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title Integrate a slack-native AI agent
excerpt Unify company knowledge with slack-native AI agents
products
cloud
keywords
ai
vector
pgvector
TigerData vector
pgvectorizer
tags
ai
vector
pgvectorizer

Integrate a slack-native AI agent

import PrereqAccount from "versionContent/_partials/_prereqs-cloud-project-and-self.mdx";

$AGENTS_LONG is a Slack-native AI agent that you use to unify the knowledge in your company. This includes your Slack history, docs, GitHub repositories, Salesforce and so on. You use your $AGENTS_SHORT to get instant answers for real business, technical, and operations questions in your Slack channels.

Query Tiger Agent

$AGENTS_LONG can handle concurrent conversations with enterprise-grade reliability. They have the following features:

  • Durable and atomic event handling: $PG-backed event claiming ensures exactly-once processing, even under high concurrency and failure conditions
  • Bounded concurrency: fixed worker pools prevent resource exhaustion while maintaining predictable performance under load
  • Immediate event processing: $AGENTS_LONG provide real-time responsiveness. Events are processed within milliseconds of arrival rather than waiting for polling cycles
  • Resilient retry logic: automatic retry with visibility thresholds, plus stuck or expired event cleanup
  • Horizontal scalability: run multiple $AGENTS_SHORT instances simultaneously with coordinated work distribution across all instances
  • AI-Powered Responses: use the AI model of your choice, you can also integrate with MCP servers
  • Extensible architecture: zero code integration for basic agents. For more specialized use cases, easily customize your agent using Jinja templates
  • Complete observability: detailed tracing of event flow, worker activity, and database operations with full Logfire instrumentation

This page shows you how to install the $AGENTS_CLI, connect to the $COMPANY MCP server, and customize prompts for your specific needs.

Prerequisites

Create a Slack app

Before installing $AGENTS_LONG, you need to create a Slack app that the $AGENTS_SHORT will connect to. This app provides the security tokens for Slack integration with your $AGENTS_SHORT:

  1. Create a manifest for your Slack App

    1. In a temporary directory, download the $AGENTS_SHORT Slack manifest template:

      curl -O https://raw.githubusercontent.com/timescale/tiger-agents-for-work/main/slack-manifest.json

    2. Edit slack-manifest.json and customize your name and description of your Slack App. For example:

      "display_information": {
  "name": "Tiger Agent",
  "description": "Tiger AI Agent helps you easily access your business information, and tune your Tiger Cloud services",
  "background_color": "#000000"
},
"features": {
  "bot_user": {
    "display_name": "Tiger Agent",
    "always_online": true
  }
},

    3. Copy the contents of slack-manifest.json to the clipboard:

      cat slack-manifest.json| pbcopy

  2. Create the Slack app

    1. Go to api.slack.com/apps.
    2. Click Create New App.
    3. Select From a manifest.
    4. Choose your workspace, then click Next.
    5. Paste the contents of slack-manifest.json and click Next.
    6. Click Create.

  3. Generate an app-level token

    1. In your app settings, go to Basic Information.
    2. Scroll to App-Level Tokens.
    3. Click Generate Token and Scopes.
    4. Add a Token Name, then click Add Scope, add connections:write then click Generate.
    5. Copy the xapp-* token locally and click Done.

  4. Install your app to a Slack workspace

    1. In the sidebar, under Settings, click Install App.
    2. Click Install to <workspace name>, then click Allow.
    3. Copy the xoxb- Bot User OAuth Token locally.

You have created a Slack app and obtained the necessary tokens for $AGENTS_SHORT integration.

Install and configure your $AGENTS_SHORT instance

$AGENTS_LONG are a production-ready library and CLI written in Python that you use to create Slack-native AI agents. This section shows you how to configure a $AGENTS_SHORT to connect to your Slack app, and give it access to your data and analytics stored in $CLOUD_LONG.

  1. Create a project directory

    mkdir my-tiger-agent
cd my-tiger-agent

  2. Create a $AGENTS_SHORT environment with your Slack, AI Assistant, and database configuration

    1. Download .env.sample to a local .env file:
    curl -L -o .env https://raw.githubusercontent.com/timescale/tiger-agent/refs/heads/main/.env.sample
    1. In .env, add your Slack tokens and Anthropic API key:
    # Slack tokens (from the Slack app you created)
SLACK_APP_TOKEN=xapp-your-app-token
SLACK_BOT_TOKEN=xoxb-your-bot-token

# Anthropic API key
ANTHROPIC_API_KEY=sk-ant-your-api-key

# Optional: Logfire token for enhanced logging
LOGFIRE_TOKEN=your-logfire-token
    1. Add the connection details for the $SERVICE_LONG you are using for this $AGENTS_SHORT:
    PGHOST=<host>
PGDATABASE=tsdb
PGPORT=<port>
PGUSER=tsdbadmin
PGPASSWORD=<password>
    1. Save and close .env.

  3. Add the default $AGENTS_SHORT prompts to your project

    mkdir prompts
curl -L -o prompts/system_prompt.md https://raw.githubusercontent.com/timescale/tiger-agent/refs/heads/main/prompts/system_prompt.md
curl -L -o prompts/user_prompt.md https://raw.githubusercontent.com/timescale/tiger-agent/refs/heads/main/prompts/user_prompt.md

  4. Install $AGENTS_LONG to manage and run your AI-powered Slack bots

    1. Install the $AGENTS_CLI using uv.

      uv tool install --from git+https://github.com/timescale/tiger-agents-for-work.git tiger-agent

      tiger-agent is installed in ~/.local/bin/tiger-agent. If necessary, add this folder to your PATH.

    2. Verify the installation.

      tiger-agent --help

      You see the $AGENTS_CLI help output with the available commands and options.

  5. Connect your $AGENTS_SHORT with Slack

    1. Run your $AGENTS_SHORT:

      tiger-agent run --prompts prompts/  --env .env

      If you open the explorer in $CONSOLE, you can see the tables used by your $AGENTS_SHORT.

    2. In Slack, open a public channel app and ask $AGENTS_SHORT a couple of questions. You see the response in your public channel and log messages in the terminal.

    Query Tiger Agent

Add information from MCP servers to your $AGENTS_SHORT

To increase the amount of specialized information your AI Assistant can use, you can add MCP servers supplying data your users need. For example, to add the $COMPANY MCP server to your $AGENTS_SHORT:

  1. Copy the example mcp_config.json to your project

    In my-tiger-agent, run the following command:

    ```bash
 curl -L -o mcp_config.json https://raw.githubusercontent.com/timescale/tiger-agent/refs/heads/main/examples/mcp_config.json
 ```

  2. Configure your $AGENTS_SHORT to connect to the most useful MCP servers for your organization

    For example, to add the $COMPANY documentation MCP server to your $AGENTS_SHORT, update the docs entry to the following:

    "docs": {
  "tool_prefix": "docs",
  "url": "https://mcp.tigerdata.com/docs",
  "allow_sampling": false
},

    To avoid errors, delete all entries in mcp_config.json with invalid URLs. For example the github entry with http://github-mcp-server/mcp.

  3. Restart your $AGENTS_SHORT

    tiger-agent run --prompts prompts/ --mcp-config mcp_config.json

You have configured your $AGENTS_SHORT to connect to $MCP_SHORT. For more information, see MCP Server Configuration.

Customize prompts for personalization

$AGENTS_LONG uses Jinja2 templates for dynamic, context-aware prompt generation. This system allows for sophisticated prompts that adapt to conversation context, user preferences, and event metadata. $AGENTS_LONG uses the following templates:

  • system_prompt.md: defines the AI Assistant's role, capabilities, and behavior patterns. This template sets the foundation for the way your $AGENTS_SHORT will respond and interact.
  • user_prompt.md: formats the user's request with relevant context, providing the AI Assistant with the information necessary to generate an appropriate response.

To change the way your $AGENTS_SHORTs interact with users in your Slack app:

  1. Update the prompt

    For example, in prompts/system_prompt.md, add another item in the Response Protocol section to fine tune the behavior of your $AGENTS_SHORTs. For example:

    5. Be snarky but vaguely amusing

  2. Test your configuration

    Run $AGENTS_SHORT with your custom prompt:

    tiger-agent run --mcp-config mcp_config.json --prompts prompts/

For more information, see Prompt tempates.

Advanced configuration options

For additional customization, you can modify the following $AGENTS_SHORT parameters:

  • --model: change AI model (default: anthropic:claude-sonnet-4-20250514)
  • --num-workers: adjust concurrent workers (default: 5)
  • --max-attempts: set retry attempts per event (default: 3)

Example with custom settings:

tiger-agent run \
  --model claude-3-5-sonnet-latest \
  --mcp-config mcp_config.json \
  --prompts prompts/ \
  --num-workers 10 \
  --max-attempts 5

Your $AGENTS_SHORTs are now configured with $COMPANY MCP server access and personalized prompts.