# Build a Lark (Feishu) AI Bot with Ollama

Source: https://langbot.app/docs/en/recipes/ollama/lark

Step-by-step guide to running a Ollama-powered AI bot on Lark (Feishu) with LangBot: model setup, bot credentials, networking, testing and troubleshooting.

This recipe puts a Ollama model behind a Lark (Feishu) bot. LangBot receives messages from Lark (Feishu), 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](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 Ollama account or server you can call from LangBot.
- A Feishu or Lark custom app (`app_id` and `app_secret`). The one-click setup in LangBot can create and authorize it for you.

## Networking

The default WebSocket long connection needs no public IP. Where long connection is unavailable (for example some Lark international setups), webhook mode requires a public HTTPS address; LangBot Cloud includes one.

## 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.

| 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 Lark (Feishu) bot

1. Create a bot with the **Lark** adapter, select the platform domain (Feishu or Lark) and use **One-Click Setup**, or enter `app_id` and `app_secret` manually.
2. Add the message permissions and event subscriptions from the guide (and the card permissions if you want streaming cards), then publish an app version.

## 4. Bind and test

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

Search for the bot in Feishu/Lark, send it a direct message, then add it to a group and mention it.

## Troubleshooting

- Small local models may not support tool calling well; leave **Tool Use** off for models that do not support it.
- No messages arrive: confirm the app version is published and the event subscription uses the same mode (long connection or webhook) as LangBot.
- 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 Lark (Feishu)?

Not necessarily. The default WebSocket long connection needs no public IP. Where long connection is unavailable (for example some Lark international setups), webhook mode requires a public HTTPS address; LangBot Cloud includes one.

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