Build a WeCom Customer Service AI Bot with Gemini
Step-by-step guide to running a Gemini-powered AI bot on WeCom Customer Service with LangBot: model setup, bot credentials, networking, testing and troubleshooting.
This recipe puts a Gemini model behind a WeCom Customer Service bot. LangBot receives messages from WeCom Customer Service, runs them through a pipeline, and sends the answer back, so you do not write any platform code.
Use Google's Gemini models with LangBot's built-in Agent, including image input when the model supports vision.
What you need
- A running LangBot instance: LangBot Cloud (no server needed) or a self-hosted deployment with Docker or
uvx langbot. - A Gemini account or server you can call from LangBot.
- WeCom Customer Service (WeChat Kefu) with API access: Corporate ID, Secret, and a callback Token and EncodingAESKey.
Networking
Customer Service delivers events to a callback URL, so LangBot needs a public HTTPS address. LangBot Cloud includes one.
1. Add the Gemini model
- Create an API key in the Google Gemini console.
- In LangBot, open Models, add an LLM model with the provider set to Google Gemini, then paste the API key and the model ID.
- Enable Tool Use if the model supports function calling; MCP tools, Skills and the sandbox need it.
- Create the key in Google AI Studio. Enable Vision on the model if you want the bot to read images.
2. Create a pipeline
Open Pipelines in the LangBot WebUI, create a pipeline (or edit the default one) and set AI as follows.
| Field | Value |
|---|---|
| Runner | Local Agent (built-in Agent) |
| Model | The model you just added. You can add fallback models. |
| Prompt | Instructions: who the bot is, tone, what it must not do. |
| Knowledge Bases | Optional. Attach a knowledge base so answers come from your documents. |
3. Create the WeCom Customer Service bot
- In WeCom Customer Service Developer Settings, generate the Token and EncodingAESKey and enter them with the Corporate ID in a bot using the WeCom Customer Service adapter.
- Enable the bot, paste its webhook callback URL into the admin console, then fill in the Secret and restart the bot. Generate the customer service link or QR code users will open.
4. Bind and test
Open the bot, select the pipeline you created, save and make sure the bot is enabled.
Open the customer service link or scan the QR code in WeChat and send a message.
Troubleshooting
- Images are ignored: enable Vision on the model and check that the platform adapter forwards images.
- Welcome messages can only be sent when WeChat supplies a
welcome_code, and only once before it expires. - Check Monitoring in the WebUI: every message, pipeline run and error is recorded there.
FAQ
Do I need a server with a public IP for WeCom Customer Service?
Yes, or use LangBot Cloud. Customer Service delivers events to a callback URL, so LangBot needs a public HTTPS address. LangBot Cloud includes one.
Does the WeCom Customer Service bot support streaming replies?
Check the platform feature matrix for what this platform supports. Replies are delivered once the AI back end has finished.
Can the same Gemini setup serve other platforms too?
Yes. A pipeline can be bound to many bots, so one configuration can answer on several platforms at once.
Related guides
- Full WeCom Customer Service setup guide
- Model configuration
- Other AI back ends on WeCom Customer Service: Dify · n8n · Coze · Langflow · DeepSeek
- Gemini on other platforms: Telegram · Discord · Slack · Lark (Feishu) · DingTalk · WeCom (internal app)
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