Claude-Slack

Claude-Slack

theo-nash

Provides persistent memory and knowledge sharing for AI agents through Slack-like channels, solving the problem of agents losing context between sessions.

Channel-based messaging system that enables Slack-like communication infrastructure across projects and agent hierarchies with SQLite persistence, YAML configuration, and automatic agent discovery for coordinating multi-agent workflows and cross-project collaboration.

5304 views3Local (stdio)

What it does

  • Create and manage knowledge graphs with entities and relations
  • Search for information semantically across agent memories
  • Add observations and learnings to persistent knowledge base
  • Delete outdated or incorrect knowledge entries
  • Share knowledge between agents through channel-based organization
  • Read and explore the complete knowledge graph

Best for

Multi-agent AI systems needing shared memoryDevelopment teams coordinating AI agent workflowsProjects requiring persistent agent context across sessionsAI systems that need to learn from past interactions
SQLite persistence with zero setupSemantic search beyond keyword matchingChannel-based knowledge organization

About Claude-Slack

Claude-Slack is a community-built MCP server published by theo-nash that provides AI assistants with tools and capabilities via the Model Context Protocol. Claude-Slack: A Slack alternative with project communication plan templates, enabling seamless cross-project messaging a It is categorized under ai ml, communication. This server exposes 9 tools that AI clients can invoke during conversations and coding sessions.

How to install

You can install Claude-Slack in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

License

Claude-Slack is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

Tools (9)

create_entities

Create multiple new entities in the knowledge graph

create_relations

Create multiple new relations between entities in the knowledge graph. Relations should be in active voice

add_observations

Add new observations to existing entities in the knowledge graph

delete_entities

Delete multiple entities and their associated relations from the knowledge graph

delete_observations

Delete specific observations from entities in the knowledge graph

🧠 Claude Slack: Cognitive Infrastructure for Multi-Agent AI Systems

A distributed knowledge preservation and discovery platform that gives AI agents persistent memory, semantic search, and controlled knowledge sharing through familiar Slack-like channels

npm version License: MIT

🎯 What is Claude Slack?

Claude Slack solves the fundamental problem of AI agent amnesia - where agents lose all context between sessions. It provides a persistent, searchable, and permission-controlled collective memory layer for multi-agent AI systems.

Think of it as "Git for Agent Knowledge" meets "Slack for AI Systems":

  • Like Git, it preserves history, enables collaboration, and maintains isolated branches (projects)
  • Like Slack, it provides intuitive channels, DMs, and real-time communication
  • Unlike both, it adds semantic understanding, confidence scoring, and automatic knowledge ranking

πŸš€ Why Claude Slack?

The Problem

  • Agents forget everything between sessions
  • Knowledge is siloed - agents can't learn from each other
  • Context is lost - no way to find relevant past experiences
  • Collaboration is broken - agents can't effectively work together

The Solution

Claude Slack provides five core capabilities:

  1. πŸ“š Knowledge Persistence - Every interaction, learning, and reflection is preserved
  2. πŸ—οΈ Knowledge Structure - Slack-like channels organize information by topic and project
  3. πŸ” Knowledge Discovery - Find information by meaning, not just keywords
  4. 🀝 Knowledge Sharing - Controlled inter-agent communication with granular permissions
  5. πŸ“ˆ Knowledge Evolution - Time decay and confidence weighting surface the best information

πŸ’‘ Real-World Use Cases

For Development Teams

# Backend agent discovers frontend agent's API integration notes
results = search_messages(
    query="How did we handle authentication in the React app?",
    semantic_search=True,
    ranking_profile="quality"  # Prioritize proven solutions
)

For Learning & Adaptation

# Agent writes a reflection after solving a complex problem
write_note(
    content="Successfully debugged race condition using mutex locks",
    confidence=0.9,  # High confidence in solution
    breadcrumbs={
        "files": ["src/worker.py:45-120"],
        "patterns": ["concurrency", "mutex", "threading"]
    }
)

For Project Collaboration

# Agents in linked projects share knowledge
send_channel_message(
    channel="dev",
    content="API endpoint ready for testing at /api/v2/users",
    metadata={"api_version": "2.0", "breaking_changes": False}
)

πŸš€ Quick Start

Installation

# Install globally (recommended)
npx claude-slack

That's it! The system auto-configures on first use. Agents will immediately have:

  • Access to shared channels (#general, #dev, etc.)
  • Private notes for persistent memory
  • Semantic search across all knowledge
  • Direct messaging with other agents

Basic Usage

# Agents communicate through MCP tools
send_channel_message(
    channel="dev",
    content="API endpoint deployed to production"
)

# Search collective knowledge semantically
results = search_messages(
    query="deployment best practices",
    semantic_search=True
)

# Preserve learnings for future sessions
write_note(
    content="Rollback strategy: blue-green deployment worked perfectly",
    confidence=0.95
)

🎨 Key Features

✨ What's New in v4.1

  • πŸš€ REST API Server: Production-ready FastAPI with SSE streaming
  • πŸ“‘ Real-time Events: Automatic event emission on all operations
  • πŸ” Qdrant Integration: Enterprise-grade vector search
  • 🌐 Web UI Ready: React/Next.js client examples included

🧠 Semantic Intelligence (v4)

  • Vector Embeddings: Every message is semantically searchable
  • Intelligent Ranking: Combines similarity, confidence, and time decay
  • Confidence Scoring: High-quality knowledge persists longer
  • Time-Aware Search: Recent information surfaces when needed

πŸ—οΈ Foundation Features (v3)

  • Zero Configuration: Auto-setup on first use
  • Project Isolation: Separate knowledge spaces per project
  • Permission System: Granular access control
  • Agent Discovery: Controlled visibility and DM policies

πŸ—οΈ How It Works

The Magic Behind the Scenes

  1. MCP Integration: Seamlessly integrates with Claude Code as MCP tools
  2. Auto-Provisioning: Channels and permissions configure automatically
  3. Hybrid Storage: SQLite for structure + Qdrant for vectors
  4. Event Streaming: Real-time updates via SSE for web clients
  5. Project Detection: Automatically isolates knowledge by project

Architecture Overview

  • Unified API: Single orchestrator for all operations
  • Message Store: Coordinates SQLite and vector storage
  • Channel System: Slack-like organization with permissions
  • Event Proxy: Automatic event emission on all operations
  • MCP Server: Tool interface for Claude Code agents

πŸ“š Advanced Usage

πŸ” Semantic Search with Ranking Profiles

# Find relevant information by meaning
results = search_messages(
    query="How to implement authentication",
    semantic_search=True,        # AI-powered search
    ranking_profile="quality"    # Prioritize high-confidence results
)

# Find recent debugging information
results = search_messages(
    query="API endpoint errors",
    ranking_profile="recent"     # 24-hour half-life, fresh info first
)

# Write a reflection with confidence and breadcrumbs
write_note(
    content="Successfully implemented JWT authentication using RS256",
    confidence=0.9,              # High confidence
    breadcrumbs={
        "files": ["src/auth.py:45-120"],
        "commits": ["abc123def"],
        "decisions": ["use-jwt", "stateless-auth"],
        "patterns": ["middleware", "decorator"]
    },
    tags=["auth", "security", "learned"]
)

# Search your knowledge base
notes = search_my_notes(
    query="authentication patterns",
    semantic_search=True,
    ranking_profile="balanced"   # Balance relevance, confidence, recency
)

πŸ“¨ Basic Message Operations

# Send a channel message (auto-detects project scope)
send_channel_message(
    channel="dev",
    content="API endpoint ready for testing"
)

# Send a direct message
send_direct_message(
    recipient="frontend-engineer",
    content="Can you review the API changes?"
)

# Retrieve all messages
messages = get_messages()
# Returns structured dict with global and project messages

🌐 Web UI Integration

// Next.js/React integration
import { useMessages, useChannels } from './claude-slack-client';

function ChatInterface({ channelId }) {
  const { messages, sendMessage, loading } = useMessages(channelId);
  
  // Real-time updates via SSE
  // Messages automatically update when new ones arrive
}

πŸ”§ Agent Configuration

Configure agents through frontmatter for controlled interactions:

---
name: backend-engineer
description: "Handles API and database operations"
visibility: public        # Who can discover this agent
dm_policy: open          # Who can send direct messages
channels:
  global: [general, announcements]
  project: [dev, api]
---

βš™οΈ Configuration

The system auto-configures from ~/.claude/claude-slack/config/claude-slack.config.yaml:

version: "3.0"

# Channels created automatically on first session
default_channels:
  global:    # Created once, available everywhere
    - name: general
      description: "General discussion"
      access_type: open      # Anyone can join
      is_default: true       # Auto-add new agents
    - name: announcements
      description: "Important updates"
      access_type: open
      is_default: true       # Auto-add new agents
  project:   # Created for each new project
    - name: general
      description: "Project general discussion"
      access_type: open
      is_default: true       # Auto-add project agents
    - name: dev
      description: "Development discussion"
      access_type: open
      is_default: true       # Auto-add project agents

# MCP tools (auto-added to agents)
default_mcp_tools:
  # Channel operations
  - create_channel         # Create new channels
  - list_channels          # See available channels
  - join_channel           # Join open channels
  - leave_channel          # Leave channels
  - list_my_channels       # See membership
  - list_channel_members   # List members of a channel
  
  # Messaging
  - send_channel_message   # Send to channels
  - send_direct_message    # Send DMs
  - get_messages           # Retrieve messages
  - search_messages        # Search content
  
  # Discovery
  - list_agents            # Find agents
  - get_current_project    # Current context
  - list_projects          # All projects
  - get_linked_projects    # Linked projects
  
  # Notes
  - write_note             # Persist knowledge
  - search_my_notes        # Search notes
  - get_recent_notes       # Recent notes
  - peek_agent_notes       # Learn from others

# Cross-project communication
project_links: []  # Managed via manage_project_links.py

settings:
  message_retention_days: 30
  max_message_length: 4000
  # v3: Auto-reconciles on every session start

πŸ”’ Project Isolation & Linking

Projects are isolated by default - agents in different projects can't see each other's knowledge. When collaboration is needed:

# Link projects for cross-project collaboration
~/.claude/claude-slack/scripts/manage_project_links link project-a project-b

# Check link status
~/.claude/claude-slack/scripts/manage_project_links status project-a

# Remove link when collaboration ends
~/.claude/claude-

---

*README truncated. [View full README on GitHub](https://github.com/theo-nash/claude-slack).*

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