# 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.

---

## ✅ 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`

---

### 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`

---

### 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`

---

### 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`

---

### 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`

---

## ✅ Integration

### Workspace Configuration

**File:** `~/node2/config/workspaces.json`

**Added:** `memory_core` workspace

```json
{
  "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"

---

## 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

---

## 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

---

## 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
```

---

## 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

---

## Status

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

**Ready for:** Memory operations and testing

---

**Date:** 2025-11-22  
**Version:** 1.0