Reddit Comment & Reddit/LinkedIn Content Discovery

Toolkit for Reddit engagement and content repurposing (OpenAI + Apify). Scans Reddit for relevant posts to comment on and makes suggestions for comments. Also collects posts that can be replicated on Reddit or LinkedIn. Also collects the list of subreddits with domain relevant posts over lifetime. Also collects promotional/launch posts that can be replicated.

Features

• Scans Reddit for relevant posts to comment on and makes suggestions for comments.

• Also collects posts that can be replicated on Reddit or LinkedIn.

• Also collects the list of subreddits with domain relevant posts over lifetime.

• Also collects promotional/launch posts that can be replicated.

• Reddit Post Fetching: Scan subreddits by URL or keywords via Apify

• Comment Generation: OpenAI (OPENAI_MODEL_GENERATION, default gpt-5.6-terra) generates 2 comment suggestions per post in parallel

• Comment Validation: Single batched LLM call scores and tags (PASS/UNSURE/FAIL) all comments

• Reddit + LinkedIn Repurposing: Identifies viral posts I can repurpose as my own Reddit or LinkedIn content

• Rewrite Strategies: Single batched call generates strategy paragraphs (not actual posts), calling out best channel

• Run History: SQLite-backed storage with 7-day retention (configurable), survives server restart

Quick Start

1. Backend adn Frontend Setup

python3.13 -m venv .venv && source .venv/bin/activate 
cd backend
pip install -r requirements.txt

# Create .env file with your API keys and access password
cp .env.example .env
# Edit .env and add:
# OPENAI_API_KEY=sk-your-openai-api-key
# APIFY_API_TOKEN=apify_api_your-token
# ACCESS_PASSWORD=your-strong-password-here
# (optional model overrides) OPENAI_MODEL_ANALYSIS / OPENAI_MODEL_GENERATION
Similarly for config.json
cp config.example.json config.json

# Start the server
uvicorn main:app --port 8007

1.2. Frontend Setup

cd frontend
npm install
npm run dev

add .env.production file: NEXT_PUBLIC_API_BASE=http://${EC2_IP}:8007

2. Quick internal deployment on IP:Port ec2:

create logs and data directory inside backend cd backend && mkdir -p data logs

Add files backend/.env backend/config.json and frontend/.env.production frontend/.env.production to have : NEXT_PUBLIC_API_BASE=http://IP:8007 Start backend with PM2 (uses the venv's uvicorn) pm2 start "/home/ubuntu/reddit-scanner/backend/.venv/bin/uvicorn main:app --host 0.0.0.0 --port 8007" --name reddit-scanner-backend --cwd /home/ubuntu/reddit-scanner/backend

Start Frontend

  cd frontend
  # EC2_IP=$(curl -s http://169.254.169.254/latest/meta-data/public-ipv4)
  echo "NEXT_PUBLIC_API_BASE=http://<<IP>>:8007" > .env.production
  npm install
  npm run build
  pm2 start "npm run start" --name reddit-scanner-frontend --cwd /home/ubuntu/reddit-scanner/frontend

What you need to do (post-pull checklist)

• Add OPENAI_API_KEY to .env
• Drop BEDROCK_API_KEY / BEDROCK_MODEL_ID from .env
• pip install -r requirements.txt
• Restart backend (creates SQLite DB)
• Smoke-test OpenAI curl
• Run single subreddit
• Confirm ~10 LLM calls
• Verify history persists after restart

3. Use the App

1. Open http://localhost:3007
2. Enter the access password (from ACCESS_PASSWORD in backend .env)
3. Select "Subreddit URLs" or "Keywords"
4. Enter your inputs (one per line or comma-separated)
5. Click "Run Now"
6. Wait for results (auto-polls every 5 seconds)

Configuration

Copy backend/config.example.json to backend/config.json and edit:

{
  "user_context": "Your professional background, expertise, interests...",
  "product_context": "Optional: product/project you want to promote. The LLM will favor relevant posts. Leave as empty string if not applicable.",
  "default_urls": ["https://www.reddit.com/r/AI_Agents/top/?t=day"],
  "min_score": 5,
  "post_score_threshold": 5,
  "retention_days": 7
}

config.json is gitignored, so your personal details stay local.

LLM (OpenAI)

All LLM calls go through OpenAI's Chat Completions API (raw httpx, no openai SDK):

• Endpoint: https://api.openai.com/v1/chat/completions
• Models: two tiers chosen per call type — OPENAI_MODEL_ANALYSIS (default gpt-5.6-luna) for high-context analysis/scoring/classification, and OPENAI_MODEL_GENERATION (default gpt-5.6-terra) for the generation calls (comments, rewrite strategies). Both overridable via env.
• Auth: Authorization: Bearer ${OPENAI_API_KEY}
• No output-token cap or reasoning_effort sent — the model uses its full default output budget; temperature left at model default
• Concurrency cap: Semaphore(5) per workflow run

API Endpoints

Endpoint	Method	Auth	Description
/auth/verify	POST	No	Verify password (login)
/health	GET	No	Health check
/run	POST	Yes	Start a new workflow run
/status/{task_id}	GET	Yes	Get task status and progress
/results/{task_id}	GET	Yes	Get full results
/history	GET	Yes	List all runs from last retention_days (default 7)

Protected endpoints require X-Access-Password header.

Deployment

Recommended path: AWS EC2 + PM2 (single uvicorn worker), with backend/data/tasks.db on the instance disk for SQLite persistence across deploys.

# On EC2
pm2 start "uvicorn main:app --host 0.0.0.0 --port 8007" --name reddit-scanner-backend --cwd /path/to/backend
pm2 save

Key production steps:

1. Set NEXT_PUBLIC_API_BASE to your backend URL
2. Restrict CORS in main.py to your frontend domain
3. Use a strong ACCESS_PASSWORD (20+ characters)
4. Ensure backend/data/ is writable and persists across deploys

Tech Stack

• Backend: FastAPI, Python 3.13
• Frontend: Next.js 15, React 19
• LLM: OpenAI Chat Completions API (two models by call type via env: OPENAI_MODEL_ANALYSIS / OPENAI_MODEL_GENERATION)
• Reddit API: Apify Reddit Scraper
• Storage: SQLite at backend/data/tasks.db with in-memory cache (7-day retention)

Responsible Use

This tool generates AI-assisted suggestions for manual review. Do not use it for spam, impersonation, or mass-automated posting. Respect Reddit and LinkedIn Terms of Service.

License

MIT — see LICENSE.

Okay, so now I want you to separately maintain counts from keyword runs only, both for the subreddit memory bank as well as the promotional bank. In the subreddit memory bank, the score_by should add one more criterion, which is posts from keyword searches, and make this criterion default. Feel free to name it properly. And for context of the promotional bank, keep a counter (say X) which is promotional posts fetched from keyword searches only. In the subreddit filter, the brackets can have the numbers that denotes how many unbiased and how many total promotional posts exist in that subreddit, but they should be sorted in order of counts from the unbiased counter X e.g. r/SaaS (7 unbiased, 35 total) . feel free to use a different or better fitting word than unbiased if you want. (Note: name the counter properly and dont call it X - X is just for reference in this instruction) The guided tour tip should highlight that these are in descending order of unbiased criteria and hence in descending order of occurrence of promotional posts in blind fetch. Similarly, guided tour tip in subreddit memory bank also should highlight this fact.