The WeaveClient class is the primary interface for interacting with the Weave service. It provides methods for managing calls, datasets, models, and other Weave objects.

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Constructor

from weave import WeaveClient

client = WeaveClient(project: str)

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Parameters

  • project (str) - The project identifier in the format “entity/project”

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Methods

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add_cost

Add custom cost tracking to a call. 
client.add_cost(
    costs: Dict[str, float],
    call_id: Optional[str] = None
) -> None

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Parameters

  • costs (Dict[str, float]) - Dictionary of cost metrics (e.g., {"prompt_tokens": 100, "completion_tokens": 50})
  • call_id (Optional[str]) - The call ID to add costs to. If None, uses the current call context.

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Example

client.add_cost({
    "prompt_tokens": 100,
    "completion_tokens": 50,
    "total_cost": 0.002
})

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query_costs

Query costs for calls with various filters. 
client.query_costs(
    project_id: Optional[str] = None,
    filters: Optional[Dict] = None
) -> List[Dict]

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purge_costs

Remove custom costs by their IDs. 
client.purge_costs(cost_ids: List[str]) -> None

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get_call

Retrieve a specific call by its ID. 
call = client.get_call(call_id: str) -> Call

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Parameters

  • call_id (str) - The unique identifier of the call

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Returns

  • Call - A Call object containing the call details

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Example

call = client.get_call("call_123456")
print(call.inputs)
print(call.output)

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get_calls

Query multiple calls with filters. 
calls = client.get_calls(
    filter: Optional[CallsFilter] = None,
    limit: Optional[int] = None,
    offset: Optional[int] = None
) -> List[Call]

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Parameters

  • filter (Optional[CallsFilter]) - Filter criteria for calls
  • limit (Optional[int]) - Maximum number of calls to return
  • offset (Optional[int]) - Number of calls to skip

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delete

Delete a call or calls. 
# Delete a single call
call.delete()

# Delete multiple calls
client.delete_calls(call_ids: List[str])

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set_display_name

Set or update the display name of a call. 
call.set_display_name(display_name: str) -> None

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Working with Datasets

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save

Save a dataset to Weave. 
dataset = weave.Dataset(rows=[...])
weave.publish(dataset, "my-dataset")

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get

Retrieve a dataset by name. 
dataset = weave.ref("my-dataset").get()

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Working with Models

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save

Save a model to Weave. 
model = MyModel(...)
weave.publish(model, "my-model")

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get

Retrieve a model by reference. 
model = weave.ref("my-model").get()
For complete examples and advanced usage, see the Weave documentation.