Swarm

# Swarm — Cut Your LLM Costs by 200x

**Turn your expensive model into an affordable daily driver. Offload the boring stuff to Gemini Flash workers — parallel, batch, research — at a fraction of the cost.**

## At a Glance

| 30 tasks via | Time | Cost | |--------------|------|------| | Opus (sequential) | ~30s | ~$0.50 | | Swarm (parallel) | ~1s | ~$0.003 |

## When to Use

Swarm is ideal for: - **3+ independent tasks** (research, summaries, comparisons) - **Comparing or researching multiple subjects** - **Multiple URLs** to fetch/analyze - **Batch processing** (documents, entities, facts) - **Complex analysis** needing multiple perspectives → use chain

## Quick Reference

```bash # Check daemon (do this every session) swarm status

# Start if not running swarm start

# Parallel prompts swarm parallel "What is X?" "What is Y?" "What is Z?"

# Research multiple subjects swarm research "OpenAI" "Anthropic" "Mistral" --topic "AI safety"

# Discover capabilities swarm capabilities ```

## Execution Modes

### Parallel (v1.0) N prompts → N workers simultaneously. Best for independent tasks.

```bash swarm parallel "prompt1" "prompt2" "prompt3" ```

### Research (v1.1) Multi-phase: search → fetch → analyze. Uses Google Search grounding.

```bash swarm research "Buildertrend" "Jobber" --topic "pricing 2026" ```

### Chain (v1.3) — Refinement Pipelines Data flows through multiple stages, each with a different perspective/filter. Stages run in sequence; tasks within a stage run in parallel.

**Stage modes:** - `parallel` — N inputs → N workers (same perspective) - `single` — merged input → 1 worker - `fan-out` — 1 input → N workers with DIFFERENT perspectives - `reduce` — N inputs → 1 synthesized output

**Auto-chain** — describe what you want, get an optimal pipeline: ```bash curl -X POST http://localhost:9999/chain/auto \ -d '{"task":"Find business opportunities","data":"...market data...","depth":"standard"}' ```

**Manual chain:** ```bash swarm chain pipeline.json # or echo '{"stages":[...]}' | swarm chain --stdin ```

**Depth presets:** `quick` (2 stages), `standard` (4), `deep` (6), `exhaustive` (8)

**Built-in perspectives:** extractor, filter, enricher, analyst, synthesizer, challenger, optimizer, strategist, researcher, critic

**Preview without executing:** ```bash curl -X POST http://localhost:9999/chain/preview \ -d '{"task":"...","depth":"standard"}' ```

### Benchmark (v1.3) Compare single vs parallel vs chain on the same task with LLM-as-judge scoring.

```bash curl -X POST http://localhost:9999/benchmark \ -d '{"task":"Analyze X","data":"...","depth":"standard"}' ```

Scores on 6 FLASK dimensions: accuracy (2x weight), depth (1.5x), completeness, coherence, actionability (1.5x), nuance.

### Capabilities Discovery (v1.3) Lets the orchestrator discover what execution modes are available: ```bash swarm capabilities # or curl http://localhost:9999/capabilities ```

## Prompt Cache (v1.3.2)

LRU cache for LLM responses. **212x speedup on cache hits** (parallel), **514x on chains**.

- Keyed by hash of instruction + input + perspective - 500 entries max, 1 hour TTL - Skips web search tasks (need fresh data) - Persists to disk across daemon restarts - Per-task bypass: set `task.cache = false`

```bash # View cache stats curl http://localhost:9999/cache

# Clear cache curl -X DELETE http://localhost:9999/cache ```

Cache stats show in `swarm status`.

## Stage Retry (v1.3.2)

If tasks fail within a chain stage, only the failed tasks get retried (not the whole stage). Default: 1 retry. Configurable per-phase via `phase.retries` or globally via `options.stageRetries`.

## Cost Tracking (v1.3.1)

All endpoints return cost data in their `complete` event: - `session` — current daemon session totals - `daily` — persisted across restarts, accumulates all day

```bash swarm status # Shows session + daily cost swarm savings # Monthly savings report ```

## Web Search (v1.1)

Workers search the live web via Google Search grounding (Gemini only, no extra cost).

```bash # Research uses web search by default swarm research "Subject" --topic "angle"

# Parallel with web search curl -X POST http://localhost:9999/parallel \ -d '{"prompts":["Current price of X?"],"options":{"webSearch":true}}' ```

## JavaScript API

```javascript const { parallel, research } = require('~/clawd/skills/node-scaling/lib'); const { SwarmClient } = require('~/clawd/skills/node-scaling/lib/client');

// Simple parallel const result = await parallel(['prompt1', 'prompt2', 'prompt3']);

// Client with streaming const client = new SwarmClient(); for await (const event of client.parallel(prompts)) { ... } for await (const event of client.research(subjects, topic)) { ... }

// Chain const result = await client.chainSync({ task, data, depth }); ```

## Daemon Management

```bash swarm start # Start daemon (background) swarm stop # Stop daemon swarm status # Status, cost, cache stats swarm restart # Restart daemon swarm savings # Monthly savings report swarm logs [N] # Last N lines of daemon log ```

## Performance (v1.3.2)

| Mode | Tasks | Time | Notes | |------|-------|------|-------| | Parallel (simple) | 5 | ~700ms | 142ms/task effective | | Parallel (stress) | 10 | ~1.2s | 123ms/task effective | | Chain (standard) | 5 | ~14s | 3-stage multi-perspective | | Chain (quick) | 2 | ~3s | 2-stage extract+synthesize | | Cache hit | any | ~3-5ms | 200-500x speedup | | Research (web) | 2 | ~15s | Google grounding latency |

## Config

Location: `~/.config/clawdbot/node-scaling.yaml`

```yaml node_scaling: enabled: true limits: max_nodes: 16 max_concurrent_api: 16 provider: name: gemini model: gemini-2.0-flash web_search: enabled: true parallel_default: false cost: max_daily_spend: 10.00 ```

## Troubleshooting

| Issue | Fix | |-------|-----| | Daemon not running | `swarm start` | | No API key | Set `GEMINI_API_KEY` or run `npm run setup` | | Rate limited | Lower `max_concurrent_api` in config | | Web search not working | Ensure provider is gemini + web_search.enabled | | Cache stale results | `curl -X DELETE http://localhost:9999/cache` | | Chain too slow | Use `depth: "quick"` or check context size |

## Structured Output (v1.3.7)

Force JSON output with schema validation — zero parse failures on structured tasks.

```bash # With built-in schema curl -X POST http://localhost:9999/structured \ -d '{"prompt":"Extract entities from: Tim Cook announced iPhone 17","schema":"entities"}'

# With custom schema curl -X POST http://localhost:9999/structured \ -d '{"prompt":"Classify this text","data":"...","schema":{"type":"object","properties":{"category":{"type":"string"}}}}'

# JSON mode (no schema, just force JSON) curl -X POST http://localhost:9999/structured \ -d '{"prompt":"Return a JSON object with name, age, city for a fictional person"}'

# List available schemas curl http://localhost:9999/structured/schemas ```

**Built-in schemas:** `entities`, `summary`, `comparison`, `actions`, `classification`, `qa`

Uses Gemini's native `response_mime_type: application/json` + `responseSchema` for guaranteed JSON output. Includes schema validation on the response.

## Majority Voting (v1.3.7)

Same prompt → N parallel executions → pick the best answer. Higher accuracy on factual/analytical tasks.

```bash # Judge strategy (LLM picks best — most reliable) curl -X POST http://localhost:9999/vote \ -d '{"prompt":"What are the key factors in SaaS pricing?","n":3,"strategy":"judge"}'

# Similarity strategy (consensus — zero extra cost) curl -X POST http://localhost:9999/vote \ -d '{"prompt":"What year was Python released?","n":3,"strategy":"similarity"}'

# Longest strategy (heuristic — zero extra cost) curl -X POST http://localhost:9999/vote \ -d '{"prompt":"Explain recursion","n":3,"strategy":"longest"}' ```

**Strategies:** - `judge` — LLM scores all candidates on accuracy/completeness/clarity/actionability, picks winner (N+1 calls) - `similarity` — Jaccard word-set similarity, picks consensus answer (N calls, zero extra cost) - `longest` — Picks longest response as heuristic for thoroughness (N calls, zero extra cost)

**When to use:** Factual questions, critical decisions, or any task where accuracy > speed.

| Strategy | Calls | Extra Cost | Quality | |----------|-------|-----------|---------| | similarity | N | $0 | Good (consensus) | | longest | N | $0 | Decent (heuristic) | | judge | N+1 | ~$0.0001 | Best (LLM-scored) |

## Self-Reflection (v1.3.5)

Optional critic pass after chain/skeleton output. Scores 5 dimensions, auto-refines if below threshold.

```bash # Add reflect:true to any chain or skeleton request curl -X POST http://localhost:9999/chain/auto \ -d '{"task":"Analyze the AI chip market","data":"...","reflect":true}'

curl -X POST http://localhost:9999/skeleton \ -d '{"task":"Write a market analysis","reflect":true}' ```

Proven: improved weak output from 5.0 → 7.6 avg score. Skeleton + reflect scored 9.4/10.

## Skeleton-of-Thought (v1.3.6)

Generate outline → expand each section in parallel → merge into coherent document. Best for long-form content.

```bash curl -X POST http://localhost:9999/skeleton \ -d '{"task":"Write a comprehensive guide to SaaS pricing","maxSections":6,"reflect":true}' ```

**Performance:** 14,478 chars in 21s (675 chars/sec) — 5.1x more content than chain at 2.9x higher throughput.

| Metric | Chain | Skeleton-of-Thought | Winner | |--------|-------|---------------------|--------| | Output size | 2,856 chars | 14,478 chars | SoT (5.1x) | | Throughput | 234 chars/sec | 675 chars/sec | SoT (2.9x) | | Duration | 12s | 21s | Chain (faster) | | Quality (w/ reflect) | ~7-8/10 | 9.4/10 | SoT |

**When to use what:** - **SoT** → long-form content, reports, guides, docs (anything with natural sections) - **Chain** → analysis, research, adversarial review (anything needing multiple perspectives) - **Parallel** → independent tasks, batch processing - **Structured** → entity extraction, classification, any task needing reliable JSON - **Voting** → factual accuracy, critical decisions, consensus-building

## API Endpoints

| Method | Path | Description | |--------|------|-------------| | GET | /health | Health check | | GET | /status | Detailed status + cost + cache | | GET | /capabilities | Discover execution modes | | POST | /parallel | Execute N prompts in parallel | | POST | /research | Multi-phase web research | | POST | /skeleton | Skeleton-of-Thought (outline → expand → merge) | | POST | /chain | Manual chain pipeline | | POST | /chain/auto | Auto-build + execute chain | | POST | /chain/preview | Preview chain without executing | | POST | /chain/template | Execute pre-built template | | POST | /structured | Forced JSON with schema validation | | GET | /structured/schemas | List built-in schemas | | POST | /vote | Majority voting (best-of-N) | | POST | /benchmark | Quality comparison test | | GET | /templates | List chain templates | | GET | /cache | Cache statistics | | DELETE | /cache | Clear cache |

## Cost Comparison

| Model | Cost per 1M tokens | Relative | |-------|-------------------|----------| | Claude Opus 4 | ~$15 input / $75 output | 1x | | GPT-4o | ~$2.50 input / $10 output | ~7x cheaper | | Gemini Flash | ~$0.075 input / $0.30 output | **200x cheaper** |

Cache hits are essentially free (~3-5ms, no API call).