claude-flow
Version:
Ruflo - Enterprise AI agent orchestration for Claude Code. Deploy 60+ specialized agents in coordinated swarms with self-learning, fault-tolerant consensus, vector memory, and MCP integration
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{
"name": "@claude-flow/neural",
"version": "3.0.0-alpha.8",
"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.2",
"@ruvector/sona": "latest"
},
"optionalDependencies": {
"agentdb": "^2.0.0 || ^3.0.0-alpha.1"
},
"publishConfig": {
"access": "public",
"tag": "v3alpha"
}
}