Python client

pip install webzio-news-search
export WEBZ_API_TOKEN="your-webz-api-token"

from webzio_news_search import WebzNewsSearch

client = WebzNewsSearch()  # reads WEBZ_API_TOKEN
# client = WebzNewsSearch(api_token="your-webz-api-token")

response = client.search("recent developments on EU AI regulation", k=10, days=30)

print(response.total_results, "results |", response.credits_used, "credits used")
for result in response:
    print(result.score, result.title, result.url)
    print("  ", result.text)

client.search(
    "trade agreements between USA and Germany",
    k=10,
    days=7,
    language=["english"],
    country=["US", "DE"],
)

client.search(
    "earnings guidance and analyst reactions",
    ticker=["NVDA"],
    domain=["yahoo.com", "cnn.com"],
    score_gte=5,
    k=5,
)

client.search(
    "central bank interest rate decision",
    filters={
        "published_from": "2026-07-01",
        "published_to": "2026-07-31",
        "sentiment": ["negative"],
        "political_bias": ["center"],
    },
)

response.query            # echoed query
response.total_results    # matches returned, up to k
response.requests_left    # remaining credit balance
response.credits_used     # credits charged for this call
response.raw              # full JSON payload

result = response.results[0]
result.score              # match quality, 0–10
result.article            # article_id, url, title, published_at, summary, main_image
result.chunk.text         # the most relevant excerpt
result.metadata           # language, country, category, sentiment, domain, site_type,
                          # topic, person, organization, location, ticker,
                          # political_bias, trust_category, source_type, domain_rank

print(response.to_text())   # prompt-friendly text
rows = response.to_dicts()  # one flat dict per result

from webzio_news_search import WebzNewsSearch

async with WebzNewsSearch() as client:
    response = await client.asearch("renewable energy investments", k=5)

from webzio_news_search import news_search, anews_search

response = news_search("semiconductor export controls", k=3)
response = await anews_search("semiconductor export controls", k=3)

from openai import OpenAI
from webzio_news_search import WebzNewsSearch

openai_client = OpenAI()
news = WebzNewsSearch()

messages = [{"role": "user", "content": "What happened with Nvidia this week? Cite sources."}]
completion = openai_client.chat.completions.create(
    model="gpt-4.1-mini",
    messages=messages,
    tools=[WebzNewsSearch.openai_tool_definition()],
)

for call in completion.choices[0].message.tool_calls or []:
    messages.append({
        "role": "tool",
        "tool_call_id": call.id,
        "content": news.run_tool(call.function.arguments),
    })