for openclaw users — v1.0

run ai workflows with
100% precision

deterministic, repeatable agent workflows — written in plain language, executed exactly as defined.

whipflow — summarize-and-post.whip

# fetch, summarize, and post — every run, identical output session summarize-and-post var source_url = "https://news.example.com/feed"var max_words = 120 step fetch-contentfetch {{source_url}} → raw_content step summarizeskill summarize input={{raw_content}} words={{max_words}} → summary step post-to-slackskill slack-notify message={{summary}} $ whipflow run summarize-and-post.whip ✓ fetch-content 42ms ✓ summarize 1.2s ✓ post-to-slack done

// the problem

prompt engineering is not a deployment strategy.

openclaw lets you build with capable models — but capability and consistency are different problems. when your workflow runs differently on tuesday than it did on monday, you're not shipping features. you're debugging runs, tracing model quirks, and patching output formats — again.

→non-deterministic runs don't fail loudly. they return plausible-looking output that silently breaks downstream steps. harness farm gives the runtime a contract to enforce — so you catch the drift at execution time, not in production.

// features

write it once. run it the same way every time.

three things that let openclaw users ship workflows they trust.

⬡

01 / precision

precision workflow control

declare every step explicitly — execution order, inputs, and outputs are guaranteed by the runtime, not the model.

⌘

02 / language

natural language first

write workflows in .whip — readable, diffable, no sdk wiring, no glue code.

◈

03 / portability

model-agnostic execution

swap claude for gpt or a local model in one line — your workflow logic stays exactly the same.

// getting started

from zero to running workflow in under 5 minutes.

01

write your workflow in .whip

define steps, input variables, and skill calls in plain language. no imports, no async/await, no boilerplate — just the logic you care about.

touch my-workflow.whip

02

run it and watch every step execute

the runtime resolves variables, calls skills in order, and logs each step with timing and output — full observability, no black box.

whipflow run my-workflow.whip

03

iterate on prompts or swap models — deploy with confidence

change the underlying model in your config and re-run. your workflow doesn't care — the contract it enforces stays identical.

whipflow config set model claude-sonnet-4-6

// faq

honest answers to skeptic questions.

stop debugging runs.
start shipping workflows.

harness farm is built for openclaw users who need repeatability, not just capability.

start building →free to use · no api key needed to try