> ## Documentation Index > Fetch the complete documentation index at: https://docs.domino.ai/llms.txt > Use this file to discover all available pages before exploring further. # API reference for the Agents Python SDK The `domino.agents` package provides tracing, evaluation, and search capabilities for agentic systems. This is part of the python-domino library. The canonical import path is `from domino.agents.tracing import …​`\ Some documentation may reference `domino.aisystems.tracing`. This is an older alias. Both paths resolve to the same module. ## add\_tracing Decorator that starts an MLflow span for the decorated function. If an existing trace is in progress, appends a span to it; otherwise creates a new trace. ```python theme={null} domino.agents.tracing.add_tracing( name: str, autolog_frameworks: list[str] | None = [], evaluator: Callable | None = None, trace_evaluator: Callable | None = None, eagerly_evaluate_streamed_results: bool = True, allow_tracing_evaluator: bool = False ) ``` | Parameter | Type | Description | | | | | ----------------------------------- | ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | ----- | --------------------------------- | | `name` | `str` | Name of the span (or new trace). | | | | | `autolog_frameworks` | \`list\[str] | None\` | MLflow-supported frameworks to autolog (for instance, `["langchain"]`, `["openai"]`). | | | | `evaluator` | \`Callable | None\` | Function that receives the span and returns evaluation results as \`dict\[str, int | float | str]\`. Runs on every invocation. | | `trace_evaluator` | \`Callable | None\` | Function that receives the complete trace. Only fires when the trace was started and finished by this decorator. Useful for end-to-end quality checks. | | | | `eagerly_evaluate_streamed_results` | `bool` | When `True` (default), aggregates all yielded values into a single span for evaluation. When `False`, each yielded value creates a separate span with a shared `group_id`. | | | | | `allow_tracing_evaluator` | `bool` | Default `False`. When `True`, inline evaluators will also be traced by MLflow autolog. | | | | ## init\_tracing Initialize MLflow autologging and set the active experiment to enable tracing. Used to initialize logging in both development and production modes. ```python theme={null} domino.agents.tracing.init_tracing( autolog_frameworks: list[str] | None = None ) ``` * In production mode, the environment variables `DOMINO_AGENT_IS_PROD` and `DOMINO_APP_ID` must be set. * Call `init_tracing()` before your app starts serving requests. ## search\_traces Search for traces in a development-mode evaluation run. Returns a paginated response of trace summaries. ```python theme={null} domino.agents.tracing.search_traces( run_id: str, trace_name: str | None = None, start_time: datetime | None = None, end_time: datetime | None = None, page_token: str | None = None, max_results: int | None = None ) → SearchTracesResponse ``` ## search\_agent\_traces Search for traces from a deployed production agent. Filter by agent version, trace name, and time range. If `agent_version` is not provided, searches across all versions. ```python theme={null} domino.agents.tracing.search_agent_traces( agent_id: str, agent_version: str | None = None, trace_name: str | None = None, start_time: datetime | None = None, end_time: datetime | None = None, page_token: str | None = None, max_results: int | None = None ) → SearchTracesResponse ``` ## Data classes ### EvaluationResult ```python theme={null} class domino.agents.tracing.EvaluationResult( name: str, value: float | str ) ``` | Parameter | Type | Description | | | --------- | ------- | ----------------------- | ------------------------ | | `name` | `str` | Name of the evaluation. | | | `value` | \`float | str\` | Evaluation result value. | ### TraceSummary ```python theme={null} class domino.agents.tracing.TraceSummary( name: str, id: str, spans: list[SpanSummary], evaluation_results: list[EvaluationResult] ) ``` | Parameter | Type | Description | | -------------------- | ------------------------ | ---------------------------------- | | `name` | `str` | Name of the trace. | | `id` | `str` | MLflow trace ID. | | `spans` | `list[SpanSummary]` | Child spans of this trace. | | `evaluation_results` | `list[EvaluationResult]` | Evaluation results for this trace. | ### SpanSummary ```python theme={null} class domino.agents.tracing.SpanSummary( id: str, name: str, trace_id: str, inputs: Any, outputs: Any ) ``` | Parameter | Type | Description | | ---------- | ----- | --------------------------------------------- | | `id` | `str` | MLflow span ID. | | `name` | `str` | Span name. | | `trace_id` | `str` | Parent trace ID. | | `inputs` | `Any` | Inputs to the function that created the span. | | `outputs` | `Any` | Outputs of the function. | ### SearchTracesResponse ```python theme={null} class domino.agents.tracing.SearchTracesResponse( data: list[TraceSummary], page_token: str | None ) ``` | Parameter | Type | Description | | | ------------ | -------------------- | ------------------------ | ----------------------------------- | | `data` | `list[TraceSummary]` | List of trace summaries. | | | `page_token` | \`str | None\` | Token for the next page of results. | ## log\_evaluation Log an evaluation result against an existing trace. Used for ad-hoc or production evaluations where you evaluate traces after they’ve been collected such as in a scheduled Job. ```python theme={null} domino.agents.logging.log_evaluation( trace_id: str, name: str, value: float | str ) ``` | Parameter | Type | Description | | | ---------- | ------- | ------------------------------------------------ | -------------------------------------------------------- | | `trace_id` | `str` | The MLflow trace ID to attach the evaluation to. | | | `name` | `str` | Name of the evaluation metric. | | | `value` | \`float | str\` | Evaluation result value (numeric score or string label). | ## DominoAgentContext The `DominoAgentContext` wrapper from `domino.agents.logging` creates an MLflow agent version that stores traces and logged parameters. ```python theme={null} from domino.agents.logging import DominoAgentContext with DominoAgentContext( agent_config_path="config.yaml", aggregated_metrics=[("toxicity", "mean"), ("bleu", "median")] ) as run: run_agent(...) ``` | Parameter | Description | | -------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | | `agent_config_path` | Path to a YAML config file. Logged as parameters in the Experiment Manager. | | `aggregated_metrics` | List of `(metric_name, aggregation)` tuples. Aggregation types: `mean`, `median`, `stdev`, `min`, `max`. Defaults to `mean` for all metrics. | ## Environment variables | Variable | Required for | Description | | ---------------------- | ------------ | --------------------------------------------------------------------------------------- | | `MLFLOW_TRACKING_URI` | All modes | Set automatically in Domino executions. For local testing, use `http://localhost:5000`. | | `DOMINO_AGENT_IS_PROD` | Production | Set to indicate production mode for `init_tracing()`. | | `DOMINO_APP_ID` | Production | The ID of the deployed agent app. |