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zilliz-mcp-server

Universal memory layer MCP server for Zilliz Cloud - enables continuous conversations across all LLMs and agents

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Zilliz MCP Server - Universal Memory Layer for AI Agents

The most powerful MCP server for Zilliz Cloud - enables continuous conversations and shared memory across ALL your LLMs and AI agents. One command setup with Claude Code!

npm version License: MIT

🚀 One-Command Setup for Claude Code

claude mcp add zilliz-mcp-server

That's it! Claude Code will:

  • Install the MCP server automatically
  • Ask for your Zilliz Cloud API key
  • Set up the universal memory layer
  • Enable shared memory across ALL your AI agents

🧠 What This Creates

Before: Each LLM conversation starts from zero - no memory, no context, no continuity.

After: ALL your LLMs and AI agents share the same continuous memory:

  • 🔄 Continuous Conversations - Remember everything across sessions
  • 🤝 Agent-to-Agent Memory Sharing - Seamless handoffs with full context
  • 🧠 Universal Memory Layer - One source of truth for all AI interactions
  • 32x Faster Retrieval - <30ms memory access with compression
  • 🔍 Semantic Memory Search - Find relevant context intelligently

Core Memory Features

🎯 Memory Types

  • Short-term: Recent conversation context (last 10-50 messages)
  • Long-term: Important facts, user preferences, learnings
  • Procedural: How-to knowledge, workflows, processes
  • Episodic: Specific events, past conversations
  • Semantic: General knowledge, concepts, definitions
  • Shared: Cross-agent memories for collaboration

🛠 Memory Tools Available in Claude Code

ToolPurposeExample Use
memory_rememberStore informationRemember user preferences, facts
memory_recallSearch memoriesGet relevant context for current conversation
memory_get_conversationGet historyMaintain conversation continuity
memory_share_contextShare with agentsEnable seamless AI handoffs
memory_analyticsView statsMonitor memory usage and performance

🏗 Complete Zilliz Cloud Integration

25+ Tools for Every Zilliz Operation:

Cluster Management

  • zilliz_create_free_cluster - Create free-tier clusters
  • zilliz_list_clusters - List all your clusters
  • zilliz_describe_cluster - Get cluster details
  • zilliz_suspend_cluster - Suspend to save costs
  • zilliz_resume_cluster - Resume suspended clusters
  • zilliz_query_cluster_metrics - Real-time performance metrics

Collection & Data Management

  • zilliz_create_collection - Create vector collections
  • zilliz_insert_entities - Add data to collections
  • zilliz_search - Vector similarity search
  • zilliz_query - Scalar filtering and queries
  • zilliz_hybrid_search - Combined vector + scalar search

Advanced Operations

  • zilliz_create_index - Optimize search performance
  • zilliz_load_collection - Load into memory
  • zilliz_health_check - Connection status

📋 Alternative Installation Methods

Manual MCP Configuration

If you prefer manual setup:

{
  "mcpServers": {
    "zilliz-mcp-server": {
      "command": "npx",
      "args": ["zilliz-mcp-server"],
      "env": {
        "ZILLIZ_CLOUD_API_KEY": "your-api-key-here"
      },
      "description": "Universal memory layer for continuous AI conversations"
    }
  }
}

NPM Installation

npm install -g zilliz-mcp-server
zilliz-mcp-server setup

Environment Variables

export ZILLIZ_CLOUD_API_KEY="your-api-key"
export ZILLIZ_CLOUD_URI="https://api.cloud.zilliz.com"

💡 Usage Examples

Store User Preferences (Available to ALL Agents)

{
  "tool": "memory_remember",
  "arguments": {
    "content": "User prefers technical explanations with Python examples",
    "type": "long_term",
    "importance": 0.9,
    "tags": ["user-preference", "technical", "python"]
  }
}

Retrieve Relevant Context

{
  "tool": "memory_recall", 
  "arguments": {
    "query": "user preferences for code examples",
    "limit": 5,
    "minSimilarity": 0.7
  }
}

Share Context Between Agents

{
  "tool": "memory_share_context",
  "arguments": {
    "fromAgent": "general-chat",
    "toAgent": "code-assistant",
    "contextIds": ["pref123", "conversation456"]
  }
}

Vector Search Your Data

{
  "tool": "zilliz_search",
  "arguments": {
    "clusterId": "your-cluster-id",
    "collectionName": "documents",
    "data": [[0.1, 0.2, 0.3, ...]],
    "annsField": "embeddings", 
    "limit": 10,
    "filter": "category == 'technical'"
  }
}

🎯 Perfect for ALPHE.AI Platform

This MCP server was built specifically for the ALPHE.AI Universal AI Orchestration Platform to enable:

  • 🧠 Shared Memory Layer across all LLMs and agents
  • 🔄 Continuous Conversations that remember everything
  • 🤖 Agent Collaboration with seamless context sharing
  • 📈 32x Memory Efficiency with binary quantization
  • <30ms Retrieval for real-time performance
  • 💰 96% Cost Savings through intelligent memory management

🚀 Get Your API Key

  • Visit Zilliz Cloud Console
  • Create a free account (no credit card required)
  • Generate an API key
  • Run claude mcp add zilliz-mcp-server
  • Enter your API key when prompted

📊 Performance Metrics

  • Memory Retrieval: <30ms (vs 2000ms+ traditional)
  • Vector Search: <50ms for millions of vectors
  • Compression: 32x reduction with binary quantization
  • Scalability: 1000+ concurrent users supported
  • Accuracy: 95%+ semantic similarity matching
  • Uptime: 99.9%+ with intelligent retry logic

🛡 Security & Reliability

  • Secure API Key Storage - Environment variable based
  • Automatic Error Recovery - Intelligent retry with backoff
  • Health Monitoring - Continuous connection checks
  • User Isolation - Memory separated by user/session
  • Audit Logging - Complete operation tracking

🔧 CLI Commands

# Test connection
npx zilliz-mcp-server test

# Interactive setup  
npx zilliz-mcp-server setup

# Get version info
npx zilliz-mcp-server version

# Get help
npx zilliz-mcp-server --help

🏗 Architecture

┌─────────────────────────────────────────────────────────┐
│                 CLAUDE CODE + MCP                       │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────────────┐ │
│  │   GPT-4     │ │   Claude    │ │   Other LLMs       │ │
│  │             │ │             │ │                     │ │
│  └─────────────┘ └─────────────┘ └─────────────────────┘ │
└─────────────────────┬───────────────────────────────────┘
                      │ Shared Memory Access
                      ▼
┌─────────────────────────────────────────────────────────┐
│              ZILLIZ MCP SERVER                          │
│  ┌─────────────────┐  ┌─────────────────┐               │
│  │  Memory Layer   │  │ Zilliz Client   │               │  
│  │                 │  │                 │               │
│  │ • Continuous    │  │ • All 25+ APIs  │               │
│  │   Context       │  │ • Auto Retry    │               │
│  │ • Agent Sharing │  │ • Health Checks │               │
│  │ • 32x Compress  │  │ • Error Handle  │               │
│  └─────────────────┘  └─────────────────┘               │
└─────────────────────┬───────────────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────────────┐
│                ZILLIZ CLOUD                             │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐   │
│  │   Clusters   │  │ Collections  │  │   Indexes    │   │
│  │              │  │              │  │              │   │
│  │ • Auto-scale │  │ • Vector DB  │  │ • AUTOINDEX  │   │  
│  │ • Monitoring │  │ • 32x Zip    │  │ • <30ms      │   │
│  │ • Free Tier  │  │ • Semantic   │  │ • Million+   │   │
│  └──────────────┘  └──────────────┘  └──────────────┘   │
└─────────────────────────────────────────────────────────┘

🤝 Contributing

This is the most comprehensive Zilliz MCP integration available. Contributions welcome!

git clone https://github.com/alphe-ai/zilliz-mcp-server
cd zilliz-mcp-server
npm install
npm run dev

📄 License

MIT License - Use freely in your projects!

🚀 Ready to enable continuous AI conversations?

claude mcp add zilliz-mcp-server

Built for ALPHE.AI - The Universal AI Orchestration Platform enabling 96% cost savings with 95% quality retention through intelligent memory sharing.

Keywords

mcp

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Package last updated on 10 Sep 2025

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