> ## Documentation Index
> Fetch the complete documentation index at: https://docs.comput3.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# ELIZAOS Integration
> Integrate ELIZAOS agents with existing systems and workflows
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:
Connect agents to REST APIs, GraphQL endpoints, and webhook systems.
Direct integration with SQL and NoSQL databases for data processing.
Integrate with RabbitMQ, Apache Kafka, and other messaging systems.
Connect with existing workflow engines and business process automation.
## Common Integration Patterns
### CI/CD Pipeline Integration
Deploy an agent that automatically reviews code changes in your CI/CD pipeline.
```yaml theme={null}
# 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 }}
}'
```
Automatically generate test cases for new code changes.
```json theme={null}
{
"name": "test-generator",
"triggers": [
{
"type": "webhook",
"source": "github",
"event": "push",
"filter": "src/**/*.py"
}
],
"actions": [
{
"type": "analyze_code",
"generate_tests": true,
"coverage_target": 80
}
]
}
```
Keep documentation up-to-date with code changes.
```python theme={null}
# 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
**Intelligent support ticket classification and routing**
```json theme={null}
{
"name": "ticket-router",
"model": "hermes4:70b",
"specialization": "customer_support",
"integrations": [
{
"type": "zendesk",
"config": {
"subdomain": "yourcompany",
"email": "support@yourcompany.com",
"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
**Dynamic knowledge base management**
**Features**:
* Automatic FAQ generation from tickets
* Knowledge article updates based on resolutions
* Search and recommendation improvements
* Multi-language support
**Integration Example**:
```python theme={null}
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
**Automated data processing and analysis**
```json theme={null}
{
"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"]
}
]
}
```
**Automated content review and moderation**
**Agent Configuration**:
```json theme={null}
{
"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"]
}
```
**Automated financial data processing and reporting**
**Capabilities**:
* Transaction analysis and categorization
* Fraud detection and alerting
* Financial report generation
* Budget tracking and forecasting
* Compliance monitoring
**Example Agent**:
```python theme={null}
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
**Connect with SAP ERP systems**
```json theme={null}
{
"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"]
}
}
}
```
**CRM data analysis and automation**
**Capabilities**:
* Lead scoring and qualification
* Opportunity analysis and forecasting
* Customer data enrichment
* Automated follow-up sequences
**Setup**:
```json theme={null}
{
"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": "ai-integration@yourcompany.com",
"password_env": "SF_PASSWORD"
}
}
]
}
```
### Monitoring and Observability
**Metrics collection and monitoring**
* Agent performance metrics
* Resource utilization tracking
* Custom business metrics
* Alert manager integration
**Visualization and alerting**
* Real-time agent performance dashboards
* Resource usage visualization
* Cost tracking and optimization
* Custom alert configurations
**Centralized logging with ELK stack**
* Elasticsearch for log storage
* Logstash for log processing
* Kibana for log visualization
* Custom log analysis agents
**End-to-end request tracing**
* Jaeger or Zipkin integration
* Agent interaction tracing
* Performance bottleneck identification
* Service dependency mapping
## Webhook and Event Integration
### Webhook Configuration
```json Incoming Webhooks theme={null}
{
"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"
}
]
}
}
}
```
```json Outgoing Webhooks theme={null}
{
"outgoing_webhooks": {
"task_completion": {
"url": "https://your-system.com/api/task-complete",
"method": "POST",
"headers": {
"Authorization": "Bearer YOUR_API_KEY",
"Content-Type": "application/json"
},
"triggers": [
"agent_task_completed",
"workflow_finished",
"error_occurred"
]
}
}
}
```
### Event-Driven Architecture
**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**
```python theme={null}
# 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"]
}
}
```
**Intelligent event routing to appropriate agents**
* Content-based routing (analyze event content)
* Load-based routing (distribute to available agents)
* Skill-based routing (match events to specialized agents)
* Priority-based routing (handle urgent events first)
## Best Practices
### Agent Design Patterns
**Design agents with focused, specific purposes**
✅ **Good**: Separate agents for code review, testing, and documentation
❌ **Avoid**: One agent trying to handle all development tasks
**Benefits**:
* Easier debugging and maintenance
* Better resource utilization
* Improved fault isolation
* Simpler scaling decisions
**Design agents to be stateless when possible**
✅ **Good**: Store state in external databases or shared storage
❌ **Avoid**: Relying on agent memory for critical data
**Implementation**:
```python theme={null}
# 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
```
**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
```python theme={null}
# 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
**Optimize agent resource usage**
* Use appropriate model sizes for tasks
* Implement connection pooling
* Cache frequently accessed data
* Batch similar operations together
**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
**Comprehensive agent monitoring**
* Track agent performance metrics
* Monitor resource utilization
* Set up alerting for failures
* Analyze usage patterns for optimization
**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
```python Research Pipeline theme={null}
# 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"
}
]
}
```
```json E-commerce Automation theme={null}
{
"name": "ecommerce-automation",
"description": "Complete e-commerce order processing automation",
"agents": [
{
"name": "order-processor",
"model": "hermes4:70b",
"responsibilities": [
"validate_orders",
"check_inventory",
"process_payments"
]
},
{
"name": "inventory-manager",
"model": "hermes4:70b",
"responsibilities": [
"track_inventory",
"reorder_products",
"optimize_stock_levels"
]
},
{
"name": "customer-communicator",
"model": "kimi-k2",
"responsibilities": [
"send_confirmations",
"provide_updates",
"handle_inquiries"
]
}
],
"integrations": [
"shopify_store",
"inventory_database",
"payment_processor",
"shipping_apis",
"email_service"
]
}
```
### Real-time System Integration
**Manage and respond to IoT device data**
```python theme={null}
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**
```json theme={null}
{
"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**
```python theme={null}
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
**Test individual agent functions**
```python theme={null}
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)
```
**Test agent interactions with external systems**
```python theme={null}
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
```
**Test agent performance under load**
```bash theme={null}
# Load test agent endpoints
elizaos test load \
--agent document-analyzer \
--concurrent-requests 100 \
--duration 300s \
--ramp-up 30s
```
## Security Considerations
### Agent Security
**Isolate agent execution environments**
* Process isolation using containers
* Network namespace separation
* File system access restrictions
* Resource quota enforcement
**Fine-grained access controls**
```json theme={null}
{
"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
}
}
}
```
**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
Learn how to deploy ELIZAOS on Comput3 Network infrastructure.
Connect external data sources and tools to your ELIZAOS agents.
Choose the right GPU instances for your ELIZAOS deployment.