Layerr is a 12-layer orchestration intelligence system that sits between your applications and LLM providers. A dedicated router model classifies every prompt into 8 intent categories, then a workload analyzer, workspace selector, 12-dimensional scoring engine, and strategy engine determine the optimal model. Fallback chains, provider health checks, explainability, replay intelligence, adaptive learning, and benchmark harnesses are all built in. Currently v0.8.0-alpha - early but real.

Does Layerr generate AI responses?

No. Layerr never generates responses itself. It acts as an intelligent routing and orchestration layer, forwarding your prompts to existing providers like Anthropic, OpenAI, Mistral, Google, and others, then returning the response back to your tool.

Is it compatible with Claude Code and Cursor?

Yes. Layerr exposes a fully OpenAI-compatible API endpoint. Set OPENAI_BASE_URL to Layerr and Claude Code, Cursor, Roo Code, or any OpenAI SDK client routes through it instantly. Zero code changes. The author has been running this against Claude Code for weeks - local models for quick patches, escalated to sonnet for heavy reasoning.

How does intelligent routing work?

Layer 1: a router model classifies intent into CODING, REASONING, CREATIVE, VISION, DOCUMENT, FAST, SECURITY, or GENERAL. Layer 2: deterministic workload analysis scores complexity, latency sensitivity, cost sensitivity, context size, and reasoning depth with zero LLM calls. Layer 3: a workspace profile (General, Coding, Research, Creative, Local-First, Cost, or Performance) is selected. Layer 4: the strategy engine builds a 12-dimensional scoring profile with latency tolerance, cost sensitivity, reasoning depth, and fallback thresholds. Layer 5: every candidate model is scored across 12 dimensions plus runtime enrichment. Layer 6: the primary model is selected and a fallback chain of up to 4 candidates is prepared. If the primary fails, the next candidate is tried automatically. Every decision produces a structured explainability output.

Can I self-host Layerr?

Yes. The Layerr runtime deploys via Docker or Kubernetes on any cloud or bare metal. Self-hosted deployments use your own API keys, run all routing logic locally, and receive all 9 capabilities including workspace customization, replay intelligence, benchmark harness, and entitlements. In v0.8.0-alpha the focus is on getting the local + cloud hybrid right first.

What models are supported?

Any model available through OpenRouter (100+ models), plus direct integrations with Anthropic, OpenAI, Mistral, Google Gemini, Cohere, and Ollama for local inference. The router is currently qwen2.5:14b or similar. If a provider exposes an API, Layerr can route to it.

Is my code or data sent to Layerr?

Only routing metadata is processed - estimated prompt complexity, context size, and task type. Your actual code and prompts go directly to the chosen LLM provider. Self-hosted deployments send zero data to Layerr servers. The router model runs locally if you self-host.

What's the difference between Layerr and OpenRouter?

OpenRouter is a model marketplace - one API key, many models. Layerr is an orchestration intelligence system that decides WHICH model to use, WHEN to escalate, and HOW to recover from failures. It adds intent classification, workspace profiles, 12-dimensional scoring, fallback chains up to 4 deep, explainability, replay intelligence, adaptive learning, benchmark harnesses, and governance. OpenRouter gives you access. Layerr gives you decisions.

About

Why Aspyre Labs exists, and why we built Layerr.

I spent fifteen years running infrastructure. Windows Server, cloud deployments, virtualization, network infrastructure. I watched enterprise software get slower, more expensive, and less respectful of your data. The pattern was always the same. Vendors lock you into bloated suites that demand bigger budgets every renewal cycle. Then they treat your data as their product, mining it for insights they sell back to you as analytics or AI features you never asked for.

I built enough servers and managed enough cloud migrations to know there is a better way. Infrastructure should serve the operator, not the vendor. When cloud computing promised freedom, it delivered another layer of billing complexity and egress fees. When AI tools arrived, they repeated the same playbook. Premium prices for every autocomplete. Opaque pricing that punishes you for using the tool more. Your code, your prompts, your intellectual property flowing into someone else's training pipeline.

I started Aspyre Labs because I was tired of watching developers get squeezed. Aspyre Labs is an infrastructure-focused product studio. I build tools that do not require enterprise budgets or enterprise patience. Self-hosted is not a nostalgia trip for me. It is a requirement. It means your data stays on your hardware. It means your costs are predictable. It means you are not one pricing email away from rewriting your workflow.

Layerr is our first release. It is a self-hosted intelligence layer for AI coding agents. Not a router. An orchestration runtime that learns which LLM works best for your codebase and actually improves its decisions over time. I built it because I was tired of paying premium prices for every autocomplete and trivial refactor. Your agent should not burn expensive tokens on one-liners. Layerr stops that waste by making intelligent decisions about when to use which model, and it gets smarter the longer you run it.

This is the beginning. One tool at a time, one honest price at a time, one line of code that actually belongs to you.

Aasim Pathan, Founder, Aspyre Labs