plano/demos/llm_routing/openclaw_routing

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)

Plano uses a preference-aligned router to analyze each prompt and select the best backend based on configured routing preferences.

Prerequisites

• Docker running
• Plano CLI: uv tool install planoai or pip install planoai
• OpenClaw: npm install -g openclaw@latest
• API keys:
  • MOONSHOT_API_KEY — from Moonshot AI
  • ANTHROPIC_API_KEY — from Anthropic

Quick Start

1. Set Environment Variables

export MOONSHOT_API_KEY="your-moonshot-key"
export ANTHROPIC_API_KEY="your-anthropic-key"

2. Start Plano

cd demos/llm_routing/openclaw_routing
planoai up --service plano --foreground

3. Set Up OpenClaw

Install OpenClaw (requires Node >= 22):

npm install -g openclaw@latest

Install the gateway daemon and connect your messaging channels:

openclaw onboard --install-daemon

This installs the gateway as a background service (launchd on macOS, systemd on Linux). To connect messaging channels like WhatsApp or Telegram, see the OpenClaw channel setup docs.

Run openclaw doctor to verify everything is working.

4. Point OpenClaw at Plano

During the OpenClaw onboarding wizard, when prompted to choose an LLM provider:

1. Select Custom OpenAI-compatible as the provider
2. Set the base URL to http://127.0.0.1:12000/v1
3. Enter any value for the API key (e.g. none) — Plano handles auth to the actual providers
4. Set the context window to at least 128000 tokens

This registers Plano as OpenClaw's LLM backend. All requests go through Plano on port 12000, which routes them to Kimi K2.5 or Claude based on the prompt content.

If you've already onboarded, re-run the wizard to update the provider:

openclaw onboard --install-daemon

5. Test Routing Through OpenClaw

Send messages through any connected channel (WhatsApp, Telegram, Slack, etc.) and watch routing decisions in a separate terminal:

planoai logs --service plano | grep MODEL_RESOLUTION

Try these messages to see routing in action:

#	Message (via your messaging channel)	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 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:

bash test_routing.sh

Monitoring

Routing Decisions

Watch Plano logs for model selection:

docker logs plano 2>&1 | grep MODEL_RESOLUTION

Jaeger Tracing (Optional)

To visualize full request traces and routing decisions, start Jaeger:

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 to see traces for 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

planoai down