Add Reddit Search to Your Local Research Agent

Reddit is where real users complain, recommend, and debug in public. For research agents, Reddit threads contain signal that polished marketing pages do not: honest opinions, workarounds, and deal-breaker bugs. The problem is access. Reddit's API requires OAuth app registration, rate limits at 100 requests per minute per OAuth client, and returns raw post/comment data that needs parsing. For a local research agent, this is too much plumbing for a single data source.

Reddit API vs search API approach

• Reddit API: register app, get client ID and secret, handle OAuth token refresh, deal with rate limits, parse markdown body text
• Search API with Reddit filter: one POST request, get Reddit results ranked by relevance, structured title/URL/snippet, no OAuth

The tradeoff is control vs convenience. The Reddit API gives you full thread data including all comments and votes. A search API gives you discovery -- which threads exist and are relevant -- but not the full comment tree.

Adding Reddit search to a local agent

import requests, os, json

def search_reddit(query: str, limit: int = 10) -> list:
    """Search Reddit via Scavio. Returns top threads for a query."""
    resp = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": os.environ["SCAVIO_API_KEY"]},
        json={
            "query": f"site:reddit.com {query}",
            "num_results": limit,
        },
        timeout=10,
    )
    results = resp.json().get("results", [])
    return [
        {
            "title": r["title"],
            "url": r["url"],
            "snippet": r.get("snippet", ""),
            "subreddit": extract_subreddit(r["url"]),
        }
        for r in results
    ]

def extract_subreddit(url: str) -> str:
    parts = url.split("/")
    try:
        idx = parts.index("r")
        return parts[idx + 1]
    except (ValueError, IndexError):
        return "unknown"

Integrating into a research agent

A local research agent typically has a tool registry. Add the Reddit search as one tool alongside web search and document retrieval. The agent decides when to use Reddit based on the query type.

class ResearchAgent:
    def __init__(self):
        self.tools = {
            "web_search": self.web_search,
            "reddit_search": self.reddit_search,
            "summarize": self.summarize,
        }

    def reddit_search(self, query: str) -> str:
        threads = search_reddit(query)
        return json.dumps(threads, indent=2)

    def web_search(self, query: str) -> str:
        resp = requests.post(
            "https://api.scavio.dev/api/v1/search",
            headers={"x-api-key": os.environ["SCAVIO_API_KEY"]},
            json={"query": query, "num_results": 5},
            timeout=10,
        )
        return json.dumps(resp.json().get("results", []), indent=2)

    def research(self, topic: str) -> dict:
        """Run a research workflow: web search + Reddit for real opinions."""
        web_results = self.web_search(topic)
        reddit_results = self.reddit_search(topic)
        return {
            "web": json.loads(web_results),
            "reddit": json.loads(reddit_results),
        }

agent = ResearchAgent()
findings = agent.research("best database for real-time analytics 2026")
print(f"Web results: {len(findings['web'])}")
print(f"Reddit threads: {len(findings['reddit'])}")

What you get vs what you miss

The search approach gives you thread discovery: which subreddits discuss your topic, which threads are most relevant, and a snippet of the top-level content. This is enough for many research tasks where you need sentiment and recommendations.

What you miss: full comment threads. A Reddit post with 200 comments might have the real answer buried in comment #47. The search API returns the post title and a snippet, not the full discussion. If deep comment analysis matters, you need either the Reddit API directly or a follow-up scrape of the specific thread URL.

Cost for a research-heavy workflow

A research agent that runs 10 Reddit searches and 10 web searches per research task uses 20 credits per task. At $0.005/credit, that is $0.10 per research task. 100 research tasks per month costs $10. The 250 free credits per month cover about 12 research tasks at no cost.

Hybrid approach for deep threads

Use the search API for discovery, then hit specific thread URLs with a scraping tool or the Reddit API for full comments. This gives you the best of both: fast discovery without OAuth overhead, and deep data only when you need it. Most research tasks need discovery, not full thread dumps.