# How to Vibe Code Your Own Gumloop (and Stop Paying for It) > Build AI agents for work - Site: https://gumloop.com - Category: AI Automation & Agents - Platforms: Web app - Verdict: **Serious undertaking** (38/100 vibecodeable) - Estimated effort: 4-6 weeks of intensive development and debugging ## Verdict Build a personal subset with Next.js and React Flow, but keep paying forGumloop if you need enterprise-grade security and 160+ production connectors. Vibecoding a full personal clone of Gumloop requires building a complex non-linear graph execution engine, isolated Docker code sandboxes, and real-time stateful chat streaming. While an AI agent can rapidly scaffold the Next.js frontend and React Flow canvas, wiring robust node execution semantics, error recovery mid-run, and secure python execution environments involves immense debugging of async graph state and security edge cases. For personal automation experiments, building a simplified execution runner is a rewarding multi-week project, but commercial replacement is unrealistic. ### What you can't replicate - Enterprise compliance certifications (SOC 2 Type II, DPA contracts with model providers) - Pre-existing deep integration ecosystem with 160+ authenticated third-party SaaS apps - Network effects of enterprise teams using shared governance and Gumstack gateways ## What it does An AI automation platform and agent-builder workspace that allows non-technical users and enterprises to orchestrate complex, multi-step workflows and autonomous AI agents using a visual drag-and-drop canvas. ### Core features - Visual drag-and-drop workflow canvas with topological sort and graph execution engine - Multi-agent conversation and streaming chat interface with subagent delegation - Code execution sandboxes ('Agent Computers') running isolated Python and JavaScript scripts - Knowledge base sync ('Company Brain') connecting external documentation sources - Third-party tool integrations and MCP (Model Context Protocol) client inventory - Enterprise security gateway (Gumstack) with audit logging, RBAC, and policy guardrails ## The business ### Pricing - Pro: $37/mo - Enterprise: Custom ### Funding $70.6M raised. - Seed Round ($3.1M) - Jul 2024 - Series A ($17M) - Jan 2025 - Series B ($50M) - Mar 2026 Investors: Benchmark, First Round Capital, Nexus Venture Partners, Y Combinator, Shopify Ventures Founded 2023. Team size: 30-50. ## The hard parts - Building a reliable non-linear graph execution engine that handles parallel branch state, loops, and variable mapping cleanly - Spinning up secure, isolated, ephemeral code sandboxes ('Agent Computers') that execute arbitrary user-generated scripts safely - Managing robust real-time streaming state for multi-turn agent chats involving tool calling and subagent handoffs - Maintaining API credential rotation and OAuth/Key-pair auth sync across dozens of enterprise integrations ## How to vibe code Gumloop ### Prerequisites - Node.js (free): Required for running the Next.js full-stack development environment. - GitHub (free): Source control and integration with deployment infrastructure. - Anthropic API Key (Pay-as-you-go): Powers the LLM intelligence engine and agent reasoning loops. ### Recommended AI tools - Claude Code: Best-in-class agentic coding tool for scaffolding complex multi-file graph execution engines and database schemas straight from the terminal. - Cursor: Excellent AI editor for fine-tuning the React Flow visual canvas UI and debugging intricate frontend state interactions. ### Stack - Frontend: Next.js with React Flow and Tailwind CSS - Backend: Next.js Server Actions and API Routes - Database: Neon (Serverless Postgres) - Auth: better-auth - Payments: None (Personal clone) - Other: Docker for Agent Computers, Resend for email alerts ### Hosting - Vercel (Hosting the Next.js frontend and serverless API dashboard): $0-20/mo - Fly.io (Running isolated Docker code execution sandboxes ('Agent Computers')): $5-15/mo ### Build guide 1. **Project Scaffolding & Database Schema** — Initialize a Next.js project with Tailwind CSS and set up the Neon Postgres database schema to store workflows, nodes, edges, agent definitions, and execution logs. ``` Initialize a new Next.js 16 project with TypeScript and Tailwind CSS. Configure Drizzle ORM with a Neon Postgres connection. Create database tables for 'workflows' (id, name, user_id, canvas_json, created_at), 'nodes' (id, workflow_id, type, config_json, position_x, position_y), 'edges' (id, workflow_id, source_node_id, target_node_id), and 'executions' (id, workflow_id, status, logs_json, started_at, completed_at). Set up better-auth for user authentication with email/password and Google login. Ensure clean directory structure following App Router conventions. ``` 2. **Visual Drag-and-Drop Canvas** — Build the visual workflow builder canvas using React Flow, allowing users to add, drag, and connect different automation and AI model nodes. ``` Implement the workflow editor page using React Flow (@xyflow/react). Create custom node components for 'AI Prompt', 'Python Code Sandbox', 'HTTP Request', 'Webhook Trigger', and 'Data Transform'. Support dragging nodes from a sidebar palette onto the canvas, connecting ports with validation for data types, and updating node configuration parameters in a side panel. Save canvas state (nodes and edges) back to the Neon database via Server Actions with debounce or explicit save triggers. ``` 3. **Graph Execution Engine** — Develop the backend graph execution engine that performs topological sorting on workflow nodes and executes them sequentially or in parallel. ``` Build a robust graph execution engine in Next.js backend utilities. When a workflow run is triggered, load all nodes and edges, construct an adjacency list, perform a topological sort to establish execution order, and handle branching logic and loops. Pass outputs from upstream nodes as inputs to downstream nodes via a shared execution context dictionary. Log every step's input, output, execution time, and errors to the executions table in real time. ``` 4. **Agent Computers (Code Execution Sandboxes)** — Implement secure code sandboxes where agents and workflows can execute Python and JavaScript scripts dynamically. ``` Create a secure code execution service that interfaces with an isolated container environment hosted on Fly.io. When a 'Python Code Sandbox' workflow node runs, send the user-defined script and input variables over a secure HTTP/gRPC bridge to the container runner, execute the code with a strict 30-second timeout and memory limit, capture stdout/stderr, and return the resulting JSON objects back to the workflow execution engine securely. Handle unresponsiveness or infinite loops gracefully. ``` 5. **Stateful Agent Chat & Subagent Delegation** — Build an interactive chat interface for communicating directly with configured AI agents, supporting tool calling and streaming responses. ``` Build an agent chat interface supporting real-time streaming responses via AI SDK or direct LLM tool calling. Allow agents to invoke workflows or internal tools (such as searching a connected knowledge base or running code snippets). Implement subagent delegation tracking and show credit/token consumption breakdowns per turn in a collapsible debug panel. Store chat history and message states in the database. ``` 6. **Knowledge Base 'Company Brain' & Polish** — Add document ingestion capabilities and polish UI themes, error handling, and execution history views. ``` Implement a 'Company Brain' knowledge management section allowing users to upload text files or connect URLs. Use pgvector on Neon to store chunked embeddings generated via OpenAI or Anthropic embeddings APIs. Wire vector search retrieval into agent chat and workflow prompt nodes as a context source. Add an execution history dashboard showing run statuses, duration logs, and detailed step traces with filtering options. ``` ### Cost vs paying **Starting costs (one-time):** - Domain name (optional): $12 one-time - AI coding tool credits: $20 - Total: ~$32 one-time **Ongoing costs (monthly):** - Vercel / Fly.io / Neon hosting: $15/mo - LLM API usage (Anthropic/OpenAI): Pay-as-you-go (~$10-30/mo) - Total: ~$25-45/mo - Paying for the SaaS instead: $37/mo (Pro Plan) - Build time: 60-80 hours - AI tool credits: $20 (Cursor/Claude Code subscription) - Break-even: Equivalent cost; build is worthwhile primarily for custom learning and private sandbox control. ## Sources - [Gumloop Official Website & Documentation](https://gumloop.com) - [TechCrunch: Benchmark bets $50M on Gumloop's AI agents](https://techcrunch.com) - [Gumloop Pricing & Enterprise Plans](https://gumloop.com/pricing)