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magic

polterguy · polterguy/magic

This tool generates instant, secure full-stack applications and AI agents from plain English, running on your hardware, and functions as an MCP server exposing endpoints to agents like Claude and Cursor.

Instant SECURE Full Stack Apps and AI Agents

1.2k stars175License: MITUpdated 19 days ago0 opens

See it in action

API Wizard generating secure REST endpoints from a database

From the author's READMEAPI Wizard generating secure REST endpoints from a database

1/3

From the author's README polterguy ·Source

What you can do with it

  • Generate a secure full-stack CRM system from a natural language description, including a database, API, and frontend.
  • Create database-driven AI agents that can query and update your custom database through a secure API.
  • Expose your application's API endpoints as tools to Claude or Cursor, allowing agents to interact with your services programmatically.

Safety check

Safe

No risky patterns found in the files we scanned.

Automated scan of the repo files. Always review code you run.

From the README

Instant SECURE Full Stack Apps and AI Agents

Turn plain English into a working full-stack app — database, secure API, business logic, and frontend — running on your own hardware, with zero lock-in. An open-source alternative to Lovable, Bolt, and Replit that gives you the whole backend, plus an MCP server that turns every endpoint into a tool for Claude, Cursor, or Codex. Read more about Hyperlambda.

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Run it in 60 seconds

curl -fsSL https://hyperlambda.dev/docker-compose.yaml | docker compose -f - up

Then open localhost:5555, point it at localhost:4444, and log in with root / root.

Point the API Wizard at your database, and it generates a complete, secured REST API — then extend it in plain English, without a build or deploy step.

⭐ If this saves you time, star the repo — it's the main way other developers find it.

What you can build

  • Full-stack business apps — CRM systems, admin panels, booking systems, internal tools; database, secure API and frontend generated from natural language
  • Database-driven AI agents — agents that query and update your database and invoke your endpoints, behind your own role-based access control
  • AI chatbots and expert systems — crawl a website, get an embeddable chatbot grounded in your own content
  • Secure APIs over legacy databases — point it at an existing MySQL, PostgreSQL, SQL Server or MariaDB schema and get a CRUD API in seconds
  • Wrappers around third-party APIs — paste an OpenAPI or Swagger URL and get typed, documented endpoints wrapping Stripe, GitHub, Slack or anything else publishing a spec
  • MCP tool servers — expose your endpoints to Claude, Cursor, Codex, Qoder or any MCP client
  • Background jobs and automation — scheduled tasks written in Hyperlambda, or generated from a plain English description
  • Static sites and SPAs — your cloudlet serves files directly, next to the APIs powering them

How it compares

Magic CloudLovable / Boltn8n / Zapier / Make
LicenseMIT, fully open sourceProprietaryMixed
Self-hostableYes — your hardware, your dataNoPartly
Backend includedDatabase, API, auth, RBAC, jobsFrontend + third-party BaaSWorkflows only
Deploy step before you can testNone — save and runDeploy to third parties firstPublish step
Execution modelCompiled .NET runtime—Interprets JSON/YAML workflows
MCP serverBuilt inNoNo
Vendor lock-inNoneYesYes

The LLM that cannot hallucinate functions

Magic runs Hyperlambda, which can be generated by our own proprietary LLM. Because we generate an AST rather than text, the output is analysed and rejected if it contains functions that don't exist. The Hyperlambda Generator cannot return hallucinated function invocations — like any LLM it can still write logically wrong code, but every function it invokes is guaranteed to exist.

Combined with the ability to restrict the vocabulary, this lets you ship AI agents that grow their own tool space on demand, without widening your attack surface.

Unique security model

Hyperlambda runs sandboxed, with no file system access outside its sandbox, and can whitelist individual functions through its RBAC system — so your server can accept code as input and execute it safely without knowing where it came from. Restricting invocations at the execution level makes Hyperlambda, as far as we know, the only language that currently does this.

I'm so confident in the codebase quality, I'll give you $100 if you can find a severe security-related bug in its backend code — and another $100 if you can exploit the natural language API, which has accepted arbitrary public input for 3 months with nobody succeeding.

MCP support — and an 80% token saving

Install the mcp plugin, point Claude Code, Cowork, or OpenAI's Codex at your cloudlet, and every HTTP endpoint in your modules folder becomes a tool the agent can invoke. In our measurements this also cuts token consumption by roughly 80%. Calculate your own savings here.

Performance

In our measurements Hyperlambda is roughly 20× faster than FastAPI or Flask, around 50× faster than LangChain, and 100–1,000× faster than graphical workflow tools such as n8n, Zapier and Make — because it runs a real compiled runtime instead of interpreting logic out of JSON, XML or YAML. Hyperlambda solutions are broadly on par with C# and Entity Framework on both performance and scalability.

The dashboard

The sidebar is the whole platform: Hyper IDE for editing, running and replaying any file on the server, Playground for executing Hyperlambda without saving it first, SQL Studio for querying and designing databases, Endpoint Generator for turning tables into secured CRUD endpoints, wrapping caller-supplied code in a role-scoped sandbox, and importing third-party APIs from their OpenAPI specifications, plus users and roles, scheduled tasks, machine learning, and the plugin store.

Your cloudlet is also an AI agent. With the MCP plugin installed, the URL at the top hands any MCP-capable agent your endpoints as tools. The Chatbot Wizard goes the other way: give it a website, and it crawls the site, turns what it finds into training data, and hands you an embeddable chatbot grounded in your own content.

Once you save the code, you can test it — no deployment or publish step required.

Notice the prompt bar below the editor, where "the Machine Creates the Code". Describe what you want in plain English and the built-in generator writes it straight into the file you're editing. The same bar follows you into the Playground and SQL Studio, generating Hyperlambda, SQL, HTML, or whatever fits the file you have open.

Web

View source on GitHub