BlockRunAI/ClawRouter

Most teams optimize prompts. The real cost leak is model selection. → ClawRouter (BlockRunAI) This is an agent-native LLM routing layer that sits between your app and 30+ models. Goal: 👉 Route every request to the cheapest model that can handle it Automatically. Core architecture ↓ • Local routing (<1ms latency, no external calls) • 14–15 dimension scoring (complexity, reasoning, code, length) • Tiered execution → SIMPLE / MEDIUM / COMPLEX / REASONING • Picks lowest-cost viable model per request This is not a wrapper. It's a decision engine. Cost layer ↓ • Up to ~78–90% inference savings • Blended pricing vs always hitting premium models • Handles 30–40+ models (OpenAI, Claude, Gemini, DeepSeek, etc.) Infra primitives ↓ • Runs as local proxy / gateway • Stateless routing logic (client-side) • Works with agent frameworks (OpenClaw) • Zero API keys → wallet-based auth Payment model ↓ • x402 micropayments (USDC) • Pay-per-request (non-custodial wallet) • No vendor lock-in / no prepaid credits Why this matters: Most stacks today: App → Hardcoded model → $$$ bleed With ClawRouter: App → Router → Optimal model → minimized cost Same output class. Lower spend. Use cases: • Multi-agent systems (dynamic routing) • Cost-aware SaaS AI backends • High-volume inference pipelines • Autonomous agents optimizing their own spend This is the missing layer in AI infra: 👉 Not better models 👉 Better routing between models If you're scaling LLM usage, this is mandatory infra. Try it: https://t.co/UUPH7CSsjQ Follow for more on AI infra, cost optimization, and agent systems. #AI #AIAgents #LLM #DevOps #CloudNative #FinOps #OpenSource #PlatformEngineering