AI工作流

Install

```bash

Install the CLI (recommended)

uv tool install hud-python --python 3.12

Deploy on the platform

From the platform UI you can run batches, compare models on the same taskset, and inspect every trace.

→ Run & deploy

Environments & templates

A template is an async generator registered with @env.template(): yield a prompt, receive the agent's answer, yield a reward. Calling the template mints a runnable Task; one function spans a whole dataset of variants. The simplest needs no capabilities — just a prompt and a grader:

from hud import Environment

env = Environment(name="letter-count")

@env.template()
async def count_letter(word: str = "strawberry", letter: str = "r"):
    answer = yield f"How many '{letter}'s are in '{word}'? Reply with just the number."
    yield 1.0 if answer and str(word.count(letter)) in answer else 0.0

tasks = [count_letter(word=w) for w in ("strawberry", "raspberry", "blueberry")]

Run it immediately against any model:

hud eval tasks.py claude --group 3

Each graded evaluation is a trace (the SDK's live handle is a Run). With HUD_API_KEY set, every rollout is recorded on hud.ai. Tasks that need a shell, browser, GUI, or robot declare capabilities (below); everything else — variants, grading, batching — stays identical.

→ Quickstart · Tasks & tasksets

or: export HUD_API_KEY=your-key-here

Then scaffold your first environment:

Package & run anywhere

A built image is the end product for your tasks: one build packs every task from a single definition. The recommended path is hud deploy, which builds and registers your environment on HUD in one step; then sync a taskset and run remotely:

hud deploy
hud sync tasks my-taskset
hud eval my-taskset --remote

For local iteration, the same protocol works against a container on your laptop:

docker build -f Dockerfile.hud -t my-env .
docker run -d --name run1 -p 8765:8765 my-env
hud task start fix_bug --url tcp://127.0.0.1:8765
hud task grade fix_bug --url tcp://127.0.0.1:8765 --answer "..."
docker rm -f run1

→ Run & deploy