Key Concepts

const llm = LLMCall.create({  provider: provider ?? exampleProvider('core', { reply: "It's sunny in San Francisco." }),  model: 'mock-weather',  temperature: 0.2,})  .system('You are a terse weather assistant. One sentence answers.')  .build();

const agent = Agent.create({  provider: provider ?? exampleProvider('feature', { respond: weatherRespond }),  model: 'mock',  maxIterations: 5,  // reactMode: 'dynamic-grouped' wraps the LLM turn in an sf-llm-call subflow,  // so Lens renders the agent's reasoning as an LLM group with its context  // slots (system-prompt / messages / tools) nested inside — the SAME shape  // the LLMCall primitive shows — instead of a bare "Final · RUNNER" card.  reactMode: 'dynamic-grouped',})  .system('You answer weather questions using the `weather` tool.')  .tool({    schema: {      name: 'weather',      description: 'Get current weather for a city.',      inputSchema: {        type: 'object',        properties: { city: { type: 'string' } },        required: ['city'],      },    },    execute: async (args) => `${(args as { city: string }).city}: sunny, 72°F`,  })  .build();

const pipeline = Sequence.create({ name: 'IntakePipeline' })  .step('classify', classify)  .pipeVia((label) => ({ message: `Intent: ${label.trim()}` }))  .step('respond', respond)  .build();

if (mode === 'strict') {  // STRICT (default), 2 BRANCHES: any branch failure → whole Parallel throws.  // The smaller committee is the most common shape — two specialists vote.  // Both votes are REQUIRED (losing 1 of 2 is not fine), so each branch  // is marked `{ required: true }`: with every branch required, the  // fan-out runs fail-fast — the first failure rejects the whole run  // immediately, naming the branch, without waiting on the sibling.  const committee = Parallel.create({ name: 'Committee' })    .branch('legal', brief('legal'), { required: true })    .branch('ethics', brief('ethics'), { required: true })    .mergeWithFn((results) =>      Object.entries(results)        .map(([id, r]) => `  ${id}: ${r}`)        .join('\n'),    )    .build();  console.log('--- strict mode (2 agents) ---');  const strict = await committee.run({ message: cleanInput });  console.log(strict);  return { mode, strict };}// TOLERANT, 3 BRANCHES: the merge fn receives the full outcomes map so// it can decide how to handle partial failure. Larger committees are// also where tolerant mode pays off — losing 1 of 3 voices is fine,// losing 1 of 2 is not.const tolerantCommittee = Parallel.create({ name: 'TolerantCommittee' })  .branch('legal', brief('legal'))  .branch('ethics', brief('ethics'))  .branch('cost', brief('cost'))  .mergeOutcomesWithFn((outcomes) => {    const lines = Object.entries(outcomes).map(([id, o]) =>      o.ok ? `  ${id}: ${o.value}` : `  ${id}: [FAILED] ${o.error}`,    );    return lines.join('\n');  })  .build();

const triage = Conditional.create({ name: 'Triage' })  .when(    'urgent',    (input) => /\b(urgent|asap|outage|down|critical)\b/i.test(input.message),    urgent,  )  .otherwise('normal', normal)  .build();

const runner = reflection({  provider: provider ?? exampleProvider('pattern', { respond: () => replies[i++ % replies.length]! }),  model: 'mock',  proposerPrompt: 'Write or revise a short poem about night.',  criticPrompt:    'Critique the poem. When it is good enough include the marker DONE.',  maxIterations: 5,});

const router = swarm({  agents: [    { id: 'triage', runner: triage },    { id: 'billing', runner: billing },    { id: 'tech', runner: tech },  ],  // Route function — pure sync over the current message. First turn goes  // to triage, then to billing or tech based on content, then halts.  route: (input) => {    const msg = input.message.toLowerCase();    if (msg.includes('[billing]')) return undefined; // billing done → halt    if (msg.includes('[tech]')) return undefined; // tech done → halt    if (msg.includes('refund') || msg.includes('bill')) return 'billing';    if (msg.includes('status') || msg.includes('error')) return 'tech';    return 'triage'; // first turn  },  maxHandoffs: 5,});

const docs = defineRAG({  id: 'product-docs',  description: 'Product documentation chunks',  store,  embedder,  topK: 2,           // up to 2 most-relevant docs per query  threshold: 0.5,    // strict — drop weak matches  asRole: 'user',    // chunks land as user-role context (RAG default)});const agent = Agent.create({  provider: provider ?? mock({ reply: 'Refunds are processed within 3 business days.' }),  model: 'mock',  maxIterations: 1,})  .system('You answer support questions using the retrieved docs.')  .rag(docs)  .build();