AI Slack 机器人的对话记忆

Tiger Slack 赋予 Slack 原生 AI 助手自然理解并参与团队对话的能力。通过 MCP 服务器实时访问 Slack 消息历史和上下文，使 AI 机器人能够以与人类相同的上下文意识回应问题——理解过去讨论、正在进行的项目和团队动态的引用。

Tiger Slack 是 Eon 背后的超级力量，Eon 是 TigerData 原生的 Slack 原生 AI 助手。

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概述

为希望AI助手能够：

• 理解对话语境及对过去讨论的引用
• 回答有关项目历史和决策的问题
• **了解参与不同话题和讨论的人员
• 关注正在进行的对话并提供相关意见
• 在所有团队频道建立对话记忆

关键组成部分

Ingest Service （ingest/）

该导入服务是一个Python应用，它：

• 通过WebSocket连接到Slack（套接字模式）
• 实时捕捉消息、反应、用户变更和频道更新

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 ServicePython]
    D[TimescaleDBTime-series Database]
    M[MCP/HTTP ServerTypeScript]
    
    %% 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 [email protected]:timescale/tiger-slack.git
cd tiger-slack

The ingest service requires an app configured in Slack with proper privileges and tokens.