Swarm

Swarm

Process many independent items in parallel. create builds a table handle; run fans work out across rows and merges results back. One row = one unit of work — swarm handles batching automatically.

Flow

1. Create. Build a table from a source — files, a glob pattern, or pre-parsed records. One row per item. Returns a handle.
2. Run. Dispatch an instruction template across rows. Results are merged back into the table. Returns { completed, failed, skipped, failures }.
3. Aggregate. Use rows() and plain JS to count, filter, or summarize. Do not spawn additional subagents for aggregation.
4. Retry. Re-run with filter: { column: "<col>", exists: false } to reprocess only failed rows.

Choosing a source

glob / filePaths — one file = one row. Use when each file is an independent unit of work. Each row gets { id, file }; the subagent reads the file itself via the {file} placeholder.

tasks — pass pre-built records directly. Use when the data lives inside a file (JSONL, CSV, JSON array). Read and parse the file first inside eval, then pass the records. One record = one row — do not group multiple items into a single row.

For small files (under ~500 lines), parse and create in one block:

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const { create } = await import("@/skills/swarm");
const raw = await tools.readFile({ file_path: "/data.jsonl" });
const records = raw.trim().split("\n").map(l => JSON.parse(l));
const table = await create({ tasks: records });
console.log(table);

For large files, read in chunks of 500 lines to avoid truncation:

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const { create } = await import("@/skills/swarm");
let records = [];
let offset = 0;
while (true) {
  const chunk = await tools.readFile({ file_path: "/data.txt", offset, limit: 500 });
  const lines = chunk.split("\n").filter(l => l.trim());
  for (const l of lines) { records.push({ id: `r${records.length}`, text: l }); }
  if (lines.length < 500) break;
  offset += 500;
}
const table = await create({ tasks: records });
console.log(table);

When the file is too large to parse and dispatch in one eval call, split across two blocks. Only the block that calls swarm functions needs the import:

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// eval 1: parse only — no swarm import needed
const raw = await tools.readFile({ file_path: "/data.jsonl" });
globalThis.records = raw.trim().split("\n").map(l => JSON.parse(l));
console.log(`Parsed ${globalThis.records.length} records`);

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// eval 2: create and dispatch
const { create, run } = await import("@/skills/swarm");
const table = await create({ tasks: globalThis.records });
const result = await run(table.id, {
  instruction: "Classify {text}",
  responseSchema: {
    type: "object",
    properties: { label: { type: "string" } },
    required: ["label"],
  },
});
console.log(result);

Passing filePaths: ["/data.jsonl"] would produce a table with one row pointing at the file — not one row per record inside it.

When to use subagentType

Omit subagentType for classification, extraction, labeling, and any task where a single model call with structured output is sufficient. This is the default and is significantly cheaper and faster — each dispatch is a direct model call, no tools, no iteration.

Set subagentType when the task requires tools, file access, or multi-step reasoning. Each dispatch runs a full agentic loop with the named subagent.

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// Direct model call — classification, no tools needed
await run(table.id, {
  instruction: "Classify {text}",
  responseSchema: { type: "object", properties: { label: { type: "string" } }, required: ["label"] },
});

// Subagent — needs to read files and reason over multiple steps
await run(table.id, {
  subagentType: "reviewer",
  instruction: "Review {file} for security issues.",
  responseSchema: { type: "object", properties: { finding: { type: "string" } }, required: ["finding"] },
});

Instruction + context

instruction is a per-item template with {column} placeholders. Placeholders are resolved by the framework — your column names appear in prompts as references to the values listed alongside, never as raw template syntax. Subagents do the work — do not process items yourself in JS and write the results into rows.

context is free-form prose prepended to every subagent prompt. Use it for shared background: domain terms, classification rules, examples, etc.

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const { create, run } = await import("@/skills/swarm");

const table = await create({ glob: "src/**/*.ts" });
const r = await run(table.id, {
  subagentType: "reviewer",
  instruction: "Review {file} for security issues. List findings or write 'no issues'.",
  context: "TypeScript Express backend using Prisma ORM. Focus on injection, auth bypass, path traversal.",
  responseSchema: {
    type: "object",
    properties: { review: { type: "string" } },
    required: ["review"],
  },
});
console.log(r);
// → { completed: 45, failed: 2, skipped: 0, failures: [...] }

Structured output

responseSchema is required. Schema properties become top-level columns on each row and constrain what subagents can return.

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const { run } = await import("@/skills/swarm");
await run(table.id, {
  instruction: "Classify: {text}",
  responseSchema: {
    type: "object",
    properties: {
      sentiment: { type: "string", enum: ["positive", "negative", "neutral"] },
    },
    required: ["sentiment"],
  },
});
// Row after: { id: "r1", text: "...", sentiment: "positive" }

Batching

By default, swarm auto-batches to keep total dispatches under 10. For small tables (≤10 rows) each row gets its own subagent call. For larger tables, rows are grouped automatically.

Set batchSize to control grouping:

• Number — uniform batch size for all rows. batchSize: 1 forces per-row dispatch; batchSize: 20 groups in twenties.
• Function — (row, rowCount) => number. Returns the desired batch size for each row. Rows with the same batch size are grouped together, then chunked. Allows mixed dispatch where some rows go solo and others batch.

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const { create, run } = await import("@/skills/swarm");
const table = await create({ tasks: items });

// Complex items get individual attention; simple ones batch together
await run(table.id, {
  instruction: "Analyze {text}",
  responseSchema: {
    type: "object",
    properties: { analysis: { type: "string" } },
    required: ["analysis"],
  },
  batchSize: (row) => (row.token_count > 1000 ? 1 : 10),
});

Batch sizes are clamped to [1, 50] after evaluation.

Aggregation

After run(), use rows() and plain JS — no additional subagents needed.

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const { rows } = await import("@/skills/swarm");
const data = await rows(table.id, { columns: ["sentiment"] });
const counts = {};
data.forEach(r => { counts[r.sentiment] = (counts[r.sentiment] || 0) + 1 });
console.log(counts);
// → { positive: 120, negative: 45, neutral: 35 }

Chaining passes

run updates the table in place — chain calls to accumulate columns.

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const { create, run } = await import("@/skills/swarm");
const table = await create({ tasks: interviews });
await run(table.id, {
  instruction: "Classify sentiment of {text}",
  responseSchema: {
    type: "object",
    properties: { sentiment: { type: "string", enum: ["positive", "negative", "neutral"] } },
    required: ["sentiment"],
  },
});
await run(table.id, {
  filter: { column: "sentiment", equals: "negative" },
  instruction: "Summarize why {text} had negative sentiment.",
  responseSchema: {
    type: "object",
    properties: { summary: { type: "string" } },
    required: ["summary"],
  },
});

Action-only tasks

When subagents perform actions (write a file, apply a fix) rather than return data, use a simple schema with a status or marker field. The exists: false filter still works for retries.

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const { create, run } = await import("@/skills/swarm");
const fixedSchema = {
  type: "object",
  properties: { fixed: { type: "string" } },
  required: ["fixed"],
};
const table = await create({ glob: "src/**/*.ts" });
await run(table.id, {
  subagentType: "fixer",
  instruction: "Add missing JSDoc to all exported functions in {file}.",
  responseSchema: fixedSchema,
});
// retry any that failed
await run(table.id, {
  subagentType: "fixer",
  instruction: "Add missing JSDoc to all exported functions in {file}.",
  responseSchema: fixedSchema,
  filter: { column: "fixed", exists: false },
});

Filtering

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{ column: "status", equals: "done" }
{ column: "status", notEquals: "done" }
{ column: "category", in: ["A", "B"] }
{ column: "result", exists: false }      // not yet processed
{ and: [filter1, filter2] }
{ or: [filter1, filter2] }

Technical notes

• Only import @/skills/swarm in blocks where you call swarm functions. Data preparation (reading files, parsing, storing in globalThis) does not need the import. Destructure only what you use: { create }, { run }, { create, run }, etc.
• Console output is capped at ~5 KB. Never log raw file contents — log only counts and short samples.
• readFile inside eval returns raw content — no line-number prefixes. Request at most 500 lines per call. For files with more than 500 lines, loop with incrementing offset.
• When building a table from a file, read it inside eval. Data read inside the sandbox stays there; it never enters the agent's context window.
• Never write to .swarm/ directly. Always use create().
• Everything the subagent needs must be in instruction + context. Subagents can't see the agent's context.
• Row ids must be unique. create() rejects sources that produce duplicate ids. For tasks, that's a caller-side responsibility; for glob / filePaths, ids are auto-disambiguated by parent directory.
• Unknown columns fail fast. If instruction references {foo} and no matched row provides foo, run() throws before any subagent is dispatched.

API Reference

create(source)

Create a table. Returns a handle { id, count, columns }.

run(tableId, options)

Dispatch work across rows. Returns { completed, failed, skipped, failures }.

rows(tableId, options?)

Retrieve rows. Use for inspection and JS-based aggregation.