Launch live integrations first. Go deeper only when you need to.

pip install trendsagi

from trendsagi import TrendsAGIClient, APIError
import os

# Initialize the signal layer
client = TrendsAGIClient(api_key=os.getenv("TRENDSAGI_API_KEY"))

try:
    # Check for high-velocity signals in the Technology category
    signals = client.get_trends(limit=5, category="Technology", sort_by="velocity")
    
    print(f"Signal Layer Initialized. Detected {len(signals.trends)} active signals.")
    for signal in signals.trends:
        print(f" - Signal Detected: {signal.name} (Velocity: {signal.volume})")
        
except APIError as e:
    print(f"Signal Fetch Failed ({e.status_code}): {e.error_detail}")

# Example: preparing prompt context for an ad copy workflow
trend_id = 123 
insights = client.get_ai_insights(trend_id=trend_id)

# Constructing the System Prompt Context
# Note: These are retrieved from the cache. New insights must be triggered via the dashboard.
system_context = {
    "topic": insights.trend_name,
    "current_public_mood": insights.sentiment_summary,
    "marketing_hook": insights.suggested_marketing_angle,
    "taboo_subjects": insights.negative_themes # Guardrails
}

prompt = f"""
You are an expert copywriter. 
Topic: {system_context['topic']}
Mood: {system_context['current_public_mood']}
Hook: {system_context['marketing_hook']}

Task: Write 3 Facebook Ad headlines that align with this mood.
"""

# llm.predict(prompt)

1) GET /api/integrations/bigquery/status
2) GET /api/integrations/bigquery/projects
3) GET /api/integrations/bigquery/datasets
4) GET /api/integrations/bigquery/tables
5) PUT /api/integrations/bigquery/selection

or

1) GET /api/integrations/google-sheets/status
2) GET /api/integrations/google-sheets/spreadsheets
3) GET /api/integrations/google-sheets/sheets
4) PUT /api/integrations/google-sheets/selection

1) GET /api/integrations/slack/auth
2) POST /api/integrations/slack/save-channel
3) POST /api/integrations/n8n/connect
4) PUT /api/integrations/n8n/selection
5) POST /api/integrations/n8n/push
6) POST /api/integrations/webhooks
7) POST /api/integrations/webhooks/{id}/test

# Fetch grounded, cached context before generation
trends_response = client.get_trends(search="sustainable fashion", limit=1)
trend = trends_response.trends[0]
insights = client.get_ai_insights(trend_id=trend.id)

print(f"--- Fact Sheet For Workflow ---")
print(f"Context: {insights.trend_name}")
print(f"Verified Themes: {', '.join(insights.key_themes)}")
print(f"Audience Sentiment: {insights.sentiment_category}")

# Use 'Verified Themes' as a checklist for content sections.

1) GET /api/integrations/shopify/auth?shop=example-store.myshopify.com
2) Complete authorization in browser
3) GET /api/integrations/shopify/products
4) PUT /api/integrations/shopify/selection

from langchain.agents import tool
from trendsagi import TrendsAGIClient

# Initialize the Context Layer
trends_client = TrendsAGIClient(api_key="YOUR_KEY")

@tool
def check_market_signals(sector: str) -> str:
    """
    Useful for when you need to know what is currently trending or 
    popular in a specific market sector. Returns real-time data.
    """
    try:
        response = trends_client.get_trends(category=sector, limit=5)
        return str([t.name for t in response.trends])
    except Exception:
        return "Data unavailable."

# The LLM now has a "sense" of the market.
# agent_chain = initialize_agent(tools=[check_market_signals], ...)

# 1. Create a dedicated knowledge base
project = client.create_context_project(name="Support Bot Knowledge")

# 2. Upload the official policy document (PDF/Text/Image)
client.upload_context_file(
    project_id=project.id,
    file_path="./refund_policy_2025.pdf",
    item_type="reference_doc"
)

# 3. Runtime: retrieve context relevant to user query
user_query = "Can I get a refund after 30 days?"
relevant_items = client.query_context(
    project_id=project.id,
    search=user_query
)

# 4. Inject into your LLM or support workflow
context_block = "
".join([item.content for item in relevant_items])
# response = llm.predict(f"Context: {context_block}
Question: {user_query}")

exports.trendsReactor = async (req, res) => {
    // 1. Verify Signature (Security)
    const signature = req.headers['x-webhook-signature'];
    if (!verifySignature(req.body, signature)) {
        return res.status(401).send('Unauthorized');
    }

    // 2. Parse Payload
    const { trend_name, velocity, sentiment } = req.body;

    // 3. Execute Business Logic
    console.log(`Reacting to trend: ${trend_name} (Velocity: ${velocity})`);
    await triggerMarketingCampaign(trend_name);

    res.status(200).send('Reactor Executed');
};

import asyncio
import websockets
import json

URI = f"wss://api.trendsagi.com/ws/industry-live?token={API_KEY}"

async def run_listener():
    async with websockets.connect(URI) as websocket:
        print("--> Listener connected")
        while True:
            message = await websocket.recv()
            data = json.loads(message)
            
            # Immediate Reaction
            if data['type'] == 'trend_velocity_alert' and data['payload']['velocity_bucket'] == 'high':
                await trigger_response_protocol(data['payload'])

# asyncio.run(run_listener())

wss://api.trendsagi.com/ws/trends-live?token=KEY&trends=AI,Technology