# How to Vibe Code Your Own FlowHunt (and Stop Paying for It) > No-Code AI Agent Builder for Marketing, SEO & Sales - Site: https://flowhunt.io - Category: AI Agents & Workflow Automation - Verdict: **Serious undertaking** (42/100 vibecodeable) - Estimated effort: 2-3 months of focused engineering ## Verdict Build a simplified personal task runner instead of trying to clone FlowHunt's entire multi-agent integration empire. FlowHunt combines a complex visual node canvas, asynchronous agent supervisor loops, vector knowledge retrieval, and over a hundred third-party OAuth and API integrations. While you can spin up the core LLM orchestration and a basic chat interface over a weekend, building the full agentic kanban task runner with live API execution trees requires substantial asynchronous queue architecture and state management. ### What you can't replicate - Pre-existing enterprise integrations with 100+ production services - SOC 2 compliance certifications and on-premise Kubernetes deployment tooling - The massive customer base and established multi-tenant infrastructure of QualityUnit ## What it does FlowHunt is an autonomous multi-agent orchestration and workflow building platform tailored for marketing, SEO, sales, and customer support operations. ### Core features - Visual node-based drag-and-drop workflow canvas - Multi-agent supervisor and sub-worker task delegation - Kanban project management board for asynchronous AI tasks - Document and URL knowledge base ingestion - Multi-LLM orchestration router (OpenAI, Anthropic, Gemini, etc.) - Third-party integrations (Shopify, HubSpot, WordPress, Slack) - Model Context Protocol (MCP) server support - Photomatic image and video generation pipeline ## The business ### Pricing - Starter: €50/mo - Pro: €120/mo - Premium: €500/mo - Enterprise: From €1,500/mo Founded 2024. Team size: 15+. ## The hard parts - Building a performant, custom interactive node graph editor with real-time execution tracing - Designing asynchronous state machines and queue workers for autonomous multi-agent loops - Maintaining robust API connectors and handling upstream schema changes across dozens of external services - Managing complex rate limiting, cost guardrails, and token consumption tracking per workspace ## How to vibe code FlowHunt ### Prerequisites - Node.js (free): Required for running the full-stack TypeScript project and package managers. - GitHub (free): Source control and CI/CD repository hosting. - Anthropic API Key (pay-as-you-go): Provides frontier reasoning models for the agent supervisor and worker nodes. ### Recommended AI tools - Claude Code: Best-in-class terminal agent for scaffolding complex multi-file TypeScript architectures and managing backend state machines. - Cursor: Ideal for fine-tuning the visual React node editor canvas and complex Tailwind CSS layouts. ### Stack - Frontend: Next.js with React Flow for the visual workflow canvas and Tailwind CSS - Backend: Next.js API routes paired with Inngest for durable agent execution steps - Database: Turso for serverless SQLite database storage - Auth: better-auth for self-hosted TypeScript authentication - Other: Vercel AI SDK for multi-LLM streaming and tool calls, Inngest for background workflow orchestration, Fal.ai for Photomatic image generation features ### Hosting - Vercel (Hosting the Next.js frontend and serverless API endpoints): $0-20/mo - Turso (Storing user workflows, agent definitions, and task execution history): $0/mo ### Build guide 1. **Project Scaffolding and Database Schema** — Initialize the Next.js project with Tailwind CSS, configure better-auth, and set up Turso database models for users, workspaces, workflows, and task runs. ``` Scaffold a new Next.js TypeScript project using App Router. Configure Tailwind CSS and Lucide icons. Set up better-auth with email/password authentication using Turso as the database driver via @libsql/client. Create database tables for users, workspaces, workflows (storing node graphs as JSON), and task_runs with fields for status, logs, and token usage. Write a robust database connection utility and ensure migrations run cleanly on startup. ``` 2. **Visual Node-Based Flow Builder** — Implement a drag-and-drop workflow canvas using React Flow where users can link trigger, LLM, tool, and action nodes together. ``` Implement a visual workflow builder page using React Flow (@xyflow/react). Create custom node components for Triggers (Webhook, Schedule), LLM Processors (OpenAI, Anthropic selector with system prompts), Tools (Shopify, Web Search), and Actions (Slack, Turso). Allow users to drag nodes from a sidebar, connect handles with validation rules, and serialize the canvas state (nodes and edges) into JSON to save to the Turso workflows table. ``` 3. **Multi-Agent Supervisor & Task Kanban** — Build an AI Project kanban board where a supervisor agent breaks down high-level goals into sub-tasks and delegates them to worker agents. ``` Build an AI Projects kanban board view with columns for Open, In Progress, and Done. Implement a supervisor agent backend loop using the Vercel AI SDK that accepts a project goal, uses an LLM to generate structured sub-tasks, and assigns them to specialized worker agents (e.g., Content Writer, SEO Auditor). Display live execution status, logs, and real-time card movements on the dashboard. ``` 4. **Durable Background Execution with Inngest** — Set up Inngest to handle long-running multi-step agent workflows asynchronously with automatic retries and step persistence. ``` Integrate Inngest into the Next.js backend to handle asynchronous workflow execution. Create a durable function that parses saved workflow JSON graphs, resolves node dependencies, executes LLM calls sequentially or in parallel, handles tool execution errors with automatic retries, and writes execution logs and audit trails back to the database. ``` 5. **Knowledge Base Ingestion & Vector Search** — Add functionality to ingest URLs and uploaded documents, chunk text, generate embeddings, and query them during agent runs. ``` Add a Knowledge Sources module where users can input URLs or upload text documents. Use Turso with sqlite-vec to store and query vector embeddings. Build a retrieval tool node that agents can call during workflow execution to fetch relevant context chunks from ingested documents before prompting the LLM. ``` 6. **Photomatic AI Media & Polish** — Implement the Photomatic image generation tool utilizing Fal.ai and finalize workspace usage monitoring metrics. ``` Create a Photomatic media generator page that integrates with Fal.ai to generate images and videos from text prompts and base photos. Build a workspace settings dashboard that aggregates monthly token consumption, active agent run counts, and estimated API spend into clean summary cards and charts. ``` ### Cost vs paying **Starting costs (one-time):** - Domain name: $12 one-time - Total: ~$12 one-time **Ongoing costs (monthly):** - Anthropic/OpenAI API usage: ~$15/mo - Vercel & Turso hosting: $0/mo (Hobby tier) - Total: ~$15/mo - Paying for the SaaS instead: €50/mo - €120/mo - Build time: 60-80 hours - AI tool credits: $20 one-time - Break-even: 4 months ## Sources - [FlowHunt Official Website](https://flowhunt.io) - [FlowHunt Pricing Page](https://flowhunt.io/pricing/) - [FlowHunt About Us Page](https://flowhunt.io/about-us/) - [FlowHunt Enterprise Pricing Page](https://flowhunt.io/enterprise-pricing/)