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Scenario

Competitors are discussed daily on Reddit. This job searches for mentions across subreddits, feeds text into Mavera Chat, and produces a structured sentiment breakdown: positive, negative, neutral, and opportunity. Run weekly.

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Architecture

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Code

import os, requests, time

# --- Auth setup same as Job 1 ---

COMPETITORS = ["Competitor A", "Competitor B", "Competitor C"]
SUBS = ["SaaS", "startups", "productivity", "marketing"]

# 1. Search each competitor across subreddits
all_results = []
for comp in COMPETITORS:
    for sub in SUBS:
        r = requests.get(f"{RD}/r/{sub}/search", headers=RD_H,
            params={"q": comp, "restrict_sr": "true", "sort": "relevance", "t": "month", "limit": 10, "raw_json": 1})
        if r.status_code == 429: time.sleep(int(r.headers.get("X-Ratelimit-Reset", 60))); continue
        if not r.ok: continue
        for p in r.json().get("data",{}).get("children",[]):
            d = p["data"]
            all_results.append({"competitor": comp, "subreddit": sub, "title": d.get("title",""),
                "text": d.get("selftext","")[:400], "score": d.get("score",0), "post_id": d.get("id","")})
        time.sleep(0.7)

# 2. Enrich top posts with comments
enriched = []
for item in sorted(all_results, key=lambda x: -x["score"])[:20]:
    enriched.append(f"[/r/{item['subreddit']}] {item['competitor']} | score:{item['score']}\n{item['title']}\n{item['text'][:250]}")
    cr = requests.get(f"{RD}/comments/{item['post_id']}", headers=RD_H,
        params={"limit": 5, "depth": 1, "sort": "top", "raw_json": 1})
    if cr.ok and len(cr.json()) > 1:
        for c in cr.json()[1]["data"]["children"]:
            body = c.get("data",{}).get("body","")
            if body and body not in ("[removed]","[deleted]"):
                enriched.append(f"  COMMENT ({item['competitor']}): {body[:250]}")
    time.sleep(0.7)

# 3. Structured analysis
analysis = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
    "message": f"Competitive intelligence analyst: analyze Reddit mentions.\n\nCOMPETITORS: {', '.join(COMPETITORS)}\n\nDATA:\n"
        + "\n".join(enriched[:30])
        + "\n\nFor EACH competitor: Sentiment breakdown (positive/negative/neutral with quotes), Top themes, Opportunities for us, Sentiment trend. End with comparative ranking."
}).json()

print(f"Collected {len(all_results)} mentions across {len(COMPETITORS)} competitors")
print(analysis.get("content", "")[:2000])

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Example Output

Collected 87 mentions across 3 competitors

## Competitor A — Mixed-Negative
- Positive (34%): Onboarding, integrations, support
- Negative (41%): Pricing ("doubled overnight"), performance
- Opportunity: Pricing transparency + dashboard speed

## Competitor B — Positive
- Positive (62%): UX, mobile, free tier
- Negative (18%): "Outgrows you — no enterprise features"
- Opportunity: Enterprise gap we can fill

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Error Handling

Use quoted search or add product-category terms for precision.
N × M API calls. 700ms delays keep you under 100 req/min.

Reddit Integration

Mave Agent