microdao-daarion/PHASE-5-COMPLETE.md

PHASE 5 — Memory Layer Agents - Complete ✅

Summary

Успішно створено 5 агентів Memory Layer для microDAO Node-2. Всі агенти налаштовані, інтегровані з NodeAgent та додані до монітора.

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✅ Created Agents

1. Omnimind (Collective Memory Core)

Directory: ~/node2/agents/omnimind/

Configuration:

• Model: deepseek-r1:70b (local, Ollama)
• Priority: highest
• Role: Collective Memory Core
• Workspace: memory_core
• Orchestrator: Yes

Responsibilities:

• Unify all memory systems (Qdrant, Milvus, Neo4j)
• Decide storage location for information
• Support high-context queries for Solarius and Sofia
• Maintain long-term memory integrity

Files Created:

• agent.json
• system_prompt.md
• README.md

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2. Qdrant Keeper (Vector Storage Manager)

Directory: ~/node2/agents/qdrantkeeper/

Configuration:

• Model: mistral-nemo:12b (local, Ollama)
• Priority: medium
• Role: Vector Storage Manager
• Workspace: memory_core
• Database: Qdrant

Responsibilities:

• Manage vector collections in Qdrant
• Store and retrieve embeddings efficiently
• Maintain indexes for fast search
• Support fast vector queries

Files Created:

• agent.json
• system_prompt.md
• README.md

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3. Milvus Curator (Long-Range Embedding Curator)

Directory: ~/node2/agents/milvuscurator/

Configuration:

• Model: gemma2:27b (local, Ollama)
• Priority: medium
• Role: Long-Range Embedding Curator
• Workspace: memory_core
• Database: Milvus

Responsibilities:

• Manage large embedding collections in Milvus
• Handle long-range vector storage
• Support complex filtering and search
• Maintain indexes for heavy workloads

Files Created:

• agent.json
• system_prompt.md
• README.md

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4. GraphMind (Semantic Graph Agent)

Directory: ~/node2/agents/graphmind/

Configuration:

• Model: qwen2.5-coder:32b (local, Ollama)
• Priority: high
• Role: Semantic Graph Agent
• Workspace: memory_core
• Database: Neo4j

Responsibilities:

• Build and maintain knowledge graphs in Neo4j
• Create relationships between entities
• Query semantic structures
• Support graph-based reasoning

Files Created:

• agent.json
• system_prompt.md
• README.md

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5. RAG Router (RAG Query Orchestrator)

Directory: ~/node2/agents/ragrouter/

Configuration:

• Model: phi3:latest (local, Ollama)
• Priority: medium
• Role: RAG Query Orchestrator
• Workspace: memory_core
• Memory Binding: Qdrant, Milvus, Neo4j

Responsibilities:

• Analyze query requirements
• Route to appropriate memory system
• Coordinate multi-system queries
• Optimize query performance

Files Created:

• agent.json
• system_prompt.md
• README.md

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✅ Integration

Workspace Configuration

File: ~/node2/config/workspaces.json

Added: memory_core workspace

{
  "memory_core": {
    "participants": [
      "Omnimind",
      "Qdrant Keeper",
      "Milvus Curator",
      "GraphMind",
      "RAG Router"
    ],
    "description": "Memory Layer workspace for unified memory management across Qdrant, Milvus, and Neo4j. Led by Omnimind (Collective Memory Core)."
  }
}

Monitor Integration

File: fixed_monitor.py

Added: All 5 agents to AGENTS list with:

• Full configuration
• System prompts
• Node assignment (node2)
• Workspace assignment (memory_core)
• Category: "Memory"

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Memory Stack Architecture

Omnimind (Orchestrator)
├── Qdrant Keeper → Qdrant (fast vectors)
├── Milvus Curator → Milvus (long-range embeddings)
├── GraphMind → Neo4j (semantic graphs)
└── RAG Router → Routes queries to appropriate system

All agents:

• Run locally via Ollama
• Bound to NodeAgent (node2-nodeagent)
• Use NodeAgent for all memory operations
• Part of memory_core workspace

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Agent Relationships

Omnimind (Orchestrator):

• Coordinates all memory operations
• Delegates to specialized agents
• Maintains memory integrity

Qdrant Keeper:

• Fast vector storage
• Small to medium collections
• Real-time RAG

Milvus Curator:

• Large embedding collections
• Complex filtering
• Heavy workloads

GraphMind:

• Knowledge graphs
• Relationship queries
• Semantic reasoning

RAG Router:

• Query routing
• Multi-system coordination
• Performance optimization

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File Structure

~/node2/agents/
├── omnimind/
│   ├── agent.json
│   ├── system_prompt.md
│   └── README.md
├── qdrantkeeper/
│   ├── agent.json
│   ├── system_prompt.md
│   └── README.md
├── milvuscurator/
│   ├── agent.json
│   ├── system_prompt.md
│   └── README.md
├── graphmind/
│   ├── agent.json
│   ├── system_prompt.md
│   └── README.md
└── ragrouter/
    ├── agent.json
    ├── system_prompt.md
    └── README.md

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Next Steps

1. Verify agents in monitor:

   • Open http://localhost:8899/agents
   • Check that all 5 Memory Layer agents are listed
   • Verify they appear in memory_core workspace

2. Test agent cabinets:

   • Open each agent's cabinet
   • Verify metrics, configuration, and chat functionality

3. Memory Stack Integration:

   • Ensure Memory Stack services are running (from PHASE 5A)
   • Test agent connections to Qdrant, Milvus, Neo4j

4. CrewAI Integration:

   • Create CrewAI crew from memory_core workspace
   • Test multi-agent memory operations

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Status

All Agents: ✅ Created
Workspace: ✅ Configured
Monitor: ✅ Integrated
Documentation: ✅ Complete

Ready for: Memory operations and testing

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Date: 2025-11-22
Version: 1.0