Infrastructure

Runtime operations layer for monitoring deployed services, managing gateway connections, and debugging live systems.

What is Infrastructure?

The Infrastructure section provides real-time visibility into running M3 Forge services and their connections to Marie-AI backend gateways. Infrastructure focuses on operational monitoring — what services are deployed, how they’re performing, and what’s connected right now.

Infrastructure enables you to:

Monitor executor deployments across multiple gateways with health status and capacity tracking
Manage webapp runners that host Gradio, Streamlit, and custom web applications
Track gateway connections with health checks and bearer token authentication
Register MCP servers to extend agent capabilities with external tools and services
View live events from workflow executions and system operations
Inspect capacity utilization to plan scaling and resource allocation
Debug production issues with log aggregation and trace inspection
Query service registry for discovering available endpoints and capabilities

Infrastructure dashboard showing deployments, gateways, capacity metrics, and system health status

Infrastructure vs Administration vs Self-Hosting

Understanding the distinction between these three sections:

Section	Focus	Examples
Infrastructure	RUNTIME operations — monitoring what’s running NOW	Deployment status, executor health, capacity utilization, live logs
Administration	SETTINGS and configuration — managing users, roles, API keys	User management, RBAC, LLM provider connections, workspace settings
Self-Hosting	INSTALLATION and setup — how to deploy M3 Forge	Docker Compose, environment variables, database migrations, gateway config

If you’re asking “What’s running and how is it performing?” — that’s Infrastructure.

If you’re asking “Who has access and what are my settings?” — that’s Administration.

If you’re asking “How do I install and configure M3 Forge?” — that’s Self-Hosting.

Key Capabilities

Deployment Monitoring

Track executor deployments across all configured gateways. Each deployment shows:

Status metrics - Total executors, nodes, active deployments, healthy percentage
Executor details - Name, status (SERVING/STOPPED/ERROR), node count, heartbeat timestamps
Capacity tracking - Total/used/available slots, utilization percentages
Desired states - Expected executor configuration grouped by epoch
Multi-gateway support - Switch between gateways with health testing before data fetch

See Deployments for comprehensive monitoring features.

Webapp Management

Manage webapp runners and deployed web applications with full lifecycle control:

Webapp lifecycle - Create, start, stop, redeploy web applications
App types - Gradio, Streamlit, FastAPI, static sites, custom Docker containers
Git integration - Deploy from Git repositories with branch tracking and status monitoring
Log viewing - Real-time logs streamed via SSE for each webapp
Runner health - Monitor webapp runner status, capabilities, and active webapp count
Deployment history - View past deployments with timestamps and outcomes

See Webapps for webapp deployment and management.

Gateway Connections

Monitor and manage connections to Marie-AI backend gateways:

Health checks - Automatic 5-second interval health testing with status indicators
Gateway configuration - Read from environment variables, conventional paths, or config files
Bearer token authentication - Secure gateway access with GATEWAY_TOKEN_<ID> env vars
Multi-gateway switching - Select active gateway from dropdown with health validation
Connection diagnostics - Test connectivity before attempting data operations

See Gateways for gateway configuration and health monitoring.

Event Tracking

View and analyze events from workflow executions and system operations:

Event streams - Real-time display of workflow events, LLM calls, validation results
Event details - Timestamps, event types, payloads, associated workflow/node IDs
Filtering - By workflow, event type, time range, severity
Vector embeddings - Event payloads stored with embeddings for semantic search
Audit trail - Complete history of system events for debugging and compliance

See Events for event monitoring and inspection.

Capacity Management

Track resource utilization across executor deployments:

Capacity overview - Total, used, and available capacity across all gateways
Slot tracking - Individual capacity holders with utilization percentages
Resource planning - Identify over/under-provisioned executors
Historical trends - Capacity usage over time to inform scaling decisions

See Capacity for resource tracking and planning.

Debugging Tools

Diagnose production issues with comprehensive debugging capabilities:

Log aggregation - Centralized logs from all services and executors
Trace inspection - Follow request flows across distributed services
Real-time updates - SSE-based log streaming with auto-scroll
Error analysis - Stack traces, validation failures, timeout events
Context reconstruction - Full execution context for workflow runs

See Debug for troubleshooting and diagnostics.

Service Registry

Discover available services, endpoints, and capabilities:

Service catalog - All registered services with health status and uptime
Endpoint discovery - REST and tRPC endpoints with schemas
Capability listings - What each executor/runner supports
Version tracking - Service versions and compatibility matrix

See Registry for service discovery and documentation.

Infrastructure data is fetched in real-time from Marie-AI backend gateways. Ensure gateways are configured and healthy before using Infrastructure features.

Real-Time Updates

Infrastructure views use Server-Sent Events (SSE) and auto-refresh intervals for real-time monitoring:

Health checks - Every 5 seconds for gateway connectivity
Deployment status - Every 10 seconds for executor heartbeats
Log streams - Real-time SSE for webapp and workflow logs
Event updates - Live event tracking with auto-scroll to latest
Capacity metrics - Refreshed on-demand and every 30 seconds

This ensures you always see current system state without manual refresh.

Multi-Gateway Architecture

M3 Forge supports connections to multiple Marie-AI backend gateways simultaneously. Each gateway represents a separate cluster or environment:

Gateway selection is available in all Infrastructure views via a dropdown selector. The system tests gateway health before fetching data to avoid timeouts.

Best Practices

Gateway Configuration

For production deployments:

Use environment variables for bearer tokens - Store GATEWAY_TOKEN_<ID> in secure secrets management
Configure multiple gateways - Redundancy and geographic distribution
Test connectivity before use - Health checks prevent timeout errors
Monitor gateway health - Set up alerts for gateway downtime

Resource Monitoring

Track capacity proactively:

Set capacity alerts - Notify when utilization exceeds 80%
Review capacity trends - Identify growth patterns for scaling decisions
Monitor executor health - Unhealthy nodes reduce available capacity
Balance load across gateways - Distribute workloads geographically

Debugging Workflow

When investigating production issues:

Check gateway health - Ensure connectivity to backend
Review recent events - Look for errors or unusual patterns
Inspect deployment status - Verify executors are SERVING and healthy
Examine logs - Use Debug view for centralized log aggregation
Compare capacity - Ensure sufficient resources for workload

Security Considerations

Gateway Authentication

Bearer tokens required - All gateway API calls use Authorization: Bearer <token> headers
Token storage - Tokens stored in environment variables, never in frontend code
Token rotation - Update GATEWAY_TOKEN_* env vars and restart API server
Per-gateway tokens - Each gateway has independent authentication

Access Control

Infrastructure data access is role-based:

Admins - Full access to all Infrastructure views and operations
Operators - View-only access to deployments, events, capacity
Developers - Access to logs and debugging tools for their workflows
Viewers - No access to Infrastructure section

Audit Logging

All Infrastructure operations are logged:

Gateway configuration changes
Webapp deployments and lifecycle operations
Debug log access with filter criteria
Capacity threshold violations

Next Steps

Configure gateway connections to Marie-AI backends
Monitor executor deployments and health status
Set up webapp runners for Gradio and Streamlit apps
Track capacity utilization for scaling decisions
Use debugging tools to diagnose production issues