A minimal starter template for building AI-powered Slack agents with Bolt for Python. Works with the Slack MCP Server to search messages, read channels, send messages, and manage canvases — all from within your agent. Includes one example tool (emoji reactions), giving you a clean foundation to build on.

This repo contains the same app built with three different AI agent frameworks. Pick the one that fits your stack:

All implementations share the same Slack listener layer and the same user experience. The only difference is how the agent is defined and executed under the hood.

The starter agent interacts with users through four entry points:

• App Home — Displays a welcome message with instructions on how to interact.
• Direct Messages — Users message the agent directly. It responds in-thread, maintaining context across follow-ups.
• Channel @mentions — Mention the agent in any channel to get a response without leaving the conversation.
• Assistant Panel — Users click Add Agent in Slack, select the agent, and pick from suggested prompts or type a message.

When connected to the Slack MCP Server, the agent can search messages and files, read channel history and threads, send and schedule messages, and create and update canvases. The template also includes one example tool (emoji reactions). Add your own tools to customize the agent for your use case.

Once the agent is running, there are several ways to interact:

• App Home — Open the agent in Slack and click the Home tab to see a welcome message.
• Direct Messages — Open a DM with the agent. Pick a suggested prompt or type your own message, and the agent will reply in a thread.
• Channel @mentions — Invite the agent to a channel by typing /invite @agent-name, then @mention it with your message. It responds in a thread so the channel stays clean.
• Assistant Panel — Click Add Agent in the top-right corner of Slack, select the agent, and choose a suggested prompt like Write a Message, Summarize, or Brainstorm.

For full setup instructions, pick a framework above and follow the README in that directory.

This repo uses slack-bolt from PyPI.