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CLI & Library reference

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CLI

adhd "design a rate limiter that survives a leader election"

adhd "name this function" --frames 3 --ideas 8 --top 2

adhd "we have a CLI that hangs for 90s on LLM calls. what's the right retry/UX?" \
    --frames 5 --ideas 6 --top 3 --context ./client.ts

adhd "..." --json > result.json

Flags

Flag Default What
--frames N 5 parallel divergence branches
--ideas N 6 ideas per branch
--top N 3 how many to deepen / focus
--concurrency N 4 max parallel LLM calls
--context PATH — inject a file as context (code, stack, constraints)
--model NAME SDK default override model (generator + critic)
--critic-model NAME = --model override model for the critic passes only (score + cluster) — use a different family to decorrelate critic errors
--no-code-mode — don't bias frames toward engineering
--json — emit machine-readable RunResult
--quiet — suppress progress events

Library (TypeScript)

import { run, renderText, FRAMES, selectFrames } from "adhd-agent";
import type {
  RunOptions, RunResult, Idea, Branch, Cluster,
  DeepenedIdea, Score, RunEvent,
} from "adhd-agent";

type RunOptions = {
  problem: string;
  context?: string;
  framesPerRun?: number;   // default 5
  ideasPerFrame?: number;  // default 6
  topK?: number;           // default 3
  concurrency?: number;    // default 4
  codeMode?: boolean;      // default true
  model?: string;          // generator + critic
  criticModel?: string;    // critic (score + cluster) only; defaults to `model`
  onEvent?: (e: RunEvent) => void;
};

A full run:

const result = await run({
  problem: "How should we shard this queue under bursty load?",
  context: readFileSync("./queue.ts", "utf8"),
  framesPerRun: 6,
  ideasPerFrame: 8,
  topK: 3,
  onEvent: (e) => console.error(e),
});

console.log(renderText(result));
// or operate on:
//   result.shortlist        → 2–4 most promising ideas with scores
//   result.nonObviousPick   → the highest-novelty viable one
//   result.traps            → "looks good but isn't" list, with reasons
//   result.deepened         → top-K expanded: sketch + risk + first step + child ideas
//   result.clusters         → the SHAPE of the idea space

Everything in RunResult is structured — clusters, scored ideas with novelty / viability / fit, trap reasons, deepened sketches with child ideas. You can route it into your own renderer, downstream agent, or planning loop.

Use ADHD inside your own agent

The shape that pays the most: call run() at decision points inside a larger agent loop.

// inside your planning / coding / review agent
if (agentIsAtADecisionPoint) {
  const { shortlist, nonObviousPick, traps, deepened } = await run({
    problem: framedDecision,
    context: relevantCode,
    framesPerRun: 4,
    topK: 2,
    codeMode: true,
  });
  // feed the deepened sketches back into your agent's context
}

Good moments to call it:

  • agent stuck after N attempts on a bug — widen the hypothesis space
  • planning agent at a branch point with high uncertainty
  • code-review agent asked "what could go wrong here"
  • refactor agent picking which abstraction to introduce
  • test-generation agent generating adversarial inputs (inversion frame)