Build a WeCom (internal app) AI Bot with Ollama

This recipe puts a Ollama 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.

Run open models locally with Ollama and serve them through LangBot's built-in Agent, so conversations never leave your machines.

What you need

• A running LangBot instance: LangBot Cloud (no server needed) or a self-hosted deployment with Docker or uvx langbot.
• A Ollama 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 Ollama model

1. Install Ollama and pull a model (for example ollama pull followed by the model name).
2. In LangBot, open Models and add an LLM model with the provider set to Ollama and the request URL pointing at your Ollama server (by default port 11434).
3. If LangBot runs in Docker, localhost is the container itself: use the host's address (for example host.docker.internal) instead.

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

• Small local models may not support tool calling well; leave Tool Use off for models that do not support it.
• 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 Ollama 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
• Ollama on other platforms: Telegram · Discord · Slack · Lark (Feishu) · DingTalk · WeCom Intelligent Bot