Tiger Slack

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](https://github.com/timescale/tiger-eon), TigerData's Slack-native AI assistant.

Ready to get started? Jump to the [quick start](#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](/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](/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 compressio