ELIZAOS Integration - Comput3 Network

Integrate ELIZAOS agents with your existing business systems, development workflows, and data sources for seamless AI automation.

Integration Overview

ELIZAOS agents can be integrated with virtually any system through:

API Integrations

Connect agents to REST APIs, GraphQL endpoints, and webhook systems.

Database Connections

Direct integration with SQL and NoSQL databases for data processing.

Message Queues

Integrate with RabbitMQ, Apache Kafka, and other messaging systems.

Workflow Orchestration

Connect with existing workflow engines and business process automation.

Common Integration Patterns

CI/CD Pipeline Integration

Code Review Agent

Deploy an agent that automatically reviews code changes in your CI/CD pipeline.

# GitHub Actions integration
name: ELIZAOS Code Review
on:
  pull_request:
    types: [opened, synchronize]

jobs:
  ai-review:
    runs-on: ubuntu-latest
    steps:
      - name: Trigger ELIZAOS Review
        run: |
          curl -X POST "https://your-elizaos.com/api/agents/code-reviewer/trigger" \
            -H "Authorization: Bearer ${{ secrets.ELIZAOS_API_KEY }}" \
            -d '{
              "repository": "${{ github.repository }}",
              "pull_request": ${{ github.event.number }},
              "files_changed": ${{ github.event.pull_request.changed_files }}
            }'

Test Generation Agent

Automatically generate test cases for new code changes.

{
  "name": "test-generator",
  "triggers": [
    {
      "type": "webhook",
      "source": "github",
      "event": "push",
      "filter": "src/**/*.py"
    }
  ],
  "actions": [
    {
      "type": "analyze_code",
      "generate_tests": true,
      "coverage_target": 80
    }
  ]
}

Documentation Agent

Keep documentation up-to-date with code changes.

# Agent configuration for doc updates
doc_agent = {
    "name": "doc-updater",
    "model": "hermes4:70b",
    "specialization": "documentation",
    "triggers": [
        {
            "type": "code_change",
            "patterns": ["*.py", "*.js", "*.ts"],
            "action": "update_documentation"
        }
    ],
    "integrations": [
        "github_repo",
        "confluence_wiki",
        "notion_docs"
    ]
}

Customer Support Automation

Ticket Routing Agent

Intelligent support ticket classification and routing

{
  "name": "ticket-router",
  "model": "hermes4:70b",
  "specialization": "customer_support",
  "integrations": [
    {
      "type": "zendesk",
      "config": {
        "subdomain": "yourcompany",
        "email": "[email protected]",
        "token_env": "ZENDESK_TOKEN"
      }
    }
  ],
  "rules": [
    {
      "condition": "contains_code",
      "action": "route_to_technical_team",
      "priority": "high"
    },
    {
      "condition": "billing_related",
      "action": "route_to_billing_team",
      "auto_respond": true
    }
  ]
}

Capabilities:

Automatic ticket classification
Priority assignment based on content
Intelligent routing to appropriate teams
Auto-responses for common issues

Knowledge Base Agent

Dynamic knowledge base managementFeatures:

Automatic FAQ generation from tickets
Knowledge article updates based on resolutions
Search and recommendation improvements
Multi-language support

Integration Example:

knowledge_agent = {
    "name": "kb-manager",
    "model": "kimi-k2",
    "data_sources": [
        "support_tickets",
        "product_documentation", 
        "community_forums"
    ],
    "capabilities": [
        "extract_solutions",
        "generate_faqs",
        "update_articles",
        "recommend_content"
    ]
}

Business Process Automation

Data Processing Pipeline

Automated data processing and analysis

{
  "name": "data-pipeline",
  "type": "workflow_agent",
  "steps": [
    {
      "name": "data_ingestion",
      "agent": "data-collector",
      "sources": ["api_endpoints", "file_uploads", "database_changes"]
    },
    {
      "name": "data_validation",
      "agent": "data-validator", 
      "rules": ["schema_validation", "quality_checks", "anomaly_detection"]
    },
    {
      "name": "data_analysis",
      "agent": "data-analyzer",
      "models": ["statistical_analysis", "trend_detection", "forecasting"]
    },
    {
      "name": "report_generation",
      "agent": "report-generator",
      "outputs": ["dashboard_updates", "email_reports", "slack_notifications"]
    }
  ]
}

Content Moderation

Automated content review and moderationAgent Configuration:

{
  "name": "content-moderator",
  "model": "hermes4:405b",
  "specialization": "content_moderation",
  "rules": [
    {
      "type": "toxicity_detection",
      "threshold": 0.8,
      "action": "flag_for_review"
    },
    {
      "type": "spam_detection", 
      "action": "auto_remove"
    },
    {
      "type": "inappropriate_content",
      "action": "blur_and_flag"
    }
  ],
  "integrations": ["user_database", "moderation_queue", "notification_system"]
}

Financial Analysis

Automated financial data processing and reportingCapabilities:

Transaction analysis and categorization
Fraud detection and alerting
Financial report generation
Budget tracking and forecasting
Compliance monitoring

Example Agent:

financial_agent = {
    "name": "financial-analyzer",
    "model": "hermes4:405b",
    "data_sources": [
        "accounting_system",
        "bank_apis",
        "expense_reports",
        "invoice_system"
    ],
    "capabilities": [
        "transaction_categorization",
        "anomaly_detection",
        "report_generation",
        "compliance_checking"
    ],
    "security": {
        "encryption_required": True,
        "audit_logging": True,
        "access_controls": "strict"
    }
}

Enterprise Integrations

ERP System Integration

SAP Integration

Connect with SAP ERP systems

{
  "name": "sap-integration",
  "type": "erp_agent",
  "config": {
    "sap_system": {
      "host": "sap.company.com",
      "client": "100",
      "user": "AI_USER",
      "password_env": "SAP_PASSWORD"
    },
    "modules": [
      "FI", "CO", "MM", "SD", "HR"
    ],
    "permissions": {
      "read_only": true,
      "allowed_tables": ["VBAK", "VBAP", "KNA1", "MARA"]
    }
  }
}

Salesforce Integration

CRM data analysis and automationCapabilities:

Lead scoring and qualification
Opportunity analysis and forecasting
Customer data enrichment
Automated follow-up sequences

Setup:

{
  "name": "salesforce-agent",
  "integrations": [
    {
      "type": "salesforce",
      "config": {
        "instance_url": "https://yourcompany.salesforce.com",
        "client_id": "your-connected-app-id",
        "client_secret_env": "SF_CLIENT_SECRET",
        "username": "[email protected]",
        "password_env": "SF_PASSWORD"
      }
    }
  ]
}

Monitoring and Observability

Prometheus Integration

Metrics collection and monitoring

Agent performance metrics
Resource utilization tracking
Custom business metrics
Alert manager integration

Grafana Dashboards

Visualization and alerting

Real-time agent performance dashboards
Resource usage visualization
Cost tracking and optimization
Custom alert configurations

Log Aggregation

Centralized logging with ELK stack

Elasticsearch for log storage
Logstash for log processing
Kibana for log visualization
Custom log analysis agents

Distributed Tracing

End-to-end request tracing

Jaeger or Zipkin integration
Agent interaction tracing
Performance bottleneck identification
Service dependency mapping

Webhook and Event Integration

Webhook Configuration

{
  "webhooks": {
    "github_events": {
      "url": "/webhooks/github",
      "secret_env": "GITHUB_WEBHOOK_SECRET",
      "events": ["push", "pull_request", "issues"],
      "agent_triggers": [
        {
          "event": "push",
          "agent": "code-analyzer",
          "action": "analyze_changes"
        }
      ]
    },
    "slack_events": {
      "url": "/webhooks/slack",
      "verification_token_env": "SLACK_VERIFICATION_TOKEN",
      "events": ["message", "file_shared"],
      "agent_triggers": [
        {
          "event": "message",
          "condition": "contains(@ai)",
          "agent": "slack-assistant"
        }
      ]
    }
  }
}

Event-Driven Architecture

Event Sources
Event Processing
Event Routing

Systems that can trigger agent actions

File system changes (new files, modifications)
Database changes (inserts, updates, deletes)
API calls and webhook events
Time-based triggers (cron-like scheduling)
User interactions and manual triggers

How agents handle and process events

# Event handler configuration
event_handlers = {
    "file_uploaded": {
        "agent": "document-processor",
        "action": "process_document",
        "parameters": {
            "extract_text": True,
            "generate_summary": True,
            "detect_language": True
        }
    },
    "user_message": {
        "agent": "chat-assistant",
        "action": "respond_to_user",
        "context_sources": ["conversation_history", "user_profile"]
    }
}

Best Practices

Agent Design Patterns

Single Responsibility

Design agents with focused, specific purposes✅ Good: Separate agents for code review, testing, and documentation ❌ Avoid: One agent trying to handle all development tasksBenefits:

Easier debugging and maintenance
Better resource utilization
Improved fault isolation
Simpler scaling decisions

Stateless Design

Design agents to be stateless when possible✅ Good: Store state in external databases or shared storage ❌ Avoid: Relying on agent memory for critical dataImplementation:

# Stateless agent design
def process_request(request_data):
    # Load context from external source
    context = load_context(request_data.user_id)
    
    # Process with loaded context
    result = process_with_context(request_data, context)
    
    # Save updated context externally
    save_context(request_data.user_id, result.context)
    
    return result.output

Error Handling

Robust error handling and recovery

Implement retry logic with exponential backoff
Use circuit breakers for external service calls
Provide meaningful error messages and logging
Design graceful degradation for partial failures

# Error handling example
@retry(max_attempts=3, backoff=exponential_backoff)
def call_external_api(data):
    try:
        response = requests.post(api_url, json=data, timeout=30)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.Timeout:
        logger.error("API call timed out")
        return {"error": "timeout", "retry": True}
    except requests.exceptions.RequestException as e:
        logger.error(f"API call failed: {e}")
        return {"error": "api_failure", "retry": False}

Performance Optimization

Resource Efficiency

Optimize agent resource usage

Use appropriate model sizes for tasks
Implement connection pooling
Cache frequently accessed data
Batch similar operations together

Scaling Strategy

Plan for growth and load increases

Design agents for horizontal scaling
Use load balancing across agent instances
Implement auto-scaling based on metrics
Plan resource allocation strategies

Monitoring

Comprehensive agent monitoring

Track agent performance metrics
Monitor resource utilization
Set up alerting for failures
Analyze usage patterns for optimization

Cost Management

Optimize costs while maintaining performance

Use appropriate GPU instances for workloads
Implement auto-shutdown for idle agents
Monitor and optimize token usage
Use spot instances when appropriate

Advanced Integration Scenarios

Multi-Agent Workflows

# Multi-agent research workflow
research_pipeline = {
    "name": "research-pipeline",
    "agents": [
        {
            "name": "paper-finder",
            "model": "hermes4:70b",
            "role": "find_relevant_papers",
            "tools": ["arxiv_search", "pubmed_search", "google_scholar"]
        },
        {
            "name": "paper-analyzer",
            "model": "hermes4:405b",
            "role": "analyze_papers",
            "tools": ["pdf_reader", "citation_extractor", "summary_generator"]
        },
        {
            "name": "insight-synthesizer",
            "model": "hermes4:405b", 
            "role": "synthesize_insights",
            "tools": ["trend_analyzer", "gap_identifier", "recommendation_engine"]
        },
        {
            "name": "report-writer",
            "model": "kimi-k2",
            "role": "generate_report",
            "tools": ["markdown_generator", "chart_creator", "reference_formatter"]
        }
    ],
    "workflow": [
        {
            "step": 1,
            "agent": "paper-finder",
            "input": "research_topic",
            "output": "relevant_papers"
        },
        {
            "step": 2,
            "agent": "paper-analyzer",
            "input": "relevant_papers",
            "output": "paper_analysis",
            "parallel": True
        },
        {
            "step": 3,
            "agent": "insight-synthesizer",
            "input": "paper_analysis",
            "output": "research_insights"
        },
        {
            "step": 4,
            "agent": "report-writer",
            "input": "research_insights",
            "output": "final_report"
        }
    ]
}

Real-time System Integration

IoT Device Management
Trading System
Security Monitoring

Manage and respond to IoT device data

iot_agent = {
    "name": "iot-manager",
    "model": "hermes4:70b",
    "data_streams": [
        {
            "type": "mqtt",
            "broker": "iot.company.com",
            "topics": ["sensors/+/temperature", "devices/+/status"]
        }
    ],
    "actions": [
        {
            "trigger": "temperature > 75",
            "action": "send_alert",
            "targets": ["facilities_team", "hvac_system"]
        },
        {
            "trigger": "device_offline",
            "action": "diagnose_and_repair",
            "escalate_after": "5 minutes"
        }
    ]
}

Automated trading and market analysis

{
  "name": "trading-system",
  "agents": [
    {
      "name": "market-analyzer",
      "model": "hermes4:405b",
      "data_feeds": ["market_data", "news_feeds", "social_sentiment"],
      "analysis_frequency": "1s"
    },
    {
      "name": "risk-manager",
      "model": "hermes4:70b",
      "monitoring": ["portfolio_exposure", "var_calculations", "stress_tests"],
      "limits": {"max_position_size": 100000, "daily_loss_limit": 50000}
    },
    {
      "name": "execution-agent",
      "model": "hermes4:70b",
      "capabilities": ["order_placement", "position_management", "liquidity_analysis"],
      "exchanges": ["binance", "coinbase", "kraken"]
    }
  ]
}

Automated security monitoring and response

security_agents = {
    "threat_detector": {
        "model": "hermes4:405b",
        "data_sources": [
            "network_logs",
            "system_logs", 
            "application_logs",
            "threat_intelligence_feeds"
        ],
        "detection_rules": [
            "anomalous_network_traffic",
            "suspicious_login_patterns",
            "malware_signatures",
            "data_exfiltration_attempts"
        ]
    },
    "incident_responder": {
        "model": "hermes4:70b",
        "capabilities": [
            "isolate_compromised_systems",
            "collect_forensic_evidence",
            "notify_security_team",
            "generate_incident_reports"
        ]
    }
}

Testing and Validation

Agent Testing Framework

Unit Testing

Test individual agent functions

import unittest
from elizaos.testing import AgentTestCase

class TestDocumentAnalyzer(AgentTestCase):
    def setUp(self):
        self.agent = self.create_test_agent("document-analyzer")
    
    def test_pdf_analysis(self):
        result = self.agent.process({
            "type": "pdf",
            "content": "test_document.pdf"
        })
        self.assertIn("summary", result)
        self.assertGreater(len(result["summary"]), 50)

Integration Testing

Test agent interactions with external systems

def test_github_integration():
    # Test GitHub API integration
    agent = create_agent("github-reviewer")
    
    # Mock GitHub API responses
    with mock_github_api():
        result = agent.review_pr("owner/repo", 123)
        assert result["status"] == "completed"
        assert "feedback" in result

Load Testing

Test agent performance under load

# Load test agent endpoints
elizaos test load \
  --agent document-analyzer \
  --concurrent-requests 100 \
  --duration 300s \
  --ramp-up 30s

Security Considerations

Agent Security

Sandboxing

Isolate agent execution environments

Process isolation using containers
Network namespace separation
File system access restrictions
Resource quota enforcement

Permission Management

Fine-grained access controls

{
  "agent_permissions": {
    "data_access": {
      "allowed_databases": ["analytics"],
      "allowed_tables": ["users", "events"],
      "operations": ["SELECT"],
      "row_limit": 10000
    },
    "api_access": {
      "allowed_endpoints": ["/api/users", "/api/reports"],
      "rate_limit": "100/hour",
      "authentication_required": true
    }
  }
}

Audit and Compliance

Comprehensive logging and compliance

All agent actions logged with timestamps
Data access tracking and reporting
Compliance with GDPR, HIPAA, SOX
Automated compliance reporting

Next Steps

Setup Guide

Learn how to deploy ELIZAOS on Comput3 Network infrastructure.

MCP Integration

Connect external data sources and tools to your ELIZAOS agents.

GPU Instances

Choose the right GPU instances for your ELIZAOS deployment.

Getting Started

API

Chat

IDE/CLI

Launch GPU

Generate Medias

COM Token

MCP

ELIZAOS

​Integration Overview

API Integrations

Database Connections

Message Queues

Workflow Orchestration

​Common Integration Patterns

​CI/CD Pipeline Integration

​Customer Support Automation

​Business Process Automation

​Enterprise Integrations

​ERP System Integration

​Monitoring and Observability