Set Up Observability

Omnia includes an optional observability stack with Prometheus, Grafana, Loki, and Tempo for comprehensive monitoring of your agent deployments.

Prerequisites

• Kubernetes cluster with Helm 3.x
• Omnia Helm chart installed

Enable the Observability Stack

The observability components are disabled by default. Enable them in your Helm values:

prometheus:
  enabled: true

grafana:
  enabled: true

loki:
  enabled: true

tempo:
  enabled: true

alloy:
  enabled: true

Install or upgrade with these values:

helm upgrade --install omnia oci://ghcr.io/altairalabs/omnia \
  --namespace omnia-system \
  --create-namespace \
  -f values.yaml

Access Grafana

Port Forward

For development, port-forward to access Grafana:

kubectl port-forward svc/omnia-grafana 3000:80 -n omnia-system

Open http://localhost:3000 and log in with:

• Username: admin
• Password: admin (change this in production)

Via Internal Gateway

If you’ve enabled the internal gateway (with Istio), Grafana is available at /grafana:

kubectl get gateway omnia-internal -n omnia-system -o jsonpath='{.status.addresses[0].value}'

Then access http://<gateway-ip>:8080/grafana/

View Agent Metrics

Omnia agents expose Prometheus metrics automatically on the /metrics endpoint. Metrics are organized into several categories.

Facade Metrics

Connection and session metrics from the WebSocket facade:

Metric	Type	Labels	Description
omnia_agent_connections_active	Gauge	-	Current WebSocket connections
omnia_agent_connections_total	Counter	-	Total connections since startup
omnia_agent_requests_inflight	Gauge	-	Pending LLM requests
omnia_agent_request_duration_seconds	Histogram	-	Request latency
omnia_agent_messages_received_total	Counter	-	Messages received
omnia_agent_messages_sent_total	Counter	-	Messages sent

LLM Metrics

Token usage and cost metrics from LLM provider calls (via PromptKit SDK collector):

Metric	Type	Labels	Description
omnia_provider_input_tokens_total	Counter	provider, model	Input tokens sent to LLMs
omnia_provider_output_tokens_total	Counter	provider, model	Output tokens received
omnia_provider_requests_total	Counter	provider, model, status	Total LLM requests
omnia_provider_cost_total	Counter	provider, model	Estimated cost in USD
omnia_provider_request_duration_seconds	Histogram	provider, model	LLM request duration

Runtime Metrics

Detailed metrics for pipelines, stages, tools, and validations:

Pipeline Metrics:

Metric	Type	Labels	Description
omnia_runtime_pipelines_active	Gauge	-	Currently active pipelines
omnia_runtime_pipeline_duration_seconds	Histogram	status	Pipeline execution duration

Stage Metrics:

Metric	Type	Labels	Description
omnia_runtime_stage_elements_total	Counter	stage, status	Total stage executions
omnia_runtime_stage_duration_seconds	Histogram	stage, stage_type	Stage execution duration

Tool Metrics:

Metric	Type	Labels	Description
omnia_runtime_tool_calls_total	Counter	tool, status	Total tool invocations
omnia_runtime_tool_call_duration_seconds	Histogram	tool	Tool execution duration

Validation Metrics:

Metric	Type	Labels	Description
omnia_runtime_validations_total	Counter	validator, validator_type, status	Total validations
omnia_runtime_validation_duration_seconds	Histogram	validator, validator_type	Validation duration

Eval Worker Metrics

The eval worker exposes metrics for event processing, eval execution, sampling, and result persistence. These are available when enterprise.evalWorker.enabled is true. The eval worker pod includes Prometheus scrape annotations automatically.

Event Processing:

Metric	Type	Labels	Description
omnia_eval_worker_events_received_total	Counter	event_type	Session events consumed from Redis Streams
omnia_eval_worker_event_processing_duration_seconds	Histogram	event_type	End-to-end time to process a stream event
omnia_eval_worker_stream_lag	Gauge	stream	Pending messages per Redis stream (consumer lag)

Eval Execution:

Metric	Type	Labels	Description
omnia_eval_worker_evals_executed_total	Counter	eval_type, trigger, status	Total eval executions (success/error)
omnia_eval_worker_eval_duration_seconds	Histogram	eval_type, trigger	Eval execution duration
omnia_eval_worker_evals_sampled_total	Counter	eval_type, decision	Sampling decisions (sampled vs skipped)
omnia_eval_worker_results_written_total	Counter	status	Eval results written to session-api

Session API Metrics

The session-api exposes HTTP request metrics and event publishing metrics. Prometheus scrape annotations are included on the deployment by default.

HTTP Requests:

Metric	Type	Labels	Description
omnia_session_api_requests_total	Counter	method, route, status_code	Total HTTP requests
omnia_session_api_request_duration_seconds	Histogram	method, route, status_code	HTTP request duration

Event Publishing (requires Redis):

Metric	Type	Labels	Description
omnia_session_api_events_published_total	Counter	status	Redis stream publish attempts (success/error)
omnia_session_api_event_publish_duration_seconds	Histogram	—	Time to publish an event to Redis Streams

Query Metrics in Grafana

1. Open Grafana and go to Explore
2. Select the Prometheus datasource
3. Try these queries:

# Active connections
omnia_agent_connections_active

# Request rate
rate(omnia_agent_requests_total[5m])

# P95 request latency
histogram_quantile(0.95, rate(omnia_agent_request_duration_seconds_bucket[5m]))

# LLM cost per model (last hour)
sum by (model) (increase(omnia_provider_cost_total[1h]))

# Token usage rate by provider
sum by (provider) (rate(omnia_provider_input_tokens_total[5m]) + rate(omnia_provider_output_tokens_total[5m]))

# Tool call error rate
sum(rate(omnia_runtime_tool_calls_total{status="error"}[5m])) / sum(rate(omnia_runtime_tool_calls_total[5m]))

# Average pipeline duration
histogram_quantile(0.5, rate(omnia_runtime_pipeline_duration_seconds_bucket[5m]))

# Eval worker: evals per second by type
sum by (eval_type) (rate(omnia_eval_worker_evals_executed_total[5m]))

# Eval worker: eval pass/fail rate
sum(rate(omnia_eval_worker_evals_executed_total{status="error"}[5m])) / sum(rate(omnia_eval_worker_evals_executed_total[5m]))

# Eval worker: consumer lag across all streams
omnia_eval_worker_stream_lag

# Eval worker: P95 eval duration by type
histogram_quantile(0.95, rate(omnia_eval_worker_eval_duration_seconds_bucket[5m]))

# Session API: request rate by route
sum by (route) (rate(omnia_session_api_requests_total[5m]))

# Session API: P99 request latency
histogram_quantile(0.99, rate(omnia_session_api_request_duration_seconds_bucket[5m]))

# Session API: event publish error rate
rate(omnia_session_api_events_published_total{status="error"}[5m])

View Agent Logs

Logs are collected by Alloy and stored in Loki.

Query Logs in Grafana

1. Open Grafana and go to Explore
2. Select the Loki datasource
3. Use LogQL queries:

{namespace="omnia-system", container="agent"}

{namespace="omnia-system"} |= "error"

{namespace="omnia-system", app_name="my-agent"}

Agent Tracing with OpenTelemetry

The runtime container supports OpenTelemetry tracing for detailed visibility into conversations, LLM calls, and tool executions.

Enable Tracing

Tracing is configured via environment variables on the AgentRuntime. The operator will pass these to the runtime container:

apiVersion: omnia.altairalabs.ai/v1alpha1
kind: AgentRuntime
metadata:
  name: my-agent
spec:
  # ... other config ...
  runtime:
    env:
      - name: OMNIA_TRACING_ENABLED
        value: "true"
      - name: OMNIA_TRACING_ENDPOINT
        value: "tempo.omnia-system.svc.cluster.local:4317"
      - name: OMNIA_TRACING_SAMPLE_RATE
        value: "1.0"
      - name: OMNIA_TRACING_INSECURE
        value: "true"

Tracing Configuration Options

Environment Variable	Description	Default
OMNIA_TRACING_ENABLED	Enable OpenTelemetry tracing	false
OMNIA_TRACING_ENDPOINT	OTLP collector endpoint (gRPC)	-
OMNIA_TRACING_SAMPLE_RATE	Sampling rate (0.0 to 1.0)	1.0
OMNIA_TRACING_INSECURE	Disable TLS for OTLP connection	false

Span Types

The runtime creates three types of spans:

Conversation Spans (conversation.turn)

• Created for each message exchange
• Includes session ID, message length, response length
• Parent span for LLM and tool spans

LLM Spans (llm.call)

• Created for each LLM API call
• Includes model name, token counts (input/output), cost

Tool Spans (tool.<name>)

• Created for each tool execution
• Includes tool name, success/error status, result size

Trace Attributes

Traces include rich metadata for debugging:

Attribute	Description
omnia.session_id	Conversation session identifier
llm.model	LLM model used
llm.input_tokens	Input token count
llm.output_tokens	Output token count
llm.cost_usd	Estimated cost in USD
tool.name	Tool that was called
tool.is_error	Whether tool returned an error
tool.result_size	Size of tool result

View Traces in Tempo

Tempo collects distributed traces from agents.

Query Traces in Grafana

1. Open Grafana and go to Explore
2. Select the Tempo datasource
3. Search by:
   • Service name (e.g., omnia-runtime-my-agent)
   • Trace ID
   • Duration
   • Tags (e.g., omnia.session_id)

Example Trace Query

Find slow conversations:

{ duration > 5s && resource.service.name =~ "omnia-runtime.*" }

Find tool errors:

{ span.tool.is_error = true }

Production Considerations

Persistent Storage

Enable persistent storage for production:

prometheus:
  server:
    persistentVolume:
      enabled: true
      size: 50Gi

loki:
  singleBinary:
    persistence:
      enabled: true
      size: 50Gi

tempo:
  persistence:
    enabled: true
    size: 10Gi

Change Grafana Password

grafana:
  adminPassword: your-secure-password

Or use a secret:

grafana:
  admin:
    existingSecret: grafana-admin-secret
    userKey: admin-user
    passwordKey: admin-password

Enable Loki Ruler (Log-based Alerting)

The Loki ruler is disabled by default to avoid startup issues on local development environments. For production deployments that need log-based alerting, enable it:

loki:
  ruler:
    enabled: true
    storage:
      type: local
      local:
        directory: /var/loki/rules
    alertmanager_url: http://alertmanager:9093
  singleBinary:
    extraVolumes:
      - name: rules
        emptyDir: {}
    extraVolumeMounts:
      - name: rules
        mountPath: /var/loki/rules

The ruler allows you to:

• Alerting rules: Fire alerts based on LogQL queries (e.g., error rate thresholds)
• Recording rules: Pre-compute expensive queries for faster dashboards

For cloud storage backends (S3, GCS), configure ruler storage accordingly:

loki:
  ruler:
    enabled: true
    storage:
      type: s3
      s3:
        bucketnames: loki-rules
        region: us-west-2

Resource Limits

Adjust resources based on your cluster size:

prometheus:
  server:
    resources:
      requests:
        cpu: 500m
        memory: 512Mi
      limits:
        cpu: 1000m
        memory: 1Gi

grafana:
  resources:
    requests:
      cpu: 100m
      memory: 128Mi
    limits:
      cpu: 500m
      memory: 256Mi

Disable Individual Components

You can enable only the components you need:

prometheus:
  enabled: true
grafana:
  enabled: true
loki:
  enabled: false
tempo:
  enabled: false
alloy:
  enabled: false

Use External Observability

If you have existing observability infrastructure, disable the subcharts and configure agents to export to your systems:

prometheus:
  enabled: false
grafana:
  enabled: false
loki:
  enabled: false
tempo:
  enabled: false

Agent pods include Prometheus scrape annotations by default, so your existing Prometheus can scrape them automatically.