Build a WeCom (internal app) AI Bot with OpenAI (ChatGPT models)

This recipe puts a OpenAI (ChatGPT models) model behind a WeCom (internal app) bot. LangBot receives messages from WeCom (internal app), runs them through a pipeline, and sends the answer back, so you do not write any platform code.

Use OpenAI's GPT models with LangBot's built-in Agent, with optional knowledge bases, MCP tools, Skills and a code sandbox.

What you need

• A running LangBot instance: LangBot Cloud (no server needed) or a self-hosted deployment with Docker or uvx langbot.
• A OpenAI (ChatGPT models) account or server you can call from LangBot.
• A WeCom self-built app: Corporate ID, the app Secret, the Contact Sync Secret, and the callback Token and EncodingAESKey.

Networking

WeCom delivers messages to a callback URL, so LangBot needs a public domain with HTTPS (see reverse proxy and SSL). LangBot Cloud includes a ready-to-use public URL.

1. Add the OpenAI (ChatGPT models) model

1. Create an API key in the OpenAI console.
2. In LangBot, open Models, add an LLM model with the provider set to OpenAI and request URL https://api.openai.com/v1, then paste the API key and the model ID.
3. Enable Tool Use if the model supports function calling; MCP tools, Skills and the sandbox need it.
4. Use a current model ID from OpenAI's model list. Any OpenAI-compatible endpoint works the same way with its own request URL.

2. Create a pipeline

Open Pipelines in the LangBot WebUI, create a pipeline (or edit the default one) and set AI as follows.

3. Create the WeCom (internal app) bot

1. Create a bot with the WeCom adapter, enter the Corporate ID, app Secret and Contact Sync Secret, enable it and copy the webhook callback URL.
2. In the WeCom admin console, open Receive Messages → Set API Reception, paste the URL, generate the Token and EncodingAESKey, save them in LangBot first and then in WeCom. Add LangBot's outbound IP to Corporate Trusted IP.

4. Bind and test

Open the bot, select the pipeline you created, save and make sure the bot is enabled.

Open the app in the WeCom client and send it a message.

Troubleshooting

• Region or network errors: LangBot must be able to reach the request URL. Behind a restricted network, use a compatible gateway you can reach.
• request URL failed when saving the callback: the URL is not publicly reachable over HTTPS, or the Token/EncodingAESKey in LangBot does not match.
• Replies fail to send: add LangBot's outbound IP to the corporate trusted IP list.
• 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 (internal app)?

Yes, or use LangBot Cloud. WeCom delivers messages to a callback URL, so LangBot needs a public domain with HTTPS (see reverse proxy and SSL). LangBot Cloud includes a ready-to-use public URL.

Does the WeCom (internal app) 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 OpenAI (ChatGPT models) 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 (internal app) setup guide
• Model configuration
• Other AI back ends on WeCom (internal app): Dify · n8n · Coze · Langflow · DeepSeek
• OpenAI (ChatGPT models) on other platforms: Telegram · Discord · Slack · Lark (Feishu) · DingTalk · WeCom Intelligent Bot