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.

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

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 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.

Chat Ops

Ctrl+. anywhere in the dashboard opens Chat Ops: a conversation with your cloudlet that slides in over whatever screen you are on, and stays there while you navigate. Ask it to generate an endpoint from Hyper IDE and the file lands in the tree behind you — the conversation is still open when you turn round to look at it.

It talks to the default model, the developer-tools model, and it does not merely answer. It executes AI functions on your behalf: crawl a site, query a database, generate a CRUD API, scrape a URL, run a Hyperlambda snippet. Each invocation appears as a pill — amber while it runs, green when it succeeds, red when it throws — and clicking one opens the exact file and JSON arguments the model sent, so you can see what it actually did rather than trust a summary.

The whole loop is enforced server-side. A function the model tries to invoke that is not declared on the type is rejected before it executes, every function file carries its own auth.ticket.verify, and the invocation runs under your JWT — so Chat Ops can never do anything you could not do yourself from the dashboard.

Answers stream in as Markdown with syntax-highlighted code (Hyperlambda included), Mermaid diagrams render inline, and you can attach up to five files — either uploaded to the cloudlet for a function to work on, or handed to the model to read. Copy any code block, or the whole response, with one click.

Chat Ops appears only when an OpenAI API key is configured, and the key dialog is where you pick which model default runs.

Import any API

Point the Import API tab at any OpenAPI or Swagger URL — OpenAPI 3.x or Swagger 2.0, JSON or YAML — and Magic generates Hyperlambda endpoints wrapping whichever operations you tick. Query parameters and form fields become named, typed arguments carrying the specification's own types, defaults, enums and descriptions, and required ones get validators, so a missing argument is refused before the upstream API is ever contacted.

Because Magic publishes an endpoint's file comment as its MCP tool description and each argument's comment as that argument's description, an imported API arrives at your agent as a set of self-describing tools. Slack's chat.postMessage becomes fifteen individually typed, individually described arguments — not one of them written by hand.

The upstream credential is never written into the generated files. It is read from your configuration at the moment the endpoint is invoked, so the files stay safe to commit, and you declare which of your roles are allowed to invoke the wrapper.

Rewind — step through an execution after it ran

A stack trace tells you where your code died. It rarely tells you why, because the values are gone by the time you read it.

Click Execute on any Hyperlambda file, fill in your arguments, then click Debug instead of Invoke. Magic runs the file while recording every slot it invokes, and hands you the recording. Each step carries your entire program as it looked immediately afterwards — so stepping through the list is watching your own state evolve, rather than guessing at it.

Click a step and the code highlights the statement that was executing. Arrow keys step back and forward. When something throws, the statement that threw is marked in red and everything that happened before it survives — if a loop ran forty times and the forty-first line failed, you can see the state the loop had built up at the moment it broke.

Recording happens only when you ask for it and lives only in memory. Nothing is written to disk, nothing leaves the server, and code running normally is unaffected — there is nothing switched on to slow it down.

OIDC sign-in

The dashboard signs in over OpenID Connect against Google, GitHub, LinkedIn, Microsoft Entra ID, Okta, Auth0, Keycloak and Slack — configured from the Configuration screen with a client ID (and, where the provider requires it, a secret) per provider. Code-flow exchanges run server-side with PKCE, only provider-verified email addresses are trusted, and identities are provider-scoped so a sign-in can never collide with another account. A provider is a single Hyperlambda file — drop in a new one and the login screen picks it up automatically.

Getting started

Deploy to DigitalOcean (recommended)

One droplet running the full stack, with persistent volumes for your data, configuration and modules. Create your droplet — edit the single DOMAIN= line in .do/cloud-init.yaml and paste it into the User Data field. HTTPS is automatic through Caddy and Let's Encrypt. Full guide: DigitalOcean deployment guide

Kubernetes (own cluster)

The root docker.backend/docker.frontend images run unmodified in your own cluster. To make request.ip/request.scheme resolve the true end user behind your ingress (and Cloudflare, if in front), follow the client-IP deployment note.

Docker Compose (self-hosted)

The one-liner at the top of this README is the fastest route. If you'd rather keep the file around:

version: "3.8"

services:
  backend:
    image: servergardens/magic-backend:latest
    platform: linux/amd64
    container_name: magic_backend
    restart: unless-stopped

    ports:
      - "4444:4444"

    volumes:
      - magic_files_etc:/magic/files/etc
      - magic_files_data:/magic/files/data
      - magic_files_config:/magic/files/config
      - magic_files_modules:/magic/files/modules

  frontend:
    image: servergardens/magic-frontend:latest
    container_name: magic_frontend
    restart: unless-stopped

    depends_on:
      - backend

    ports:
      - "5555:80"

volumes:
  magic_files_etc:
  magic_files_data:
  magic_files_config:
  magic_files_modules:

Run docker compose up, visit localhost:5555, log in with root / root, and configure the system. More installation options here.

Run from source

You'll need .NET 10 and Node.js (latest LTS).

# Backend — starts the API on http://localhost:5000
cd backend
dotnet run

# Frontend (in a second terminal) — dashboard on http://localhost:4201
cd frontend
npm install
npm run dev

Open http://localhost:4201 and log in using http://localhost:5000 as your backend URL, with root / root.

Bring your own OpenAI API key

Magic uses OpenAI by default — create a key here. If you'd rather not use OpenAI, there are Ollama and HuggingFace plugins that override the inference functions, and chatbots can use BM25 keyword retrieval instead of embeddings — full-text search over your training data, running entirely inside SQLite, needing no OpenAI key and no vectorisation. Semantic and hybrid retrieval still use OpenAI's embeddings API. And if you drive Magic through the MCP server, you don't need an OpenAI key at all.

AI agents

Below is Magic's AI agent autonomously browsing the web and filling out a "contact us" form, using the integrated headless browser that lets your agent see the web and solve tasks on it.

You can vibe code AI agents integrated with your CRM, ERP, or legacy databases. Magic fundamentally is an AI agent for building software and AI agents — what you use it for is up to you.

Headless browser

Magic embeds PuppeteerSharp, so agents can browse like a human — filling forms, clicking buttons. An example prompt: "Go to xyz website, identify their contact us form and change URLs if required, and fill out their contact us form." The screenshot above is the result of exactly that prompt.

Python, terminal, and C# integration

Generate and execute Python on the fly and let the LLM use it as a tool, use Bash and the underlying terminal, and create Hyperlambda keywords in C#. Persisted Python scripts can be referenced later as tools, permanently widening your agents' capabilities.

Notice — you must be logged in as root to generate and execute Python or terminal scripts, or create C# extensions. Magic's security model eliminates entire classes of holes, but it is not a magic pill; don't expose endpoints that let third parties execute arbitrary code.

AI chatbots and expert systems

Deliver your agents as password-protected AI expert systems, or as embeddable chatbots on any website. Try ours here.

Chatbots are grounded in your own content through RAG, with three retrieval modes per model: semantic (cosine similarity over OpenAI embeddings), keyword (BM25 full-text search — built on SQLite's FTS5, no OpenAI key and no vectorise step needed), or hybrid, fusing both rankings so paraphrased questions and exact terms such as product names each find the right training snippet. The crawler splits pages into subject-scoped snippets titled as the questions users actually ask, which is what makes retrieval precise in the first place.

Also a web server

Magic serves your frontend itself, so there's no compilation, build process or pipeline between writing code and testing it:

1. Create your prompt
2. Press enter
3. Test

This is in stark contrast to tools such as Lovable and Bolt, which require deploying into two different third-party providers before you can even test. It also ships pre-built frontends, such as the AI Expert System for serving password-protected agents or entire SaaS AI solutions.

Git integration

Magic was built for software developers from day one, so Git is integrated as part of the platform. Create a project, vibe code your tools and even your GitHub workflows, then commit and push.

DIY home cloud

Magic installs happily on something like a Mac Mini using the Docker images. Combined with a CloudFlare tunnel you can serve applications and data out of your home in minutes. The link below runs out of a house in Larnaca, Cyprus, through such a tunnel — tested from the US, Norway and elsewhere, and surprisingly responsive for the connection it's served over.

• CRM dashboard served from a DIY web server

A setup like this keeps your applications and your data entirely under your own control, on hardware you own.

The LLM

Internally the system defaults to OpenAI's gpt-5.6-luna, but everything is tunable, and with some effort you can swap the defaults for Ollama or HuggingFace models. The Hyperlambda Generator's training dataset is not public and won't be. Worst case, you keep running everything you've already generated; you'd only lose the ability to generate new code.

The screenshot above is a publicly available page accepting input from any visitor. That input is transformed into Hyperlambda by our LLM and executed in-process behind our DMZ — with a standing $100 bounty for anyone who can exploit it to reach PII. Nobody has.

Technology

Magic Cloud is built in .NET 10, with a React, Vite and TypeScript dashboard. Hyperlambda is built on a design pattern called Active Events ("slots and signals"), an in-process model for executing dynamic functions that eliminates cross-project dependencies and yields extreme encapsulation. That pattern is what makes the security model possible: AI-generated code can be trusted because it simply lacks permission to do anything harmful unless you granted it.

• Make C# more Dynamic with Hyperlambda
• Active events, one design pattern instead of a dozen

FAQ

Is it really free? Yes — MIT licensed, self-hostable, no feature gates. The hosted Hyperlambda code generator is currently free to use; future pricing is expected at $49 per 1,000 requests, and there is no payment wall today.

Do I need an OpenAI API key? For AI code generation and semantic (embeddings) retrieval, yes. If you drive Magic over MCP from Claude or Codex, no. Ollama and HuggingFace plugins can replace inference, and chatbots configured for BM25 keyword retrieval search your training data with SQLite full-text search — no OpenAI involved.

Can I run it completely on my own hardware? Yes. Everything — database, API, frontend, scheduler, chatbots — runs in your own containers, on your own machine.

How is this different from Lovable or Bolt? They generate a frontend and hand the backend to a third-party service. Magic generates and runs the whole backend, needs no deployment step before you can test, and is yours to self-host under MIT.

What database does it use? SQLite out of the box, with full support for MySQL, PostgreSQL, MariaDB and SQL Server. No connectors required.

Do I have to learn Hyperlambda? No. The generator writes it from plain English, and the dashboard's prompt bars cover most workflows. Reading Hyperlambda is much easier than writing it.

What happens if AINIRO disappears? Everything you've generated keeps running — it's MIT-licensed code on your hardware. You'd only lose the ability to generate new code with the hosted generator.

Is AI-generated code safe to execute? That's the point of the security model: Hyperlambda is sandboxed and its vocabulary can be whitelisted per role, so generated code can only invoke functions you explicitly allowed.

Contributing

Contributions are welcome — bug reports, feature requests, documentation, and code.

Repository structure

Building and testing

# Run the backend — builds and starts the API on http://localhost:5000
cd backend
dotnet run

# Run a plugin's unit tests (each plugin's tests live in its *.tests project)
cd plugins/<plugin>/<plugin>.tests
dotnet test

# Type-check and build the dashboard
cd frontend && npm install && npm run build

Submitting changes

1. Fork the repo and create a feature branch.
2. Make your change, and make sure dotnet test and the frontend build both pass.
3. Open a pull request with a clear description of what changed and why.

Found a security issue? Please read SECURITY.md and report it privately rather than opening a public issue.

Maintenance

Magic Cloud and Hyperlambda are developed and professionally maintained by AINIRO.IO, copyright Thomas Hansen 2019 – 2026. We offer hosting, support, and software development services on top of Magic Cloud, in addition to delivering AI agents, chatbots, and AI solutions.

License

MIT, as published by the Open Source Initiative — see LICENSE. For licensing inquiries contact Thomas Hansen, thomas@ainiro.io.