Natural language in, deterministic AST out

Build Deterministic AI Agents and Backend Tools

Standard AI predicts text. Hyperlambda compiles strict ASTs for deterministic execution.

terminal

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

Try the AST Playground

Deterministic Output

Hallucination-Resistant Execution

The model can propose structures, but only valid, whitelisted AST nodes can execute.

C# 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,
    "invoice_number": "INV-10042",
    "amount": 1299.00,
    "due_date": "2026-05-15"
  }
]

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.

Frequently asked questions

What exactly does Hyperlambda compile

Hyperlambda compiles natural language into a strict executable AST rather than free-form source code. The output is designed for deterministic execution inside a constrained runtime.

Why is this safer than generating Python or JavaScript

Traditional code generation produces text first and relies on validation later. Hyperlambda generates structured execution trees and enforces what those trees can bind to at runtime.

What is the actual security boundary

The security boundary is the runtime, not the prompt. Generated AST nodes can only invoke explicitly whitelisted capabilities available in the current execution context.

What happens if the model generates something invalid

If the generated structure is invalid, it fails at compilation or execution binding. The model is not trusted to define permissions or bypass runtime constraints.

Can agents generate new backend tools at runtime

Yes. Hyperlambda can generate working backend functionality in 5 to 10 seconds, but the resulting executable tree still runs inside a capability-constrained runtime.

I dare you to break the sandbox

Find a verified bug in the backend C# or Hyperlambda code, and I will pay you $100.

Go to Playground