Types

Reference for HUD serializable types: Run, Trace, Grade, Step, AgentStep, ToolStep, Citation, Answer, and the typed task input and output shapes.

The serializable shapes agents, tasks, and graders exchange.

from hud import Grade, Job, Run, Trace
from hud.types import Step
from hud.agents.types import AgentStep, Citation, SubagentStep, ToolStep
from hud.environment import Answer

Run

The live handle for one task - the lifecycle plus the agent’s Trace. You get them in job.runs from task.run(agent) / taskset.run(agent), or construct one over a connected client for manual driving.

Member	Type	Description
run.prompt	str | list | None	The task’s opening prompt as tasks.start returned it (text, or chat-style message list).
run.prompt_messages	list[PromptMessage]	The prompt as normalized user/assistant turns - what agents consume.
run.prompt_text	str	The prompt flattened to plain text, for string-only backends.
run.trace	Trace	The trajectory the agent fills. The answer is run.trace.content.
run.grade	Grade	Structured grade result.
run.reward	float	The graded reward (grade.reward, set on exit).
run.evaluation	dict	The raw grade payload (grade.raw).
run.runtime	str | None	Control-channel url the run executed against (the placement record).
run.trace_id	str | None	Keys the trajectory for training.
run.slug	str | None	The task slug this run came from, set by the runner; how Job.results keys runs back to their task without positional zip.
run.job_id / run.group_id	str | None	Batch + GRPO group, set by the runner.

A rollout that fails before its session is live comes back as a synthesized failed run (no prompt, no runtime); a mid-run failure keeps the real run - prompt, runtime, partial trace - with the error on run.trace.

Grade

Structured result from grading one run, parsed from the wire grade frame ({"score": ..., "done": ..., "isError": ..., ...}).

Field	Type	Description
reward	float	The frame’s score.
done	bool	Whether the task is complete.
content	str | None	Human-readable grade content.
info	dict	Extra metadata.
is_error	bool	Whether grading failed.
raw	dict	The full original frame.

Job

The receipt for one execution: the graded Runs of a batch under one platform job id. Every run reports under a job, so even a single task.run returns a job of one. You get one back from task.run / taskset.run.

Member	Type	Description
id	str	Platform job id.
name	str	Display name.
runs	list[Run]	The graded runs, in expansion order (task-major, then group).
group	int	Rollouts per task.
taskset_id	str | None	Platform taskset id when the job runs a synced taskset (Taskset.from_api), else None.
reward	float	Property: mean reward across runs (0.0 for an empty job).
results	dict[str, list[Run]]	Property: runs grouped by task slug - the alignment-safe alternative to zip(tasks, runs), list-valued since group > 1 gives several runs per task.

job = await ts.run(agent, runtime=LocalRuntime("env.py"), group=4)
print(job.reward)   # mean across every run

Each run call mints its own job by default. To gather many calls under one id - a training session, a multi-turn chat - open one with Job.start(name, *, group=1, taskset_id=None) and pass it as job=; the training agents guide uses this session pattern.

Trace

The agent’s trajectory for one rollout - an ordered collection of Steps plus the run summary, and the unit of training data. Every recorded step also streams to the platform as one schema-tagged span.

Field	Type	Description
steps	list[Step]	The ordered trajectory.
status	"completed" | "error" | "cancelled" | None	How the run ended (trace.is_error reads it).
content	str | None	The final answer (graded).
trace_id	str | None	Keys server-side trajectories.

hud.types.Step is the shared skeleton (source, timing, error, plus the harness payloads: prompt messages and task_call lifecycle RPCs). The tool-agent family subclasses it in hud.agents.types, flat on the skeleton:

• AgentStep - the model’s turn: content, reasoning, tool_calls, done, plus model, usage, and token-level sample when the backend is trainable.
• ToolStep - one tool round-trip: the MCPToolCall paired with its MCPToolResult.
• SubagentStep - a nested rollout’s Trace, embedded whole.

Derived reads go through the trace’s two query shapes - trace.final(get) (newest non-None answer wins; trace.error is a view on it) and trace.collect(get) (every answer, in step order). Family vocabulary stays at the call site:

samples = trace.collect(lambda s: s.sample if isinstance(s, AgentStep) else None)
citations = trace.final(lambda s: s.citations if isinstance(s, AgentStep) else None)

Answer & result types

Answer[T]

When a task declares returns=T, the answer arrives wrapped (from hud.environment import Answer): content is the answer parsed into T (or the original string when parsing failed - grade it accordingly), raw is always the string as submitted.

@env.template(returns=int)
async def count(word: str = "strawberry"):
    answer = yield f"How many letters in '{word}'?"
    yield 1.0 if answer.content == len(word) else 0.0

Citation

A normalized citation across providers (hud.agents.types.Citation): type, text, source, title, start_index, end_index. A reply annotation, not a grading input - provider agents attach them to AgentStep.citations, and chat surfaces read the final reply’s via the trace.final(...) query above. A task that wants to grade sources should declare them in its returns= schema so the agent submits them as part of the answer.

Grading shapes

SubScore and EvaluationResult live with the graders - see Graders.

Typed task I/O

Declare input= / returns= on @env.template to surface JSON schemas in the manifest and parse the agent’s answer into a typed Answer[T]. Any Pydantic model or standard type works.

These shapes flow through a task run; the scores they carry come from the graders.