@Armin Šarić Hey Armin, classic stateless context problem! Two clean solutions: Option 1 — Pass conversation history to the Bodyguard (best fix) Before the Bodyguard node, inject the last 1-2 messages from your session memory into the prompt context. Instead of just sending "Yes", send: Previous message: "Would you like to book 3 PM?" User reply: "Yes" The Bodyguard now has enough context to classify it correctly. Pull the history from whatever memory store your AI Agent is using (Postgres, Redis, etc.). Option 2 — Session state flag (simpler) Store a conversation_state variable (e.g., "awaiting_booking_confirmation") in your session. Add a check before the Bodyguard: if state = awaiting confirmation → skip Bodyguard entirely and route straight to the AI Agent. My recommendation: Option 2 is faster to build. Option 1 is more robust long-term since it handles any ambiguous follow-up, not just confirmations. The core principle, Armin: your Bodyguard needs either memory or a state bypass — right now it's operating blind. 😊

Hey @Evelyn Taylor , I just finished reading your request, and I am definitely the man for the job.I am currently working with two US-based AI agencies as their technical back-end guy, handling all their delivery. Here are videos I have recorded showcasing real-world projects I have delivered to my clients:https://docs.google.com/document/d/1F64YiLfin6P1YkgL8nfI7U6CtSdNLd8Kppjwi-HOovo/edit?usp=sharing A little bit about me - I’m an AI automation Engineer, currently working as a solo freelancer. Everything I build is ROI based, and I’ve built systems that have saved 10’s of hours each week for my clients. If you feel like I might be a fit, leave me a message, and we'll go from there. Here's my Emai : mofedulalamjoy09@gmail.com Here's my WhatsApp: +8801628675223

Hey @Aubrey Walker , I just finished reading your request, and I am interested.I am currently working with two US-based AI agencies as their technical back-end guy, handling all their delivery. Here are videos I have recorded showcasing real-world projects I have delivered to my clients:https://docs.google.com/document/d/1F64YiLfin6P1YkgL8nfI7U6CtSdNLd8Kppjwi-HOovo/edit?usp=sharing A little bit about me - I’m an AI automation Engineer, currently working as a solo freelancer. Everything I build is ROI based, and I’ve built systems that have saved 10’s of hours each week for my clients. If you feel like I might be a fit, leave me a message, and we'll go from there. Here's my Emai : mofedulalamjoy09@gmail.com Here's my WhatsApp: +8801628675223

@Benjamin Adams Hey Benjamin, the no-paid-API constraint is actually a useful filter — here's the open-source stack I'd use for this and why. For scraping across 4-5 major platforms locally, the approach splits by site type. For JavaScript-rendered platforms (LinkedIn, Twitter/X, modern marketplaces), Playwright with the playwright-stealth plugin handles bot detection significantly better than standard Playwright — it spoofs browser fingerprints, which is the main reason scrapers get rate-limited on those platforms. For static or semi-static platforms, requests + BeautifulSoup is faster and lower overhead. A Scrapy spider works well if you need structured crawls across many pages with built-in retry logic. For email enrichment without paid tools: direct extraction from scraped page content using regex covers a meaningful portion of business contacts. For the gap, SMTP verification runs locally — pysmtp or a similar library does MX record lookup + SMTP handshake to validate without sending, no external API required. It's slower than ZeroBounce but zero cost and surprisingly accurate for business domains. For the rules and funnel logic before cold email, I'd build this as a local Python pipeline with SQLite (or Postgres if volume warrants it) storing each lead's state as it moves through qualification rules — industry match, company size signals pulled from scraped data, contact role filtering, dedup logic. Each funnel rule is a deterministic function, not an AI call, which keeps it fast and auditable. The critical piece with multi-platform scraping locally is session and proxy management — rotating user agents and adding randomized delays between requests is non-negotiable to avoid IP blocks, especially without paid residential proxies. Happy to go into the specific architecture for whichever platforms you're targeting — the approach differs enough per platform that it's worth getting specific. What are the 4-5 you're working with?