Swarm-Tools

The ultimate weapon for Claude Code multi-agent swarms ~80-110%+ token/cost reductions - Near Zero deadlocks - True autonomous scaling

Sick of context bloat, Ralph loops, "context low" deadlocks, and swarms that collapse?

Swarm-Tools is here to help ease this burden.

Built on cutting-edge 2025 research (Optima, RCR-Router, Trajectory Reduction, BAMAS, CodeAgents), this Rust-native plugin transforms Claude Code into a battle-hardened swarm engine capable of 10-20+ parallel agents with minimal tokens and maximum reliability.

No other plugin comes close, this is the most advanced swarm optimizer available today (that i know of).

Quick Install

1. Add the marketplace (once):

   /plugin marketplace add lazerusrm/Swarm-Tools
   

2. Install:

   /plugin install swarm-tools
   

Done. Auto-downloads binaries, wires hooks (precompact, subagentStop), and keeps you updated forever. (Requires Claude Code v2.0+ with marketplace support)

Manual Install

git clone https://github.com/lazerusrm/Swarm-Tools.git
cd Swarm-Tools
cargo build --release

Add to settings.json:

{
  "plugins": {
    "swarm-tools": {
      "path": "/path/to/Swarm-Tools/target/release",
      "hooks": {
        "precompact": "precompact",
        "subagentStop": "subagent_stop"
      }
    }
  }
}

Pre-built binaries in Releases.

Primary Features

• Semantic Task Routing - ML-powered embedding-based role assignment using BERT (all-MiniLM-L6-v2)
• Persistent Multi-Type Loop Detection - Crushes Ralph loops before they start
• Role-Aware Routing - Recency + impact boosted (45-65% communication savings)
• Sparse Trajectory Compression - Impact-based, expired/redundant filtering (25-40% context reduction)
• Quality Gates + Closed-Loop Refinement - Objective scoring drives perfect outputs
• Codified Reasoning - Structured plans with priority/impact/token estimates
• MCP/Tool Routing - Selective approval + arg stripping (20-40% external waste gone)
• Auto-Model Tiering - Haiku/Sonnet/Opus routing (30-50% cost savings)
• Self-Healing Topology - Contribution-tracked auto-pruning + rebalancing
• Parallel Execution Planning - Smart batching + mode comparison
• Communication Optimization - Redundancy/irrelevance pattern removal
• Fully Configurable - JSON overrides for every heuristic, weight, pattern, and threshold

Semantic Engine

The semantic engine uses transformer-based embeddings for intelligent task-to-role routing:

• BERT embeddings - all-MiniLM-L6-v2 model (384-dimensional vectors)
• Cosine similarity - Precise matching between user prompts and role descriptions
• Cross-platform support - Full ONNX inference on Linux/macOS, runtime DLL loading on Windows
• Graceful fallback - TF-IDF style hash embeddings if ML unavailable

Role routing examples:

• "Review this pull request for security issues" → Reviewer
• "Show me the git diff for recent changes" → Extractor
• "Analyze the codebase metrics" → Analyzer
• "Write documentation for this API" → Documenter

Everything is optional, lightweight (no heavy deps), and runtime-safe.

Configuration

Drop overrides in ~/.config/swarm-tools/config.json. Ready-made presets in config_examples/:

• coding_swarm.json - Code-heavy beast mode
• research_swarm.json - Web/browse domination
• large_scale.json - Aggressive pruning for massive swarms

Why Swarm-Tools

Vanilla Claude Code swarms hit walls: unbounded context, redundant loops, exploding costs, context deadlock. Swarm-Tools rewrites the rules—proactive heuristics, research-backed autonomy, and tenacious efficiency let you run large, reliable, cheap swarms!

Backed by 2025 research breakthroughs:

• Optima / OMAC multi-dimension optimization
• RCR-Router role-aware relevance
• Trajectory Reduction / AgentDiet sparse compression
• BAMAS budget-aware topology + pruning
• CodeAgents structured planning

Contributing

Issues, PRs, and real-world benchmarks welcome.

MIT © lazerusrm

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Attribution

This project uses the following open-source components:

Semantic Embedding Model

• Model: all-MiniLM-L6-v2 by Sentence Transformers
• License: Apache-2.0
• Purpose: 384-dimensional sentence embeddings for semantic task routing

The model is downloaded automatically during build from Hugging Face. See the model card for details.