AINL helps turn AI from "a smart conversation" into "a structured worker." It is designed for teams building AI workflows that need multiple steps, state and memory, tool use, repeatable execution, validation and control, and lower dependence on long prompt loops. AINL is a compact, graph-canonical, AI-native programming system for (READ: README)

Deterministic agent runtime with explicit plans, typed tools, permissions, and replay. Implements Google DeepMind's Intelligent AI Delegation framework. Named after ELP's Karn Evil 9.

Stop Claude Code from doing irreversible damage. Policy-gated execution + receipts so you can ship agents without sweating production.

HELM OSS — Open-source core of the HELM Autonomous OS. Policy enforcement, kernel runtime, proof graphs, and audit infrastructure.

A kernel-userland protocol enforcing information-theoretic bounds on AI adaptivity leakage, benchmark gaming, and capability spillover.

Execution OS with human approval gates and pressure-driven task control.

Offline-first LLM orchestration CLI for planning and executing human-approved tasks via validated git patches, with reproducible runs and full auditability.

Constitutional Verification Model (CVM), a layered framework for reasoning about how decentralized systems achieve legitimate rule authority and independently verifiable execution through invariant correctness, deterministic execution, and replayable history.

Deterministic graph execution engine built in Rust

AIΩN – A formal framework for deterministic, history-native computation.

R-LAM is a reproducibility-constrained execution framework for Large Action Models in scientific workflow automation. It enables adaptive, agent-driven workflow execution while enforcing strict guarantees on auditability, determinism, and replayability.

Deterministic business workflow reference application with scenario-driven execution and AI-assisted insights.

Deterministic execution boundary for AI systems enforcing signed approvals, replay protection, and cryptographic receipts.

The Deterministic Execution Layer for AI Agents. A high-performance infrastructure protocol designed to eliminate probabilistic uncertainty in Large Language Models (LLMs) through sandbox-validated execution.

The Security Kernel for AI Agents — MCP/A2A gateway with policy enforcement, taint tracking, sandboxed execution, deterministic envelopes, and Sigstore audit. OWASP ASI 2026 compliant.

Example skills, manifests, and reference projects for building on Inactu.

HeartFire | Open-source kernel-level framework for Digital Sovereignty and Social AI. High-performance, low-latency infrastructure bridging resilient cybersecurity with inclusive robotics. Designed for European technological autonomy.

Built to answer: How do you execute millions of smart contract transactions — concurrently — without breaking determinism?

Secure execution substrate for immutable agent skills with explicit capabilities, cryptographic provenance, and auditable deterministic runs.

The Universal Goal Execution Model. A deterministic computational substrate for autonomous agents and post-procedural software. Stop programming execution. Start declaring intent.