feat: Add presence heartbeat for Matrix online status
- matrix-gateway: POST /internal/matrix/presence/online endpoint - usePresenceHeartbeat hook with activity tracking - Auto away after 5 min inactivity - Offline on page close/visibility change - Integrated in MatrixChatRoom component
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Apple
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PHASE-5-COMPLETE.md
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PHASE-5-COMPLETE.md
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# PHASE 5 — Memory Layer Agents - Complete ✅
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## Summary
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Успішно створено 5 агентів Memory Layer для microDAO Node-2. Всі агенти налаштовані, інтегровані з NodeAgent та додані до монітора.
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---
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## ✅ Created Agents
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### 1. Omnimind (Collective Memory Core)
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**Directory:** `~/node2/agents/omnimind/`
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**Configuration:**
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- Model: `deepseek-r1:70b` (local, Ollama)
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- Priority: `highest`
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- Role: Collective Memory Core
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- Workspace: `memory_core`
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- Orchestrator: Yes
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**Responsibilities:**
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- Unify all memory systems (Qdrant, Milvus, Neo4j)
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- Decide storage location for information
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- Support high-context queries for Solarius and Sofia
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- Maintain long-term memory integrity
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**Files Created:**
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- `agent.json`
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- `system_prompt.md`
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- `README.md`
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---
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### 2. Qdrant Keeper (Vector Storage Manager)
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**Directory:** `~/node2/agents/qdrantkeeper/`
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**Configuration:**
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- Model: `mistral-nemo:12b` (local, Ollama)
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- Priority: `medium`
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- Role: Vector Storage Manager
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- Workspace: `memory_core`
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- Database: Qdrant
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**Responsibilities:**
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- Manage vector collections in Qdrant
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- Store and retrieve embeddings efficiently
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- Maintain indexes for fast search
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- Support fast vector queries
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**Files Created:**
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- `agent.json`
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- `system_prompt.md`
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- `README.md`
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---
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### 3. Milvus Curator (Long-Range Embedding Curator)
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**Directory:** `~/node2/agents/milvuscurator/`
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**Configuration:**
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- Model: `gemma2:27b` (local, Ollama)
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- Priority: `medium`
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- Role: Long-Range Embedding Curator
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- Workspace: `memory_core`
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- Database: Milvus
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**Responsibilities:**
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- Manage large embedding collections in Milvus
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- Handle long-range vector storage
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- Support complex filtering and search
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- Maintain indexes for heavy workloads
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**Files Created:**
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- `agent.json`
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- `system_prompt.md`
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- `README.md`
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---
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### 4. GraphMind (Semantic Graph Agent)
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**Directory:** `~/node2/agents/graphmind/`
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**Configuration:**
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- Model: `qwen2.5-coder:32b` (local, Ollama)
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- Priority: `high`
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- Role: Semantic Graph Agent
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- Workspace: `memory_core`
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- Database: Neo4j
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**Responsibilities:**
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- Build and maintain knowledge graphs in Neo4j
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- Create relationships between entities
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- Query semantic structures
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- Support graph-based reasoning
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**Files Created:**
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- `agent.json`
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- `system_prompt.md`
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- `README.md`
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---
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### 5. RAG Router (RAG Query Orchestrator)
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**Directory:** `~/node2/agents/ragrouter/`
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**Configuration:**
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- Model: `phi3:latest` (local, Ollama)
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- Priority: `medium`
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- Role: RAG Query Orchestrator
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- Workspace: `memory_core`
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- Memory Binding: Qdrant, Milvus, Neo4j
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**Responsibilities:**
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- Analyze query requirements
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- Route to appropriate memory system
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- Coordinate multi-system queries
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- Optimize query performance
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**Files Created:**
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- `agent.json`
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- `system_prompt.md`
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- `README.md`
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---
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## ✅ Integration
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### Workspace Configuration
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**File:** `~/node2/config/workspaces.json`
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**Added:** `memory_core` workspace
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```json
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{
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"memory_core": {
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"participants": [
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"Omnimind",
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"Qdrant Keeper",
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"Milvus Curator",
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"GraphMind",
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"RAG Router"
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],
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"description": "Memory Layer workspace for unified memory management across Qdrant, Milvus, and Neo4j. Led by Omnimind (Collective Memory Core)."
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}
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}
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```
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### Monitor Integration
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**File:** `fixed_monitor.py`
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**Added:** All 5 agents to `AGENTS` list with:
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- Full configuration
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- System prompts
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- Node assignment (node2)
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- Workspace assignment (memory_core)
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- Category: "Memory"
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---
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## Memory Stack Architecture
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```
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Omnimind (Orchestrator)
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├── Qdrant Keeper → Qdrant (fast vectors)
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├── Milvus Curator → Milvus (long-range embeddings)
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├── GraphMind → Neo4j (semantic graphs)
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└── RAG Router → Routes queries to appropriate system
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```
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**All agents:**
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- Run locally via Ollama
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- Bound to NodeAgent (`node2-nodeagent`)
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- Use NodeAgent for all memory operations
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- Part of `memory_core` workspace
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---
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## Agent Relationships
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**Omnimind** (Orchestrator):
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- Coordinates all memory operations
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- Delegates to specialized agents
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- Maintains memory integrity
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**Qdrant Keeper:**
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- Fast vector storage
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- Small to medium collections
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- Real-time RAG
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**Milvus Curator:**
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- Large embedding collections
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- Complex filtering
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- Heavy workloads
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**GraphMind:**
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- Knowledge graphs
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- Relationship queries
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- Semantic reasoning
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**RAG Router:**
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- Query routing
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- Multi-system coordination
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- Performance optimization
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---
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## File Structure
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```
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~/node2/agents/
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├── omnimind/
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│ ├── agent.json
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│ ├── system_prompt.md
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│ └── README.md
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├── qdrantkeeper/
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│ ├── agent.json
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│ ├── system_prompt.md
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│ └── README.md
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├── milvuscurator/
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│ ├── agent.json
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│ ├── system_prompt.md
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│ └── README.md
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├── graphmind/
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│ ├── agent.json
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│ ├── system_prompt.md
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│ └── README.md
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└── ragrouter/
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├── agent.json
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├── system_prompt.md
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└── README.md
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```
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---
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## Next Steps
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1. **Verify agents in monitor:**
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- Open `http://localhost:8899/agents`
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- Check that all 5 Memory Layer agents are listed
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- Verify they appear in `memory_core` workspace
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2. **Test agent cabinets:**
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- Open each agent's cabinet
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- Verify metrics, configuration, and chat functionality
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3. **Memory Stack Integration:**
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- Ensure Memory Stack services are running (from PHASE 5A)
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- Test agent connections to Qdrant, Milvus, Neo4j
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4. **CrewAI Integration:**
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- Create CrewAI crew from `memory_core` workspace
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- Test multi-agent memory operations
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---
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## Status
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**All Agents:** ✅ Created
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**Workspace:** ✅ Configured
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**Monitor:** ✅ Integrated
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**Documentation:** ✅ Complete
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**Ready for:** Memory operations and testing
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---
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**Date:** 2025-11-22
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**Version:** 1.0
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