""",
    tools=[FunctionTool(func=get_briefing), FunctionTool(func=search_news)],
)


async def main():
    svc = InMemorySessionService()
    runner = Runner(agent=agent, app_name="wpnews", session_service=svc)
    session = await svc.create_session(app_name="wpnews", user_id="u1")
    msg = types.Content(role="user", parts=[types.Part(text="What's the top AI news today?")])
    async for event in runner.run_async(user_id="u1", session_id=session.id, new_message=msg):
        if event.is_final_response():
            print(event.content.parts[0].text)

asyncio.run(main())