January 22, 2026

Reddit to Gmail, executive customer insights delivered

Lisa Granqvist Partner Workflow Automation Expert

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You don’t have a “research” problem. You have a tab problem. Reddit threads, X posts, a couple web articles, a half-finished spreadsheet, and then someone asks, “So what are customers really complaining about this week?”

This is where product marketers lose hours, ops leads lose clarity, and founders lose patience. With Reddit Gmail insights automation, you get a clean, executive-ready brief in your inbox and a running log in Google Sheets without babysitting the process.

Below is how the workflow pulls signals from Reddit, X, and the web, labels pain points, and emails a polished summary you can forward upstairs.

How This Automation Works

The full n8n workflow, from trigger to final output:

n8n Workflow Template: Reddit to Gmail, executive customer insights delivered

Click to explore

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The Problem: Customer research turns into unpaid detective work

Manual customer intelligence sounds simple until you actually do it. You search Google, skim a few “top results,” then jump to Reddit for real talk, and end up on X for the latest blow-ups. After 45 minutes you’ve collected a pile of screenshots and half-quotes, but you still can’t answer the real question: what’s the pattern, and what should we do about it? It gets worse when you try to repeat this weekly. The process is fragile, inconsistent, and honestly a little demoralizing.

The friction compounds. Here’s where it breaks down in the real world.

Signals end up scattered across tabs and bookmarks, so every “quick update” becomes another mini research project.
It’s easy to overweight whatever you read last, which means your summary changes depending on what you happened to click.
Manual notes don’t give you a historical baseline, so you can’t tell if “pricing complaints” are spiking or just loud today.
By the time the findings reach leadership, they’re either too long to read or too vague to act on.

The Solution: A multisource customer intelligence brief, automatically emailed

This n8n workflow acts like a customer intelligence engine you can run on demand (via a simple form) or on a schedule. It pulls three different kinds of signal at the same time: web search results for broader context, Reddit posts for detailed “here’s what happened” stories, and X for real-time complaints and sentiment. Then it cleans up the text, tags each item by source, and merges everything into one dataset. A lightweight categorization step labels pain points and assigns a sentiment score without sending every single item to a paid AI model. Finally, it generates a polished HTML executive brief using an LLM and sends it through Gmail, while also appending the metrics to Google Sheets so you can track trends over time.

The workflow starts with your keywords and target subreddits from a form trigger. It runs parallel retrieval from SERP, Reddit, and X, then merges the results and classifies pain points. After aggregation and deduping, the LLM writes the executive-ready brief and Gmail delivers it, with Google Sheets keeping the running log.

What You Get: Automation vs. Results

What This Workflow Automates

Results You’ll Get

Collects web, Reddit, and X signals in parallel from one input.
Cleans and normalizes text so sources can be compared apples-to-apples.
Labels each item with a pain point category and sentiment score automatically.
Generates an HTML executive brief and emails it via Gmail, while logging counts to Google Sheets.

Get a brief you can forward in about 10 minutes, instead of spending about 2 hours reading threads.
See the top pain points ranked, not buried in anecdotes.
Build a simple historical trend line in Google Sheets without extra work.
Make strategy discussions more concrete because the evidence is attached.
Reduce “we should look into that” loops by showing patterns and suggested actions.

Example: What This Looks Like

Say you run a weekly customer intel check for an HVAC service brand. Manually, you might scan 20 Reddit posts (about 3 minutes each), 30 X posts (maybe a minute each), plus 10 web results (2 minutes each). That’s roughly 2 hours before you even start writing. With this workflow, you submit keywords and subreddits in a form (about 5 minutes), wait for retrieval and synthesis (often around 10–15 minutes), and the Gmail brief shows up ready to share. You also get the week’s counts added to Google Sheets automatically.

What You’ll Need

n8n instance (try n8n Cloud free)
Self-hosting option if you prefer (Hostinger works well)
Google Sheets for historical logging and trend tracking
Gmail to deliver the executive brief to stakeholders
SERP API key (get it from your SERP API provider dashboard)

Skill level: Intermediate. You’ll connect a few accounts, add API keys, and map form fields to the right nodes.

Don’t want to set this up yourself? Talk to an automation expert (free 15-minute consultation).

How It Works

You submit the research focus. The workflow begins when you fill out an n8n form with target keywords and the subreddits you want to monitor (and it can also be adapted to run on a schedule).

Sources are pulled in parallel. One branch retrieves web results through SERP API, another builds a subreddit list and fetches Reddit discussions, and a third queries X through an HTTP request so you get the “right now” complaints too.

Everything gets cleaned, merged, and labeled. The workflow normalizes text, tags each item with a source label, then combines the streams into one dataset. A categorization step assigns pain point categories and a sentiment score so you can count patterns without expensive per-item LLM calls.

Exec outputs land where people actually read them. The workflow aggregates and deduplicates, generates a polished HTML brief with an LLM, emails it via Gmail, and appends the key metrics to Google Sheets for historical tracking.

You can easily modify the keywords, subreddits, and briefing format to match your market and internal reporting style. See the full implementation guide below for customization options.

Step-by-Step Implementation Guide

Step 1: Configure the Form Trigger

Set up the intake form that captures user inputs and launches the workflow.

Add and open Input Capture Form.
Set Form Title to Customer intelligence Briefing.
Set Form Description to The system will analyze the web, categorize complaints, and deliver a strategic executive summary to the sales team within minutes.
Add the seven fields as shown, including required fields for name, email, location, and search queries.

Tip: Ensure the field labels match exactly, because downstream expressions reference these labels from Input Capture Form.

Step 2: Connect the Data Sources and Parallel Inputs

Configure the three parallel data streams that run immediately after form submission.

In Web Search Retrieval, set Query to HVAC company customer service problems and Location to ={{ $json["Location (where you want the search to originate):"] }}.
Credential Required: Connect your serpApi credentials in Web Search Retrieval.
In Social API Query, set URL to https://api.twitterapi.io/twitter/tweet/advanced_search, enable Send Query, and set query parameter query to ={{ $json["Mention X (Twitter) Search Query:"] }} and queryType to Latest.
Credential Required: Connect your httpHeaderAuth credentials in Social API Query.
In Subreddit List Builder, keep the JavaScript that creates two subreddit entries from the form fields.
Input Capture Form outputs to both Web Search Retrieval, Subreddit List Builder, and Social API Query in parallel.

⚠️ Common Pitfall: If the form field labels differ, expressions like ={{ $json["Mention X (Twitter) Search Query:"] }} will fail and return empty results.

Step 3: Normalize and Merge the Data Streams

Transform each data source into a consistent schema, then merge into a single stream.

In Normalize Web Results, keep the JavaScript that maps SERP results into title, text, source_url, and full_text fields.
In Community Post Fetch, set Subreddit to ={{ $json.subreddit }} and Limit to 50.
Credential Required: Connect your redditOAuth2Api credentials in Community Post Fetch.
In Reddit Content Filter and Twitter Data Cleanse, keep the JavaScript transforms that output standardized fields like title, text, source_url, and full_text.
In Combine Data Streams, set Number of Inputs to 3 to merge results from web, Reddit, and Twitter.

Tip: The code nodes (Normalize Web Results, Subreddit List Builder, Reddit Content Filter, Twitter Data Cleanse, Classify Sentiment Tags, Aggregate Unique Insights, Extract Brief Summary) are grouped for clarity—review them if you want to adjust filtering thresholds or text mappings.

Step 4: Classify, Aggregate, and Summarize Insights

Classify sentiment, aggregate unique insights, and prepare a summary for the AI model.

In Classify Sentiment Tags, keep the JavaScript logic that tags pain points and sentiment scores based on keyword matches.
In Aggregate Unique Insights, keep the JavaScript that deduplicates content, builds summary stats, and outputs sheet-ready rows.
Aggregate Unique Insights outputs to both Extract Brief Summary and Record Search Log in parallel.
In Extract Brief Summary, keep the JavaScript that pulls LLM_SUMMARY_HOLDER from Aggregate Unique Insights and formats the final summary text.

Step 5: Configure the AI Executive Brief

Generate a professional HTML executive summary using the Anthropic model.

Open Generate Executive Brief and verify Model is set to claude-haiku-4-5-20251001.
In Messages, set Content to =DATA TO ANALYZE: {{ $json.summary }} so it receives the output from Extract Brief Summary.
Credential Required: Connect your anthropicApi credentials in Generate Executive Brief.

⚠️ Common Pitfall: If the AI returns markdown or wrapped HTML, the email formatting may break. The system prompt in Generate Executive Brief enforces raw HTML output—do not remove it.

Step 6: Configure Output Destinations

Send the brief by email and log aggregated insights to Google Sheets.

In Dispatch Email Brief, set Send To to ={{ $('Input Capture Form').item.json["What is your Email?"] }}.
Set Subject to =re: Market briefing for {{ $('Input Capture Form').item.json["What is your Name?"] }} and Message to ={{ $json.content[0].text }}.
Credential Required: Connect your gmailOAuth2 credentials in Dispatch Email Brief.
In Record Search Log, set Operation to append, Document to your Sheet ID, and Sheet Name to Sheet1.
Verify column mappings like Execution_Date set to ={{ $now.format('dd/LL/yyyy') }} and Summary_Sample_Example set to ={{ $json.LLM_SUMMARY_HOLDER }}.
Credential Required: Connect your googleSheetsOAuth2Api credentials in Record Search Log.

Step 7: Test and Activate Your Workflow

Run a full test to validate the parallel data retrieval, aggregation, AI summary, and outputs.

Click Test workflow and submit Input Capture Form with real values for location, Reddit subreddits, and X query.
Confirm Input Capture Form runs and that Web Search Retrieval, Subreddit List Builder, and Social API Query execute in parallel.
Check that Combine Data Streams receives three inputs and Classify Sentiment Tags outputs items with pain_point and sentiment_score.
Verify Generate Executive Brief returns HTML content, then confirm Dispatch Email Brief sends the summary and Record Search Log appends rows to your sheet.
Once confirmed, toggle the workflow to Active for production use.

🔒

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Common Gotchas

Google (Gmail/Sheets) credentials can expire or need specific permissions. If things break, check the n8n credential connection status and your Google OAuth consent/scope settings first.
If you’re using Wait nodes or external retrieval, processing times vary. Bump up the wait duration if downstream nodes fail on empty responses.
Default prompts in AI nodes are generic. Add your brand voice early or you’ll be editing outputs forever.

Frequently Asked Questions

How long does it take to set up this Reddit Gmail insights automation?

About 45 minutes if you already have your API keys and Google connected.

Do I need coding skills to automate Reddit Gmail insights?

No. You’ll mostly paste API keys, connect Google, and map a few fields from the form trigger.

Is n8n free to use for this Reddit Gmail insights workflow?

Yes. n8n has a free self-hosted option and a free trial on n8n Cloud. Cloud plans start at $20/month for higher volume. You’ll also need to factor in your SERP API plan and LLM usage costs for generating the executive brief.

Where can I host n8n to run this Reddit Gmail insights automation?

Two options: n8n Cloud (managed, easiest setup) or self-hosting on a VPS. For self-hosting, Hostinger VPS is affordable and handles n8n well. Self-hosting gives you unlimited executions but requires basic server management.

Can I customize this Reddit Gmail insights workflow for a different industry (not HVAC)?

Yes. Change the keywords in the Input Capture Form, swap the target subreddits in the Subreddit List Builder, and adjust the categorization logic in the Classify Sentiment Tags code step to match your industry’s pain-point language. Most teams also tweak the system prompt in Generate Executive Brief so the email uses their preferred headings and action recommendations.

Why is my Gmail connection failing in this Reddit Gmail insights workflow?

Usually it’s an expired Google OAuth token or missing Gmail scopes in the credential setup. Reconnect the Gmail credential in n8n and confirm the account has permission to send from the address you selected. If you’re sending to a large distribution list, Google can also flag unusual sending patterns, so try a smaller recipient list while testing.

How many items can this Reddit Gmail insights automation handle?

In practice, most teams run a few hundred items per briefing without issues, as long as you keep retrieval limits reasonable and your APIs don’t throttle.

Is this Reddit Gmail insights automation better than using Zapier or Make?

Often, yes, because this workflow needs branching, merging, and code-based cleanup, and those get clunky (and expensive) in simpler automation tools. n8n handles parallel paths cleanly, then recombines them with a Merge, which is the core trick here. You also get the option to self-host, which matters if you want to run frequent scans without worrying about task caps. The tradeoff is setup: you’ll spend a bit more time configuring credentials and testing. If you’re unsure, Talk to an automation expert and get a straight recommendation for your volume and team.

Once this is running, your “weekly customer voice” stops being a half-day task and becomes an email you can rely on. The workflow handles the busywork, and you get to focus on decisions.

Need Help Setting This Up?

Our automation experts can build and customize this workflow for your specific needs. Free 15-minute consultation—no commitment required.

Lisa Granqvist

Workflow Automation Expert

Expert in workflow automation and no-code tools.

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"id": "5ac35644-fdd6-42f8-9e6f-86ada4386e80",
"name": "Aggregate Unique Insights",
"type": "n8n-nodes-base.code",
"position": [
150,
325
],
"parameters": {
"jsCode": "const inputItems = $input.all();\n\nif (!Array.isArray(inputItems) || inputItems.length === 0) {\n    return []; \n}\n\n\nconst uniqueItems = new Map();\nconst painPointCounts = {};\nconst sourceCounts = {}; \nlet totalSentiment = 0; \n\nfor (const item of inputItems) {\n    if (!item.json || !item.json.unique_key) continue; \n    \n    if (!uniqueItems.has(item.json.unique_key)) {\n        uniqueItems.set(item.json.unique_key, item);\n        totalSentiment += item.json.sentiment_score;\n    }\n    \n    painPointCounts[item.json.pain_point] = (painPointCounts[item.json.pain_point] || 0) + 1;\n    sourceCounts[item.json.source] = (sourceCounts[item.json.source] || 0) + 1; \n}\n\nconst totalUniqueItems = uniqueItems.size;\nconst averageSentiment = totalUniqueItems > 0 ? totalSentiment / totalUniqueItems : 0;\nconst roundedAvgSentiment = Math.round(averageSentiment * 100) / 100;\n\n\nconst summaryString = `\nCurrent Market Intelligence Brief (Total Unique Complaints: ${totalUniqueItems}):\nAverage Sentiment Intensity (Lower is Worse): ${roundedAvgSentiment}\nPain Point Frequencies: ${JSON.stringify(painPointCounts, null, 2)}\nSource Distribution: ${JSON.stringify(sourceCounts, null, 2)}\nTop 5 Unique Complaint Examples: \n${Array.from(uniqueItems.values()).slice(0, 5).map(item => \n    `- [${item.json.pain_point}] \"${item.json.title?.substring(0, 60)}...\" (Source: ${item.json.source})`\n).join('\\n')}\n`;\n\n\nconst outputForSheets = [];\nconst painPointKeys = Object.keys(painPointCounts);\n\nif (painPointKeys.length > 0) {\n    for (const key of painPointKeys) {\n        const latestSourceItem = Array.from(uniqueItems.values()).find(item => item.json.pain_point === key);\n        \n        const sheetObject = {\n            'Pain_Point': key,\n            'Count': painPointCounts[key],\n            'Average_Sentiment': roundedAvgSentiment, \n            'Latest_Source': latestSourceItem?.json.source || 'N/A', \n            'LLM_SUMMARY_HOLDER': summaryString \n        };\n        \n        outputForSheets.push({ json: sheetObject });\n    }\n}\n\n\nreturn outputForSheets;"
},
"typeVersion": 2
},
{
"id": "89ee7c6d-9af1-4406-9e55-1d555be8c69b",
"name": "Extract Brief Summary",
"type": "n8n-nodes-base.code",
"position": [
0,
285
],
"parameters": {
"jsCode": "const previousNodeName = 'Aggregate Unique Insights'; \nconst previousNodeOutput = $items(previousNodeName, 0);\n\nif (!previousNodeOutput || previousNodeOutput.length === 0 || !previousNodeOutput[0].json) {\n    return [{ json: { summary: \"Error: Could not retrieve summary string from upstream node.\" } }];\n}\n\nconst summary = previousNodeOutput[0].json.LLM_SUMMARY_HOLDER || \"No market intelligence data found for this run.\"; \n\nif (summary.includes(\"No market intelligence data found\")) {\n    const cleanSummary = \"The system ran, but no relevant market complaints were found in the data sources. No brief is required.\";\n    return [{ json: { summary: cleanSummary } }];\n}\n\nreturn [{ json: { summary: summary } }];"
},
"typeVersion": 2
},
{
"id": "06b06b67-849a-4881-ba06-0ea2428789dd",
"name": "Generate Executive Brief",
"type": "@n8n/n8n-nodes-langchain.anthropic",
"position": [
10,
530
],
"parameters": {
"modelId": {
"__rl": true,
"mode": "list",
"value": "claude-haiku-4-5-20251001",
"cachedResultName": "claude-haiku-4-5-20251001"
},
"options": {
"system": "You are a strategic AI product consultant tasked with generating a professional executive summary for a sales team. \n\n***CRITICAL CONSTRAINT: Do NOT use markdown code blocks (e.g., ```html or ```) or any external delimiters. Output ONLY the raw HTML content. Do NOT include <html>, <head>, or <body> tags.***\n\nYour response must strictly adhere to the following structure and formatting rules:\n\n<h1>AI Voice Agent Executive Summary \u2013 HVAC Customer Service Market</h1>\n\n<h2>OPPORTUNITY STATEMENT</h2>\n(Provide a two-sentence summary of the biggest market opportunity for the AI Voice Agent based on the data. Use <p> tags for paragraphs.)\n\n<hr>\n\n<h2>TOP 3 PAIN/SELLING POINTS</h2>\n(List the three highest-count pain points. For each point, the pain point must be formatted using a large **<h3> tag** and bold text: <b>1. [Pain Point Name]</b>. Provide a <b>corresponding AI Voice Agent Feature Suggestion</b> that solves that specific pain. Use HTML unordered lists <ul> for clean formatting of feature suggestions.)\n\n<hr>\n\n<h2>SOURCE TRUST ASSESSMENT</h2>\n(Provide a complete analysis of the data's reliability, ensuring the table is fully rendered and includes ALL THREE SOURCES (Reddit, Twitter, SERP API). \n\n***CRITICAL STYLING FIX: The table MUST be wrapped in a <div align=\"center\"> container. The table MUST have width=\"80%\" set as an attribute and the border=\"1\" attribute.***\n\nUse a single HTML table for the analysis. For a clean, single-line border, the table MUST include the following inline style: **style=\"border-collapse: collapse; border: 1px solid black;\"**\n\nThe table header cells (<th>) MUST have the following combined inline style: **style=\"background-color: #f2f2f2; padding: 10px; text-align: center;\"** (This sets the background to light gray, adds padding, and center-aligns the text).\n\nThe table data cells (<td>) MUST have the following inline style: **style=\"padding: 10px;\"** (This adds padding to the regular data cells).\n\nThe table MUST include the following columns:\n1. <b>Data Source</b>\n2. <b>Volume</b> (Must include both **number of mentions** AND **percentage of total**\u2014e.g., 200 mentions (87%))\n3. <b>Reliability Rating</b> (Use text and star emojis)\n4. <b>Context & Limitations</b>\n\nFollow the table with a final key insight summary paragraph.)"
},
"messages": {
"values": [
{
"content": "=DATA TO ANALYZE:\n{{ $json.summary }}"
}
]
}
},
"credentials": {
"anthropicApi": {
"id": "CFRCoeDLcmUNPQT7",
"name": ""
}
},
"typeVersion": 1
},
{
"id": "d2e6ff58-1af5-486c-aa84-52ab8e4f9824",
"name": "Dispatch Email Brief",
"type": "n8n-nodes-base.gmail",
"position": [
25,
660
],
"webhookId": "3ac6cf9d-8d4d-4055-86e8-9c82a7d13d41",
"parameters": {
"sendTo": "={{ $('Input Capture Form').item.json[\"What is your Email?\"] }}",
"message": "={{ $json.content[0].text }}",
"options": {
"appendAttribution": false
},
"subject": "=re: Market briefing for {{ $('Input Capture Form').item.json[\"What is your Name?\"] }}"
},
"credentials": {
"gmailOAuth2": {
"id": "PJrUDp0UTEVBDOMT",
"name": ""
}
},
"typeVersion": 2.1
},
{
"id": "6d8947d0-ca54-4036-bb15-77152ed4bf8b",
"name": "Record Search Log",
"type": "n8n-nodes-base.googleSheets",
"position": [
155,
540
],
"parameters": {
"columns": {
"value": {
"Count": "={{ $json.Count }}",
"Pain_Point": "={{ $json.Pain_Point }}",
"Latest_Source": "={{ $json.Latest_Source }}",
"Execution_Date": "={{ $now.format('dd/LL/yyyy') }}",
"Average_Sentiment": "={{ $json.Average_Sentiment }}",
"Summary_Sample_Example": "={{ $json.LLM_SUMMARY_HOLDER }}"
},
"schema": [
{
"id": "Execution_Date",
"type": "string",
"display": true,
"required": false,
"displayName": "Execution_Date",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Latest_Source",
"type": "string",
"display": true,
"required": false,
"displayName": "Latest_Source",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Pain_Point",
"type": "string",
"display": true,
"required": false,
"displayName": "Pain_Point",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Count",
"type": "string",
"display": true,
"required": false,
"displayName": "Count",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Average_Sentiment",
"type": "string",
"display": true,
"required": false,
"displayName": "Average_Sentiment",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Summary_Sample_Example",
"type": "string",
"display": true,
"required": false,
"displayName": "Summary_Sample_Example",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": [],
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "",
"cachedResultName": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "[YOUR_ID]",
"cachedResultUrl": "",
"cachedResultName": "Customer Intent"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "jBABhxE6xPp1vBwL",
"name": ""
}
},
"typeVersion": 4.7
}
],
"active": false,
"pinData": [],
"settings": {
"executionOrder": "v1"
},
"versionId": null,
"connections": {
"Input Capture Form": {
"main": [
[
{
"node": "Web Search Retrieval",
"type": "main",
"index": 0
},
{
"node": "Subreddit List Builder",
"type": "main",
"index": 0
},
{
"node": "Social API Query",
"type": "main",
"index": 0
}
]
]
},
"Combine Data Streams": {
"main": [
[
{
"node": "Classify Sentiment Tags",
"type": "main",
"index": 0
}
]
]
},
"Social API Query": {
"main": [
[
{
"node": "Twitter Data Cleanse",
"type": "main",
"index": 0
}
]
]
},
"Extract Brief Summary": {
"main": [
[
{
"node": "Generate Executive Brief",
"type": "main",
"index": 0
}
]
]
},
"Subreddit List Builder": {
"main": [
[
{
"node": "Community Post Fetch",
"type": "main",
"index": 0
}
]
]
},
"Web Search Retrieval": {
"main": [
[
{
"node": "Normalize Web Results",
"type": "main",
"index": 0
}
]
]
},
"Community Post Fetch": {
"main": [
[
{
"node": "Reddit Content Filter",
"type": "main",
"index": 0
}
]
]
},
"Generate Executive Brief": {
"main": [
[
{
"node": "Dispatch Email Brief",
"type": "main",
"index": 0
}
]
]
},
"Twitter Data Cleanse": {
"main": [
[
{
"node": "Combine Data Streams",
"type": "main",
"index": 2
}
]
]
},
"Record Search Log": {
"main": [
[]
]
},
"Normalize Web Results": {
"main": [
[
{
"node": "Combine Data Streams",
"type": "main",
"index": 0
}
]
]
},
"Reddit Content Filter": {
"main": [
[
{
"node": "Combine Data Streams",
"type": "main",
"index": 1
}
]
]
},
"Classify Sentiment Tags": {
"main": [
[
{
"node": "Aggregate Unique Insights",
"type": "main",
"index": 0
}
]
]
},
"Aggregate Unique Insights": {
"main": [
[
{
"node": "Extract Brief Summary",
"type": "main",
"index": 0
},
{
"node": "Record Search Log",
"type": "main",
"index": 0
}
]
]
}
}
}