API Overview

Classes

obj.Object
dataset.Dataset: Dataset object with easy saving and automatic versioning
model.Model: Intended to capture a combination of code and data the operates on an input.
prompt.Prompt
prompt.StringPrompt
prompt.MessagesPrompt
eval.Evaluation: Sets up an evaluation which includes a set of scorers and a dataset.
eval_imperative.EvaluationLogger: This class provides an imperative interface for logging evaluations.
scorer.Scorer
annotation_spec.AnnotationSpec
file.File: A class representing a file with path, mimetype, and size information.
markdown.Markdown: A Markdown renderable.
monitor.Monitor: Sets up a monitor to score incoming calls automatically.
saved_view.SavedView: A fluent-style class for working with SavedView objects.
audio.Audio: A class representing audio data in a supported format (wav or mp3).

Functions

api.init: Initialize weave tracking, logging to a wandb project.
api.publish: Save and version a python object.
api.ref: Construct a Ref to a Weave object.
api.get: A convenience function for getting an object from a URI.
call_context.require_current_call: Get the Call object for the currently executing Op, within that Op.
call_context.get_current_call: Get the Call object for the currently executing Op, within that Op.
api.finish: Stops logging to weave.
op.op: A decorator to weave op-ify a function or method. Works for both sync and async.
api.attributes: Context manager for setting attributes on a call.

function `init`

init( project_name: 'str', settings: 'UserSettings | dict[str, Any] | None' = None, autopatch_settings: 'AutopatchSettings | None' = None, global_postprocess_inputs: 'PostprocessInputsFunc | None' = None, global_postprocess_output: 'PostprocessOutputFunc | None' = None, global_attributes: 'dict[str, Any] | None' = None) → WeaveClient

Initialize weave tracking, logging to a wandb project. Logging is initialized globally, so you do not need to keep a reference to the return value of init. Following init, calls of weave.op() decorated functions will be logged to the specified project. Args:

project_name**: The name of the Weights & Biases project to log to.
settings: Configuration for the Weave client generally.
autopatch_settings: Configuration for autopatch integrations, e.g. openai
global_postprocess_inputs: A function that will be applied to all inputs of all ops.
global_postprocess_output: A function that will be applied to all outputs of all ops.
global_attributes: A dictionary of attributes that will be applied to all traces.

NOTE: Global postprocessing settings are applied to all ops after each op’s own postprocessing. The order is always: 1. Op-specific postprocessing 2. Global postprocessing Returns: A Weave client.

function `publish`

publish(obj: 'Any', name: 'str | None' = None) → ObjectRef

Save and version a python object. If an object with name already exists, and the content hash of obj does not match the latest version of that object, a new version will be created. TODO: Need to document how name works with this change. Args:

obj**: The object to save and version.
name: The name to save the object under.

Returns: A weave Ref to the saved object.

function `ref`

ref(location: 'str') → ObjectRef

Construct a Ref to a Weave object. TODO: what happens if obj does not exist Args:

location**: A fully-qualified weave ref URI, or if weave.init() has been called, “name:version” or just “name” (“latest” will be used for version in this case).

Returns: A weave Ref to the object.

function `get`

get(uri: 'str | ObjectRef') → Any

A convenience function for getting an object from a URI. Many objects logged by Weave are automatically registered with the Weave server. This function allows you to retrieve those objects by their URI. Args:

uri**: A fully-qualified weave ref URI.

Returns: The object. Example:

weave.init("weave_get_example")dataset = weave.Dataset(rows=[{"a": 1, "b": 2}])ref = weave.publish(dataset)dataset2 = weave.get(ref) # same as dataset!

function `require_current_call`

require_current_call() → Call

Get the Call object for the currently executing Op, within that Op. This allows you to access attributes of the Call such as its id or feedback while it is running.

@weave.opdef hello(name: str) -> None: print(f"Hello {name}!") current_call = weave.require_current_call() print(current_call.id)

It is also possible to access a Call after the Op has returned. If you have the Call’s id, perhaps from the UI, you can use the get_call method on the WeaveClient returned from weave.init to retrieve the Call object.

client = weave.init("
")mycall = client.get_call("")

Alternately, after defining your Op you can use its call method. For example:

@weave.opdef add(a: int, b: int) -> int: return a + bresult, call = add.call(1, 2)print(call.id)

Returns: The Call object for the currently executing Op Raises:

NoCurrentCallError**: If tracking has not been initialized or this method is invoked outside an Op.

function `get_current_call`

get_current_call() → Call | None

Get the Call object for the currently executing Op, within that Op. Returns: The Call object for the currently executing Op, or None if tracking has not been initialized or this method is invoked outside an Op.

function `finish`

finish() → None

Stops logging to weave. Following finish, calls of weave.op() decorated functions will no longer be logged. You will need to run weave.init() again to resume logging.

function `op`

op( func: 'Callable[P, R] | None' = None, name: 'str | None' = None, call_display_name: 'str | CallDisplayNameFunc | None' = None, postprocess_inputs: 'PostprocessInputsFunc | None' = None, postprocess_output: 'PostprocessOutputFunc | None' = None, tracing_sample_rate: 'float' = 1.0, enable_code_capture: 'bool' = True, accumulator: 'Callable[[Any | None, Any], Any] | None' = None) → Callable[[Callable[P, R]], Op[P, R]] | Op[P, R]

A decorator to weave op-ify a function or method. Works for both sync and async. Automatically detects iterator functions and applies appropriate behavior.

function `attributes`

attributes(attributes: 'dict[str, Any]') → Iterator

Context manager for setting attributes on a call. Attributes become immutable once a call begins execution. Use this context manager to provide metadata before the call starts. Example:

with weave.attributes({'env': 'production'}): print(my_function.call("World"))

class `Object`

Pydantic Fields:

name: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]

classmethod `from_uri`

from_uri(uri: str, objectify: bool = True) → Self

classmethod `handle_relocatable_object`

handle_relocatable_object( v: Any, handler: ValidatorFunctionWrapHandler, info: ValidationInfo) → Any

class `Dataset`

Dataset object with easy saving and automatic versioning Examples:

# Create a datasetdataset = Dataset(name='grammar', rows=[ {'id': '0', 'sentence': "He no likes ice cream.", 'correction': "He doesn't like ice cream."}, {'id': '1', 'sentence': "She goed to the store.", 'correction': "She went to the store."}, {'id': '2', 'sentence': "They plays video games all day.", 'correction': "They play video games all day."}])# Publish the datasetweave.publish(dataset)# Retrieve the datasetdataset_ref = weave.ref('grammar').get()# Access a specific exampleexample_label = dataset_ref.rows[2]['sentence']

Pydantic Fields:

name: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]
rows: typing.Union[trace.table.Table, trace.vals.WeaveTable]

method `add_rows`

add_rows(rows: Iterable[dict]) → Dataset

Create a new dataset version by appending rows to the existing dataset. This is useful for adding examples to large datasets without having to load the entire dataset into memory. Args:

rows**: The rows to add to the dataset.

Returns: The updated dataset.

classmethod `convert_to_table`

convert_to_table(rows: Any) → Union[Table, WeaveTable]

classmethod `from_calls`

from_calls(calls: Iterable[Call]) → Self

classmethod `from_obj`

from_obj(obj: WeaveObject) → Self

classmethod `from_pandas`

from_pandas(df: 'DataFrame') → Self

method `select`

select(indices: Iterable[int]) → Self

Select rows from the dataset based on the provided indices. Args:

indices**: An iterable of integer indices specifying which rows to select.

Returns: A new Dataset object containing only the selected rows.

method `to_pandas`

to_pandas() → DataFrame

class `Model`

Intended to capture a combination of code and data the operates on an input. For example it might call an LLM with a prompt to make a prediction or generate text. When you change the attributes or the code that defines your model, these changes will be logged and the version will be updated. This ensures that you can compare the predictions across different versions of your model. Use this to iterate on prompts or to try the latest LLM and compare predictions across different settings Examples:

class YourModel(Model): attribute1: str attribute2: int @weave.op() def predict(self, input_data: str) -> dict: # Model logic goes here prediction = self.attribute1 + ' ' + input_data return {'pred': prediction}

Pydantic Fields:

name: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]

method `get_infer_method`

get_infer_method() → Callable

class `Prompt`

Pydantic Fields:

name: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]

method `format`

format(**kwargs: Any) → Any

class `StringPrompt`

method `init`

__init__(content: str)

Pydantic Fields:

name: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]
content: “

method `format`

format(**kwargs: Any) → str

classmethod `from_obj`

from_obj(obj: WeaveObject) → Self

class `MessagesPrompt`

method `init`

__init__(messages: list[dict])

Pydantic Fields:

name: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]
messages: list[dict]

method `format`

format(**kwargs: Any) → list

method `format_message`

format_message(message: dict, **kwargs: Any) → dict

classmethod `from_obj`

from_obj(obj: WeaveObject) → Self

class `Evaluation`

Sets up an evaluation which includes a set of scorers and a dataset. Calling evaluation.evaluate(model) will pass in rows from a dataset into a model matching the names of the columns of the dataset to the argument names in model.predict. Then it will call all of the scorers and save the results in weave. If you want to preprocess the rows from the dataset you can pass in a function to preprocess_model_input. Examples:

# Collect your examplesexamples = [ {"question": "What is the capital of France?", "expected": "Paris"}, {"question": "Who wrote 'To Kill a Mockingbird'?", "expected": "Harper Lee"}, {"question": "What is the square root of 64?", "expected": "8"},]# Define any custom scoring function@weave.op()def match_score1(expected: str, model_output: dict) -> dict: # Here is where you'd define the logic to score the model output return {'match': expected == model_output['generated_text']}@weave.op()def function_to_evaluate(question: str): # here's where you would add your LLM call and return the output return {'generated_text': 'Paris'}# Score your examples using scoring functionsevaluation = Evaluation( dataset=examples, scorers=[match_score1])# Start tracking the evaluationweave.init('intro-example')# Run the evaluationasyncio.run(evaluation.evaluate(function_to_evaluate))

Pydantic Fields:

name: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]
dataset: “
scorers: typing.Optional[list[typing.Annotated[typing.Union[trace.op.Op, flow.scorer.Scorer], BeforeValidator(func=)]]]
preprocess_model_input: typing.Optional[typing.Callable[[dict], dict]]
trials: “
evaluation_name: typing.Union[str, typing.Callable[[trace.weave_client.Call], str], NoneType]

method `evaluate`

evaluate(model: Union[Op, Model]) → dict

classmethod `from_obj`

from_obj(obj: WeaveObject) → Self

method `get_eval_results`

get_eval_results(model: Union[Op, Model]) → EvaluationResults

method `predict_and_score`

predict_and_score(model: Union[Op, Model], example: dict) → dict

method `summarize`

summarize(eval_table: EvaluationResults) → dict

class `EvaluationLogger`

This class provides an imperative interface for logging evaluations. An evaluation is started automatically when the first prediction is logged using the log_prediction method, and finished when the log_summary method is called. Each time you log a prediction, you will get back a ScoreLogger object. You can use this object to log scores and metadata for that specific prediction. For more information, see the ScoreLogger class. Example:

 ev = EvaluationLogger() pred = ev.log_prediction(inputs, output) pred.log_score(scorer_name, score) ev.log_summary(summary)

Pydantic Fields:

name: str | None
model: flow.model.Model | dict | str
dataset: flow.dataset.Dataset | list[dict] | str

property ui_url

method `finish`

finish() → None

Clean up the evaluation resources explicitly without logging a summary. Ensures all prediction calls and the main evaluation call are finalized. This is automatically called if the logger is used as a context manager.

method `log_prediction`

log_prediction(inputs: 'dict', output: 'Any') → ScoreLogger

Log a prediction to the Evaluation, and return a reference. The reference can be used to log scores which are attached to the specific prediction instance.

method `log_summary`

log_summary(summary: 'dict | None' = None, auto_summarize: 'bool' = True) → None

Log a summary dict to the Evaluation. This will calculate the summary, call the summarize op, and then finalize the evaluation, meaning no more predictions or scores can be logged.

class `Scorer`

Pydantic Fields:

name: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]
column_map: typing.Optional[dict[str, str]]

method `model_post_init`

model_post_init(_Scorer__context: Any) → None

method `score`

score(output: Any, **kwargs: Any) → Any

method `summarize`

summarize(score_rows: list) → Optional[dict]

class `AnnotationSpec`

Pydantic Fields:

name: typing.Optional[str]
description: typing.Optional[str]
field_schema: dict[str, typing.Any]
unique_among_creators: “
op_scope: typing.Optional[list[str]]

classmethod `preprocess_field_schema`

preprocess_field_schema(data: dict[str, Any]) → dict[str, Any]

classmethod `validate_field_schema`

validate_field_schema(schema: dict[str, Any]) → dict[str, Any]

method `value_is_valid`

value_is_valid(payload: Any) → bool

Validates a payload against this annotation spec’s schema. Args:

payload**: The data to validate against the schema

Returns:

bool: True if validation succeeds, False otherwise

class `File`

A class representing a file with path, mimetype, and size information.

method `init`

__init__(path: 'str | Path', mimetype: 'str | None' = None)

Initialize a File object. Args:

path**: Path to the file (string or pathlib.Path)
mimetype: Optional MIME type of the file - will be inferred from extension if not provided

property filename

Get the filename of the file. Returns:

str: The name of the file without the directory path.

method `open`

open() → bool

Open the file using the operating system’s default application. This method uses the platform-specific mechanism to open the file with the default application associated with the file’s type. Returns:

bool**: True if the file was successfully opened, False otherwise.

method `save`

save(dest: 'str | Path') → None

Copy the file to the specified destination path. Args:

dest**: Destination path where the file will be copied to (string or pathlib.Path) The destination path can be a file or a directory.

class `Markdown`

A Markdown renderable. Args:

markup (str): A string containing markdown.
code_theme (str, optional): Pygments theme for code blocks. Defaults to “monokai”.
justify (JustifyMethod, optional): Justify value for paragraphs. Defaults to None.
style (Union[str, Style], optional): Optional style to apply to markdown.
hyperlinks (bool, optional): Enable hyperlinks. Defaults to True.
inline_code_lexer: (str, optional): Lexer to use if inline code highlighting is enabled. Defaults to None.
inline_code_theme: (Optional[str], optional): Pygments theme for inline code highlighting, or None for no highlighting. Defaults to None.

method `init`

__init__( markup: 'str', code_theme: 'str' = 'monokai', justify: 'Optional[JustifyMethod]' = None, style: 'Union[str, Style]' = 'none', hyperlinks: 'bool' = True, inline_code_lexer: 'Optional[str]' = None, inline_code_theme: 'Optional[str]' = None) → None

class `Monitor`

Sets up a monitor to score incoming calls automatically. Examples:

import weavefrom weave.scorers import ValidJSONScorerjson_scorer = ValidJSONScorer()my_monitor = weave.Monitor( name="my-monitor", description="This is a test monitor", sampling_rate=0.5, op_names=["my_op"], query={ "$expr": { "$gt": [ { "$getField": "started_at" }, { "$literal": 1742540400 } ] } } }, scorers=[json_scorer],)my_monitor.activate()

Pydantic Fields:

name: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]
sampling_rate: “
scorers: list[flow.scorer.Scorer]
op_names: list[str]
query: typing.Optional[trace_server.interface.query.Query]
active: “

method `activate`

activate() → ObjectRef

Activates the monitor. Returns: The ref to the monitor.

method `deactivate`

deactivate() → ObjectRef

Deactivates the monitor. Returns: The ref to the monitor.

classmethod `from_obj`

from_obj(obj: WeaveObject) → Self

class `SavedView`

A fluent-style class for working with SavedView objects.

method `init`

__init__(view_type: 'str' = 'traces', label: 'str' = 'SavedView') → None

property entity

property label

property project

property view_type

method `add_column`

add_column(path: 'str | ObjectPath', label: 'str | None' = None) → SavedView

method `add_columns`

add_columns(*columns: 'str') → SavedView

Convenience method for adding multiple columns to the grid.

method `add_filter`

add_filter( field: 'str', operator: 'str', value: 'Any | None' = None) → SavedView

method `add_sort`

add_sort(field: 'str', direction: 'SortDirection') → SavedView

method `column_index`

column_index(path: 'int | str | ObjectPath') → int

method `filter_op`

filter_op(op_name: 'str | None') → SavedView

method `get_calls`

get_calls( limit: 'int | None' = None, offset: 'int | None' = None, include_costs: 'bool' = False, include_feedback: 'bool' = False, all_columns: 'bool' = False) → CallsIter

Get calls matching this saved view’s filters and settings.

method `get_known_columns`

get_known_columns(num_calls_to_query: 'int | None' = None) → list[str]

Get the set of columns that are known to exist.

method `get_table_columns`

get_table_columns() → list[TableColumn]

method `hide_column`

hide_column(col_name: 'str') → SavedView

method `insert_column`

insert_column( idx: 'int', path: 'str | ObjectPath', label: 'str | None' = None) → SavedView

classmethod `load`

load(ref: 'str') → SavedView

method `page_size`

page_size(page_size: 'int') → SavedView

method `pin_column_left`

pin_column_left(col_name: 'str') → SavedView

method `pin_column_right`

pin_column_right(col_name: 'str') → SavedView

method `remove_column`

remove_column(path: 'int | str | ObjectPath') → SavedView

method `remove_columns`

remove_columns(*columns: 'str') → SavedView

Remove columns from the saved view.

method `remove_filter`

remove_filter(index_or_field: 'int | str') → SavedView

method `remove_filters`

remove_filters() → SavedView

Remove all filters from the saved view.

method `rename`

rename(label: 'str') → SavedView

method `rename_column`

rename_column(path: 'int | str | ObjectPath', label: 'str') → SavedView

method `save`

save() → SavedView

Publish the saved view to the server.

method `set_columns`

set_columns(*columns: 'str') → SavedView

Set the columns to be displayed in the grid.

method `show_column`

show_column(col_name: 'str') → SavedView

method `sort_by`

sort_by(field: 'str', direction: 'SortDirection') → SavedView

method `to_grid`

to_grid(limit: 'int | None' = None) → Grid

method `to_rich_table_str`

to_rich_table_str() → str

method `ui_url`

ui_url() → str | None

URL to show this saved view in the UI. Note this is the “result” page with traces etc, not the URL for the view object.

method `unpin_column`

unpin_column(col_name: 'str') → SavedView

class `Audio`

A class representing audio data in a supported format (wav or mp3). This class handles audio data storage and provides methods for loading from different sources and exporting to files. Attributes:

format**: The audio format (currently supports ‘wav’ or ‘mp3’)
data: The raw audio data as bytes

Args:

data: The audio data (bytes or base64 encoded string)
format: The audio format (‘wav’ or ‘mp3’)
validate_base64: Whether to attempt base64 decoding of the input data

Raises:

ValueError: If audio data is empty or format is not supported

method `init`

__init__( data: 'bytes', format: 'SUPPORTED_FORMATS_TYPE', validate_base64: 'bool' = True) → None

method `export`

export(path: 'str | bytes | Path | PathLike') → None

Export audio data to a file. Args:

path**: Path where the audio file should be written

classmethod `from_data`

from_data(data: 'str | bytes', format: 'str') → Audio

Create an Audio object from raw data and specified format. Args:

data**: Audio data as bytes or base64 encoded string
format: Audio format (‘wav’ or ‘mp3’)

Returns:

Audio: A new Audio instance

Raises:

ValueError: If format is not supported

classmethod `from_path`

from_path(path: 'str | bytes | Path | PathLike') → Audio

Create an Audio object from a file path. Args:

path**: Path to an audio file (must have .wav or .mp3 extension)

Returns:

Audio: A new Audio instance loaded from the file

Raises:

ValueError: If file doesn’t exist or has unsupported extension

Edit this pageLast updated on Jul 14, 2025

⌘I

​API Overview

​Classes​

​Functions​

​function init​

​function publish​

​function ref​

​function get​

​function require_current_call​

​function get_current_call​

​function finish​

​function op​

​function attributes​

​class Object​

​classmethod from_uri​

​classmethod handle_relocatable_object​

​class Dataset​

​method add_rows​

​classmethod convert_to_table​

​classmethod from_calls​

​classmethod from_obj​

​classmethod from_pandas​

​method select​

​method to_pandas​

​class Model​

​method get_infer_method​

​class Prompt​

​method format​

​class StringPrompt​

​method __init__​

​method format​

​classmethod from_obj​

​class MessagesPrompt​

​method __init__​

​method format​

​method format_message​

​classmethod from_obj​

​class Evaluation​

​method evaluate​

​classmethod from_obj​

​method get_eval_results​

​method predict_and_score​

​method summarize​

​class EvaluationLogger​

​property ui_url​

​method finish​

​method log_prediction​

​method log_summary​

​class Scorer​

​method model_post_init​

​method score​

​method summarize​

​class AnnotationSpec​

​classmethod preprocess_field_schema​

​classmethod validate_field_schema​

​method value_is_valid​

​class File​

​method __init__​

​property filename​

​method open​

​method save​

​class Markdown​

​method __init__​

​class Monitor​

​method activate​

​method deactivate​

​classmethod from_obj​

​class SavedView​

​method __init__​

​property entity​

​property label​

​property project​

​property view_type​

​method add_column​

​method add_columns​

​method add_filter​

​method add_sort​

​method column_index​

​method filter_op​

​method get_calls​

​method get_known_columns​

API Overview

Classes

Functions

function `init`

function `publish`

function `ref`

function `get`

function `require_current_call`

function `get_current_call`

function `finish`

function `op`

function `attributes`

class `Object`

classmethod `from_uri`

classmethod `handle_relocatable_object`

class `Dataset`

method `add_rows`

classmethod `convert_to_table`

classmethod `from_calls`

classmethod `from_obj`

classmethod `from_pandas`

method `select`

method `to_pandas`

class `Model`

method `get_infer_method`

class `Prompt`

method `format`

class `StringPrompt`

method `init`

method `format`

classmethod `from_obj`

class `MessagesPrompt`

method `init`

method `format`

method `format_message`

classmethod `from_obj`

class `Evaluation`

method `evaluate`

classmethod `from_obj`

method `get_eval_results`

method `predict_and_score`

method `summarize`

class `EvaluationLogger`

property ui_url

method `finish`

method `log_prediction`

method `log_summary`

class `Scorer`

method `model_post_init`

method `score`

method `summarize`

class `AnnotationSpec`

classmethod `preprocess_field_schema`

classmethod `validate_field_schema`

method `value_is_valid`

class `File`

method `init`

property filename

method `open`

method `save`

class `Markdown`

method `init`

class `Monitor`

method `activate`

method `deactivate`

classmethod `from_obj`

class `SavedView`

method `init`

property entity

property label

property project

property view_type

method `add_column`

method `add_columns`

method `add_filter`

method `add_sort`

method `column_index`

method `filter_op`

method `get_calls`

method `get_known_columns`