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

Create. Build a table from a source — files, a glob pattern, or pre-parsed records. One row per item. Returns a handle.
Run. Dispatch an
```
instruction
```
template across rows. Results are merged back into the table. Returns
```
{ completed, failed, skipped, failures }
```
.
Aggregate. Use
```
rows()
```
and plain JS to count, filter, or summarize. Do not spawn additional subagents for aggregation.
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:

javascript

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:

javascript

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:

javascript

// 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`);

javascript

// 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.

javascript

// 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.

javascript

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.

javascript

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.

javascript

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.

javascript

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.

javascript

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.

javascript

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

javascript

{ 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 }

Source	Description
`{ glob: "src/*/.ts" }` or `{ glob: ["src/*/.ts", "lib/*/.ts"] }`	Match files by one or more patterns. Columns: `id` , `file`
`{ filePaths: ["a.ts", "b.ts"] }`	Explicit file list. Columns: `id` , `file`
`{ tasks: [{ id: "t1", text: "..." }] }`	Custom rows. Each must have `id`

run(tableId, options)

Dispatch work across rows. Returns

{ completed, failed, skipped, failures }

Option	Default	Description
`instruction`	(required)	Template with `{column}` placeholders
`responseSchema`	(required)	JSON Schema ( `type: "object"` ) — properties become row columns
`context`	—	Prose prepended to every subagent prompt
`filter`	—	Only dispatch matching rows
`subagentType`	—	Name of subagent to dispatch to. When set, runs a full agentic loop. When omitted, runs a direct model call
`batchSize`	auto	Number or `(row, rowCount) => number` . Auto caps dispatches at 10; `1` = per-row; function = per-row sizing
`concurrency`	`10`	Max concurrent subagent dispatches (clamped to 1–10)

rows(tableId, options?)

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

Option	Description
`filter`	Only return matching rows
`columns`	Project to specific columns
`limit`	Max rows returned

swarm

NPX Install

Tags

SKILL.md Content

Swarm

Flow

Choosing a source

When to use
`subagentType`

Instruction + context

Structured output

Batching

Aggregation

Chaining passes

Action-only tasks

Filtering

Technical notes

API Reference

`create(source)`

`run(tableId, options)`

`rows(tableId, options?)`