DeepSwarm 2.0 — Task-Agnostic Parallel Worker Orchestration

Spawn N parallel API workers for any batch task. Auto-optimizes worker count + stagger. Tiered model delegation: orchestrator plans (V4 Pro) → workers execute (V4 Flash). 99.95% API success rate.

Install

hermes skills tap add amanning3390/deepswarm

Quick Start

# 1. Define your task
cp task.yaml my_task.yaml
# Edit: prompt_template, worker_model, max_tokens

# 2. Generate seeds
python3 scripts/seed.py --count 1000 --template "Generate {{seed}}" > seeds.jsonl

# 3. Launch (auto-optimizes everything)
export DEEPSEEK_API_KEY=sk-...
python3 scripts/swarm.py --task my_task.yaml --total 1000

Tiered Delegation

orchestrator_model: deepseek-v4-pro   # Plans (few calls, frontier quality)
worker_model: deepseek-v4-flash       # Executes (many calls, cheaper)

V4 Pro costs ~3× V4 Flash per token. For batch tasks with thousands of calls, tiered delegation saves 60-70%.

Auto-Optimization

Call Duration	Workers	Stagger	Success	Throughput
<10s	16	1s	99.9%	~5,760/hr
10-30s	12	2s	99.9%	~1,440/hr
30-60s	8	5s	99.95%	~440/hr
60-90s	6	10s	99.9%	~240/hr

Omit workers and stagger in task.yaml — DeepSwarm runs a calibration call and picks optimal values.

Task Types

Built-in: generation, translation, summarization, classification, custom

For multi-turn tasks (tool calling, conversation loops):

multi_turn: true
max_turns: 20

Files

deepswarm/
├── SKILL.md              # Hermes skill definition
├── README.md
├── task.yaml             # Sample task config
├── architecture.html     # Pipeline diagram
├── scripts/
│   ├── seed.py           # Seed generator (simple + rich templates)
│   ├── swarm.py          # Orchestrator (auto-optimize + launch)
│   ├── worker.py         # Task-agnostic batch processor
│   └── filter.py         # Quality filter with JSON repair
├── templates/
│   └── prompts.py        # v2 prompt templates
└── references/
    ├── api-rate-limits.md
    └── generation-patterns.md

Post-Processing

After generation, filter the raw output to remove malformed traces:

python3 scripts/filter.py --input-dir output/ --output clean.jsonl --errors errors.jsonl

The filter applies 3 passes: JSON repair (fixes 17% API error rate), structural validation (think blocks, tag balance), and length thresholds. Typically lifts pass rate from ~28% to ~62%.

Provenance

Built from the DeepSeek Hermes Reasoning Traces project:

• 19,331 traces · 192K tool calls
• 96 workers · 31K API calls
• 99.95% success rate
• 8 workers + 5s stagger = the magic formula