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Slack Agent

  • 🤖 Slack Agent is an LLM-powered agent built using the LangGraph ReAct Agent workflow and Slack MCP Server.
  • 🌐 Protocol Support: Compatible with A2A protocol for integration with external user clients.
  • 🛡️ Secure by Design: Enforces Slack API token-based RBAC and supports secondary external authentication for strong access control.
  • 🏭 MCP Server: The MCP server is generated by our first-party openapi-mcp-codegen utility, ensuring version/API compatibility and software supply chain integrity.
  • 🔌 MCP Tools: Uses langchain-mcp-adapters to glue the tools from Slack MCP server to LangGraph ReAct Agent Graph.

🏗️ Architecture​

Detailed Sequence Diagram with Agentgateway

System Diagram​

Sequence Diagram​

⚙️ Local Development Setup​

Use this setup to test the agent against Slack.

🔑 Get Slack API Credentials​

  1. Go to Slack API
  2. Create a new Slack app
  3. Configure Bot Token Scopes:
    • app_mentions:read
    • channels:history
    • chat:write
    • users:read
    • groups:history
    • im:history
    • mpim:history
  4. Enable Socket Mode and generate an app-level token
  5. Install the app to your workspace
  6. Save the Bot User OAuth Token

Add to your .env:

SLACK_BOT_TOKEN=<your-bot-token>
SLACK_APP_TOKEN=<your-app-token>
SLACK_SIGNING_SECRET=<your-signing-secret>
SLACK_CLIENT_SECRET=<your-client-secret>
SLACK_TEAM_ID=<your-team-id>

Local Development​

# Navigate to the Slack agent directory
cd ai_platform_engineering/agents/slack

# Run the MCP server in stdio mode
make run-a2a

✨ Features​

  • Channel Management: List, join, leave, and manage channels
  • Message Management: Send, update, delete, and search messages
  • User Management: List users, get user info, and manage status
  • File Management: Upload, list, and delete files
  • Reaction Management: Add and remove reactions from messages
  • Thread Management: Reply to messages and follow threads
  • Webhook Management: Configure and manage webhooks
  • API Integration: Full Slack API coverage through MCP tools

🎯 Example Use Cases​

Ask the agent natural language questions like:

  • Channel Operations: "List all channels in the workspace"
  • Message Management: "Send a message to #general saying 'Hello team!'"
  • User Management: "Show me information about user 'john.doe'"
  • File Operations: "Upload the 'project-plan.pdf' file to #project-updates"
  • Reaction Management: "Add a thumbs up reaction to the latest message in #announcements"
  • Thread Management: "Reply to the message in #bug-reports about the login issue"
  • Search Operations: "Find all messages containing 'deployment' in the last week"