# Build a WeCom Intelligent Bot AI Bot with OpenAI (ChatGPT models)

Source: https://langbot.app/docs/en/recipes/openai/wecombot

Step-by-step guide to running a OpenAI (ChatGPT models)-powered AI bot on WeCom Intelligent Bot with LangBot: model setup, bot credentials, networking, testing and troubleshooting.

This recipe puts a OpenAI (ChatGPT models) model behind a WeCom Intelligent Bot bot. LangBot receives messages from WeCom Intelligent Bot, 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](https://space.langbot.app/cloud) (no server needed) or a [self-hosted deployment](https://langbot.app/docs/en/deploy/langbot/docker.md) with Docker or `uvx langbot`.
- A OpenAI (ChatGPT models) account or server you can call from LangBot.
- A WeCom intelligent bot in **long connection** API mode: its **BotID** and long-connection **Secret**. One-click setup can fill both.

## Networking

Long connection mode needs no public callback URL. The alternative callback-URL mode does, and the two modes cannot be used at the same time.

## 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.

| 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 Intelligent Bot bot

1. Create a bot with the **WeCom Intelligent Bot** adapter and use **One-Click Setup**, or create the bot in WeCom, switch its API mode to **long connection** and enter the BotID and Secret.
2. Set the bot's visibility scope so the right people can use it, then save and enable.

## 4. Bind and test

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

Find the bot in the WeCom client, chat with it directly, then add it to a group chat and mention it.

## 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.
- The long-connection Secret differs from the callback Token/EncodingAESKey. Switching the API mode to callback URL invalidates existing long connections.
- 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 Intelligent Bot?

No. Long connection mode needs no public callback URL. The alternative callback-URL mode does, and the two modes cannot be used at the same time.

### Does the WeCom Intelligent Bot bot support streaming replies?

Yes. Enable streaming on the bot; the AI back end must also stream. See the [platform feature matrix](https://langbot.app/docs/en/insight/platform-features.md) for details.

### 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 Intelligent Bot setup guide](https://langbot.app/docs/en/usage/platforms/wecom/wecombot.md)
- [Model configuration](https://langbot.app/docs/en/usage/models/readme.md)
- Other AI back ends on WeCom Intelligent Bot: [Dify](https://langbot.app/docs/en/recipes/dify/wecombot.md) · [n8n](https://langbot.app/docs/en/recipes/n8n/wecombot.md) · [Coze](https://langbot.app/docs/en/recipes/coze/wecombot.md) · [Langflow](https://langbot.app/docs/en/recipes/langflow/wecombot.md) · [DeepSeek](https://langbot.app/docs/en/recipes/deepseek/wecombot.md)
- OpenAI (ChatGPT models) on other platforms: [Telegram](https://langbot.app/docs/en/recipes/openai/telegram.md) · [Discord](https://langbot.app/docs/en/recipes/openai/discord.md) · [Slack](https://langbot.app/docs/en/recipes/openai/slack.md) · [Lark (Feishu)](https://langbot.app/docs/en/recipes/openai/lark.md) · [DingTalk](https://langbot.app/docs/en/recipes/openai/dingtalk.md) · [WeCom (internal app)](https://langbot.app/docs/en/recipes/openai/wecom.md)