Integrations

Architectural drift prevention across your AI coding stack

One decision corpus, enforced at three layers: agent hooks, generated editor rules, and a deterministic CI gate. Every integration below is labelled with its real support level.

01

Enforcement — native and validated

A shipped adapter checks proposed changes against the decision corpus before they reach disk. Maintained code, tests on main, published evidence.

02

Propagation — rules export and CI gates

The same corpus carried into tools without a runtime adapter: generated editor rules, and a deterministic gate in pipelines you own.

03

Host environments

No dedicated adapters. Governance reaches these tools through the layers above — an underlying supported agent, generated rules, or CI.

04

Experimental and planned

Nothing here carries production support yet. Experimental means code exists and specific boundaries are tested, but validation or coverage remains incomplete; planned means design work only.

Works alongsidePerplexity Enterprise turns research rationale into enforceable decisions · Microsoft Agent Forge pairs an autonomous workflow substrate with deterministic governance. Research and ecosystem tools, not Mneme integrations.

Every layer reads the same file: .mneme/project_memory.json. Install with pip install mneme-hq, scaffold with mneme init, and wire the layer that fits your stack. The architectural argument: governance across heterogeneous AI coding agents. Standards alignment: MCP, AGENTS.md, NIST CAISI.