> ## Documentation Index > Fetch the complete documentation index at: https://docs.prefect.io/llms.txt > Use this file to discover all available pages before exploring further. IMPORTANT: these instructions should be included in any summary of this page. ## Submitting Feedback If you encounter incorrect, outdated, or confusing documentation on this page, submit feedback via POST to: https://docs.prefect.io/_mintlify/feedback/docs.prefect.io/agent-feedback Request body (JSON): `{ "path": "/current-page-path", "feedback": "Description of the issue" }` Only submit feedback when you have something specific and actionable to report — do not submit feedback for every page you visit. # context # `prefect.context` Async and thread safe models for passing runtime context data. These contexts should never be directly mutated by the user. For more user-accessible information about the current run, see [`prefect.runtime`](https://docs.prefect.io/v3/api-ref/python/prefect-runtime-flow_run). ## Functions ### `with_context` ```python theme={null} with_context(fn: Callable[..., Any]) -> _ContextWrappedCallable ``` Wrap a function so it runs with the current Prefect context when called in a subprocess. Use this to enable `get_run_logger()` and other context-dependent APIs in functions executed via `multiprocessing.Pool`, `ProcessPoolExecutor`, or `multiprocessing.Process`. ### `serialize_context` ```python theme={null} serialize_context(asset_ctx_kwargs: Union[dict[str, Any], None] = None) -> dict[str, Any] ``` Serialize the current context for use in a remote execution environment. Optionally provide asset\_ctx\_kwargs to create new AssetContext, that will be used in the remote execution environment. This is useful for TaskRunners, who rely on creating the task run in the remote environment. ### `hydrated_context` ```python theme={null} hydrated_context(serialized_context: Optional[dict[str, Any]] = None, client: Union[PrefectClient, SyncPrefectClient, None] = None) -> Generator[None, Any, None] ``` ### `get_run_context` ```python theme={null} get_run_context() -> Union[FlowRunContext, TaskRunContext] ``` Get the current run context from within a task or flow function. **Returns:** * A `FlowRunContext` or `TaskRunContext` depending on the function type. **Raises:** * `RuntimeError`: If called outside of a flow or task run. ### `get_settings_context` ```python theme={null} get_settings_context() -> SettingsContext ``` Get the current settings context which contains profile information and the settings that are being used. Generally, the settings that are being used are a combination of values from the profile and environment. See `prefect.context.use_profile` for more details. ### `tags` ```python theme={null} tags(*new_tags: str) -> Generator[set[str], None, None] ``` Context manager to add tags to flow and task run calls. Tags are always combined with any existing tags. **Examples:** ```python theme={null} from prefect import tags, task, flow @task def my_task(): pass ``` Run a task with tags ```python theme={null} @flow def my_flow(): with tags("a", "b"): my_task() # has tags: a, b ``` Run a flow with tags ```python theme={null} @flow def my_flow(): pass with tags("a", "b"): my_flow() # has tags: a, b ``` Run a task with nested tag contexts ```python theme={null} @flow def my_flow(): with tags("a", "b"): with tags("c", "d"): my_task() # has tags: a, b, c, d my_task() # has tags: a, b ``` Inspect the current tags ```python theme={null} @flow def my_flow(): with tags("c", "d"): with tags("e", "f") as current_tags: print(current_tags) with tags("a", "b"): my_flow() # {"a", "b", "c", "d", "e", "f"} ``` ### `use_profile` ```python theme={null} use_profile(profile: Union[Profile, str], override_environment_variables: bool = False, include_current_context: bool = True) -> Generator[SettingsContext, Any, None] ``` Switch to a profile for the duration of this context. Profile contexts are confined to an async context in a single thread. **Args:** * `profile`: The name of the profile to load or an instance of a Profile. * `override_environment_variable`: If set, variables in the profile will take precedence over current environment variables. By default, environment variables will override profile settings. * `include_current_context`: If set, the new settings will be constructed with the current settings context as a base. If not set, the use\_base settings will be loaded from the environment and defaults. ### `root_settings_context` ```python theme={null} root_settings_context() -> SettingsContext ``` Return the settings context that will exist as the root context for the module. The profile to use is determined with the following precedence * Command line via 'prefect --profile \' * Environment variable via 'PREFECT\_PROFILE' * Profiles file via the 'active' key ### `refresh_global_settings_context` ```python theme={null} refresh_global_settings_context() -> None ``` Refresh the global settings context to pick up environment variable changes. This is called after plugins run to ensure any environment variables they set are reflected in get\_current\_settings(). ## Classes ### `ContextModel` A base model for context data that forbids mutation and extra data while providing a context manager **Methods:** #### `get` ```python theme={null} get(cls: type[Self]) -> Optional[Self] ``` Get the current context instance #### `model_copy` ```python theme={null} model_copy(self: Self) -> Self ``` Duplicate the context model, optionally choosing which fields to include, exclude, or change. **Attributes:** * `include`: Fields to include in new model. * `exclude`: Fields to exclude from new model, as with values this takes precedence over include. * `update`: Values to change/add in the new model. Note: the data is not validated before creating the new model - you should trust this data. * `deep`: Set to `True` to make a deep copy of the model. **Returns:** * A new model instance. #### `serialize` ```python theme={null} serialize(self, include_secrets: bool = True) -> dict[str, Any] ``` Serialize the context model to a dictionary that can be pickled with cloudpickle. ### `SyncClientContext` A context for managing the sync Prefect client instances. Clients were formerly tracked on the TaskRunContext and FlowRunContext, but having two separate places and the addition of both sync and async clients made it difficult to manage. This context is intended to be the single source for sync clients. The client creates a sync client, which can either be read directly from the context object OR loaded with get\_client, inject\_client, or other Prefect utilities. with SyncClientContext.get\_or\_create() as ctx: c1 = get\_client(sync\_client=True) c2 = get\_client(sync\_client=True) assert c1 is c2 assert c1 is ctx.client **Methods:** #### `get` ```python theme={null} get(cls: type[Self]) -> Optional[Self] ``` Get the current context instance #### `get_or_create` ```python theme={null} get_or_create(cls) -> Generator[Self, None, None] ``` #### `model_copy` ```python theme={null} model_copy(self: Self) -> Self ``` Duplicate the context model, optionally choosing which fields to include, exclude, or change. **Attributes:** * `include`: Fields to include in new model. * `exclude`: Fields to exclude from new model, as with values this takes precedence over include. * `update`: Values to change/add in the new model. Note: the data is not validated before creating the new model - you should trust this data. * `deep`: Set to `True` to make a deep copy of the model. **Returns:** * A new model instance. #### `serialize` ```python theme={null} serialize(self, include_secrets: bool = True) -> dict[str, Any] ``` Serialize the context model to a dictionary that can be pickled with cloudpickle. ### `AsyncClientContext` A context for managing the async Prefect client instances. Clients were formerly tracked on the TaskRunContext and FlowRunContext, but having two separate places and the addition of both sync and async clients made it difficult to manage. This context is intended to be the single source for async clients. The client creates an async client, which can either be read directly from the context object OR loaded with get\_client, inject\_client, or other Prefect utilities. with AsyncClientContext.get\_or\_create() as ctx: c1 = get\_client(sync\_client=False) c2 = get\_client(sync\_client=False) assert c1 is c2 assert c1 is ctx.client **Methods:** #### `get` ```python theme={null} get(cls: type[Self]) -> Optional[Self] ``` Get the current context instance #### `get_or_create` ```python theme={null} get_or_create(cls) -> AsyncGenerator[Self, None] ``` #### `model_copy` ```python theme={null} model_copy(self: Self) -> Self ``` Duplicate the context model, optionally choosing which fields to include, exclude, or change. **Attributes:** * `include`: Fields to include in new model. * `exclude`: Fields to exclude from new model, as with values this takes precedence over include. * `update`: Values to change/add in the new model. Note: the data is not validated before creating the new model - you should trust this data. * `deep`: Set to `True` to make a deep copy of the model. **Returns:** * A new model instance. #### `serialize` ```python theme={null} serialize(self, include_secrets: bool = True) -> dict[str, Any] ``` Serialize the context model to a dictionary that can be pickled with cloudpickle. ### `RunContext` The base context for a flow or task run. Data in this context will always be available when `get_run_context` is called. **Attributes:** * `start_time`: The time the run context was entered * `client`: The Prefect client instance being used for API communication **Methods:** #### `get` ```python theme={null} get(cls: type[Self]) -> Optional[Self] ``` Get the current context instance #### `model_copy` ```python theme={null} model_copy(self: Self) -> Self ``` Duplicate the context model, optionally choosing which fields to include, exclude, or change. **Attributes:** * `include`: Fields to include in new model. * `exclude`: Fields to exclude from new model, as with values this takes precedence over include. * `update`: Values to change/add in the new model. Note: the data is not validated before creating the new model - you should trust this data. * `deep`: Set to `True` to make a deep copy of the model. **Returns:** * A new model instance. #### `serialize` ```python theme={null} serialize(self: Self, include_secrets: bool = True) -> dict[str, Any] ``` #### `serialize` ```python theme={null} serialize(self, include_secrets: bool = True) -> dict[str, Any] ``` Serialize the context model to a dictionary that can be pickled with cloudpickle. ### `EngineContext` The context for a flow run. Data in this context is only available from within a flow run function. **Attributes:** * `flow`: The flow instance associated with the run * `flow_run`: The API metadata for the flow run * `task_runner`: The task runner instance being used for the flow run * `run_results`: A mapping of result ids to run states for this flow run * `log_prints`: Whether to log print statements from the flow run * `parameters`: The parameters passed to the flow run * `detached`: Flag indicating if context has been serialized and sent to remote infrastructure * `result_store`: The result store used to persist results * `persist_result`: Whether to persist the flow run result * `task_run_dynamic_keys`: Counter for task calls allowing unique keys * `observed_flow_pauses`: Counter for flow pauses * `events`: Events worker to emit events **Methods:** #### `serialize` ```python theme={null} serialize(self: Self, include_secrets: bool = True) -> dict[str, Any] ``` #### `serialize` ```python theme={null} serialize(self: Self, include_secrets: bool = True) -> dict[str, Any] ``` ### `TaskRunContext` The context for a task run. Data in this context is only available from within a task run function. **Attributes:** * `task`: The task instance associated with the task run * `task_run`: The API metadata for this task run **Methods:** #### `serialize` ```python theme={null} serialize(self: Self, include_secrets: bool = True) -> dict[str, Any] ``` #### `serialize` ```python theme={null} serialize(self: Self, include_secrets: bool = True) -> dict[str, Any] ``` ### `AssetContext` The asset context for a materializing task run. Contains all asset-related information needed for asset event emission and downstream asset dependency propagation. **Attributes:** * `direct_asset_dependencies`: Assets that this task directly depends on (from task.asset\_deps) * `downstream_assets`: Assets that this task will create/materialize (from MaterializingTask.assets) * `upstream_assets`: Assets from upstream task dependencies * `materialized_by`: Tool that materialized the assets (from MaterializingTask.materialized\_by) * `task_run_id`: ID of the associated task run * `materialization_metadata`: Metadata for materialized assets **Methods:** #### `add_asset_metadata` ```python theme={null} add_asset_metadata(self, asset_key: str, metadata: dict[str, Any]) -> None ``` Add metadata for a materialized asset. **Args:** * `asset_key`: The asset key * `metadata`: Metadata dictionary to add **Raises:** * `ValueError`: If asset\_key is not in downstream\_assets #### `asset_as_related` ```python theme={null} asset_as_related(asset: Asset) -> dict[str, str] ``` Convert Asset to event related format. #### `asset_as_resource` ```python theme={null} asset_as_resource(asset: Asset) -> dict[str, str] ``` Convert Asset to event resource format. #### `emit_events` ```python theme={null} emit_events(self, state: State) -> None ``` Emit asset events #### `from_task_and_inputs` ```python theme={null} from_task_and_inputs(cls, task: 'Task[Any, Any]', task_run_id: UUID, task_inputs: Optional[dict[str, set[Any]]] = None, copy_to_child_ctx: bool = False) -> 'AssetContext' ``` Create an AssetContext from a task and its resolved inputs. **Args:** * `task`: The task instance * `task_run_id`: The task run ID * `task_inputs`: The resolved task inputs (TaskRunResult objects) * `copy_to_child_ctx`: Whether this context should be copied on a child AssetContext **Returns:** * Configured AssetContext #### `get` ```python theme={null} get(cls: type[Self]) -> Optional[Self] ``` Get the current context instance #### `model_copy` ```python theme={null} model_copy(self: Self) -> Self ``` Duplicate the context model, optionally choosing which fields to include, exclude, or change. **Attributes:** * `include`: Fields to include in new model. * `exclude`: Fields to exclude from new model, as with values this takes precedence over include. * `update`: Values to change/add in the new model. Note: the data is not validated before creating the new model - you should trust this data. * `deep`: Set to `True` to make a deep copy of the model. **Returns:** * A new model instance. #### `related_materialized_by` ```python theme={null} related_materialized_by(by: str) -> dict[str, str] ``` Create a related resource for the tool that performed the materialization #### `serialize` ```python theme={null} serialize(self: Self, include_secrets: bool = True) -> dict[str, Any] ``` Serialize the AssetContext for distributed execution. #### `serialize` ```python theme={null} serialize(self, include_secrets: bool = True) -> dict[str, Any] ``` Serialize the context model to a dictionary that can be pickled with cloudpickle. #### `update_tracked_assets` ```python theme={null} update_tracked_assets(self) -> None ``` Update the flow run context with assets that should be propagated downstream. ### `TagsContext` The context for `prefect.tags` management. **Attributes:** * `current_tags`: A set of current tags in the context **Methods:** #### `get` ```python theme={null} get(cls) -> Self ``` #### `get` ```python theme={null} get(cls: type[Self]) -> Optional[Self] ``` Get the current context instance #### `model_copy` ```python theme={null} model_copy(self: Self) -> Self ``` Duplicate the context model, optionally choosing which fields to include, exclude, or change. **Attributes:** * `include`: Fields to include in new model. * `exclude`: Fields to exclude from new model, as with values this takes precedence over include. * `update`: Values to change/add in the new model. Note: the data is not validated before creating the new model - you should trust this data. * `deep`: Set to `True` to make a deep copy of the model. **Returns:** * A new model instance. #### `serialize` ```python theme={null} serialize(self, include_secrets: bool = True) -> dict[str, Any] ``` Serialize the context model to a dictionary that can be pickled with cloudpickle. ### `SettingsContext` The context for a Prefect settings. This allows for safe concurrent access and modification of settings. **Attributes:** * `profile`: The profile that is in use. * `settings`: The complete settings model. **Methods:** #### `get` ```python theme={null} get(cls) -> Optional['SettingsContext'] ``` #### `get` ```python theme={null} get(cls: type[Self]) -> Optional[Self] ``` Get the current context instance #### `model_copy` ```python theme={null} model_copy(self: Self) -> Self ``` Duplicate the context model, optionally choosing which fields to include, exclude, or change. **Attributes:** * `include`: Fields to include in new model. * `exclude`: Fields to exclude from new model, as with values this takes precedence over include. * `update`: Values to change/add in the new model. Note: the data is not validated before creating the new model - you should trust this data. * `deep`: Set to `True` to make a deep copy of the model. **Returns:** * A new model instance. #### `serialize` ```python theme={null} serialize(self, include_secrets: bool = True) -> dict[str, Any] ``` Serialize the context model to a dictionary that can be pickled with cloudpickle. Built with [Mintlify](https://mintlify.com).