diff --git a/demos/llm_routing/openclaw_routing/README.md b/demos/llm_routing/openclaw_routing/README.md
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+# OpenClaw + Plano: Smart Model Routing for Personal AI Assistants
+
+OpenClaw is an open-source personal AI assistant that connects to WhatsApp, Telegram, Slack, and Discord. By pointing it at Plano instead of a single LLM provider, every message is automatically routed to the best model — conversational requests go to Kimi K2.5 (cost-effective), while code generation, testing, and complex reasoning go to Claude (most capable) — with zero application code changes.
+
+## Architecture
+
+```
+[WhatsApp / Telegram / Slack / Discord]
+                |
+        [OpenClaw Gateway]
+         ws://127.0.0.1:18789
+                |
+        [Plano :12000]  ──────────────>  Kimi K2.5  (conversation, agentic tasks)
+                |                           $0.60/M input tokens
+                |──────────────────────>  Claude     (code, tests, reasoning)
+                |
+        [Arch-Router 1.5B]
+        (local via Ollama, ~200ms)
+```
+
+Plano's 1.5B [Arch-Router](https://arxiv.org/abs/2506.16655) model analyzes each prompt locally and selects the best backend based on configured routing preferences.
+
+## Prerequisites
+
+- **Docker** running
+- **Ollama** installed ([ollama.com](https://ollama.com))
+- **Plano CLI**: `uv tool install planoai` or `pip install planoai`
+- **OpenClaw**: `npm install -g openclaw@latest`
+- **API keys**:
+  - `MOONSHOT_API_KEY` — from [Moonshot AI](https://platform.moonshot.cn/)
+  - `ANTHROPIC_API_KEY` — from [Anthropic](https://console.anthropic.com/)
+
+## Quick Start
+
+### 1. Set Environment Variables
+
+```bash
+export MOONSHOT_API_KEY="your-moonshot-key"
+export ANTHROPIC_API_KEY="your-anthropic-key"
+```
+
+### 2. Start the Demo
+
+```bash
+cd demos/llm_routing/openclaw_routing
+bash run_demo.sh
+```
+
+This will:
+- Pull the Arch-Router model into Ollama
+- Start Jaeger for tracing
+- Start Plano on port 12000
+
+### 3. Configure OpenClaw
+
+In `~/.openclaw/openclaw.json`, set:
+
+```json
+{
+  "agent": {
+    "model": "kimi-k2.5",
+    "baseURL": "http://127.0.0.1:12000/v1"
+  }
+}
+```
+
+Then run:
+
+```bash
+openclaw onboard --install-daemon
+```
+
+### 4. Test Routing
+
+Run the test script to verify routing decisions:
+
+```bash
+bash test_routing.sh
+```
+
+## Demo Scenarios
+
+| # | Message | Expected Route | Why |
+|---|---------|---------------|-----|
+| 1 | "Hey, what's up? Tell me something interesting." | **Kimi K2.5** | General conversation — cheap and fast |
+| 2 | "Remind me tomorrow at 9am and ping Slack about the deploy" | **Kimi K2.5** | Agentic multi-step task orchestration |
+| 3 | "Write a Python rate limiter with the token bucket algorithm" | **Claude** | Code generation — needs precision |
+| 4 | "Write unit tests for the auth middleware, cover edge cases" | **Claude** | Testing & evaluation — needs thoroughness |
+| 5 | "Compare WebSockets vs SSE vs polling for 10K concurrent users" | **Claude** | Complex reasoning — needs deep analysis |
+
+OpenClaw's code doesn't change at all. It points at `http://127.0.0.1:12000/v1` instead of a direct provider URL. Plano's Arch-Router analyzes each prompt in ~200ms and picks the right backend.
+
+## Monitoring
+
+### Routing Decisions
+
+Watch Plano logs for model selection:
+
+```bash
+docker logs plano 2>&1 | grep MODEL_RESOLUTION
+```
+
+### Jaeger Tracing
+
+Open [http://localhost:16686](http://localhost:16686) to see full traces of each request, including which model was selected and the routing latency.
+
+## Cost Impact
+
+For a personal assistant handling ~1000 requests/day with a 60/40 conversation-to-code split:
+
+| Without Plano (all Claude) | With Plano (routed) |
+|---|---|
+| 1000 req x Claude pricing | 600 req x Kimi K2.5 + 400 req x Claude |
+| ~$3.00/day input tokens | ~$0.36 + $1.20 = **$1.56/day** (~48% savings) |
+
+Same quality where it matters (code, tests), lower cost where it doesn't (chat).
+
+## Stopping the Demo
+
+```bash
+bash run_demo.sh down
+```
diff --git a/demos/llm_routing/openclaw_routing/config.yaml b/demos/llm_routing/openclaw_routing/config.yaml
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+version: v0.1.0
+
+routing:
+  model: Arch-Router
+  llm_provider: arch-router
+
+listeners:
+  egress_traffic:
+    address: 0.0.0.0
+    port: 12000
+    message_format: openai
+    timeout: 30s
+
+llm_providers:
+
+  # Arch Router - the 1.5B preference-aligned routing model (runs locally via Ollama)
+  - name: arch-router
+    model: arch/hf.co/katanemo/Arch-Router-1.5B.gguf:Q4_K_M
+    base_url: http://host.docker.internal:11434
+
+  # Kimi K2.5 — Moonshot AI's open model (1T MoE, 32B active params)
+  # Great for general conversation, agentic tasks, and multimodal work
+  # OpenAI-compatible API at $0.60/M input, $2.50/M output tokens
+  - model: openai/kimi-k2.5
+    access_key: $MOONSHOT_API_KEY
+    base_url: https://api.moonshot.ai/v1
+    provider_interface: openai
+    default: true
+    routing_preferences:
+      - name: general conversation
+        description: general chat, greetings, casual conversation, Q&A, and everyday questions
+      - name: agentic tasks
+        description: coordinating multi-step workflows, device automation, scheduling, and task orchestration across channels
+
+  # Claude — Anthropic's most capable model
+  # Best for complex reasoning, code, tool use, and evaluation
+  - model: anthropic/claude-sonnet-4-5
+    access_key: $ANTHROPIC_API_KEY
+    routing_preferences:
+      - name: testing and evaluation
+        description: writing tests, running evaluations, QA checks, verifying correctness, and debugging failures
+      - name: code generation
+        description: generating code, writing scripts, implementing functions, and building tool integrations
+      - name: complex reasoning
+        description: multi-step analysis, planning, architectural decisions, and deep problem-solving
+
+tracing:
+  random_sampling: 100
diff --git a/demos/llm_routing/openclaw_routing/docker-compose.yaml b/demos/llm_routing/openclaw_routing/docker-compose.yaml
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+services:
+  jaeger:
+    build:
+      context: ../../shared/jaeger
+    ports:
+      - "16686:16686"
+      - "4317:4317"
+      - "4318:4318"
diff --git a/demos/llm_routing/openclaw_routing/run_demo.sh b/demos/llm_routing/openclaw_routing/run_demo.sh
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+#!/bin/bash
+set -e
+
+echo "=== OpenClaw + Plano Routing Demo ==="
+
+# Check prerequisites
+command -v docker >/dev/null || { echo "Error: Docker not found"; exit 1; }
+command -v ollama >/dev/null || { echo "Error: Ollama not found. Install from https://ollama.com"; exit 1; }
+
+# Check/create .env file
+if [ -f ".env" ]; then
+  echo ".env file already exists"
+else
+  if [ -z "${MOONSHOT_API_KEY:-}" ]; then
+    echo "Error: MOONSHOT_API_KEY not set"
+    exit 1
+  fi
+  if [ -z "${ANTHROPIC_API_KEY:-}" ]; then
+    echo "Error: ANTHROPIC_API_KEY not set"
+    exit 1
+  fi
+  echo "Creating .env file..."
+  echo "MOONSHOT_API_KEY=$MOONSHOT_API_KEY" > .env
+  echo "ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY" >> .env
+fi
+
+# Pull Arch-Router model if needed
+echo "Pulling Arch-Router model..."
+ollama pull hf.co/katanemo/Arch-Router-1.5B.gguf:Q4_K_M
+
+start_demo() {
+  # Start Jaeger for tracing
+  echo "Starting Jaeger..."
+  docker compose up -d
+
+  # Start Plano gateway
+  echo "Starting Plano..."
+  planoai up --service plano --foreground
+}
+
+stop_demo() {
+  docker compose down
+  planoai down
+}
+
+if [ "${1:-}" == "down" ]; then
+  stop_demo
+else
+  start_demo
+  echo ""
+  echo "=== Plano is running on http://localhost:12000 ==="
+  echo "=== Jaeger UI at http://localhost:16686 ==="
+  echo ""
+  echo "Configure OpenClaw to use Plano as its LLM endpoint:"
+  echo '  In ~/.openclaw/openclaw.json, set:'
+  echo '    { "agent": { "model": "kimi-k2.5", "baseURL": "http://127.0.0.1:12000/v1" } }'
+  echo ""
+  echo "Then run: openclaw onboard --install-daemon"
+fi
diff --git a/demos/llm_routing/openclaw_routing/test_routing.sh b/demos/llm_routing/openclaw_routing/test_routing.sh
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+#!/usr/bin/env bash
+set -euo pipefail
+
+PLANO_URL="http://localhost:12000/v1/chat/completions"
+
+echo "=== Testing Plano Routing Decisions ==="
+echo ""
+
+# Scenario 1: General conversation -> should route to Kimi K2.5
+echo "--- Scenario 1: General Conversation (expect: Kimi K2.5) ---"
+curl -s "$PLANO_URL" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "kimi-k2.5",
+    "messages": [{"role": "user", "content": "Hey! What is the weather like today? Can you tell me a fun fact?"}]
+  }' | jq '{model: .model, content: .choices[0].message.content[:100]}'
+echo ""
+
+# Scenario 2: Agentic task -> should route to Kimi K2.5
+echo "--- Scenario 2: Agentic Task (expect: Kimi K2.5) ---"
+curl -s "$PLANO_URL" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "kimi-k2.5",
+    "messages": [{"role": "user", "content": "Schedule a reminder for tomorrow at 9am to review the pull request, then send a message to the team Slack channel about the deployment."}]
+  }' | jq '{model: .model, content: .choices[0].message.content[:100]}'
+echo ""
+
+# Scenario 3: Code generation -> should route to Claude
+echo "--- Scenario 3: Code Generation (expect: Claude) ---"
+curl -s "$PLANO_URL" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "kimi-k2.5",
+    "messages": [{"role": "user", "content": "Write a Python function that implements a rate limiter using the token bucket algorithm with async support."}]
+  }' | jq '{model: .model, content: .choices[0].message.content[:100]}'
+echo ""
+
+# Scenario 4: Testing/evaluation -> should route to Claude
+echo "--- Scenario 4: Testing & Evaluation (expect: Claude) ---"
+curl -s "$PLANO_URL" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "kimi-k2.5",
+    "messages": [{"role": "user", "content": "Write unit tests for this authentication middleware. Test edge cases: expired tokens, malformed headers, missing credentials, and concurrent requests."}]
+  }' | jq '{model: .model, content: .choices[0].message.content[:100]}'
+echo ""
+
+# Scenario 5: Complex reasoning -> should route to Claude
+echo "--- Scenario 5: Complex Reasoning (expect: Claude) ---"
+curl -s "$PLANO_URL" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "kimi-k2.5",
+    "messages": [{"role": "user", "content": "Analyze the trade-offs between using WebSockets vs SSE vs long-polling for real-time notifications in a distributed messaging system with 10K concurrent users."}]
+  }' | jq '{model: .model, content: .choices[0].message.content[:100]}'
+echo ""
+
+echo "=== Check Plano logs for MODEL_RESOLUTION details ==="
+echo "Run: docker logs plano 2>&1 | grep MODEL_RESOLUTION"