Hyperlambda
Deterministic Output
Hallucination-Resistant Execution
The model can propose structures, but only valid, whitelisted AST nodes can execute.
Natural language in, deterministic AST out
Build Deterministic AI Agents and Backend Tools
Standard AI predicts text. Hyperlambda compiles strict ASTs for deterministic execution.
# When it's up: log in at http://localhost:5555 with root/root
# Change that password before the internet does.
$ curl -fsSL https://hyperlambda.dev/docker-compose.yaml | docker compose -f - upC# Active Events Sandbox
Structurally Constrained by Design
Hyperlambda is not free-form generated source code. It is a constrained AST validated before execution.
Instant Deployment
Evolving AI Agents
Create AI agents that evolve over time with new tools, workflows, APIs, and backend capabilities generated on demand
How it works
From English to safe execution
Run Magic Cloud locally, describe what you want in English, and Hyperlambda compiles it into strict ASTs for deterministic execution.
Step 1
Describe the task
Write what you want in natural language
Step 2
Compile to Hyperlambda
The compiler turns your request into Hyperlambda AST
Step 3
Execute safely
Magic Cloud runs the result inside a constrained C# runtime
Hyperlambda runs in a sandbox, and can restrict the execution layer on individual function level, which makes AI generated code 100% safe to execute, and the consequences of AI hallucinations become irrelevant
Concrete example
From English to Safe Backend Execution
“Create an endpoint that returns unpaid invoices for a customer.”
Generated Hyperlambda AST
.arguments
customer_id:long
data.connect:billing
data.read
table:invoices
where
and
customer_id.eq:x:@.arguments/*/customer_id
status.eq:unpaid
return-nodes:x:@data.read/*Runtime result
[
{
"id": 42,
"customer_id": 7,
"invoice_number": "INV-10042",
"amount": 1299.00,
"due_date": "2026-05-15",
"status": "unpaid"
},
{
"id": 57,
"customer_id": 7,
"invoice_number": "INV-10057",
"amount": 249.50,
"due_date": "2026-06-01",
"status": "unpaid"
}
]Safety
- The model proposed the AST.
- Magic Cloud validated the AST before execution.
- Only whitelisted nodes were available.
- Unauthorized nodes would fail before execution.
- The runtime, not the AI model, is the security boundary.
Why this matters
- Traditional AI code generation produces free-form source code.
- Hyperlambda produces constrained structures.
- Constrained structures can be validated before execution.
- This makes AI-generated backend automation more predictable and easier to secure.
The dashboard
Everything your cloudlet can do, in one place
Every Magic Cloud install ships with a dashboard for building and running your backend.
- Hyper IDE — edit and execute any file, with Hyperlambda autocomplete.
- SQL Studio & Generator — design SQLite, MySQL, PostgreSQL and SQL Server, and turn tables into secured CRUD APIs.
- MCP & Machine Learning — expose endpoints to any MCP harness, and train embeddable chatbots on your own content.
Click the screenshot to open it full size.
Take the full dashboard tour
See it in action
The whole platform, in one walkthrough
A guided tour of the most important components in Magic Cloud, straight from the dashboard — editing and running code, querying and designing databases, generating secured CRUD APIs, and turning your cloudlet into tools an AI agent can call.
Everything in the video ships with every install — MIT-licensed, self-hosted, and running on your own machine minutes after the Docker command at the top of this page.
What developers say
Developers on Magic and Hyperlambda
“Kudos for the incredible work: it's the first real IDE dedicated to AI.”
Samuele Contardi
Software Architect
“The truth is that I like Firebase, but your system is made much simpler.”
Alex Erdei
Frontend Developer
“It's so amazing and revolutionary, yet easy to use and elegant.”
Justin Lietz
Software Engineer
“Thomas, you are an absolute legend. Another great post and video by you.”
Constantine Doukas
Software Developer
Frequently asked questions
What is Hyperlambda?
Hyperlambda is a programming language whose code is a tree structure rather than free-form text — effectively an executable AST. That structure is what lets natural-language requests compile into something deterministic and inspectable before it runs, which is why it suits AI-generated backends.
Is Hyperlambda open source and free to use?
Yes. Magic Cloud, which runs Hyperlambda, is MIT-licensed and open source on GitHub. You can run the whole platform on your own hardware with no license fee.
Do I have to use a cloud, or can I self-host it?
You can self-host it entirely. Magic Cloud runs on your own server — a single Docker command brings up the full stack — and nothing leaves your machine unless an integration you configure sends it somewhere.
What makes AI-generated code safe to run?
The security boundary is the runtime, not the model. Generated Hyperlambda compiles to an AST whose nodes can only bind to explicitly whitelisted capabilities in the current context; anything outside that fails before it executes. The model proposes, the runtime decides.
Does it work with any LLM or AI coding agent?
Yes. The runtime's safety model is independent of which model generated the code. Magic integrates OpenAI, Ollama and HuggingFace for its own AI features, and with the MCP plugin installed your cloudlet exposes an MCP server that any MCP-capable harness — including Claude, Codex, Cursor and Qoder — can connect to, using your endpoints as tools.
What databases does it support?
SQLite is built in, and you can connect external MySQL, PostgreSQL and Microsoft SQL Server. Once connected, the platform treats an external database like a local one for querying, design and code generation.
What can I actually build with it?
Secured CRUD APIs, backend workflows, scheduled tasks, AI agents and tools, database integrations, and embeddable chatbots trained on your own content — generated from short natural-language prompts rather than hand-written.
Do I need to know Hyperlambda to use it?
No. You describe what you want in plain language and the generator compiles it to Hyperlambda for you. Knowing the language helps you read and adjust the result, but it is not required to build working backends.
How fast can it generate a working backend?
A single endpoint or tool typically generates in seconds. Because the output is validated structure rather than loose source code, what comes back is immediately runnable instead of needing a debug-and-fix loop.
How is this different from generating Python or JavaScript?
Traditional code generation produces text and validates it afterward, if at all. Hyperlambda produces a constrained execution tree that is checked before it runs, so an invalid or unauthorized request fails safely instead of executing something unintended.
Who builds this
Thomas Hansen — creator of Hyperlambda
I wrote my first line of code at eight years old, and 44 years later I am still at it. Hyperlambda is the part I care most about — a language I created because I believe the answer to unsafe AI code generation is not better prompts, but a runtime that cannot be talked into doing the wrong thing.
Magic Cloud has been running in production for years, and every line of it is MIT-licensed and public. That is the deal: you can read everything, run everything on your own hardware, and the code stays yours no matter what happens to me. The bounty below is not marketing — I offer it because I know how the sandbox is built.
I dare you to break the sandbox
Find a verified bug in the backend C# or Hyperlambda code, and I will pay you $100.