> ## Documentation Index > Fetch the complete documentation index at: https://docs.haiqu.ai/llms.txt > Use this file to discover all available pages before exploring further. # Log Artifact In Experiment > Log or append an artifact value under an experiment metrics key. Use this tool when the caller needs to persist run metadata, scalar values, time-series points, JSON payloads, or base64 image artifacts under an experiment. Repeated writes to the same ``data.name`` are stored as timestamp-keyed entries and may later read back as normalized timeseries or value shapes. When to Use: - Call this after a run, optimization, or analysis step when metadata or derived outputs should be attached to an experiment. - Call this when the workflow needs to persist human-readable notes, numeric values, JSON payloads, or image-like previews. Constraints: - ``kind`` must be exactly one of ``text``, ``value``, ``timeseries``, ``list``, ``dict``, or ``image``. - Repeated writes are stored under timestamp keys in the experiment metrics map, so read-side responses may normalize to ``value`` or ``timeseries`` instead of preserving the write-time ``kind`` verbatim. Notes: - The route returns an empty ``200`` response on success. - After logging, callers can use ``list_artifacts_in_experiment`` or ``get_artifact_in_experiment`` to inspect the normalized MCP read-side artifact shape. Args: experiment_id: Parent experiment identifier. user: Authenticated user resolved from the API key. data: Artifact payload. ``kind`` must be exactly one of ``text``, ``value``, ``timeseries``, ``list``, ``dict``, or ``image``. db: Active database session. Returns: An empty ``200`` response when artifact data is persisted. Raises: HTTPException: Raised with ``404`` if the experiment is missing, or ``400`` for invalid artifact shape such as missing timeseries points. ## OpenAPI ````yaml https://api.haiqu.ai/openapi.json post /ai/{experiment_id}/log_artifact openapi: 3.1.0 info: title: Haiqu API summary: Haiqu RESTful API service. description: Cloud service providing the access to Haiqu cloud runtime contact: name: Haiqu Inc. url: https://haiqu.ai/ email: info@haiqu.ai version: 1.4.0 servers: [] security: [] paths: /ai/{experiment_id}/log_artifact: post: summary: Log Artifact In Experiment description: >- Log or append an artifact value under an experiment metrics key. Use this tool when the caller needs to persist run metadata, scalar values, time-series points, JSON payloads, or base64 image artifacts under an experiment. Repeated writes to the same ``data.name`` are stored as timestamp-keyed entries and may later read back as normalized timeseries or value shapes. When to Use: - Call this after a run, optimization, or analysis step when metadata or derived outputs should be attached to an experiment. - Call this when the workflow needs to persist human-readable notes, numeric values, JSON payloads, or image-like previews. Constraints: - ``kind`` must be exactly one of ``text``, ``value``, ``timeseries``, ``list``, ``dict``, or ``image``. - Repeated writes are stored under timestamp keys in the experiment metrics map, so read-side responses may normalize to ``value`` or ``timeseries`` instead of preserving the write-time ``kind`` verbatim. Notes: - The route returns an empty ``200`` response on success. - After logging, callers can use ``list_artifacts_in_experiment`` or ``get_artifact_in_experiment`` to inspect the normalized MCP read-side artifact shape. Args: experiment_id: Parent experiment identifier. user: Authenticated user resolved from the API key. data: Artifact payload. ``kind`` must be exactly one of ``text``, ``value``, ``timeseries``, ``list``, ``dict``, or ``image``. db: Active database session. Returns: An empty ``200`` response when artifact data is persisted. Raises: HTTPException: Raised with ``404`` if the experiment is missing, or ``400`` for invalid artifact shape such as missing timeseries points. operationId: log_artifact_in_experiment parameters: - name: experiment_id in: path required: true schema: type: string title: Experiment Id requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/ContextArtifactSubmitModel' responses: '200': description: Successful Response content: application/json: schema: {} '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' security: - APIKeyQuery: [] - APIKeyHeader: [] components: schemas: ContextArtifactSubmitModel: properties: name: type: string title: Name kind: type: string enum: - text - value - timeseries - list - dict - image - file title: Kind description: >- Artifact type. Use `text` for strings, `value` for a single numeric value, `timeseries` for numeric points in `points`, `list` for JSON arrays, `dict` for JSON objects, and `image` for a base64 data URL such as `data:image/png;base64,...`. value: anyOf: - {} - type: 'null' title: Value description: >- Artifact payload for all kinds except `timeseries`. Send properly typed JSON values: string for `text`, number for `value`, array for `list`, object for `dict`, and a base64 data URL string for `image`. Leave this empty when `kind` is `timeseries`. points: anyOf: - items: $ref: '#/components/schemas/ContextTimeSeriesItem' type: array - type: 'null' title: Points description: >- Time series points to log when `kind` is `timeseries`. Each item must have numeric `x` and `y` fields. Leave this empty for all other kinds. type: object required: - name title: ContextArtifactSubmitModel description: >- Define payload for logging experiment artifacts from MCP tools. Logged artifact kinds are accepted on write, but MCP read endpoints expose a normalized artifact view and do not preserve every submitted ``kind`` as a distinct response shape. Attributes: name: Artifact key under which values are stored. kind: Artifact serialization mode accepted by the logging endpoint. value: Value payload for non-timeseries kinds. points: Time series points used when ``kind='timeseries'``. examples: - kind: value name: energy value: 42 - kind: text name: note value: Optimization converged after 18 iterations - kind: timeseries name: training_loss points: - x: 1710000000 'y': 0.91 - x: 1710000060 'y': 0.63 - x: 1710000120 'y': 0.41 - kind: list name: probabilities value: - 0.12 - 0.38 - 0.5 - kind: dict name: run_metadata value: device: simulator seed: 7 shots: 1024 - kind: image name: circuit_preview value: data:image/png;base64,aGVsbG8= HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError ContextTimeSeriesItem: properties: x: type: number title: X 'y': type: number title: 'Y' type: object title: ContextTimeSeriesItem description: |- Represent one point in a time series artifact payload. Attributes: x: Timestamp in Unix seconds. y: Numeric value for this timestamp. ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: type: string title: Error Type type: object required: - loc - msg - type title: ValidationError securitySchemes: APIKeyQuery: type: apiKey in: query name: HAIQU_API_KEY APIKeyHeader: type: apiKey in: header name: authorization ````