Tiger Slack

Conversational Memory for AI Slack Bots

Tiger Slack gives Slack-native AI assistants the ability to understand and engage with your team's conversations naturally. By providing real-time access to Slack message history and context through an MCP server, it enables AI bots to respond to questions with the same contextual awareness that humans have - understanding references to past discussions, ongoing projects, and team dynamics.

Tiger Slack is the superpower behind Eon, TigerData's Slack-native AI assistant.

Ready to get started? Jump to the quick start.

Overview

Built for teams who want their AI assistants to:

• Understand conversational context and references to past discussions
• Answer questions about project history and decisions
• Know who's involved in different topics and threads
• Follow ongoing conversations and provide relevant input
• Build conversational memory across all team channels

Key Components

Ingest Service (ingest/)

The ingest service is a Python app that:

• Connects to Slack via WebSocket (Socket Mode)
• Captures messages, reactions, user changes, and channel updates in real-time
• Runs scheduled jobs for user/channel synchronization

The ingest service can process a Slack historical export into the database.

See the ingest README.md for more details.

MCP Server (mcp/)

A Streamable HTTP Model Context Protocol server provides:

• AI-accessible APIs for Slack data analysis
• Channel and user browsing with intelligent filtering
• Conversation retrieval with full threading context
• Message permalink generation

See the mcp README.md for more details.

###️TimescaleDB Database Tiger Slack uses a TimescaleDB database to store the Slack content.

• High-throughput message ingestion with 7-day chunk partitioning
• Efficient time-based queries and analytics
• Automatic compression after 45 days (5-10x space savings)
• Channel-based segmentation for optimal query performance
• Sparse indexes (bloom filters, minmax) for fast filtering
• Chunk skipping for accelerated thread queries
• Full-text search capabilities across message content

Architecture

graph TB
    %% External Systems
    S[Slack Workspace]
    C[Claude/LLM Client]
    
    %% Core Components  
    I[Ingest Service<br/>Python]
    D[TimescaleDB<br/>Time-series Database]
    M[MCP/HTTP Server<br/>TypeScript]
    
    %% Data Flow - Ingestion
    S -->|WebSocket Events| I
    S -->|Historical Export| I
    I -->|Store Events| D
    
    %% Data Flow - Analysis
    C -->|MCP Protocol| M
    M -->|SQL Queries| D
    M -->|Structured Data| C

Quick Start

Start by cloning the repo.

git clone --recurse-submodules git@github.com:timescale/tiger-slack.git
cd tiger-slack

The ingest service requires an app configured in Slack with proper privileges and tokens. You can run a script to automate this setup, or you can do it manually.

Automated Setup

Execute the following script to guide you through the setup process:

./setup-tiger-slack.sh

Manual Setup

Create a Slack App

1. Edit the slack-app-manifest.json file to have the names and descriptions you want to use.
2. Create a new Slack app
3. Choose to create an app from a manifest.
4. Pick your workspace and click Next.
5. Paste that manifest configuration in the input field provided and click Next.
6. Review and verify that the configuration you entered matches the summary and click Create.
7. Navigate to: Basic Information → App-Level Tokens
8. Click 'Generate Token and Scopes' → Add 'connections:write' scope → Generate
9. Save your SLACK_APP_TOKEN (It starts with xapp-).
10. Navigate to: Install App → Click 'Install to [Workspace]'
11. After installation, save t

... View full README on GitHub