AI Coding Agent Memory & Semantic Change Control
Stop AI Agents from Corrupting your Code.
Nool is the first semantic change control layer built for autonomous software evolution. It provides persistent operational memory and mechanical proof to govern AI coding agents, ensuring they never outrun their reviewed intent.
- Eliminate context drift with persistent operational memory.
- Reduce token waste by avoiding redundant agent re-indexing.
- Prevent code corruption with deterministic semantic policy gates.
- Compute semantic blast radius before changes enter your repository.
- Maintain a verifiable audit trail of causal reasoning and intent.
Mechanical Proof of Self-Governance
Nool governs its own evolution. The screenshots and data on this page are live captures of Nool governing this project's development. Every semantic transition is recorded in an immutable ledger, ensuring that our AI agents never outrun their reviewed intent.
- nool propose: Semantic intent declaration and blast radius analysis. Catch drift before implementation.
- nool blast-radius: Structural impact analysis across architectural boundaries. Understand downstream effects.
- nool visualize: Project-wide topology mapping. Visualize features, entities, and invariants in a semantic DAG.
- nool task list: Fleet-wide coordination. Eliminate intent-clashes and resource contention across agents.
- nool solidify: Immutable operational truth. Seal transitions into the permanent ledger with causal attribution.
Why Persistent Agent Memory Matters
Traditional AI coding workflows suffer from 'Vibe Coding'—fragmented context and silent regressions. Nool replaces this with governed software evolution.
- Reduce Token Waste: Stop agents from re-indexing your entire repository. Nool provides high-fidelity semantic memory.
- Eliminate Regressions: Detect logical conflicts that text-based diffs miss. Enforce your architecture mechanically.
- Zero-Loss Handoffs: Preserve the 'Why' behind every change. Agents inherit the causal reasoning of previous sessions.