n8n-nodes-n8ntools-crewai
Advanced tools
Advanced multi-agent AI orchestration with CrewAI - Create agents, tasks, crews, crew executors, flows, flow executors, and tools for complex AI workflows. Now with hybrid LLM support (native + LangChain), advanced features, and integrated support system.
The most advanced multi-agent AI orchestration framework for N8N - Transform complex business processes into intelligent, autonomous agent collaborations with the power of CrewAI. Build sophisticated AI teams that think, collaborate, and execute like human experts.
CrewAI is a revolutionary multi-agent framework that enables you to create autonomous AI teams capable of complex problem-solving, decision-making, and task execution. Unlike single AI agents, CrewAI orchestrates multiple specialized agents that collaborate, delegate, and coordinate to achieve sophisticated business objectives.
Traditional AI: Single agent → Single task → Limited complexity
User Input → AI Agent → Simple Response
CrewAI Framework: Multi-agent collaboration → Complex workflows → Enterprise-grade solutions
User Input → Agent Team → Sophisticated Analysis → Coordinated Execution → Strategic Output
CrewAI follows a hierarchical multi-agent architecture designed for maximum flexibility and scalability:
┌─────────────────────────────────────────────────────────────────┐
│ CrewAI Flow │
│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │
│ │ Crew Alpha │ │ Crew Beta │ │ Crew Gamma │ │
│ │ │ │ │ │ │ │
│ │ Agent A1 ┌─────┐│ │ Agent B1 ┌─────┐│ │ Agent C1 ┌─────┐│ │
│ │ Agent A2 │Task ││ │ Agent B2 │Task ││ │ Agent C2 │Task ││ │
│ │ Agent A3 │Pool ││ │ Agent B3 │Pool ││ │ Agent C3 │Pool ││ │
│ │ └─────┘│ │ └─────┘│ │ └─────┘│ │
│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Results │ │ Results │ │ Results │
│ Aggregation │ │ Analysis │ │ Validation │
└─────────────┘ └─────────────┘ └─────────────┘
Agent 1 (Research) → Agent 2 (Analysis) → Agent 3 (Report) → Final Output
Manager Agent
/ | \
Agent A1 Agent A2 Agent A3
/ | | | \
Task 1 Task 2 Task 3 Task 4 Task 5
Agent A ──┐
├─→ Consensus Engine ─→ Final Decision
Agent B ──┤
│
Agent C ──┘
# In N8N interface:
# Settings → Community Nodes → Install → n8n-nodes-n8ntools-crewai
npm install n8n-nodes-n8ntools-crewai
Get API Access
Connect LLM Model
Create Your First Agent
LLM Model → CrewAI Agent → CrewAI Task → CrewAI Crew → Execute
Scenario: Complete content marketing campaign creation
Agents:
- Market Research Analyst: Analyzes trends, competitors, audience
- Content Strategist: Develops content pillars and messaging
- Copywriter: Creates compelling copy and headlines
- SEO Specialist: Optimizes for search engines
- Social Media Manager: Adapts content for different platforms
Result: Full-scale content campaign with multi-channel optimization
Scenario: Comprehensive investment research and recommendation
Agents:
- Data Analyst: Processes financial statements and metrics
- Market Researcher: Analyzes industry trends and competition
- Risk Assessor: Evaluates potential risks and scenarios
- Report Writer: Synthesizes findings into executive summary
- Compliance Checker: Ensures regulatory adherence
Result: Professional-grade investment analysis with risk assessment
Scenario: End-to-end product design and marketing
Agents:
- UX Researcher: Analyzes user needs and pain points
- Product Designer: Creates product concepts and features
- Technical Writer: Documents specifications and requirements
- Marketing Strategist: Develops go-to-market strategy
- Brand Designer: Creates visual identity and assets
Result: Complete product launch package with technical and marketing assets
Scenario: Multi-source data analysis with business insights
Agents:
- Data Collector: Gathers data from various sources (APIs, databases, web)
- Data Cleaner: Normalizes and validates data quality
- Statistical Analyst: Performs complex statistical analysis
- Business Analyst: Translates data insights into business recommendations
- Visualization Expert: Creates compelling charts and dashboards
Result: Executive-ready business intelligence with actionable insights
Create autonomous AI specialists with defined roles and capabilities.
Configuration Options:
interface AgentConfig {
role: string; // Agent's professional role
goal: string; // Primary objective
backstory: string; // Context and expertise background
allowDelegation: boolean; // Can delegate to other agents
verbose: boolean; // Enable detailed logging
maxIter: number; // Maximum iterations for task completion
maxExecutionTime: number; // Timeout in seconds
}
Input Connections:
Advanced Features:
Define structured objectives with clear success criteria and dependencies.
Configuration Options:
interface TaskConfig {
description: string; // Detailed task description
expectedOutput: string; // Success criteria definition
agent?: string; // Assigned agent (optional)
dependencies: string[]; // Task dependencies
context: string[]; // Additional context tasks
asyncExecution: boolean; // Enable parallel execution
outputFormat: 'text' | 'json' | 'markdown' | 'xml';
}
Task Types:
Multi-agent orchestration with advanced collaboration patterns.
Process Types:
enum CrewProcess {
Sequential = 'sequential', // Linear execution
Hierarchical = 'hierarchical', // Manager-subordinate
Consensual = 'consensual' // Democratic decision-making
}
Configuration Options:
interface CrewConfig {
process: CrewProcess;
verbose: boolean;
memory: boolean; // Enable crew memory
cache: boolean; // Cache intermediate results
maxRPM: number; // Rate limiting
shareCrewReady: boolean; // Share crew state
managerAgent?: AgentConfig; // For hierarchical process
}
Advanced Orchestration:
High-performance execution engine with advanced monitoring and control.
Execution Modes:
Monitoring Features:
Create custom capabilities for specialized operations.
Tool Types:
interface HttpApiTool {
name: string;
description: string;
baseUrl: string;
endpoints: {
method: 'GET' | 'POST' | 'PUT' | 'DELETE';
path: string;
parameters: Parameter[];
authentication: AuthConfig;
}[];
}
interface DatabaseTool {
connectionString: string;
type: 'postgresql' | 'mysql' | 'mongodb' | 'sqlite';
queries: {
name: string;
sql: string;
parameters: Parameter[];
}[];
}
interface ScrapingTool {
name: string;
url: string;
selectors: {
name: string;
cssSelector: string;
attribute?: string;
}[];
pagination: boolean;
rateLimiting: RateLimit;
}
interface PythonTool {
name: string;
code: string;
requirements: string[];
environment: 'sandbox' | 'secure' | 'isolated';
}
Complex multi-crew workflows with conditional logic and state management.
Flow Patterns:
enum FlowPattern {
Sequential = 'sequential', // Linear crew execution
Parallel = 'parallel', // Concurrent crew execution
Conditional = 'conditional', // If-then-else logic
Loop = 'loop', // Iterative processing
EventDriven = 'event-driven' // Reactive processing
}
State Management:
Advanced flow execution with enterprise-grade reliability and performance.
Enterprise Features:
Workflow: "Automated Quality Assurance Pipeline"
Flow Structure:
Crew 1: "Data Collection Team"
- Sensor Data Agent: Collects IoT sensor readings
- Image Analysis Agent: Processes visual inspection data
- Specification Agent: Validates against quality standards
Crew 2: "Analysis Team"
- Statistical Analyst: Performs trend analysis
- Defect Classifier: Categorizes quality issues
- Root Cause Agent: Identifies underlying problems
Crew 3: "Action Team"
- Report Generator: Creates quality reports
- Alert Manager: Triggers notifications for critical issues
- Process Optimizer: Recommends process improvements
Tools Used:
- Database Tool: Access quality management system
- HTTP API Tool: Connect to manufacturing execution system
- Python Tool: Advanced statistical analysis
- Web Scraping Tool: Monitor supplier quality data
Result: Automated quality control with predictive maintenance recommendations
Workflow: "Intelligent Crypto Portfolio Management"
Flow Structure:
Crew 1: "Market Intelligence"
- Market Data Agent: Collects real-time price data
- News Sentiment Agent: Analyzes market sentiment
- Technical Analysis Agent: Performs chart analysis
- On-Chain Agent: Analyzes blockchain metrics
Crew 2: "Risk Management"
- Portfolio Analyst: Evaluates current positions
- Risk Assessor: Calculates position sizing
- Correlation Analyst: Analyzes asset correlations
- Volatility Agent: Monitors market volatility
Crew 3: "Execution Team"
- Strategy Optimizer: Optimizes trading strategies
- Order Manager: Manages trade execution
- Performance Tracker: Monitors trading performance
- Report Generator: Creates performance reports
Tools Used:
- HTTP API Tool: Connect to crypto exchanges
- Database Tool: Store trading history
- Python Tool: Quantitative analysis algorithms
- Web Scraping Tool: Monitor crypto news and social media
Result: Autonomous crypto trading with risk management and performance tracking
Workflow: "AI-Assisted Medical Diagnosis"
Flow Structure:
Crew 1: "Data Intake Team"
- Symptom Analyst: Processes patient symptoms
- Medical History Agent: Reviews patient history
- Lab Results Agent: Analyzes laboratory results
- Imaging Agent: Processes medical imaging
Crew 2: "Diagnosis Team"
- Diagnostic Agent: Generates differential diagnoses
- Evidence Evaluator: Weighs diagnostic evidence
- Risk Stratifier: Assesses patient risk levels
- Treatment Planner: Suggests treatment options
Crew 3: "Validation Team"
- Guidelines Checker: Validates against medical guidelines
- Drug Interaction Agent: Checks medication conflicts
- Quality Assurance Agent: Reviews diagnostic quality
- Documentation Agent: Creates medical documentation
Tools Used:
- Database Tool: Access electronic health records
- HTTP API Tool: Connect to medical databases
- Python Tool: Medical calculation algorithms
- Web Scraping Tool: Monitor latest medical research
Result: Comprehensive diagnostic support with evidence-based recommendations
// Automatically scale agents based on workload
const dynamicCrew = {
baseAgents: 3,
maxAgents: 10,
scalingMetrics: {
queueLength: 5,
responseTime: 30,
errorRate: 0.05
},
scalingPolicy: 'exponential'
};
// Agents learn from previous executions
const learningConfig = {
memoryType: 'episodic',
learningRate: 0.1,
experienceReplay: true,
knowledgeSharing: true
};
// Enterprise security configuration
const securityConfig = {
dataEncryption: 'AES-256',
accessControl: 'RBAC',
auditLogging: true,
complianceMode: 'GDPR',
dataRetention: '90days'
};
// Optimize for different scenarios
const performanceConfig = {
executionMode: 'parallel',
caching: {
enabled: true,
ttl: 3600,
strategy: 'LRU'
},
resourceLimits: {
memory: '2GB',
cpu: '2cores',
timeout: 300
}
};
Data Sources → CrewAI Flow → Analytics Crew → Visualization → Dashboard
│ │ │ │ │
┌─────┐ ┌─────────┐ ┌──────────┐ ┌──────────┐ ┌────────┐
│APIs │ │Extract │ │Transform │ │Visualize │ │Present │
│DBs │ ───▶ │Validate │──▶│Analyze │──▶│Format │─▶│Report │
│Files│ │Clean │ │Insights │ │Charts │ │Share │
└─────┘ └─────────┘ └──────────┘ └──────────┘ └────────┘
Customer Query → Intent Analysis → Crew Routing → Resolution → Follow-up
│ │ │ │ │
┌─────────┐ ┌────────────┐ ┌──────────┐ ┌─────────┐ ┌────────┐
│Classify │ │Route to │ │Execute │ │Generate │ │Quality │
│Extract │ ──▶│Appropriate │─▶│Solution │─▶│Response │─▶│Check │
│Context │ │Crew │ │Strategy │ │Format │ │Follow │
└─────────┘ └────────────┘ └──────────┘ └─────────┘ └────────┘
Supply Data → Demand Forecasting → Optimization Crew → Execution → Monitoring
│ │ │ │ │
┌────────┐ ┌───────────┐ ┌──────────────┐ ┌─────────┐ ┌────────┐
│Collect │ │Predict │ │Optimize │ │Execute │ │Track │
│Clean │ ──▶│Analyze │ ────▶ │Schedule │─▶│Orders │─▶│Adjust │
│Validate│ │Model │ │Route │ │Notify │ │Report │
└────────┘ └───────────┘ └──────────────┘ └─────────┘ └────────┘
const domainExperts = {
financialAnalyst: {
role: "Senior Financial Analyst",
expertise: ["DCF modeling", "ratio analysis", "risk assessment"],
tools: ["bloomberg_api", "sec_filings", "financial_calculator"],
decisionThreshold: 0.85
},
legalAdvisor: {
role: "Corporate Legal Counsel",
expertise: ["contract review", "compliance", "risk mitigation"],
tools: ["legal_database", "regulation_checker", "document_analyzer"],
decisionThreshold: 0.95
}
};
const crossFunctionalCrew = {
productLaunch: {
agents: ["product_manager", "engineer", "designer", "marketer"],
collaborationPattern: "hierarchical",
communicationProtocol: "structured_handoffs",
qualityGates: ["technical_review", "design_approval", "market_validation"]
}
};
const processingPipeline = {
stages: [
{
name: "data_ingestion",
agents: ["data_collector", "data_validator"],
parallelism: 3,
timeout: 300
},
{
name: "analysis",
agents: ["analyst", "ml_specialist"],
dependencies: ["data_ingestion"],
parallelism: 2
},
{
name: "reporting",
agents: ["report_writer", "visualizer"],
dependencies: ["analysis"],
parallelism: 1
}
]
};
const eventDrivenFlow = {
triggers: {
"data_quality_alert": "data_remediation_crew",
"performance_threshold": "optimization_crew",
"compliance_violation": "legal_review_crew"
},
eventHandlers: {
retryPolicy: "exponential_backoff",
maxRetries: 3,
fallbackAction: "escalate_to_human"
}
};
interface ExecutionMetrics {
averageExecutionTime: number;
taskCompletionRate: number;
agentUtilization: number;
errorRate: number;
costPerExecution: number;
qualityScore: number;
}
interface BusinessMetrics {
automationROI: number;
processEfficiencyGain: number;
humanHoursReplaced: number;
customerSatisfactionImpact: number;
complianceScoreImprovement: number;
}
CrewAI Performance Dashboard:
Real-time Metrics:
- Active Crews: 23
- Tasks in Queue: 156
- Average Response Time: 12.3s
- Success Rate: 98.7%
- Cost per Hour: $4.23
Agent Performance:
- Top Performer: Financial Analyst (99.2% accuracy)
- Resource Usage: 67% of allocated capacity
- Collaboration Score: 8.9/10
Business Impact:
- Processes Automated: 45
- Time Saved: 234 hours/week
- Cost Reduction: 43%
- Error Reduction: 78%
const securityConfig = {
threatDetection: {
anomalyDetection: true,
behaviorAnalysis: true,
realTimeAlerts: true
},
accessControl: {
multiFactorAuth: true,
sessionTimeout: 3600,
ipWhitelisting: true
},
dataProtection: {
encryptionAtRest: true,
encryptionInTransit: true,
keyRotation: "monthly"
}
};
Production Setup:
Load Balancing:
- Multiple crew instances
- Automatic failover
- Geographic distribution
Resource Management:
- Auto-scaling based on demand
- Resource pooling
- Capacity planning
Monitoring:
- Health checks
- Performance metrics
- Alert management
CI/CD Pipeline:
Development:
- Agent testing frameworks
- Crew simulation environments
- Performance benchmarking
Deployment:
- Blue-green deployments
- Canary releases
- Rollback capabilities
Operations:
- Centralized logging
- Distributed tracing
- Performance profiling
// Good: Focused agent
const emailAnalyst = {
role: "Email Marketing Analyst",
focus: "Email campaign performance analysis",
capabilities: ["open_rates", "click_through", "conversion_tracking"]
};
// Avoid: Overly broad agent
const everythingAgent = {
role: "General Marketing Agent",
focus: "All marketing activities", // Too broad
capabilities: ["email", "social", "ads", "content", "seo"] // Too many
};
const communicationStandards = {
outputFormat: {
structured: true,
schema: "json",
validation: true
},
handoffProtocol: {
dataValidation: true,
contextPreservation: true,
qualityGates: ["completeness", "accuracy", "relevance"]
}
};
const balancedCrew = {
specialists: 60, // Deep domain expertise
generalists: 30, // Broad knowledge, integration
coordinators: 10 // Task management, quality control
};
const redundancyStrategy = {
criticalRoles: ["data_validator", "quality_checker"],
backupAgents: 2,
failoverTime: "< 30 seconds",
dataConsistency: "eventual_consistency"
};
const aiOptimization = {
autoTuning: {
agentParameters: true,
workflowOptimization: true,
resourceAllocation: true
},
predictiveScaling: {
demandForecasting: true,
capacityPlanning: true,
costOptimization: true
},
continuousLearning: {
performanceImprovement: true,
errorReduction: true,
adaptiveStrategies: true
}
};
Deployment Options:
Cloud Native:
- AWS ECS/EKS
- Google Cloud Run
- Azure Container Instances
On-Premises:
- Kubernetes clusters
- Docker Swarm
- Bare metal servers
Hybrid:
- Edge computing nodes
- Data residency compliance
- Latency optimization
Problem: Agents taking too long to complete tasks
Solutions:
- Check LLM response times
- Optimize agent instructions
- Reduce tool complexity
- Implement caching
- Scale agent instances
Problem: Agents not collaborating effectively
Solutions:
- Review communication protocols
- Simplify task handoffs
- Improve context sharing
- Add coordination agents
- Implement feedback loops
Problem: High costs or resource usage
Solutions:
- Optimize LLM usage
- Implement response caching
- Use smaller models for simple tasks
- Batch similar operations
- Monitor and alert on usage
const qualityFramework = {
validation: {
inputSanitization: true,
outputValidation: true,
businessRuleChecking: true
},
testing: {
unitTests: "agent_behavior",
integrationTests: "crew_collaboration",
performanceTests: "scalability"
},
monitoring: {
qualityMetrics: true,
errorTracking: true,
performanceMonitoring: true
}
};
MIT License - see LICENSE file for details.
This package integrates with various third-party services and APIs. Please ensure compliance with respective terms of service:
N8N Tools is the leading provider of enterprise-grade AI and automation integrations for N8N. Our CrewAI framework represents the cutting edge of multi-agent AI orchestration, enabling businesses to build sophisticated AI systems that think, collaborate, and execute like human expert teams.
Transform your business processes with intelligent AI teams.
Visit n8ntools.io to explore our complete suite of AI-powered workflow automation tools.
Built with ❤️ by the N8N Tools Team
Empowering the future of work through intelligent automation
FAQs
Advanced multi-agent AI orchestration with CrewAI - Create agents, tasks, crews, crew executors, flows, flow executors, and tools for complex AI workflows. Now with hybrid LLM support (native + LangChain), advanced features, and integrated support system.
The npm package n8n-nodes-n8ntools-crewai receives a total of 17 weekly downloads. As such, n8n-nodes-n8ntools-crewai popularity was classified as not popular.
We found that n8n-nodes-n8ntools-crewai demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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