Swarm — Cut Your LLM Costs by 200x

---

name: swarm

description: Cut your LLM costs by 200x. Offload parallel, batch, and research work to Gemini Flash workers instead of burning your expensive primary model.

homepage: https://github.com/Chair4ce/node-scaling

metadata: {"clawdbot":{"emoji":"🐝","requires":{"bins":["node"]}}}

---

# 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:

Quick Reference

# 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.

swarm parallel "prompt1" "prompt2" "prompt3"

Research (v1.1)

Multi-phase: search → fetch → analyze. Uses Google Search grounding.

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:**

**Auto-chain** — describe what you want, get an optimal pipeline:

curl -X POST http://localhost:9999/chain/auto \
  -d '{"task":"Find business opportunities","data":"...market data...","depth":"standard"}'

**Manual chain:**

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:**

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.

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:

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**.

# 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:

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).

# 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

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

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`

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.

# 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.

# 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:**

**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.

# 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.

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:**

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).