diff --git a/CLAUDE.md b/CLAUDE.md index e04eefb..962297f 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -1,6 +1,6 @@ # XClawRouter -Smart LLM router for autonomous agents. 88 models. Wallet-based auth. USDC micropayments via x402. +Smart LLM router for autonomous agents. 90 models. Wallet-based auth. USDC micropayments via x402. ## Commands diff --git a/README.md b/README.md index 0cad8f8..11e628a 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@

The LLM router built for autonomous agents

Use one BlockRun account API key or let an agent settle each request over x402.

-Local smart routing across 88 models.

+Local smart routing across 90 models.

7 models free, no crypto required.


@@ -33,7 +33,7 @@ -> **XClawRouter** is an open-source smart LLM router that reduces AI API costs by 84%. It analyzes each request across 15 dimensions and routes locally to the cheapest capable model in under 1ms. Use a BlockRun account API key with prepaid credit, or settle requests with USDC over x402 on Solana or Base. 88 models from OpenAI, Anthropic, Google, xAI, DeepSeek, and more. MIT licensed. +> **XClawRouter** is an open-source smart LLM router that reduces AI API costs by 84%. It analyzes each request across 15 dimensions and routes locally to the cheapest capable model in under 1ms. Use a BlockRun account API key with prepaid credit, or settle requests with USDC over x402 on Solana or Base. 90 models from OpenAI, Anthropic, Google, xAI, DeepSeek, and more. MIT licensed. --- @@ -306,7 +306,7 @@ Edit existing images with `/img2img`: ## Models & Pricing -88 models across 9 providers, one BlockRun API key or x402 wallet. **Starting at $0.0002/request.** +90 models across 9 providers, one BlockRun API key or x402 wallet. **Starting at $0.0002/request.** > **💡** Models are grouped by typical cost of a chat message (~500 input + 500 output tokens). @@ -590,7 +590,7 @@ npm test **The LLM router built for autonomous agents** -You're here. 88 models, local smart routing, x402 USDC payments — the only stack that lets agents operate independently. +You're here. 90 models, local smart routing, x402 USDC payments — the only stack that lets agents operate independently. `curl -fsSL https://blockrun.ai/XClawRouter-update | bash` @@ -601,7 +601,7 @@ You're here. 88 mod **BlockRun for Claude Code** -Run Claude Code with 88 models, no rate limits, no Anthropic account, no phone verification. Pay per request with USDC — your wallet is your identity. +Run Claude Code with 90 models, no rate limits, no Anthropic account, no phone verification. Pay per request with USDC — your wallet is your identity. `curl -fsSL https://blockrun.ai/brcc-install | bash` diff --git a/brand-numbers.json b/brand-numbers.json index a71dfa5..75f4dc2 100644 --- a/brand-numbers.json +++ b/brand-numbers.json @@ -2,23 +2,23 @@ "$schema": "https://blockrun.ai/brand/numbers.schema.json", "version": 1, "models": { - "chatVisible": 88, - "totalVisible": 115, + "chatVisible": 90, + "totalVisible": 117, "free": 7, - "freeWithheld": 28, + "freeWithheld": 27, "image": 12, "video": 8, "music": 1, "speech": 5, "soundfx": 1, - "withFallback": 28, - "withFallbackAllEntries": 68 + "withFallback": 29, + "withFallbackAllEntries": 69 }, "clawrouter": { "dimensions": 15, "tiers": 4, "profiles": 4, - "aliases": 276 + "aliases": 295 }, "mcp": { "tools": 19, diff --git a/docs/anthropic-cost-savings.md b/docs/anthropic-cost-savings.md index 55e67fe..57f16b4 100644 --- a/docs/anthropic-cost-savings.md +++ b/docs/anthropic-cost-savings.md @@ -50,7 +50,7 @@ This is where you're paying for real value: ## The Solution: ClawRouter -[ClawRouter](https://github.com/BlockRunAI/XClawRouter) is an open-source local proxy that sits between your app and 88 AI models. It saves you money in three ways: **smart routing**, **token optimization**, and **response caching**. +[ClawRouter](https://github.com/BlockRunAI/XClawRouter) is an open-source local proxy that sits between your app and 90 AI models. It saves you money in three ways: **smart routing**, **token optimization**, and **response caching**. ``` ┌─────────────┐ ┌──────────────────────────────┐ ┌──────────────────┐ diff --git a/docs/anthropic-third-party-harness-changes.md b/docs/anthropic-third-party-harness-changes.md index 80868ae..ee3b63c 100644 --- a/docs/anthropic-third-party-harness-changes.md +++ b/docs/anthropic-third-party-harness-changes.md @@ -31,7 +31,7 @@ This is exactly the problem [XClawRouter](https://github.com/BlockRunAI/XClawRou ## XClawRouter: Smart Routing for Agents -XClawRouter is an open-source local proxy that sits between your agent and 88 LLM models across 9 providers. It analyzes every request across 15 dimensions and routes it to the cheapest model that can handle it — in under 1ms, entirely locally. +XClawRouter is an open-source local proxy that sits between your agent and 90 LLM models across 9 providers. It analyzes every request across 15 dimensions and routes it to the cheapest model that can handle it — in under 1ms, entirely locally. ``` Your Agent → XClawRouter (localhost:8402) → Best model for the job @@ -74,7 +74,7 @@ Then point your agent at `http://localhost:8402/v1/` with any OpenAI-compatible - **No API keys to manage** — wallet-based cryptographic auth - **No subscriptions** — pay per request in USDC (Base or Solana) -- **No vendor lock-in** — 88 models, switch anytime +- **No vendor lock-in** — 90 models, switch anytime - **You control your wallet** — non-custodial, funds never held by a third party - **Budget caps** — set a max spend per session, XClawRouter gracefully downgrades when budget runs low - **Token compression** — 7-layer pipeline reduces token costs by 7–40% before they hit any provider diff --git a/docs/clawrouter-vs-openrouter-llm-routing-comparison.md b/docs/clawrouter-vs-openrouter-llm-routing-comparison.md index 360ff43..e6f87c6 100644 --- a/docs/clawrouter-vs-openrouter-llm-routing-comparison.md +++ b/docs/clawrouter-vs-openrouter-llm-routing-comparison.md @@ -222,7 +222,7 @@ OpenRouter doesn't always pass through provider-specific features correctly. Ima **Direct provider routing.** XClawRouter routes through BlockRun's API directly to providers — not through a second aggregator. One hop, not two. Provider-specific features work because there's no middleman translating them. -![Guaranteed Feature Parity & Direct Connectivity — Three-panel diagram: Vision (image_url auto-detected → vision-capable models only), Tool Calling (toolCalling flag → agentic models only), Catalog (curated 88 models with automatic legacy-to-modern redirects). Direct provider routing means no dropped payloads.](./assets/clawrouter-feature-parity-direct-connectivity.png) +![Guaranteed Feature Parity & Direct Connectivity — Three-panel diagram: Vision (image_url auto-detected → vision-capable models only), Tool Calling (toolCalling flag → agentic models only), Catalog (curated 90 models with automatic legacy-to-modern redirects). Direct provider routing means no dropped payloads.](./assets/clawrouter-feature-parity-direct-connectivity.png) --- @@ -240,7 +240,7 @@ When new models launch, OpenRouter's catalog lags. Users configure a model that ### How XClawRouter Solves This -XClawRouter maintains a curated catalog of 88 models across 9 providers (including 11 free models), updated with each release. Delisted models have automatic redirect aliases: +XClawRouter maintains a curated catalog of 90 models across 9 providers (including 11 free models), updated with each release. Delisted models have automatic redirect aliases: ```typescript // Delisted models redirect automatically @@ -268,7 +268,7 @@ No silent drops. No stale catalog. Models are benchmarked for speed, quality, an | **Rate limits** | Per-key, shared | Per-wallet, independent | | **Vision support** | Images sometimes dropped | Auto-detected, vision-only fallback | | **Tool calling** | Silent failures with some models | Flag-based filtering, guaranteed support | -| **Model catalog** | Laggy, silent drops | Curated 88 models, redirect aliases | +| **Model catalog** | Laggy, silent drops | Curated 90 models, redirect aliases | | **Budget control** | Monthly invoice | Per-session cap (`maxCostPerRun`) | | **Setup** | Create account, paste key | Agent generates wallet, auto-configured | | **Average cost** | $25/M tokens (Opus direct) | auto-routed = **84% savings** | diff --git a/docs/llm-router-benchmark-46-models-sub-1ms-routing.md b/docs/llm-router-benchmark-46-models-sub-1ms-routing.md index 0e00f17..81768e9 100644 --- a/docs/llm-router-benchmark-46-models-sub-1ms-routing.md +++ b/docs/llm-router-benchmark-46-models-sub-1ms-routing.md @@ -13,7 +13,7 @@ The results changed how we route requests. ## Why We Did This -BlockRun is an x402 micropayment gateway that sits between your AI agent and 88 LLM providers. Every request flows through our payment verification layer before hitting the model API. That means our latency numbers include everything a real user experiences: payment auth, provider API call, and response delivery. +BlockRun is an x402 micropayment gateway that sits between your AI agent and 90 LLM providers. Every request flows through our payment verification layer before hitting the model API. That means our latency numbers include everything a real user experiences: payment auth, provider API call, and response delivery. Most benchmarks measure model speed in isolation. We wanted to measure what users actually feel. @@ -136,7 +136,7 @@ Raw benchmark data: [benchmark-results.json](https://github.com/BlockRunAI/XClaw --- -_BlockRun is the x402 micropayment gateway for AI. One wallet, 88 models, pay-per-request with USDC. [Get started](https://blockrun.ai)_ +_BlockRun is the x402 micropayment gateway for AI. One wallet, 90 models, pay-per-request with USDC. [Get started](https://blockrun.ai)_ --- diff --git a/docs/plans/2026-02-03-smart-routing-design.md b/docs/plans/2026-02-03-smart-routing-design.md index c37dc3a..bf14afa 100644 --- a/docs/plans/2026-02-03-smart-routing-design.md +++ b/docs/plans/2026-02-03-smart-routing-design.md @@ -6,7 +6,7 @@ Simple queries go to Claude Opus at $75/M output tokens when Gemini Flash could handle them at $0.60/M. No cost-aware model selection. -Phase 1 solved API key management (one wallet for 88 models). Phase 2 solves cost optimization by routing queries to the cheapest capable model. +Phase 1 solved API key management (one wallet for 90 models). Phase 2 solves cost optimization by routing queries to the cheapest capable model. ## Why Client-Side diff --git a/docs/smart-llm-router-14-dimension-classifier.md b/docs/smart-llm-router-14-dimension-classifier.md index 55c8a7a..934c535 100644 --- a/docs/smart-llm-router-14-dimension-classifier.md +++ b/docs/smart-llm-router-14-dimension-classifier.md @@ -7,7 +7,7 @@ _March 20, 2026 | BlockRun Engineering_ > the benchmark. For current figures see > [blockrun.ai/brand/numbers.json](https://blockrun.ai/brand/numbers.json). -When you route AI requests across 88 models from 8 providers, you can't just pick the cheapest one. You can't just pick the fastest one either. We learned this the hard way. +When you route AI requests across 90 models from 8 providers, you can't just pick the cheapest one. You can't just pick the fastest one either. We learned this the hard way. This is the technical story of how we benchmarked every model on our platform, discovered that speed and intelligence are poorly correlated, and built a production routing system that classifies requests in under 1ms using 14 weighted dimensions with sigmoid confidence calibration. @@ -26,7 +26,7 @@ We needed a system that could classify any request and route it to the optimal m ## Step 1: Benchmarking the Fleet -Before building the router, we needed ground truth. We benchmarked all 88 models through our production payment pipeline. +Before building the router, we needed ground truth. We benchmarked all 90 models through our production payment pipeline. ### Methodology @@ -317,7 +317,7 @@ We originally designed a two-stage system where low-confidence rules-based class ## Appendix: Full Benchmark Data -Raw data (88 models, latency, throughput, IQ scores, pricing): [`benchmark-merged.json`](https://github.com/BlockRunAI/XClawRouter/blob/main/benchmark-merged.json) +Raw data (90 models, latency, throughput, IQ scores, pricing): [`benchmark-merged.json`](https://github.com/BlockRunAI/XClawRouter/blob/main/benchmark-merged.json) Routing configuration: [`src/router/config.ts`](https://github.com/BlockRunAI/XClawRouter/blob/main/src/router/config.ts) @@ -325,4 +325,4 @@ Scoring implementation: [`src/router/rules.ts`](https://github.com/BlockRunAI/XC --- -_BlockRun is the x402 micropayment gateway for AI. One wallet, 88 models, pay-per-request with USDC. [blockrun.ai](https://blockrun.ai)_ +_BlockRun is the x402 micropayment gateway for AI. One wallet, 90 models, pay-per-request with USDC. [blockrun.ai](https://blockrun.ai)_ diff --git a/skills/xclawrouter/SKILL.md b/skills/xclawrouter/SKILL.md index 48ef06e..e6298ed 100644 --- a/skills/xclawrouter/SKILL.md +++ b/skills/xclawrouter/SKILL.md @@ -19,7 +19,7 @@ metadata: { "openclaw": { "emoji": "🦀", "requires": { "config": ["models.prov # XClawRouter -Smart LLM router that saves 84% on inference costs by routing each request to the cheapest model that can handle it. 88 models across 9 providers (7 free models), all through one wallet. +Smart LLM router that saves 84% on inference costs by routing each request to the cheapest model that can handle it. 90 models across 9 providers (7 free models), all through one wallet. ## Install @@ -55,7 +55,7 @@ Rules handle ~~80% of requests in <1ms. Only ambiguous queries hit the LLM class ## Available Models -88 models including: claude-fable-5.1, claude-fable-5, claude-opus-5.5, claude-opus-5, claude-sonnet-5.5, claude-sonnet-5, gpt-6-astra, gpt-6-sol, gpt-6.1-sol, gpt-6-luna, gpt-5.6-terra, gpt-5.6-sol, gpt-5.5, gpt-5.4, gpt-5.1, gemini-3.1-pro, gemini-3.6-flash, grok-4.7, grok-4.6, grok-4.5, grok-4.3, glm-5.3, kimi-k3, qwen3.8-max, qwen3.7-max, mistral-large-4, hy4-preview, laguna-s-2.1, mimo-v2.5, deepseek-v4-pro, deepseek-chat, and the free models (nemotron-3.5-lightning [default], laguna-xs-2.1, north-mini-code, nemotron-3-nano-omni-30b-a3b-reasoning [vision], nemotron-3-ultra-550b, llama-3.2-11b-vision [vision], muse-glimmer-30b). +90 models including: claude-fable-5.1, claude-fable-5, claude-opus-5.5, claude-opus-5, claude-sonnet-5.5, claude-sonnet-5, gpt-6-astra, gpt-6-sol, gpt-6.1-sol, gpt-6-luna, gpt-5.6-terra, gpt-5.6-sol, gpt-5.5, gpt-5.4, gpt-5.1, gemini-3.1-pro, gemini-3.6-flash, grok-4.7, grok-4.6, grok-4.5, grok-4.3, glm-5.3, kimi-k3, qwen3.8-max, qwen3.7-max, mistral-large-4, hy4-preview, laguna-s-2.1, mimo-v2.5, deepseek-v4-pro, deepseek-chat, and the free models (nemotron-3.5-lightning [default], laguna-xs-2.1, north-mini-code, nemotron-3-nano-omni-30b-a3b-reasoning [vision], nemotron-3-ultra-550b, llama-3.2-11b-vision [vision], muse-glimmer-30b). ## Model Prices — Look Them Up Live