Orq span attributes reference

orq.* attributes are OpenTelemetry span attributes emitted by Orq.ai on every trace span. They appear in the Traces panel, in LLM webhook payloads, and in trace exports. Core attributes appear on every span; attributes in Invoked resources, Evaluators, and Routing sections appear only when the relevant feature was active.

Quick reference

What to find	Attributes
Span type	`orq.span_type`
Model	`orq.model.id`, `orq.model.provider`, `orq.model.type`, `orq.model.is_private`
Cost	`orq.billing.*`
Tenant scope	`orq.organization_id`, `orq.workspace_id`, `orq.project_id`, `orq.identity_id`, `orq.api_key_id`, `orq.product`
Invoked resources	`orq.deployment_id`, `orq.deployment_key`, `orq.deployment.variant_id`, `orq.prompt_id`, `orq.knowledge_id`, `orq.memory_store_id`, `orq.playground_id`, `orq.dataset_id`, `orq.schedule_id`, `orq.workflow.run_id`
Conversation	`orq.thread_id`, `orq.session_id`, `orq.agent_execution_id`
Custom metadata	`orq.metadata`, `orq.tags`, `orq.variables`, `orq.object_name`
Guardrail rules and routing rules	`orq.routing_rule.id`, `orq.guardrail_rule.ids`, `orq.guardrail.*`
Evaluators	`orq.evaluator.id`, `orq.evaluator.type`, `orq.evaluator.version`, `orq.evaluator.output_type`, `orq.evaluation.stage`
Routing and fallback	`orq.fallback.`, `orq.auto_router.`, `orq.load_balancer.*`
Framework source	`orq.trace.framework.name`

Span type

Classifies the kind of work a span represents. Use orq.span_type to filter spans by operation type in the Traces view or LLM webhook payloads.

Attribute	Description
`orq.span_type`	Span operation type

Common values:

Value	Meaning
`span.chat_completion`	Chat completion call
`span.responses`	Responses API call
`span.embedding`	Embeddings
`span.retrieval`	Knowledge retrieval
`span.evaluator`	Evaluator or guardrail
`span.agent`	Agent step within a run
`span.agent_execution`	Full agent run
`span.tool`	Tool execution
`span.fallback_selected`	Fallback model selected
`span.auto_router`	Smart Router decision
`span.load_balancer`	Load balancer decision

Model

Attribute	Description
`orq.model.id`	Orq.ai model catalog ID (UUID). For the human-readable model name, see `gen_ai.response.model` below.
`orq.model.provider`	Provider, e.g. `"openai"`, `"anthropic"`
`orq.model.type`	Model type, e.g. `"chat"`, `"embedding"`
`orq.model.is_private`	Whether a workspace-private model was used (a model added to the workspace model catalog, not a shared Orq.ai model)

Related standard fields:

gen_ai.request.model: model requested by the caller
gen_ai.response.model: model that actually ran; use this for the human-readable model name, as it reflects the true model after any routing or fallback

Cost and billing

These attributes appear in the Economics panel, trace list, and LLM webhook payloads.

Attribute	Description
`orq.billing.total_cost`	Total cost for the span (float, USD)
`orq.billing.input_cost`	Input token cost (float, USD)
`orq.billing.output_cost`	Output token cost (float, USD)
`orq.billing.cache_read_cost`	Prompt cache read cost (float, USD)
`orq.billing.cache_write_cost`	Prompt cache write cost (float, USD)
`orq.billing.reasoning_cost`	Reasoning token cost (float, USD)
`orq.billing.audio_input_cost`	Audio input cost (float, USD)
`orq.billing.audio_output_cost`	Audio output cost (float, USD)
`orq.billing.web_search_cost`	Web search fees (float, USD)
`orq.billing.billable`	Whether this span counts toward workspace usage billing (bool). `false` for cached responses and internal evaluator calls.
`orq.billing.pricing_tier`	Pricing tier: `"standard"`, `"extended_128k"`, `"extended_200k"` (string)
`orq.billing.threshold_exceeded`	A configured budget threshold was exceeded (bool). The call still completes; this flag signals that alerting or enforcement rules may apply.
`orq.billing.integration_id`	ID of the provider integration (the API key configuration for a specific provider account) used to look up pricing rates (string)

Token counts live under gen_ai.usage.*, not orq.*. See Token usage below.

Token usage

Token counts follow the OpenTelemetry gen_ai.usage.* convention. Detailed breakdowns are present only when the provider reports them.

Attribute	Description
`gen_ai.usage.input_tokens`	Input tokens, including any cached or cache-written tokens (int)
`gen_ai.usage.output_tokens`	Output tokens (int)
`gen_ai.usage.total_tokens`	Input plus output tokens (int)
`gen_ai.usage.prompt_tokens_details.cached_tokens`	Input tokens read from the prompt cache (int)
`gen_ai.usage.prompt_tokens_details.cache_creation_tokens`	Input tokens written to the prompt cache, across all TTLs (int)
`gen_ai.usage.prompt_tokens_details.cache_creation_5m_tokens`	Cache write tokens with the 5-minute TTL. Anthropic only (int)
`gen_ai.usage.prompt_tokens_details.cache_creation_1h_tokens`	Cache write tokens with the 1-hour TTL. Anthropic only (int)
`gen_ai.usage.prompt_tokens_details.audio_tokens`	Audio input tokens (int)
`gen_ai.usage.completion_tokens_details.reasoning_tokens`	Reasoning or thinking tokens (int)
`gen_ai.usage.completion_tokens_details.audio_tokens`	Audio output tokens (int)

The cached and cache-written counts are subsets of gen_ai.usage.input_tokens. Their cost is reported separately in orq.billing.cache_read_cost and orq.billing.cache_write_cost. See Prompt caching for how these appear per API.

Tenant scope

Scope a trace to the organization, workspace, and product surface.

Attribute	Description
`orq.organization_id`	Organization the call belongs to
`orq.workspace_id`	Workspace
`orq.project_id`	Project within the workspace
`orq.identity_id`	End-user Identity (preferred)
`orq.contact_id`	Legacy alias for identity; same meaning as `orq.identity_id`
`orq.api_key_id`	API key used for the request
`orq.product`	Product surface that handled the request, e.g. `"router"`, `"agents"`, `"spreadsheets"`

Filter traces by project, or assign cost to an Identity.

Invoked resources

Links a trace to the Orq.ai resources that served the request.

Attribute	Description
`orq.deployment_id`	Deployment ID
`orq.deployment_key`	Deployment key/slug
`orq.deployment.variant_id`	Variant that served the request
`orq.prompt_id`	Prompt resource
`orq.knowledge_id`	Knowledge base used in retrieval
`orq.memory_store_id`	Memory store ID, present on spans that use memory-augmented retrieval
`orq.playground_id`	Playground session ID, present on spans initiated from the Playground
`orq.dataset_id`	Dataset ID, present on spans triggered by evals or experiments
`orq.schedule_id`	Schedule ID, present on spans triggered by a schedule
`orq.workflow.run_id`	Workflow run

Use these to answer: which deployment, prompt, knowledge base, schedule, or workflow produced this trace? To identify which agent execution produced this trace, use orq.agent_execution_id.

Conversation and session grouping

Attribute	Description
`orq.thread_id`	Thread ID: groups turns within a single conversation
`orq.session_id`	Session ID: groups requests that share the same value across conversations
`orq.agent_execution_id`	A single Agent run

orq.thread_id groups individual turns into a conversation. orq.session_id groups requests across conversations for broader session tracking; when a request carries both, orq.thread_id determines the grouping. Pass orq.thread_id to group related turns in the Threads view.

The standard OTel field gen_ai.conversation.id is also supported. When both are present, orq.thread_id takes precedence as the first-class thread identifier in the UI and trace exports.

Custom metadata and attribution

Attribute	Description
`orq.metadata`	Custom key-value metadata (JSON string on the span)
`orq.tags`	Request-level tags for filtering and reporting
`orq.variables`	Template variables passed by the caller to fill prompt placeholders for this call (JSON string)
`orq.object_name`	Display name for the traced object

Arbitrary key-value context attached to the span, such as customer_tier or feature_flag. Filterable in the Traces view and in Trace Automations.

Filterable attributes follow ingest limits: string values longer than 512 bytes are not kept as filterable attributes, and attribute keys longer than 512 bytes are dropped. Up to 200 attribute keys per span are kept, raise the limit to at most 1000 with the orq.attribute_keys_limit attribute. Selection is deterministic: keys prefixed gen_ai. or orq. are kept first, then the remaining keys alphabetically. The full span payload, including values past these limits, stays available on the span detail.

Guardrail rules and routing rules

These attributes reflect static rule enforcement: which routing rule or guardrail matched this request. For dynamic model selection (fallback, Smart Router, load balancer), see Routing, fallback, and model selection.

Attribute	Description
`orq.routing_rule.id`	Routing rule that matched
`orq.guardrail_rule.ids`	IDs of Guardrail rules that matched this request (array of strings)
`orq.guardrail.enabled`	Span is a blocking guardrail check
`orq.guardrail.action`	Action taken, e.g. `"block"`

Use these to debug why a request was blocked or routed by a rule.

Evaluators

On Evaluator and Guardrail spans:

Attribute	Description
`orq.evaluator.id`	Evaluator ID
`orq.evaluator.type`	Evaluator type, e.g. `"llm_judge"`, `"regex"`, `"similarity"`
`orq.evaluator.version`	Evaluator version
`orq.evaluator.output_type`	Output type, e.g. `"boolean"`, `"score"`, `"label"`
`orq.evaluation.stage`	`"input"` when the evaluator ran before the model call (checking the prompt); `"output"` when it ran after (checking the response)

Score, pass, and fail results also appear via standard gen_ai.evaluation.* attributes. gen_ai.evaluation.explanation carries free-text reasoning. Explanations longer than 512 bytes appear shortened with an ellipsis in list views and tooltips; the full text remains on the span detail. Substring filters are not offered on this attribute.

Routing, fallback, and model selection

These attributes reflect dynamic model selection at runtime: which model was actually chosen, and why it differed from the one requested. For static rule enforcement, see Guardrail rules and routing rules.

Attribute	Description
`orq.fallback.index`	Attempt number (`0` = primary, `1` = first fallback, …)
`orq.fallback.previous_model`	Model that failed
`orq.fallback.previous_error`	Error that triggered fallback
`orq.auto_router.selected_model`	Model chosen by the Smart Router
`orq.auto_router.strong_model`	High-capability candidate
`orq.auto_router.economical_model`	Cost-efficient candidate
`orq.auto_router.profile`	Smart Router profile
`orq.load_balancer.selected_model`	Model chosen by load balancer
`orq.load_balancer.original_model`	Originally requested model

Use these to explain cost differences and latency spikes when routing changed the model.

Framework source

Attribute	Description
`orq.trace.framework.name`	Framework that emitted the trace, e.g. `"langchain"`, `"openai-agents"`, `"llama-index"`

Use this to distinguish traces emitted directly by Orq.ai from those produced by an external framework.

Webhook example

A typical LLM webhook payload includes these attributes in the data.attributes block:

{
  "orq.organization_id": "3d4c4097-3ad7-48f8-965e-71d8d25ddb1b",
  "orq.workspace_id": "391ff8f8-95cd-49bd-9e28-eddba2d570b8",
  "orq.product": "router",
  "orq.span_type": "span.chat_completion",
  "orq.model.id": "85b18e5b-76a0-48dc-99b5-5738c35c9c85",
  "orq.model.provider": "openai",
  "orq.model.type": "chat",
  "orq.model.is_private": false,
  "orq.billing.input_cost": 0.0000012,
  "orq.billing.output_cost": 0.0000054,
  "orq.billing.total_cost": 0.0000066,
  "orq.billing.billable": false,
  "orq.billing.pricing_tier": "standard"
}

See Webhooks for the full envelope shape.

Overview

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Quick reference

Span type

Model

Cost and billing

Token usage

Tenant scope

Invoked resources

Conversation and session grouping

Custom metadata and attribution

Guardrail rules and routing rules

Evaluators

Routing, fallback, and model selection

Framework source

Webhook example

​Quick reference

​Span type

​Model

​Cost and billing

​Token usage

​Tenant scope

​Invoked resources

​Conversation and session grouping

​Custom metadata and attribution

​Guardrail rules and routing rules

​Evaluators

​Routing, fallback, and model selection

​Framework source

​Webhook example

Quick reference

Span type

Model

Cost and billing

Token usage

Tenant scope

Invoked resources

Conversation and session grouping

Custom metadata and attribution

Guardrail rules and routing rules

Evaluators

Routing, fallback, and model selection

Framework source

Webhook example