Solution

Ground DeerFlow Research Agents with Scavio Search

DeerFlow research agents generate reports based on their training data, which becomes stale within weeks. Without live search grounding, research outputs contain outdated statistic

The Problem

DeerFlow research agents generate reports based on their training data, which becomes stale within weeks. Without live search grounding, research outputs contain outdated statistics, broken links, and superseded recommendations.

The Scavio Solution

Integrate Scavio as the search backend for DeerFlow agents. Each research step queries live SERP data, verifies claims against current sources, and injects timestamped citations into the final report.

Before

DeerFlow agents produce research reports with outdated data points, dead links, and statistics from months or years ago. Manual fact-checking adds hours to every report.

After

Every factual claim in DeerFlow reports is backed by a live search result with a timestamped citation. Reports are current as of the run date.

Who It Is For

DeerFlow users building multi-agent research systems.

Key Benefits

  • Live search grounding for every DeerFlow research step
  • Timestamped citations with verifiable source URLs
  • Eliminates stale data and dead links in reports
  • Works as a drop-in search backend for DeerFlow pipelines

Python Example

Python
import requests

def deerflow_search_tool(research_query: str, platform: str = "google") -> dict:
    """Drop-in search tool for DeerFlow agent pipelines."""
    resp = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": SCAVIO_API_KEY, "Content-Type": "application/json"},
        json={"query": research_query, "platform": platform, "limit": 10}
    )
    data = resp.json()
    sources = []
    for r in data.get("results", []):
        sources.append({
            "title": r["title"],
            "url": r["link"],
            "excerpt": r.get("snippet", ""),
            "platform": platform
        })
    return {
        "query": research_query,
        "source_count": len(sources),
        "sources": sources,
        "citation_block": "\n".join(
            f"[{i+1}] {s['title']} - {s['url']}" for i, s in enumerate(sources)
        )
    }

result = deerflow_search_tool("DeerFlow AI agent framework features 2026")
print(result["citation_block"])

JavaScript Example

JavaScript
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 + ' results'));

Platforms Used

Google

Web search with knowledge graph, PAA, and AI overviews

Reddit

Community, posts & threaded comments from any subreddit

YouTube

Video search with transcripts and metadata

Frequently Asked Questions

DeerFlow research agents generate reports based on their training data, which becomes stale within weeks. Without live search grounding, research outputs contain outdated statistics, broken links, and superseded recommendations.

Integrate Scavio as the search backend for DeerFlow agents. Each research step queries live SERP data, verifies claims against current sources, and injects timestamped citations into the final report.

DeerFlow users building multi-agent research systems.

Yes. Scavio's free tier includes 250 credits per month with no credit card required. That is enough to validate this solution in your workflow.

Ground DeerFlow Research Agents with Scavio Search

Integrate Scavio as the search backend for DeerFlow agents. Each research step queries live SERP data, verifies claims against current sources, and injects timestamped citations in