> ## Documentation Index > Fetch the complete documentation index at: https://docs.orq.ai/llms.txt > Use this file to discover all available pages before exploring further. # Get run metrics > Returns aggregated evaluator and built-in metrics for a run, keyed by task column and metric. Pass baseline_run_id to include the delta against another run. > [!NOTE] > > **Related guide**: Experiments guide. See the [Experiments guide](https://docs.orq.ai/ai-studio/optimize/experiments.md) for a walkthrough with examples. ## OpenAPI ````yaml get /v3/experiments/{experiment_id}/runs/{run_id}/metrics openapi: 3.1.0 info: title: orq.ai API version: '2.0' description: orq.ai API documentation servers: - url: https://my.orq.ai security: - ApiKey: [] tags: - name: Chunking description: Split text into smaller chunks for retrieval and generation workflows. - name: File Systems description: >- Create and manage persistent file systems that agents and MCP clients read from and write to. - name: Knowledge Bases description: Create and manage knowledge bases used by agents and retrieval workflows. - name: Memory Stores description: Create and manage memory stores, memories, and memory documents. - name: Evals description: Run an evaluator against a conversation and its result - name: Logs description: >- OpenTelemetry log query API. Search, filter, aggregate, and facet log records ingested via OTLP. - name: Reporting description: >- GenAI reporting API over canonical analytics rollups. Accepts a metric name, time range, grain, group-by, and filters; returns a typed time series and optional totals. - name: Traces description: >- Query and inspect ingested trace data: search trace summaries, aggregate metrics, and read individual traces and their spans. - name: Experiments description: >- Create and run experiments: dataset rows against task columns, evaluators, and human review. - description: List models available through the AI Router. name: Models - name: Alerts description: >- Alerts evaluate a Reporting API metric on a fixed interval and fire notifications through notifiers when the value breaches a threshold. Each breach opens a trigger that tracks the incident until the value recovers. - name: Annotation Queues description: Annotation queues collect spans for human review. - name: API keys description: >- API keys authenticate programmatic access to the workspace. They expose opaque tokens, per-domain access grants, and budget and rate-limit constraints. - name: Audit Logs description: Audit logs record workspace entity changes and access-relevant events. - name: Budgets description: >- Budgets govern spend, token usage, and request rate across six scopes: workspace, project, identity, API key, provider, and model. Every applicable budget is enforced, and the most restrictive limit applies per dimension. - name: Files description: File upload and retrieval operations. - name: Guardrail Rules description: >- Guardrail Rules conditionally enforce evaluators and plugins for AI Gateway traffic. Rules may be scoped to a project or the whole workspace. - name: Hub description: Hub items are reusable templates available to a workspace. - name: Identities description: >- Identities represent end users from your system for usage and engagement tracking. - name: Management keys description: >- Management keys are workspace-scoped credentials that authenticate programmatic access to workspace administration surfaces (API keys, budgets). Unlike project-scoped API keys, a management key always operates at the workspace level. - name: MCP Gateway description: >- Register upstream MCP servers, discover and sync their tools, and assemble gateways that expose a curated tool surface to MCP clients. - name: Model Catalog description: >- Browse the orq.ai model catalog: every model orq offers, across every provider, with pricing, capabilities and benchmark data. List endpoints only return models that are not deprecated. This API is public, requires no authentication, and is rate limited to 120 requests per minute per IP. Responses carry a 5-minute cache-control max-age. - name: Model Fusions description: >- Model Fusions combine independent model answers through a panel, judge, and optional synthesizer. - name: Monitors description: >- Monitors are custom observability dashboards for traces, runtime metrics, and logs. Each monitor holds widgets that chart one telemetry query. List the metrics, operations, and dimensions available to your workspace with `GET /v3/telemetry/capabilities`, or start from a preset with `GET /v2/monitors/presets`. - name: Notifiers description: Notifier destinations used to send delivery and workflow notifications. - name: Projects description: Projects organize resources within a workspace - name: Routing Rules description: >- Routing Rules conditionally select models and enforce request plugins for AI Gateway traffic. Rules are evaluated by ascending priority and may be scoped to a project or the whole workspace. - name: Threads description: Threads group related trace invocations and their aggregate usage - name: Skills description: >- Skills are modular instructions you can use to codify processes and conventions - name: Smart Routers description: >- Create and manage workspace Smart Routers. A Smart Router selects a model from an eligible pool for each request according to a quality, balanced, or cost profile. - name: Webhooks description: >- Create and manage webhooks that deliver workspace events to external HTTPS endpoints. - name: Workspaces description: >- A workspace is the tenant. Create is called from a user session during onboarding; Get, List, and Update are the public management surface. - name: Workspace Security description: >- Workspace-level domain verification and IP allowlist controls. These operations are restricted to workspace administrators. - name: Workspace Settings description: >- Workspace-level settings managed with a workspace credential. A workspace is the tenant, so these settings are a singleton — there is nothing to create or delete, only read and update. - name: Responses - description: Run agents on a cron cadence. Minimum firing interval is 1 hour. name: Agent Schedules - name: Embeddings - name: Telemetry description: >- Unified query envelope for traces, metrics, and logs. One request shape, one filter dialect, and one response shape per source, validated by a per-source registry. - description: Beta. Run typed classification questions against a classify model. name: Classify - description: Search Gateway with managed credits or BYOK. name: Web Search - description: Beta. Run classification decisions with the classify contract. name: Decisions - name: Users description: >- Workspace user administration. Requires a Management Key with user permissions or an authenticated workspace admin session. Standard API keys are not supported. externalDocs: url: https://docs.orq.ai description: orq.ai Documentation paths: /v3/experiments/{experiment_id}/runs/{run_id}/metrics: get: tags: - Experiments summary: Get run metrics description: >- Returns aggregated evaluator and built-in metrics for a run, keyed by task column and metric. Pass baseline_run_id to include the delta against another run. operationId: ExperimentRunMetricsGet parameters: - name: experiment_id in: path description: ID of the experiment. required: true schema: type: string - name: run_id in: path description: ID of the run. required: true schema: type: string - name: baseline_run_id in: query description: |- Run of the same experiment to compare against. A run without values returns no baseline. schema: type: string responses: '200': description: OK content: application/json: schema: $ref: '#/components/schemas/GetRunMetricsResponse' '401': description: >- Authentication required. See https://docs.orq.ai/reference/errors for the error codes. content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' x-code-samples: - lang: curl label: Core - Get run metrics source: > curl --get 'https://my.orq.ai/v3/experiments//runs//metrics' \ --header 'Authorization: Bearer $ORQ_API_KEY' - lang: python label: Python - Get run metrics source: | import os from orq_ai_sdk import Orq client = Orq(api_key=os.environ["ORQ_API_KEY"]) summary = client.experiments.get_run_metrics( experiment_id="", run_id="", ) for key, metric in summary.metrics.items(): print(key, metric) - lang: typescript label: Node.js - Get run metrics source: | import { Orq } from '@orq-ai/node'; const client = new Orq({ apiKey: process.env.ORQ_API_KEY }); const summary = await client.experiments.getRunMetrics({ experimentId: '', runId: '', }); for (const [key, metric] of Object.entries(summary.metrics)) { console.log(key, metric); } components: schemas: GetRunMetricsResponse: type: object properties: metrics: type: object additionalProperties: $ref: '#/components/schemas/MetricSummary' description: |- Metrics keyed `{task_column_id}_{evaluator_id}` for evaluators and `{task_column_id}_orq_{metric}` for usage metrics such as `orq_cost` and `orq_latency`. Empty when the run has no data. ErrorResponse: type: object properties: message: description: Human-readable error message. type: string code: type: integer description: Numeric error code, when one is available. details: type: array items: {} description: Additional error details, when the service reports any. required: - message MetricSummary: type: object properties: number: allOf: - $ref: '#/components/schemas/NumberMetricSummary' description: Set for numeric metrics. boolean: allOf: - $ref: '#/components/schemas/BooleanMetricSummary' description: Set for pass or fail evaluators. categorical: allOf: - $ref: '#/components/schemas/CategoricalMetricSummary' description: Set for evaluators that return labels. baseline: description: >- Comparison with the baseline run. Null unless `baseline_run_id` was given, and for categorical metrics or a baseline without values. anyOf: - allOf: - $ref: '#/components/schemas/MetricBaseline' - type: 'null' description: >- Summary of one metric. Exactly one of `number`, `boolean` and `categorical` is set, depending on the metric's output type. NumberMetricSummary: type: object properties: min: type: number description: Lowest value. format: double max: type: number description: Highest value. format: double avg: type: number description: Average value. format: double sum: type: number description: Sum of the values. format: double median: type: number description: Approximate median. format: double range: type: number description: '`max` minus `min`.' format: double std_dev: type: number description: Population standard deviation. format: double count: type: integer description: Number of rows with a value. format: int32 description: Statistics of a numeric metric. BooleanMetricSummary: type: object properties: passed_rate: type: number description: Percentage of rows that passed, 0 to 100, rounded to a whole number. format: double count: type: integer description: Number of rows with a value. format: int32 description: Statistics of a pass or fail metric. CategoricalMetricSummary: type: object properties: labels: type: object additionalProperties: type: integer format: int32 description: Number of rows per label. count: type: integer description: Number of rows with a label. format: int32 description: Statistics of a metric that returns labels. MetricBaseline: type: object properties: avg: type: number description: Average of the baseline run. format: double delta: type: number description: This run's average minus the baseline average. format: double pct_change: type: number description: |- `delta` as a percentage of the baseline average; 0 when the baseline average is 0. format: double direction: type: string description: '`up`, `down` or `same`, by the sign of `delta`.' description: Comparison with the baseline run. securitySchemes: ApiKey: type: http scheme: bearer bearerFormat: JWT ```` This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.