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Gemini

Build a Slack AI Bot with Gemini

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

This recipe puts a Gemini model behind a Slack bot. LangBot receives messages from Slack, runs them through a pipeline, and sends the answer back, so you do not write any platform code.

Use Google's Gemini models with LangBot's built-in Agent, including image input when the model supports vision.

What you need

  • A running LangBot instance: LangBot Cloud (no server needed) or a self-hosted deployment with Docker or uvx langbot.
  • A Gemini account or server you can call from LangBot.
  • A Slack workspace where you can install apps, plus App Configuration Tokens from the Slack apps page for the quick setup assistant.

Networking

With Socket Mode, LangBot connects out to Slack and no public event URL is needed. Webhook mode needs a publicly reachable HTTPS endpoint.

1. Add the Gemini model

  1. Create an API key in the Google Gemini console.
  2. In LangBot, open Models, add an LLM model with the provider set to Google Gemini, 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. Create the key in Google AI Studio. Enable Vision on the model if you want the bot to read images.

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 Slack bot

  1. Create a bot with the Slack adapter and use Quick Slack setup: it creates the app, scopes and event subscriptions for you.
  2. Choose Socket Mode (recommended) and add an App-Level Token (xapp-...), or choose Webhook if LangBot has a public HTTPS address. Then fill the adapter configuration and enable the bot.

4. Bind and test

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

Message the app from its Messages tab, or mention it in a channel it has been added to.

Troubleshooting

  • Images are ignored: enable Vision on the model and check that the platform adapter forwards images.
  • Do not paste the App Configuration Access/Refresh tokens into the App-Level Token field; it needs the xapp- token of the same app.
  • Leave token rotation disabled for the App-Level Token.
  • 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 Slack?

Not necessarily. With Socket Mode, LangBot connects out to Slack and no public event URL is needed. Webhook mode needs a publicly reachable HTTPS endpoint.

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