Competitor Report with Groq, Not AI Agent Node (2026)

A developer on r/AiAutomations built daily competitor reports using Groq via HTTP Request instead of the AI Agent node. The motivation: the AI Agent node in n8n makes its own decisions about which tools to call and how to format output. For a daily report that must be consistent and cheap, those decisions should be yours, not the agent's.

The 4-node workflow

The entire pipeline is four nodes. No branching, no conditional logic, no agent autonomy. Each node does one thing and passes structured data to the next.

• Node 1 - Schedule Trigger: fires daily at 8:00 AM. No configuration beyond the cron expression.
• Node 2 - HTTP Request (Scavio): fetches SERP data for each competitor. One request per competitor, results stored as JSON array.
• Node 3 - HTTP Request (Groq): sends the collected SERP data to Groq with a fixed summarization prompt. Llama 8B, temperature 0.2, max 500 tokens.
• Node 4 - Email/Slack: delivers the summary. Plain text, no formatting surprises.

Node 2: Fetching competitor data

The search node runs once per competitor. For three competitors, that is three API calls. Each call returns the top 5 recent results about the competitor, filtered to the last 24 hours when possible.

import requests, os

SCAVIO_KEY = os.environ["SCAVIO_API_KEY"]
GROQ_KEY = os.environ["GROQ_API_KEY"]

def fetch_competitor_data(competitors):
    """Fetch recent SERP data for each competitor."""
    all_data = {}
    for name in competitors:
        resp = requests.post(
            "https://api.scavio.dev/api/v1/search",
            headers={"x-api-key": SCAVIO_KEY},
            json={
                "query": f"{name} news announcement update",
                "num_results": 5,
                "freshness": "day"
            }
        ).json()
        all_data[name] = [
            {"title": r["title"], "snippet": r["snippet"], "url": r["url"]}
            for r in resp.get("results", [])
        ]
    return all_data

competitors = ["Ahrefs", "Semrush", "Moz"]
data = fetch_competitor_data(competitors)

Node 3: Summarizing with Groq

The Groq call uses a fixed prompt template. No tool selection, no chain-of-thought, no agent reasoning. The model receives context and produces a summary. Temperature 0.2 keeps output consistent across days. Max 500 tokens prevents runaway generation.

def generate_report(competitor_data):
    """Summarize competitor data via Groq Llama 8B."""
    context = ""
    for company, results in competitor_data.items():
        context += f"\n=== {company} ===\n"
        if not results:
            context += "No news in last 24 hours.\n"
        for r in results:
            context += f"- {r['title']}: {r['snippet']}\n"

    resp = requests.post(
        "https://api.groq.com/openai/v1/chat/completions",
        headers={"Authorization": f"Bearer {GROQ_KEY}"},
        json={
            "model": "llama-3.1-8b-instant",
            "messages": [
                {
                    "role": "system",
                    "content": (
                        "Write a daily competitor briefing. For each company: "
                        "1-2 sentence summary of notable activity. "
                        "If no news, say so. No speculation. No filler."
                    )
                },
                {"role": "user", "content": context}
            ],
            "max_tokens": 500,
            "temperature": 0.2
        }
    ).json()
    return resp["choices"][0]["message"]["content"]

report = generate_report(data)
print(report)

Cost per daily report

Scavio: 3 calls x $0.005 = $0.015. Groq Llama 8B input: roughly 1,500 tokens at $0.05/1M = $0.000075. Groq output: roughly 400 tokens at $0.08/1M = $0.000032. Daily total: $0.015. Monthly total: $0.45. Compare this to the AI Agent node approach where the agent might make 6-8 LLM calls per run to decide which tools to use, costing 4-5x more and producing inconsistent output.

Why not the AI Agent node

The AI Agent node is designed for tasks where the model needs to decide what to do. A daily competitor report is not that task. You know exactly what to search, exactly how to summarize, and exactly where to send it. The agent's autonomy adds no value and introduces three risks: inconsistent tool selection (sometimes it searches, sometimes it does not), unpredictable token usage (agent reasoning burns tokens), and format drift (the summary structure changes day to day).

Extending the workflow

• Add a fifth node: store each daily report in a database or Google Sheet for trend analysis over time.
• Add Reddit monitoring: include a Scavio call with search_type reddit for competitor brand mentions in community discussions.
• Add alerting: if the Groq summary mentions "funding," "acquisition," or "lawsuit," send to a separate urgent channel.

The design principle

For repeatable, predictable automation: use explicit HTTP nodes with fixed prompts. For exploratory, open-ended tasks: use the AI Agent node. The mistake is using the agent for everything. Most production automations are repeatable. Most benefit from the control that explicit HTTP Request nodes provide.