AI features in a WeChat Mini Program can feel like a magic trick — until you try to ship them at scale. Users expect instant responses, product wants “smarter” behavior every week, and you need to keep costs under control. The winning approach is to make the Mini Program a polished interface while OpenClaw does the heavy lifting on a server.
When that server runs on Tencent Cloud Lighthouse, you get the boring reliability you want: simple operations, high performance, and cost-effective uptime for a 24/7 agent.
Most Mini Programs don’t need a dozen AI features. They need a few that are trustworthy:
OpenClaw is ideal here because it can combine model calls with tools (retrieval, validation, formatting), instead of relying on “just a chat completion.”
Start by deploying the baseline OpenClaw environment on Lighthouse.
With that in place, your Mini Program can call a stable API and your AI functions can evolve without client rewrites.
A common mistake is creating an endpoint per feature (/summarize, /qa, /extract). It grows fast and becomes hard to govern.
Instead, expose one endpoint with an intent field:
intent: "qa"intent: "summarize"intent: "extract"intent: "draft"OpenClaw routes intent → prompt policy + tools + model profile.
{
"intent": "extract",
"traceId": "1710000123456",
"input": {
"text": "My order is #A1029, delivery to 21 King St tomorrow morning.",
"schema": {"orderId":"string","address":"string","time":"string"}
}
}
Now you can add new intents without exploding your API surface.
Your Mini Program should do three things well:
Example Mini Program request:
wx.request({
url: "https://YOUR_LIGHTHOUSE_DOMAIN/v1/mini/ai",
method: "POST",
header: {
"Content-Type": "application/json",
"Authorization": "Bearer " + token
},
data: {
intent: "qa",
traceId: Date.now().toString(),
input: {
question: userQuestion,
locale: "en",
topK: 5
}
}
})
Mini Program users are impatient. Make speed a feature.
Tactics that work well with OpenClaw:
Even small changes in prompt length can cut token usage significantly.
AI features fail in production when guardrails are added late.
traceIdYour OpenClaw backend is the correct place for these policies.
The most useful debugging signal is correlation.
traceIdtraceIdWhen a user says “the bot misunderstood me,” you can replay the exact intent and fix the policy.
Here are two functions that consistently succeed in Mini Programs because they’re structured, testable, and easy to measure.
Instead of asking the model to “know everything,” you retrieve a few relevant snippets from your own content and ask the model to answer only from that context.
Extraction becomes reliable when you constrain the output contract.
{
"orderId": "A1029",
"address": "21 King St",
"deliveryTime": "tomorrow morning",
"confidence": 0.86
}
OpenClaw can enforce schema-like behavior via policy prompts, and your Mini Program can render the result directly into UI fields.
Once users like a feature, they’ll spam it.
These are small implementation details that prevent most “AI feature” incidents.
The fastest path is to stand up the backend first and ship a single AI function end-to-end.
/v1/mini/ai endpoint and start with intent: qa.Once the first function is stable, you’ll find it surprisingly easy to add summaries, extraction, and drafting — because your architecture already treats AI as a policy-driven service.