Drive an installed LaRuche App through its declared actions, never its files.
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Hand a sub-task to another agent, a specialist model, or a background job.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Four ways to offload work. They differ in who does it, whether you wait, and what comes back. Picking wrong wastes a model call or loses the result.
| Tool | Runs | You get back | Use when |
|---|---|---|---|
delegate | now, same swarm | the finished result | a self-contained sub-task |
spawn_specialist | now, chosen model | the finished result | the sub-task needs a different model or role |
mixture_of_agents | now, several in parallel | one merged answer | quality matters more than cost |
submit_job | in the background | a job id, to poll later | it takes minutes and blocks nothing |
Before any of them, ask whether you can just do it. Delegation costs a full context build. For anything you can finish in two or three tool calls, doing it yourself is faster and more reliable.
delegate with task, plus optional context and role.
The delegate starts COLD. It does not see this conversation. Anything it needs must be in
task and context: absolute paths, the exact goal, what "done" looks like, and what it
must not touch. A vague task returns vague work, and you will not be able to tell that
it misunderstood.
Write task as a complete brief a stranger could execute. Include the acceptance
criterion explicitly.
spawn_specialist with role and task, plus optional context, provider and
model.
Same cold start, same briefing rules. Use it when the sub-task wants a specific profile: a stronger model for a hard piece of reasoning, a cheaper one for bulk extraction, a local one for something that must not leave the machine.
Leave provider and model unset unless you have a reason. The default is configured
and works; naming a model that is not available fails the whole call.
mixture_of_agents with prompt and candidates, plus optional model, provider and
api_base.
Several candidates answer the same prompt, and the answers are merged. Use it for questions where a single pass is unreliable: an open judgement, a design trade-off, a summary that must not miss anything.
Do not use it for anything deterministic. Running a command, reading a file or computing a value has one right answer; asking three models to vote on it costs several times more and can produce a merged answer that none of them actually gave.
submit_job with script, plus optional label.
Runs in the background. Returns a job id immediately.
submit_job with the script. Give it a label, because a bare id is unreadable in a
later turn.check_job_status with job_id when you need the result.Never assume a job succeeded. Poll with check_job_status and read the output before
reporting anything about it. Reporting a background job as done without checking is
fabrication.
If it is still running when you are otherwise finished, say so explicitly instead of claiming completion.
cancel_job with job_id. It asks for approval, because stopping work is a decision.
It stops the job from continuing. It does NOT undo what the job already did. A script that had written half its files keeps those files; a script that had already called a remote API has already called it. Cancelling is a way to stop spending, not a way to return to the state before the job started.
So after a cancel, read the state before you report anything: list the directory the script was writing to, or query the service it was calling. If you cannot establish what it managed to do, say exactly that. An unknown outcome is a result, and reporting it as "cancelled, nothing happened" is the fabrication this whole page is about.
Cancel when the job is provably pointless: its input was wrong, the user changed their mind, or a faster path is already finished. A job that is merely slow is not a reason.
task or
context.check_job_status says so.The delegate returns work that misses the point. The brief was thin. Do not re-run it
identically: rewrite task with the missing constraints spelled out, then retry.
The delegate says it cannot find a file that exists. A relative path was passed. It does not share your working directory. Pass absolute paths.
check_job_status reports failure with no detail. Run the same script directly with
shell_exec to see the real error. Background output is often truncated.
mixture_of_agents returns a bland merge. The prompt asked for something factual. Do
it in one pass instead.
spawn_specialist fails immediately. The named provider or model is not
configured. Drop both fields and retry with the default.
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Drive an installed LaRuche App through its declared actions, never its files.
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Find academic papers on arXiv, with citation counts and BibTeX.
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Render text or an image as ASCII art for terminal-friendly output.
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Track RSS/Atom feeds and blogs via blogwatcher-cli.
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Drive a real web browser: navigate, read, find, click, fill, screenshot
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Measure a codebase: lines of code, language mix, and symbol lookups.
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