RunArtifacts is the structured record of an agent run. Your agent populates it during execution, and verifiers consume it to evaluate task completion.
Fields
| Field | Type | Required | Description |
|---|---|---|---|
version | str | Yes | Schema version (currently "0.1") |
task_id | str | Yes | Task identifier |
task | str | Yes | Task instruction text |
model | str | No | Model used (e.g., "gpt-4o") |
provider | str | No | Model provider (e.g., "openai") |
messages | list[dict] | No | Full message history (role + content) |
tool_calls | list[ToolCall] | No | All tool invocations made |
tool_results | list[ToolCallResult] | No | Results from each tool call (parallel to tool_calls) |
metadata | dict | No | Arbitrary metadata |
final_observation | str | No | Agent’s final output/summary |
error | str | No | Error message if the run failed |
steps_taken | int | Yes | Number of tool calls executed |
max_steps | int | No | Step limit (if configured) |
created_at | str | Yes | ISO 8601 timestamp |
Helper Methods
| Method | Description |
|---|---|
record_message(role, content) | Append a message to the history |
record_tool_call(call, result) | Record a tool call + result, increments steps_taken |
set_final_observation(text) | Set the agent’s final output |
set_error(text) | Record an error |
to_dict() | Serialize to a JSON-compatible dict |
dump(path) | Write artifacts to a JSON file |
ToolCall
@dataclass
class ToolCall:
tool_server: str # e.g. "email"
tool_name: str # e.g. "send_email"
parameters: dict[str, Any] # tool-specific parameters
ToolCallResult
@dataclass
class ToolCallResult:
observation: Any # response from the tool server
is_error: bool = False # True if the tool call failed