{
  "name": "@claude-flow/neural",
  "version": "3.0.0-alpha.9",
  "type": "module",
  "description": "Self-Optimizing Neural Architecture (SONA) for Claude Flow — adaptive learning, trajectory tracking, pattern reuse, 7 RL algorithms (PPO/A2C/DQN/Q-Learning/SARSA/Decision Transformer/Curiosity), Flash Attention, MoE routing, LoRA, EWC++ for continual learning. Reproducible via seedable PRNG; persistence via serialize/deserialize.",
  "main": "dist/index.js",
  "types": "dist/index.d.ts",
  "exports": {
    ".": "./dist/index.js"
  },
  "scripts": {
    "test": "vitest run",
    "build": "tsc"
  },
  "keywords": [
    "ai",
    "agents",
    "ai-agents",
    "multi-agent",
    "multi-agent-systems",
    "agentic",
    "agentic-ai",
    "agentic-systems",
    "claude-flow",
    "ruflo",
    "claude",
    "claude-code",
    "anthropic",
    "neural",
    "neural-network",
    "neural-networks",
    "sona",
    "self-optimizing",
    "self-improving",
    "adaptive-learning",
    "continual-learning",
    "lifelong-learning",
    "online-learning",
    "reinforcement-learning",
    "rl",
    "deep-rl",
    "ppo",
    "proximal-policy-optimization",
    "dqn",
    "deep-q-network",
    "a2c",
    "advantage-actor-critic",
    "q-learning",
    "sarsa",
    "decision-transformer",
    "curiosity-driven",
    "exploration",
    "policy-gradient",
    "value-function",
    "lora",
    "low-rank-adaptation",
    "ewc",
    "elastic-weight-consolidation",
    "catastrophic-forgetting",
    "flash-attention",
    "fast-attention",
    "mixture-of-experts",
    "moe",
    "expert-routing",
    "trajectory",
    "trajectory-learning",
    "experience-replay",
    "pattern-recognition",
    "pattern-matching",
    "pattern-extraction",
    "pattern-evolution",
    "reasoning",
    "reasoning-bank",
    "reasoning-traces",
    "self-consistency",
    "ensemble",
    "embeddings",
    "vector-embeddings",
    "vector-search",
    "hnsw",
    "semantic-search",
    "cosine-similarity",
    "agentdb",
    "memory",
    "knowledge-distillation",
    "model-compression",
    "quantization",
    "fine-tuning",
    "transfer-learning",
    "meta-learning",
    "mcp",
    "model-context-protocol",
    "llm",
    "llm-agents",
    "agent-orchestration",
    "agent-coordination",
    "ml",
    "machine-learning",
    "typescript",
    "esm",
    "wasm",
    "open-source"
  ],
  "author": {
    "name": "ruvnet",
    "url": "https://github.com/ruvnet"
  },
  "license": "MIT",
  "homepage": "https://github.com/ruvnet/ruflo",
  "repository": {
    "type": "git",
    "url": "https://github.com/ruvnet/ruflo.git",
    "directory": "v3/@claude-flow/neural"
  },
  "bugs": {
    "url": "https://github.com/ruvnet/ruflo/issues"
  },
  "files": [
    "dist/",
    "README.md",
    "LICENSE"
  ],
  "dependencies": {
    "@claude-flow/memory": "^3.0.0-alpha.16",
    "@ruvector/sona": "0.1.5"
  },
  "optionalDependencies": {
    "agentdb": "^2.0.0 || ^3.0.0-alpha.1"
  },
  "publishConfig": {
    "access": "public",
    "tag": "v3alpha"
  }
}