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The Project: Babuger Babuger automates the entire outbound/inbound lifecycle. It trains on your best rep's scripts to qualify leads, handle objections, and book meetings 24/7.
The Problem: Traditional SDR teams are expensive ($150k/yr), have high turnover, and ignore "dead" leads.
The Solution: One human orchestrator managing 20+ specialized AI agents.
The Result: 90% task automation and 70% response rates on neglected pipelines.
The Tech Stack I kept it modern and modular to handle complex multi-step reasoning:
Agent Orchestration: LangGraph. This was crucial for handling non-linear conversation flows (loops, conditional routing, and state management) that standard DAGs can't touch.
LLM Framework: LangChain. Used for prompt templating, output parsing, and integrating various toolsets (Gmail/Cal.com/HubSpot).
Frontend: Next.js. Managed the dashboard, live email thread previews, and real-time pipeline analytics.
Why I’m Posting I’m looking for the "HN stress test." Is the agentic approach with LangGraph the right move for scaling to 10k+ interactions/mo, or should I be looking at a more custom state machine?
Check it out: Babuger.com