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Ollama

Build a DingTalk AI Bot with Ollama

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

This recipe puts a Ollama model behind a DingTalk bot. LangBot receives messages from DingTalk, 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 DingTalk app robot (Client ID and Client Secret). The one-click setup fills both after you authorize on your phone.

Networking

In Stream Mode LangBot connects out to DingTalk, so no public callback URL is needed.

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.

FieldValue
RunnerLocal Agent (built-in Agent)
ModelThe model you just added. You can add fallback models.
PromptInstructions: who the bot is, tone, what it must not do.
Knowledge BasesOptional. Attach a knowledge base so answers come from your documents.

3. Create the DingTalk bot

  1. Create a bot with the DingTalk adapter and use One-Click Setup, or create the robot manually and enter its credentials.
  2. Use Stream Mode for event delivery. For streaming replies, import LangBot's dingtalk_stream_card.json card template, enable Use Card Replies and paste the Card Template ID.

4. Bind and test

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

Message the robot directly, 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.
  • Replies arrive as plain text instead of streaming: the card template is not imported or Use Card Replies is off.
  • Messages do not arrive: publish the app version and check that Stream Mode is selected under event subscriptions.
  • 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 DingTalk?

No. In Stream Mode LangBot connects out to DingTalk, so no public callback URL is needed.

Does the DingTalk bot support streaming replies?

Yes. Enable streaming on the bot; the AI back end must also stream. See the platform feature matrix 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.

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On this page

What you needNetworking1. Add the Ollama model2. Create a pipeline3. Create the DingTalk bot4. Bind and testTroubleshootingFAQDo I need a server with a public IP for DingTalk?Does the DingTalk bot support streaming replies?Can the same Ollama setup serve other platforms too?Related guides