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Hierarchical multi-cluster coding swarm CLI

Project description

Autobots: Decentralized 100+ NIM Software Engine

Autobots is a massively parallelized development swarm utilizing over 100+ specialized NVIDIA NIM microservices.
By orchestrating these models through a 6-File Global State Architecture, the swarm operates as a singular, non-redundant Codex.


Swarm Hierarchy & Model Inventory

The swarm is divided into specialized clusters.
Each Autobot represents a cluster of models optimized for specific technical domains.


Optimus Prime: The Command & Router Cluster

The “Matrix of Leadership.”
Manages the 6-file state, roadmap, and model-to-task routing.

  • nemotron-3-super-120b-a12b — Master Reasoning
  • llama-3.3-nemotron-super-49b-v1.5 — High-efficiency Routing
  • mistral-large-3-675b-instruct-2512 — Long-context Instruction
  • kimi-k2-thinking — Strategic Planning
  • step-3.5-flash — Fast Agentic Decisioning
  • gpt-oss-120b — Mathematical Logic
  • glm-5.1 — High-horizon Planning
  • llama-4-maverick-17b-128e-instruct — Multilingual Command
  • stockmark-2-100b-instruct — Enterprise Documentation

Ultra Magnus: The Logic & Architect Cluster

Handles complex backend reasoning, architecture, and long-horizon coding.

  • kimi-k2.6 — 1T Multimodal MoE for Coding
  • deepseek-v4-pro — Large-scale Code Intelligence
  • qwen3.5-397b-a17b — Advanced RAG / Agentic Logic
  • mistral-medium-3.5-128b — Agentic Task Execution
  • gemma-4-31b-it — Frontier Code Reasoning
  • qwen3-next-80b-a3b-thinking — Hybrid Reasoning
  • dracarys-llama-3.1-70b-instruct — Fine-tuned Code Generation
  • mixtral-8x22b-instruct-v0.1 — Sparse MoE Logic
  • evo2-40b — Biological / Complex Sequential Logic
  • boltz-2 — Complex Structure Prediction
  • alphafold2-multimer — Systemic Complexity
  • msa-search — Sequence Alignment

Red Alert: The Security & Safety Cluster

The “Software Security” mandate.
Real-time auditing and guardrail enforcement.

  • nemotron-3-content-safety — Multilingual Safety
  • llama-3.1-nemotron-safety-guard-8b-v3 — Content Moderation
  • gliner-pii — Personally Identifiable Information Detection
  • llama-guard-4-12b — Input / Output Safety Classification
  • nemoguard-jailbreak-detect — Adversarial Protection
  • llama-3.1-nemoguard-8b-topic-control — Domain Enforcement
  • llama-3.1-nemoguard-8b-content-safety — Policy Reasoning
  • nemotron-content-safety-reasoning-4b — Context-aware Safety
  • synthetic-video-detector — Deepfake / Synthetic Detection
  • usdvalidate — Asset Validation

Jazz: The UI/UX & Creative Cluster

Responsible for the “Minimalist Dark Mode” aesthetic and frontend execution.

  • qwen-image-edit — Consistent Image Editing
  • qwen-image — High-fidelity Text Rendering
  • flux.2-klein-4b — High-speed UI Asset Generation
  • flux.1-dev — Creative Prototyping
  • flux.1-schnell — Rapid Iteration
  • stable-diffusion-3.5-large — Production Design
  • FLUX.1-Kontext-dev — In-context Design Editing
  • phi-4-multimodal-instruct — Audio / Visual UI Analysis
  • NVIDIA AI for Media Relighting — Lighting Consistency
  • TRELLIS — 3D Asset Generation
  • vista-3d — Anatomical / Structural UI Mapping

Ratchet: The Debug & Repair Cluster

The “Fixer” swarm for refactoring, unit testing, and bug squashing.

  • deepseek-v4-flash — Rapid Code Patching
  • qwen3.5-coder-480b-a35b-instruct — Agentic Bug Fixing
  • qwen2.5-coder-32b-instruct — Code Completion / Fixing
  • mistral-small-4-119b-2603 — Hybrid Generation / Fixing
  • devstral-2-123b-instruct-2512 — Deep Reasoning Debugging
  • magistral-small-2506 — Edge Efficiency Debugging
  • phi-4-mini-instruct — Latency-bound Refactoring
  • llama-3.2-3b-instruct — Lightweight Task Fixing
  • llama-3.2-1b-instruct — Micro-service Optimization
  • nemotron-mini-4b-instruct — Functional Call Debugging

Perceptor: The RAG & Data Cluster

Knowledge extraction, OCR, and semantic retrieval.

  • nemotron-ocr-v1 — Table / Document Extraction
  • nemotron-parse — Vision-language Metadata Extraction
  • paddleocr — Image-to-text Table Extraction
  • nemotron-table-structure-v1 — Layout Analysis
  • nemotron-page-elements-v3 — Object Detection
  • nemotron-graphic-elements-v1 — Chart Parsing
  • llama-3.2-nemoretriever-300m-embed-v2 — Multilingual Embedding
  • llama-3.2-nv-embedqa-1b-v2 — Context-long QA Retrieval
  • llama-3.2-nv-rerankqa-1b-v2 — Probabilistic Reranking
  • nv-embedcode-7b-v1 — Code-specific Embedding
  • bge-m3 — Multi-vector Retrieval
  • rerank-qa-mistral-4b — Ranking Probability

Bumblebee: The Communication & Media Cluster

Handling speech recognition, translation, and video processing.

  • whisper-large-v3 — Robust ASR
  • canary-1b-asr — Multilingual Transcription
  • riva-translate-4b-instruct-v1_1 — Instruction-based Translation
  • magpie-tts-zeroshot — Expressive Voice Synthesis
  • nemotron-voicechat — Conversational Audio
  • LipSync — Audio-Visual Syncing
  • Background Noise Removal — Audio Cleanup
  • Active Speaker Detection — Video Localization
  • parakeet-1.1b-rnnt-multilingual-asr — 25-language Transcription

Ironhide: The Physical & Synthetic Data Cluster

Simulation, autonomous driving logic, and physics-aware world states.

  • cosmos-reason2-8b — Physical World Understanding
  • cosmos-transfer2.5-2b — Physics-aware Video Generation
  • cosmos-predict1-5b — Future Frame Prediction
  • streampetr — 3D Object Detection
  • sparsedrive — Autonomous Driving Stack
  • bevformer — Bird’s-eye-view Perception
  • fourcastnet — Atmospheric Dynamics Prediction
  • cuopt — Route Optimization

Wheeljack: The Specialized Science Cluster

Quantum calibration, molecular generation, and biological design.

  • ising-calibration-1-35b-a3b — Quantum Computer Calibration
  • genmol — Molecular Generation
  • molmim — Controlled Molecular Search
  • rfdiffusion — Protein Binder Design
  • proteinmpnn — Amino Acid Sequence Prediction
  • esm2-650m — Protein Embedding
  • openfold3 — Biomolecular Structure Prediction

The 6-File Control Architecture

All 100+ Autobots synchronize their state through six core Markdown files to ensure zero redundancy:

architecture.md

System design and tech stack definitions.

roadmap.md

Project phases and milestone tracking.

ui-components.md

The “Jazz” Design System and Tailwind rules.

progress-tracker.md

Real-time task state, lock ownership, and completion updates.

project-briefing.md

Core business logic and intent.

security-auth.md

Encryption, authentication flows, and safety benchmarks.


Coordination Strategy For 100+ NIMs

For large swarms, a hybrid coordination strategy keeps throughput high without letting file contention become chaos.

Critical Context Files Use Pessimistic Locks

Use explicit write locks for low-churn, high-impact files such as:

  • architecture.md
  • security-auth.md

These documents define shared truth for the entire swarm, so brief waiting is preferable to conflicting edits.

progress-tracker.md Uses A Coordinator

Do not let every large model compete to write status updates. Instead, assign a lightweight Optimus model such as step-3.5-flash to act as the swarm's "Secretary":

  • receives status changes from worker clusters
  • serializes updates to progress-tracker.md
  • handles lock bookkeeping and completion markers
  • keeps larger reasoning models focused on planning and implementation

This reduces file I/O contention while preserving a single source of truth for execution state.

Prevent Deadlocks With Lock Expiration

Every lock should carry an expiration time. Recommended default:

  • lock TTL: 60 seconds

If a lock exceeds its TTL, treat it as stale and reclaim it automatically before retrying the write. This prevents two models from waiting on abandoned locks forever.


Operational Principles

Parallelized Intelligence

Each cluster executes independently while synchronizing through shared state files.

Zero-Redundancy Coordination

No duplicated task execution across models.

Deterministic Routing

Optimus Prime dynamically routes workloads to the most specialized NIMs.

Security-by-Default

Red Alert validates every generation, mutation, and deployment path.

Persistent Project Memory

The 6-file architecture acts as the swarm’s distributed cognition layer.


Example Swarm Execution Flow

User Request
    ↓
Optimus Prime
    ↓
Task Classification
    ↓
Ultra Magnus → Architecture & Backend
Jazz → UI/UX
Ratchet → Testing & Refactoring
Red Alert → Security Validation
Perceptor → Retrieval & OCR
Bumblebee → Voice / Media
Ironhide → Simulation
Wheeljack → Scientific Reasoning
    ↓
6-File Synchronization Layer
    ↓
Unified Production Output

Swarm Status

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