diff --git a/demos/llm_routing/openclaw_routing/README.md b/demos/llm_routing/openclaw_routing/README.md
index 77088ec2..7c201687 100644
--- a/demos/llm_routing/openclaw_routing/README.md
+++ b/demos/llm_routing/openclaw_routing/README.md
@@ -1,5 +1,9 @@
 # OpenClaw + Plano: Smart Model Routing for Personal AI Assistants
 
+<p align="center">
+  <img src="openclaw_plano.png" alt="OpenClaw + Plano" width="50%">
+</p>
+
 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
@@ -97,34 +101,21 @@ Try these messages to see routing in action:
 
 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 router analyzes each prompt and picks the right backend.
 
-### Verify Plano Routing Directly (Optional)
 
-To test Plano's routing without OpenClaw, run the test script which sends requests directly to the gateway:
+## Tracing
+
+For fast dev/test cycles, Plano provides built-in tracing to visualize routing decisions and LLM interactions. Start the trace listener in a separate terminal:
 
 ```bash
-bash test_routing.sh
+planoai trace
 ```
 
-## Monitoring
+Then send requests through OpenClaw. You'll see detailed traces showing:
+- Which model was selected and why
+- Token usage and latency for each request
+- Complete request/response payloads
 
-### Routing Decisions
-
-Watch Plano logs for model selection:
-
-```bash
-docker logs plano 2>&1 | grep MODEL_RESOLUTION
-```
-
-### Jaeger Tracing (Optional)
-
-To visualize full request traces and routing decisions, start Jaeger:
-
-```bash
-docker run -d --name jaeger -p 16686:16686 -p 4317:4317 -p 4318:4318 \
-  -e COLLECTOR_OTLP_ENABLED=true jaegertracing/all-in-one:latest
-```
-
-Then open [http://localhost:16686](http://localhost:16686) to see traces for each request, including which model was selected and the routing latency.
+Learn more about tracing features and configuration in the [Plano tracing guide](https://docs.planoai.dev/guides/observability/tracing.html#tracing-with-the-cli).
 
 ## Cost Impact
 
diff --git a/demos/llm_routing/openclaw_routing/openclaw_plano.png b/demos/llm_routing/openclaw_routing/openclaw_plano.png
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diff --git a/demos/llm_routing/openclaw_routing/test_routing.sh b/demos/llm_routing/openclaw_routing/test_routing.sh
deleted file mode 100755
index d630f920..00000000
--- a/demos/llm_routing/openclaw_routing/test_routing.sh
+++ /dev/null
@@ -1,60 +0,0 @@
-#!/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"