kodeagent.fca.Task

class kodeagent.fca.Task(*, id: str = <factory>, description: str, files: list[str] | None = None, result: ~typing.Any | None = None, is_finished: bool = False, is_error: bool = False, output_files: list[str] = <factory>, steps_taken: int = 0, total_llm_calls: int = 0, total_prompt_tokens: int = 0, total_completion_tokens: int = 0, total_tokens: int = 0, total_cost: float = 0.0, usage_by_component: dict[str, dict] | None = None)

Task to be solved by an agent.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__init__(**data: Any) → None

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Methods

__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
construct([_fields_set])
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])
from_orm(obj)
json(*[, include, exclude, by_alias, ...])
model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])	!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
model_dump_json(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
model_json_schema([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
model_post_init(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
update_forward_refs(**localns)
validate(value)

Attributes

model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
id	Auto-generated task ID.
description	Task description.
files	A list of file paths or URLs.
result	Task result.
is_finished	Whether the task has finished running.
is_error	Whether the task execution resulted in any error.
output_files	List of file paths generated during task execution.
steps_taken	Number of steps/iterations taken by the agent for this task.
total_llm_calls	Total number of LLM calls made during task execution.
total_prompt_tokens	Total prompt tokens used.
total_completion_tokens	Total completion tokens used.
total_tokens	Total tokens used (prompt + completion).
total_cost	Total cost in USD for all LLM calls.
usage_by_component	Breakdown of usage by component (Planner, Observer, Agent).