AILANG: AI-First Programming Language

The Problem with AI-Generated Code

AI Writes Code Fast. Debugging It Is Slow.

AI coding agents like Claude Code and Gemini CLI generate code rapidly, but when something goes wrong, you're left with print statements, breakpoints and guesswork. The AI can't replay its own execution or trace what happened.

Deterministic execution: Same input produces same output, every time
Effect boundaries: Side effects visible in types, preventing hallucinated operations
Structured traces: Detailed execution logs organized by effect type
Budget constraints: Capabilities are statically visible and resource-limited

30-Second Setup

Integrates with Claude Code, Gemini CLI, and other AI coding agent plugin systems

Get Started

The Decision Space

Every project starts as a tree of possible paths. Each decision prunes branches. The question isn't how much entropy a project has — it's when that entropy gets resolved. Decisions made at design time cost tokens. Decisions deferred to runtime cost debugging, deployments, and outages.

Click the intro card inside the frame to start, then use the play / step controls.

Open Full Screen See the Full Talk

Built on 12 Design Axioms

AILANG's design principles ensure AI-generated code is predictable, debuggable, and safe by construction.

Deterministic Replay

Every execution can be replayed exactly. When debugging AI code, you can step through exactly what happened, not guess at state changes.

Explicit Authority

Capabilities like file access or network calls are statically visible in types. AI can't accidentally generate code that exceeds its permissions.

Structured Traces

Execution traces organized by effect type give you visibility into exactly what operations occurred and in what order.

Pure functional core with lambda calculus, pattern matching, and algebraic data types

Version 0.6.2

AI Agent Integration

Works with Claude Code, Gemini CLI, and other AI coding agent plugin systems

66 Code Examples

Comprehensive examples covering all language features and common patterns

Effect System

IO, File System, Clock, and Network effects with budget constraints

Interactive Playground

Browser-based REPL for experimenting without installation

Performance Benchmarks

Documented performance comparisons across major LLM models

Capability Security

Statically visible permissions prevent unauthorized operations

Why AILANG for AI Coding Agents?

The Debugging Problem:

Non-deterministic execution: Traditional languages make it hard to replay failures
Hidden side effects: AI can generate code with unexpected mutations
Implicit permissions: No way to constrain what operations AI code can perform
Opaque traces: Difficult to understand what happened during execution

AILANG's Approach:

Replay any execution: Deterministic semantics mean perfect reproducibility
Effects in types: Side effects are visible and constrained by the type system
Budget constraints: Limit resources AI code can consume
Structured traces: Organized logs by effect type for easy debugging

See AILANG in Action

These demos showcase AILANG's core capabilities -- deterministic execution, effect boundaries, contract verification, and safe AI agent orchestration. All running live in your browser.

Document Intelligence

Document Extractor

AI-powered extraction with automatic schema detection and contract validation. WebAssembly + Gemini.

DocParse

Parse DOCX, PPTX, XLSX, and PDF files entirely in the browser via WebAssembly. No server needed.

Z3 Verify

Contract verification using the Z3 theorem prover. 43 proven-correct functions with bug detection.

AI + Contracts

Schema extraction combined with contract validation and AI integration. WebAssembly + Gemini.

Streaming & Voice

Voice DocParse

Talk to your documents through Gemini Live voice streaming with bidirectional WebSocket.

Claude Chat

Streaming text responses via Server-Sent Events using Anthropic's Claude model.

Gemini Live Audio

Text to streaming audio via bidirectional WebSocket connection with real-time playback.

Safe Agent

Contract-verified AI tool calling with safety guarantees and capability budgets.

Data & Analytics

GA4 Analytics

BigQuery queries with contract-verified SQL generation, capability budgets, and ADC auth.

Open Demo Hub

Why This Matters for AI Strategy

AILANG isn't just another programming language. It's built on a radical thesis about how AI systems should work.

                
                "Machines as Primary Readers" — AILANG deliberately prioritizes machine reasoning over human ergonomics. Every design decision optimizes for AI analysis, not developer convenience.
            

The Context Window Problem

Research shows LLMs suffer 30-50% performance degradation as context grows from 10K to 1M tokens. Architectural reasoning is a key failure mode.

AILANG directly addresses this with:

Explicit effects: No need to trace through code to understand capabilities
Static imports: Dependencies are clear without scanning files
Deterministic semantics: Predictable behavior reduces reasoning overhead

Cost as Semantic Property

In AILANG, resource consumption—time, memory, I/O operations—is part of the program's meaning, not incidental detail.

This enables:

Budget constraints: Limit what AI code can consume
Predictable costs: Know resource usage before execution
Safe autonomy: AI agents that can't accidentally exhaust resources

What's Coming

Observability Dashboards

Unified OTEL dashboards with cross-process trace linking. See exactly what your AI agents are doing across distributed systems.

Human-in-the-Loop Workflows

Dashboard approval systems for AI operations. Review and approve agent actions before they execute in production.

Global Collaboration Hub

Cross-computer agent collaboration. Multiple AI agents working together across machines with shared semantic state.

Full Roadmap on GitHub

Get Involved

AILANG is an active research project welcoming contributors of all backgrounds. Whether you're interested in programming language design, AI systems, or just curious about the future of coding, there are ways to participate.

For Developers

Read the Documentation Explore the Source Code Try the Playground

For Researchers

Study Language Design Analyze AI Coding Patterns Publish Findings

For Contributors

Report Issues Suggest Features Submit Pull Requests

AI-Generated Code That's Cheaper to Debug, Replay, and Fix