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
110
services/node2-crewai/README.md
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110
services/node2-crewai/README.md
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# CrewAI для microDAO Node-2
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## 🎯 Призначення
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CrewAI використовується для формування команд агентів (10-35 агентів) на microDAO Node-2.
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## 📁 Структура
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```
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~/node2/crewai/
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├── agents/ # Визначення агентів
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│ └── example_agent.py
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├── crews/ # Формування команд
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│ └── example_crew.py
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├── tasks/ # Задачі для агентів
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├── tools/ # Інструменти для агентів
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├── config/ # Конфігурація
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│ └── node2_crewai_config.yaml
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└── requirements.txt # Залежності
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```
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## 🚀 Швидкий старт
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### 1. Встановити залежності
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```bash
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cd ~/node2/crewai
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pip install -r requirements.txt
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```
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### 2. Створити агентів
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Коли отримаєте список агентів, створіть їх у `agents/`:
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```python
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from crewai import Agent
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my_agent = Agent(
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role="Agent Role",
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goal="Agent Goal",
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backstory="Agent Backstory",
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tools=[...],
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llm=... # Will use Swoper/Ollama
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)
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```
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### 3. Створити команди (crews)
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```python
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from crewai import Crew, Process
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my_crew = Crew(
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agents=[agent1, agent2, ...],
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tasks=[task1, task2, ...],
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process=Process.sequential,
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verbose=True,
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memory=True
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)
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```
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### 4. Запустити команду
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```python
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result = my_crew.kickoff()
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```
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## 🔗 Інтеграція з microDAO Node-2
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### LLM Provider (Swoper/Ollama)
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CrewAI буде використовувати Swoper через Ollama API:
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```python
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from langchain_community.llms import Ollama
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llm = Ollama(
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base_url="http://localhost:11434",
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model="deepseek-r1" # або інша модель
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)
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```
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### Memory (RAG Router)
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Агенти можуть використовувати локальну пам'ять через RAG Router:
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```python
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from crewai_tools import tool
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@tool("Local Memory Search")
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def local_memory_search(query: str) -> str:
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# Використовує RAG Router для пошуку
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...
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```
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## 📋 Очікується
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- Список агентів (10-35 агентів)
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- Призначення LLM для кожного агента
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- Структура команд (crews)
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## ⏭️ Наступні кроки
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1. Дочекатися списку агентів від користувача
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2. Створити агентів з CrewAI
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3. Сформувати команди
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4. Інтегрувати з NodeAgent
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5. Запустити команди агентів
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247
services/node2-crewai/setup_crewai_node2.sh
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247
services/node2-crewai/setup_crewai_node2.sh
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#!/bin/bash
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# Setup CrewAI for microDAO Node-2
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# CrewAI will be used for agent team formation and orchestration
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set -e
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echo "🚀 Setting up CrewAI for microDAO Node-2"
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echo "=================================================="
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# Colors
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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RED='\033[0;31m'
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NC='\033[0m'
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# Check if CrewAI service exists on Node-1
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CREWAI_CONTAINER="dagi-crewai"
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if docker ps --format "{{.Names}}" | grep -q "^${CREWAI_CONTAINER}$"; then
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echo -e "${GREEN}✅ CrewAI container found: ${CREWAI_CONTAINER}${NC}"
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CREWAI_URL="http://localhost:9010"
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else
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echo -e "${YELLOW}⚠️ CrewAI container not found. Will set up local CrewAI.${NC}"
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CREWAI_URL="http://localhost:9010"
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fi
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# Create CrewAI directory for Node-2
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CREWAI_DIR="$HOME/node2/crewai"
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mkdir -p "$CREWAI_DIR"/{agents,crews,tasks,tools,config}
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echo -e "${GREEN}✅ Created CrewAI directory: ${CREWAI_DIR}${NC}"
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# Create CrewAI configuration for Node-2
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cat > "$CREWAI_DIR/config/node2_crewai_config.yaml" << 'EOF'
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# CrewAI Configuration for microDAO Node-2
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microdao_id: "microdao-node2"
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# LLM Provider (via Swoper/Ollama)
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llm:
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provider: "ollama"
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base_url: "http://localhost:11434"
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models:
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default: "deepseek-r1" # Will be configured based on available models
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code: "qwen2.5-coder:72b"
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math: "deepseek-math:33b"
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vision: "qwen2-vl:32b"
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# Memory (via RAG Router)
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memory:
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rag_router_url: "http://localhost:9401"
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qdrant_url: "http://localhost:6333"
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milvus_url: "http://localhost:19530"
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neo4j_url: "http://localhost:7474"
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# Agent Team Structure
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agent_teams:
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system:
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description: "System agents for coordination and monitoring"
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agents: []
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specialists:
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description: "Specialist agents for specific tasks"
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agents: []
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custom:
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description: "Custom agents created by user"
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agents: []
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# Crew Formation
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crews:
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default:
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name: "microdao-node2-default-crew"
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description: "Default crew for microDAO Node-2"
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agents: []
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tasks: []
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verbose: true
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memory: true
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# Tools
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tools:
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enabled:
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- web_search
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- code_execution
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- file_operations
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- memory_operations
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custom_tools_path: "~/node2/crewai/tools"
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# Logging
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logging:
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level: "INFO"
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file: "~/node2/crewai/logs/crewai.log"
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EOF
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echo -e "${GREEN}✅ Created CrewAI configuration${NC}"
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# Create requirements file
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cat > "$CREWAI_DIR/requirements.txt" << 'EOF'
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crewai>=0.28.0
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crewai-tools>=0.1.0
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langchain>=0.1.0
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langchain-community>=0.0.20
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pydantic>=2.0.0
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pyyaml>=6.0.1
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httpx>=0.25.0
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EOF
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echo -e "${GREEN}✅ Created requirements.txt${NC}"
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# Create example agent template
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cat > "$CREWAI_DIR/agents/example_agent.py" << 'PYEOF'
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"""
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Example Agent Template for microDAO Node-2
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This is a template for creating agents with CrewAI
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"""
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from crewai import Agent
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from crewai_tools import tool
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# Example tool
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@tool("Local Memory Search")
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def local_memory_search(query: str) -> str:
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"""
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Search in local memory (Qdrant/Milvus/Neo4j) via RAG Router.
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Args:
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query: Search query
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Returns:
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Search results from local memory
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"""
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import httpx
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rag_router_url = "http://localhost:9401"
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try:
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response = httpx.post(
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f"{rag_router_url}/query",
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json={
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"query": query,
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"query_type": "vector_search",
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"limit": 10
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},
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timeout=30.0
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)
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if response.status_code == 200:
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results = response.json()
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return f"Found {results.get('count', 0)} results: {results.get('results', [])}"
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else:
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return f"Error: {response.status_code}"
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except Exception as e:
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return f"Error searching memory: {e}"
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# Example agent
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example_agent = Agent(
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role="Example Agent",
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goal="Help with tasks using local memory and LLM",
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backstory="You are an agent in microDAO Node-2, with access to local memory and LLM inference.",
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tools=[local_memory_search],
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verbose=True,
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allow_delegation=False
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)
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if __name__ == "__main__":
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print("Example agent created successfully!")
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PYEOF
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echo -e "${GREEN}✅ Created example agent template${NC}"
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# Create crew template
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cat > "$CREWAI_DIR/crews/example_crew.py" << 'PYEOF'
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"""
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Example Crew Template for microDAO Node-2
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This is a template for creating crews with CrewAI
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"""
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from crewai import Crew, Process
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from agents.example_agent import example_agent
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from crewai import Task
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# Example task
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example_task = Task(
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description="Example task for microDAO Node-2",
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agent=example_agent,
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expected_output="Task completion result"
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)
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# Example crew
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example_crew = Crew(
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agents=[example_agent],
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tasks=[example_task],
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process=Process.sequential,
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verbose=True,
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memory=True
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)
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if __name__ == "__main__":
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result = example_crew.kickoff()
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print(f"Result: {result}")
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PYEOF
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echo -e "${GREEN}✅ Created example crew template${NC}"
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# Create setup script
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cat > "$CREWAI_DIR/setup.sh" << 'EOF'
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#!/bin/bash
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# Setup CrewAI environment for Node-2
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echo "Setting up CrewAI for microDAO Node-2..."
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# Install dependencies
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pip install -r requirements.txt
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# Create virtual environment (optional)
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# python -m venv venv
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# source venv/bin/activate
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# pip install -r requirements.txt
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echo "✅ CrewAI setup complete!"
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echo ""
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echo "Next steps:"
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echo " 1. Create your agents in agents/"
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echo " 2. Create your crews in crews/"
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echo " 3. Run: python crews/your_crew.py"
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EOF
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chmod +x "$CREWAI_DIR/setup.sh"
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echo -e "${GREEN}✅ Created setup script${NC}"
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echo -e "\n${GREEN}=================================================="
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echo "✅ CrewAI Setup Complete"
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echo "==================================================${NC}"
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echo ""
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echo "📁 CrewAI directory: ${CREWAI_DIR}"
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echo "📝 Configuration: ${CREWAI_DIR}/config/node2_crewai_config.yaml"
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echo ""
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echo "⏭️ Next steps:"
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echo " 1. Install dependencies: cd ${CREWAI_DIR} && pip install -r requirements.txt"
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echo " 2. Create your agents (you'll provide list later)"
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echo " 3. Create crews with your agents"
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echo " 4. Run crews for agent team formation"
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echo ""
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