Overview
This workflow adds a Scavio-powered search node to your LangGraph agent graph. The agent runs daily, researches configured topics with live search data, and produces grounded reports that downstream nodes can process.
Trigger
Daily cron at 7:00 AM or triggered by a new research request in the task queue.
Schedule
Daily at 7:00 AM
Workflow Steps
Define LangGraph State Schema
Create the state schema that flows through the graph. It holds the research query, search results, and the final synthesized output.
Build the Scavio Search Node
Create a LangGraph node that takes queries from state, fetches live results from Scavio, and appends them to the state's search_results list.
Build the Synthesis Node
Create a node that takes search results and produces a grounded summary with citations. This node can call an LLM or use template-based synthesis.
Assemble and Run the Graph
Wire the nodes together in a LangGraph StateGraph and execute the daily research run.
Python Implementation
import requests, os
H = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}
data = requests.post('https://api.scavio.dev/api/v1/search', headers=H, json={'query': 'example', 'country_code': 'us'}).json()
print(len(data.get('organic_results', [])))JavaScript Implementation
const H = {'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'};
fetch('https://api.scavio.dev/api/v1/search', {method: 'POST', headers: H, body: JSON.stringify({query: 'example', country_code: 'us'})}).then(r => r.json()).then(d => console.log(d.organic_results?.length));Platforms Used
Web search with knowledge graph, PAA, and AI overviews
YouTube
Video search with transcripts and metadata
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