Theo Valmis

I built Mneme HQ after running into the same problem repeatedly while using AI coding tools on real production systems: the larger and more complex a codebase became, the less reliable AI-generated output became over time.

Models could write code quickly. They could follow instructions in the moment. But they consistently failed at one critical thing:

They forgot constraints. They reintroduced rejected patterns. They ignored prior architecture decisions unless those decisions were manually re-explained every time.

That is the gap Mneme HQ is designed to solve.

Why I built this

My background is in digital analytics, AI systems, digital transformation, and large-scale technical delivery. Worked across startups and enterprise organisations delivering analytics, AI systems, and technical platforms where consistency, governance, and architectural discipline matter as much as speed.

As AI-assisted development became part of my workflow, one pattern became obvious:

LLMs accelerate implementation. But without structured memory and governance, they introduce architectural drift.

Mneme HQ was built to provide that missing layer.

What Mneme HQ does

Mneme HQ is an open-source architectural governance layer for AI-assisted development. It stores architectural decisions, constraints, and anti-patterns as structured YAML in version control, then injects the relevant rules at prompt time — so AI coding assistants generate code that fits your architecture.

It helps teams preserve and enforce:

• Architecture decisions
• Coding standards and tooling preferences
• Anti-patterns and rejected approaches
• Project-specific implementation rules

Across tools such as Cursor, Claude Code, GitHub Copilot, LangChain, CrewAI, and custom agent pipelines.

Why this matters now

The current generation of AI coding tools is powerful, but they remain fundamentally stateless.

Without structured memory:

• Every session starts partially blind
• Every contributor re-explains context manually
• Every long-running project accumulates drift

The bottleneck in AI-assisted software development is no longer generation speed. It is continuity of context and trust in output.

Mneme HQ exists to solve that problem.

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How to engage with the project

The fastest way to understand Mneme is to read the architectural governance article, watch the interactive demo, and skim the benchmark methodology. Together they cover the why, the how, and the rigor.

If you're an engineering leader: the CTO page covers the strategic case, the platform engineering page covers the rollout pattern, and the standards landscape covers how Mneme aligns with NIST CAISI, MCP, and AGENTS.md. I track that work because long-term governance infrastructure cannot be vendor-coupled, and because the regulatory direction matters for any team operating in healthcare, finance, or public-sector environments.

If you're an individual engineer: install with pip install mneme-hq && mneme init, wire the Claude Code hook from the integration guide, and run mneme check against your repo. Everything is open source under MIT — no signup, no waitlist, no telemetry.

Connect

Mneme HQ is built openly, in public, and grounded in real implementation work. If you're exploring governance, memory, or reliability for AI-assisted development workflows, I'd be glad to connect.