> ## Documentation Index > Fetch the complete documentation index at: https://docs.pandaprobe.com/llms.txt > Use this file to discover all available pages before exploring further. # Create Monitor > Create an evaluation monitor that spawns eval runs on a recurring schedule. Monitors persist a reusable evaluation configuration (target type, metrics, filters, cadence) and the system automatically creates eval runs at each scheduled interval. If ``only_if_changed`` is true (default), runs are skipped when no new data has arrived since the previous run. **Request body fields:** - **name** *(string, required)*: Human-readable label, e.g. ``"Daily prod eval"``. - **target_type** *(string, required)*: ``"TRACE"`` or ``"SESSION"``. - **metrics** *(string[], required)*: Metric names to run. Use ``GET /evaluations/trace-metrics`` (TRACE) or ``GET /evaluations/session-metrics`` (SESSION) for available names. - **filters** *(object, optional, default {})*: Scope the data the monitor evaluates. Pass ``{}`` to match everything. Accepted keys depend on ``target_type``: - **TRACE**: ``date_from``, ``date_to`` (ISO 8601), ``status`` (PENDING/RUNNING/COMPLETED/ERROR), ``session_id``, ``user_id``, ``tags`` (string[]), ``name`` (substring match). - **SESSION**: ``date_from``, ``date_to``, ``user_id``, ``has_error`` (bool), ``tags`` (string[]), ``min_trace_count`` (int). - **cadence** *(string, required)*: Firing schedule. Predefined: ``"every_6h"``, ``"daily"``, ``"weekly"``. Custom cron: ``"cron:"``, e.g. ``"cron:0 3 * * *"`` (daily 3 AM UTC), ``"cron:0 6 * * 1-5"`` (weekdays 6 AM). - **sampling_rate** *(float, optional, default 1.0)*: Fraction of matching items to evaluate per run (0.0–1.0). - **model** *(string, optional)*: LLM model override, e.g. ``"openai/gpt-4o"``. - **only_if_changed** *(bool, optional, default true)*: Skip the run if no new traces/sessions exist since the last run. - **signal_weights** *(object, optional, SESSION only)*: Override signal aggregation weights. Keys: ``confidence``, ``loop_detection``, ``tool_correctness``, ``coherence``. Auth: ``Bearer`` + ``X-Project-ID`` | ``X-API-Key`` + ``X-Project-Name`` ## OpenAPI ````yaml /openapi.json post /evaluations/monitors openapi: 3.1.0 info: title: PandaProbe API description: >- Open-source agent engineering platform — trace LLM calls, evaluate agent performance, and monitor AI pipelines. version: 0.1.0 contact: name: PandaProbe url: https://www.pandaprobe.com email: support@pandaprobe.com servers: - url: https://api.pandaprobe.com description: Production security: [] tags: - name: health description: Service health and readiness probes. - name: user description: Authenticated user profile. - name: traces description: Trace ingestion, querying, updating, and analytics. - name: sessions description: Session-based trace aggregation and analytics. - name: evaluations description: Evaluation runs, scores, analytics, and monitoring. - name: organizations description: Organization management and membership. - name: projects description: Project CRUD within organizations. - name: api-keys description: API key lifecycle management. paths: /evaluations/monitors: post: tags: - evaluations summary: Create Monitor description: >- Create an evaluation monitor that spawns eval runs on a recurring schedule. Monitors persist a reusable evaluation configuration (target type, metrics, filters, cadence) and the system automatically creates eval runs at each scheduled interval. If ``only_if_changed`` is true (default), runs are skipped when no new data has arrived since the previous run. **Request body fields:** - **name** *(string, required)*: Human-readable label, e.g. ``"Daily prod eval"``. - **target_type** *(string, required)*: ``"TRACE"`` or ``"SESSION"``. - **metrics** *(string[], required)*: Metric names to run. Use ``GET /evaluations/trace-metrics`` (TRACE) or ``GET /evaluations/session-metrics`` (SESSION) for available names. - **filters** *(object, optional, default {})*: Scope the data the monitor evaluates. Pass ``{}`` to match everything. Accepted keys depend on ``target_type``: - **TRACE**: ``date_from``, ``date_to`` (ISO 8601), ``status`` (PENDING/RUNNING/COMPLETED/ERROR), ``session_id``, ``user_id``, ``tags`` (string[]), ``name`` (substring match). - **SESSION**: ``date_from``, ``date_to``, ``user_id``, ``has_error`` (bool), ``tags`` (string[]), ``min_trace_count`` (int). - **cadence** *(string, required)*: Firing schedule. Predefined: ``"every_6h"``, ``"daily"``, ``"weekly"``. Custom cron: ``"cron:"``, e.g. ``"cron:0 3 * * *"`` (daily 3 AM UTC), ``"cron:0 6 * * 1-5"`` (weekdays 6 AM). - **sampling_rate** *(float, optional, default 1.0)*: Fraction of matching items to evaluate per run (0.0–1.0). - **model** *(string, optional)*: LLM model override, e.g. ``"openai/gpt-4o"``. - **only_if_changed** *(bool, optional, default true)*: Skip the run if no new traces/sessions exist since the last run. - **signal_weights** *(object, optional, SESSION only)*: Override signal aggregation weights. Keys: ``confidence``, ``loop_detection``, ``tool_correctness``, ``coherence``. Auth: ``Bearer`` + ``X-Project-ID`` | ``X-API-Key`` + ``X-Project-Name`` operationId: create_monitor_evaluations_monitors_post requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/CreateMonitorRequest' responses: '201': description: Successful Response content: application/json: schema: $ref: '#/components/schemas/MonitorResponse' '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' security: - HTTPBearer: [] - ApiKey: [] - ProjectID: [] - ProjectName: [] components: schemas: CreateMonitorRequest: properties: name: type: string title: Name description: Human-readable name for the monitor, e.g. 'Daily prod trace eval'. target_type: type: string title: Target Type description: >- Evaluation scope: ``'TRACE'`` for trace-level metrics or ``'SESSION'`` for session-level metrics. metrics: items: type: string type: array minItems: 1 title: Metrics description: >- Metric names to run on each scheduled eval. For TRACE monitors use ``GET /evaluations/trace-metrics``; for SESSION monitors use ``GET /evaluations/session-metrics`` to list available names. Example: ``['task_completion', 'step_efficiency']``. filters: additionalProperties: true type: object title: Filters description: >- JSON object defining which traces/sessions the monitor targets. Pass ``{}`` to match everything in the project. **TRACE monitors** accept: ``date_from`` (ISO 8601), ``date_to`` (ISO 8601), ``status`` (PENDING|RUNNING|COMPLETED|ERROR), ``session_id``, ``user_id``, ``tags`` (string array, ANY match), ``name`` (substring, case-insensitive). **SESSION monitors** accept: ``date_from``, ``date_to``, ``user_id``, ``has_error`` (bool), ``tags``, ``min_trace_count`` (int). Example: ``{"status": "COMPLETED", "tags": ["production"]}``. sampling_rate: type: number maximum: 1 minimum: 0 title: Sampling Rate description: >- Fraction of matching traces/sessions to evaluate per run. 1.0 = all, 0.1 = random 10%. default: 1 model: anyOf: - type: string - type: 'null' title: Model description: >- LLM model override for judge calls (e.g. ``'openai/gpt-4o'``). Uses system default if null. cadence: type: string title: Cadence description: >- How often the monitor fires. Predefined intervals: ``'every_6h'``, ``'daily'``, ``'weekly'``. Custom cron: ``'cron:<5-part expression>'`` where the five parts are ``minute hour day-of-month month day-of-week``. Examples: ``'cron:0 3 * * *'`` (daily at 3 AM UTC), ``'cron:0 6 * * 1-5'`` (weekdays at 6 AM), ``'cron:0 */4 * * *'`` (every 4 hours). only_if_changed: type: boolean title: Only If Changed description: >- When true, the scheduled run is skipped if no new traces/sessions have arrived since the last run, saving LLM costs. Set to false to always run on schedule regardless of new data. default: true signal_weights: anyOf: - additionalProperties: type: number type: object - type: 'null' title: Signal Weights description: >- Override default signal weights for session-level aggregation. **Only valid for SESSION monitors** (rejected for TRACE). Keys: ``confidence``, ``loop_detection``, ``tool_correctness``, ``coherence``. Defaults: confidence=1.0, loop_detection=1.0, tool_correctness=0.8, coherence=1.0. Example: ``{"confidence": 1.0, "loop_detection": 1.5}``. type: object required: - name - target_type - metrics - cadence title: CreateMonitorRequest description: Create an evaluation monitor that spawns runs on a cadence. example: name: Daily production trace eval target_type: TRACE metrics: - task_completion - tool_correctness filters: status: COMPLETED sampling_rate: 0.3 cadence: daily only_if_changed: true model: gpt-5.4 MonitorResponse: properties: id: type: string format: uuid title: Id project_id: type: string format: uuid title: Project Id name: type: string title: Name target_type: type: string title: Target Type metric_names: items: type: string type: array title: Metric Names filters: additionalProperties: true type: object title: Filters sampling_rate: type: number title: Sampling Rate model: anyOf: - type: string - type: 'null' title: Model cadence: type: string title: Cadence only_if_changed: type: boolean title: Only If Changed status: type: string title: Status last_run_at: anyOf: - type: string - type: 'null' title: Last Run At last_run_id: anyOf: - type: string format: uuid - type: 'null' title: Last Run Id next_run_at: anyOf: - type: string - type: 'null' title: Next Run At created_at: type: string title: Created At updated_at: type: string title: Updated At type: object required: - id - project_id - name - target_type - metric_names - filters - sampling_rate - model - cadence - only_if_changed - status - last_run_at - last_run_id - next_run_at - created_at - updated_at title: MonitorResponse description: Full evaluation monitor representation. HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: type: string title: Error Type input: title: Input ctx: type: object title: Context type: object required: - loc - msg - type title: ValidationError securitySchemes: HTTPBearer: type: http scheme: bearer ApiKey: type: apiKey in: header name: X-API-Key ProjectID: type: apiKey in: header name: X-Project-ID ProjectName: type: apiKey in: header name: X-Project-Name ````