ruvector/npm/packages/ruvllm/scripts/training/ruvector-capabilities.json

{
  "metadata": {
    "name": "ruvector-ecosystem-capabilities",
    "version": "1.0.0",
    "generated": "2026-01-20",
    "description": "Comprehensive capability manifest for the RuVector ecosystem - Rust crates, NPM packages, and CLI tools"
  },
  "rust_crates": [
    {
      "name": "ruvector-core",
      "description": "High-performance Rust vector database core with HNSW indexing and SIMD-optimized distance calculations",
      "keywords": ["vector-database", "hnsw", "simd", "ann", "similarity-search", "rust"],
      "category": "vector-search",
      "features": ["simd", "parallel", "storage", "hnsw", "memory-only", "api-embeddings"],
      "example_prompts": [
        "Build a vector database with HNSW indexing",
        "Search for similar vectors using SIMD acceleration",
        "Implement approximate nearest neighbor search",
        "Store and index high-dimensional embeddings",
        "Perform semantic similarity search on vectors"
      ]
    },
    {
      "name": "ruvector-sona",
      "description": "Self-Optimizing Neural Architecture - Runtime-adaptive learning with two-tier LoRA, EWC++, and ReasoningBank for LLM routers",
      "keywords": ["neural", "learning", "lora", "ewc", "adaptive", "llm", "self-optimizing"],
      "category": "machine-learning",
      "features": ["wasm", "napi", "serde-support"],
      "example_prompts": [
        "Implement adaptive learning with SONA",
        "Use LoRA for efficient fine-tuning",
        "Prevent catastrophic forgetting with EWC++",
        "Build a self-optimizing neural router",
        "Apply continual learning patterns to LLM"
      ]
    },
    {
      "name": "ruvector-attention",
      "description": "Attention mechanisms for ruvector - geometric, graph, and sparse attention with SIMD acceleration",
      "keywords": ["attention", "machine-learning", "vector-search", "graph-attention", "transformer"],
      "category": "machine-learning",
      "features": ["simd", "wasm", "napi", "math"],
      "example_prompts": [
        "Implement graph attention mechanisms",
        "Apply sparse attention patterns",
        "Use geometric attention for vector search",
        "Build transformer attention layers",
        "Optimize attention computation with SIMD"
      ]
    },
    {
      "name": "ruvector-gnn",
      "description": "Graph Neural Network layer for Ruvector on HNSW topology with message passing and neighbor aggregation",
      "keywords": ["gnn", "graph-neural-network", "hnsw", "message-passing", "ml"],
      "category": "machine-learning",
      "features": ["simd", "wasm", "napi", "mmap"],
      "example_prompts": [
        "Build graph neural networks on HNSW topology",
        "Implement message passing between vector nodes",
        "Apply GNN for semantic understanding",
        "Aggregate neighbor embeddings in graph",
        "Train GNN models on vector relationships"
      ]
    },
    {
      "name": "ruvector-graph",
      "description": "Distributed Neo4j-compatible hypergraph database with SIMD optimization, Cypher queries, and vector embeddings",
      "keywords": ["graph-database", "hypergraph", "cypher", "neo4j", "simd", "distributed"],
      "category": "database",
      "features": ["full", "simd", "storage", "async-runtime", "compression", "distributed", "federation"],
      "example_prompts": [
        "Create a Neo4j-compatible graph database",
        "Execute Cypher queries on hypergraph",
        "Build distributed graph storage with RAFT",
        "Implement federated graph queries",
        "Store knowledge graphs with vector embeddings"
      ]
    },
    {
      "name": "ruvllm",
      "description": "LLM serving runtime with Ruvector integration - Paged attention, KV cache, SONA learning, and Metal/CUDA acceleration",
      "keywords": ["llm", "inference", "serving", "paged-attention", "kv-cache", "metal", "cuda"],
      "category": "llm-inference",
      "features": ["candle", "metal", "cuda", "parallel", "attention", "graph", "gnn", "mmap", "coreml"],
      "example_prompts": [
        "Build an LLM serving engine with paged attention",
        "Implement KV cache management for inference",
        "Use Metal acceleration for Apple Silicon",
        "Load GGUF models for inference",
        "Integrate SONA learning into LLM serving"
      ]
    },
    {
      "name": "ruvector-hyperbolic-hnsw",
      "description": "Hyperbolic (Poincare ball) embeddings with HNSW integration for hierarchy-aware vector search",
      "keywords": ["hyperbolic", "poincare", "hnsw", "vector-search", "embeddings", "hierarchy"],
      "category": "vector-search",
      "features": ["simd", "parallel", "wasm"],
      "example_prompts": [
        "Implement hyperbolic embeddings for hierarchical data",
        "Use Poincare ball model for vector search",
        "Build hierarchy-aware similarity search",
        "Apply hyperbolic geometry to embeddings",
        "Search hierarchical structures efficiently"
      ]
    },
    {
      "name": "ruvector-router-core",
      "description": "Core vector database and neural routing inference engine with semantic matching",
      "keywords": ["router", "semantic", "inference", "vector-search", "neural"],
      "category": "routing",
      "features": [],
      "example_prompts": [
        "Build semantic routing for AI agents",
        "Implement intent matching with vectors",
        "Route queries to optimal handlers",
        "Create neural-based task routing",
        "Match user intents to agent capabilities"
      ]
    },
    {
      "name": "ruvector-nervous-system",
      "description": "Bio-inspired neural system with spiking networks, BTSP learning, and EWC plasticity for neuromorphic computing",
      "keywords": ["neural", "spiking", "neuromorphic", "plasticity", "learning", "bio-inspired"],
      "category": "neuromorphic",
      "features": ["parallel", "serde"],
      "example_prompts": [
        "Build spiking neural networks",
        "Implement BTSP learning patterns",
        "Create bio-inspired neural systems",
        "Apply neuromorphic computing patterns",
        "Design plastic neural architectures"
      ]
    },
    {
      "name": "ruvector-mincut",
      "description": "World's first subpolynomial dynamic min-cut algorithm for self-healing networks and AI optimization",
      "keywords": ["graph", "minimum-cut", "network-analysis", "self-healing", "dynamic-graph", "optimization"],
      "category": "algorithms",
      "features": ["exact", "approximate", "integration", "monitoring", "simd", "agentic"],
      "example_prompts": [
        "Compute minimum cut in dynamic graphs",
        "Build self-healing network topologies",
        "Optimize graph partitioning",
        "Implement real-time graph analysis",
        "Apply min-cut to AI agent coordination"
      ]
    },
    {
      "name": "ruvector-sparse-inference",
      "description": "PowerInfer-style sparse inference engine for efficient neural network inference on edge devices",
      "keywords": ["sparse-inference", "neural-network", "quantization", "simd", "edge-ai"],
      "category": "inference",
      "features": [],
      "example_prompts": [
        "Implement sparse neural network inference",
        "Optimize inference for edge devices",
        "Build PowerInfer-style sparse engine",
        "Apply quantization for efficient inference",
        "Run models on resource-constrained hardware"
      ]
    },
    {
      "name": "ruvector-cli",
      "description": "CLI and MCP server for Ruvector with vector database operations and graph queries",
      "keywords": ["cli", "mcp", "vector-database", "graph", "server"],
      "category": "tooling",
      "features": ["postgres"],
      "example_prompts": [
        "Use ruvector CLI for vector operations",
        "Start MCP server for Ruvector",
        "Execute vector database commands",
        "Query graph data via CLI",
        "Manage vector collections from terminal"
      ]
    },
    {
      "name": "ruvector-tiny-dancer-core",
      "description": "Production-grade AI agent routing system with FastGRNN neural inference, circuit breakers, and uncertainty estimation",
      "keywords": ["router", "fastgrnn", "circuit-breaker", "uncertainty", "agent-routing"],
      "category": "routing",
      "features": [],
      "example_prompts": [
        "Build AI agent routing with FastGRNN",
        "Implement circuit breakers for reliability",
        "Estimate routing uncertainty",
        "Create production-grade agent orchestration",
        "Route tasks with confidence scoring"
      ]
    },
    {
      "name": "ruvector-math",
      "description": "Advanced mathematics for next-gen vector search: Optimal Transport, Information Geometry, Product Manifolds",
      "keywords": ["vector-search", "optimal-transport", "wasserstein", "information-geometry", "hyperbolic"],
      "category": "mathematics",
      "features": ["std", "simd", "parallel", "serde"],
      "example_prompts": [
        "Apply optimal transport to embeddings",
        "Use Wasserstein distance for similarity",
        "Implement information geometry metrics",
        "Work with product manifolds",
        "Build advanced mathematical distance functions"
      ]
    },
    {
      "name": "ruvector-dag",
      "description": "Directed Acyclic Graph structures for query plan optimization with neural learning and post-quantum cryptography",
      "keywords": ["dag", "query-optimization", "neural-learning", "post-quantum", "workflow"],
      "category": "data-structures",
      "features": ["production-crypto", "full", "wasm"],
      "example_prompts": [
        "Optimize query execution plans with DAGs",
        "Build workflow engines with neural learning",
        "Implement topological sorting",
        "Create task dependency graphs",
        "Apply post-quantum signatures to DAGs"
      ]
    },
    {
      "name": "ruvector-fpga-transformer",
      "description": "FPGA Transformer backend with deterministic latency, quantization-first design, and coherence gating",
      "keywords": ["fpga", "transformer", "inference", "quantization", "low-latency", "coherence"],
      "category": "hardware",
      "features": ["daemon", "native_sim", "pcie", "wasm", "witness"],
      "example_prompts": [
        "Build FPGA-accelerated transformer inference",
        "Implement deterministic latency inference",
        "Design quantization-first architectures",
        "Use coherence gating for quality control",
        "Deploy transformers on FPGA hardware"
      ]
    },
    {
      "name": "ruvector-mincut-gated-transformer",
      "description": "Ultra low latency transformer inference with mincut-gated coherence control and spike attention",
      "keywords": ["transformer", "inference", "mincut", "low-latency", "coherence", "spike-attention"],
      "category": "inference",
      "features": ["sliding_window", "linear_attention", "spike_attention", "spectral_pe", "sparse_attention", "energy_gate"],
      "example_prompts": [
        "Build ultra-low latency transformer inference",
        "Implement mincut-gated attention",
        "Use spike-driven attention (87x energy reduction)",
        "Apply sparse attention with mincut awareness",
        "Create energy-efficient transformer layers"
      ]
    },
    {
      "name": "cognitum-gate-kernel",
      "description": "No-std WASM kernel for 256-tile coherence gate fabric with mincut integration",
      "keywords": ["wasm", "coherence", "mincut", "distributed", "no_std", "embedded"],
      "category": "embedded",
      "features": ["std"],
      "example_prompts": [
        "Build WASM coherence gate kernels",
        "Implement 256-tile distributed fabric",
        "Create no-std embedded systems",
        "Design coherence validation kernels",
        "Deploy on edge with minimal footprint"
      ]
    },
    {
      "name": "mcp-gate",
      "description": "MCP (Model Context Protocol) server for the Anytime-Valid Coherence Gate with permission control",
      "keywords": ["mcp", "coherence", "gate", "agent", "permission", "protocol"],
      "category": "protocol",
      "features": [],
      "example_prompts": [
        "Build MCP servers for AI agents",
        "Implement coherence gate protocols",
        "Create permission-controlled AI access",
        "Design agent communication protocols",
        "Integrate with Model Context Protocol"
      ]
    },
    {
      "name": "ruqu",
      "description": "Classical nervous system for quantum machines - real-time coherence assessment via dynamic min-cut",
      "keywords": ["quantum", "coherence", "gate", "min-cut", "error-correction"],
      "category": "quantum",
      "features": ["structural", "tilezero", "decoder", "attention", "parallel", "tracing"],
      "example_prompts": [
        "Build classical control for quantum systems",
        "Implement quantum coherence assessment",
        "Apply min-cut to quantum error correction",
        "Design hybrid classical-quantum interfaces",
        "Monitor quantum gate coherence"
      ]
    },
    {
      "name": "ruvllm-cli",
      "description": "CLI for RuvLLM model management and inference on Apple Silicon with Metal acceleration",
      "keywords": ["cli", "llm", "apple-silicon", "metal", "inference", "model-management"],
      "category": "tooling",
      "features": ["metal", "cuda"],
      "example_prompts": [
        "Run LLM inference from command line",
        "Manage GGUF models with ruvllm CLI",
        "Download models from HuggingFace Hub",
        "Start inference server on Apple Silicon",
        "Benchmark model performance via CLI"
      ]
    },
    {
      "name": "rvlite",
      "description": "Standalone lightweight vector database with SQL, SPARQL, and Cypher queries - runs everywhere (Node.js, Browser, Edge)",
      "keywords": ["vector-database", "sql", "sparql", "cypher", "wasm", "lightweight"],
      "category": "database",
      "features": [],
      "example_prompts": [
        "Run vector database in the browser",
        "Query vectors with SQL syntax",
        "Use SPARQL for semantic queries",
        "Execute Cypher on embedded database",
        "Deploy lightweight vector search on edge"
      ]
    }
  ],
  "npm_packages": [
    {
      "name": "@ruvector/ruvllm",
      "version": "2.3.0",
      "description": "Self-learning LLM orchestration with SONA adaptive learning, HNSW memory, FastGRNN routing, and SIMD inference",
      "keywords": ["ruvllm", "llm", "self-learning", "adaptive-learning", "sona", "lora", "ewc", "hnsw", "fastgrnn", "simd", "inference"],
      "category": "llm-orchestration",
      "example_prompts": [
        "Build self-learning LLM systems",
        "Implement adaptive routing for AI models",
        "Use FastGRNN for intelligent task routing",
        "Apply SONA learning to Claude workflows",
        "Create federated learning pipelines"
      ]
    },
    {
      "name": "ruvector",
      "version": "0.1.88",
      "description": "High-performance vector database for Node.js with automatic native/WASM fallback and semantic search",
      "keywords": ["vector", "database", "vector-search", "embeddings", "hnsw", "ann", "ai", "rag", "wasm", "native"],
      "category": "vector-database",
      "example_prompts": [
        "Create vector database in Node.js",
        "Build RAG applications with ruvector",
        "Implement semantic search",
        "Store and query embeddings",
        "Use ONNX for automatic embeddings"
      ]
    },
    {
      "name": "@ruvector/core",
      "version": "0.1.30",
      "description": "High-performance vector database with HNSW indexing - 50k+ inserts/sec, built in Rust for AI/ML similarity search",
      "keywords": ["vector-database", "hnsw", "ann", "similarity-search", "ai", "ml", "rag", "native", "simd"],
      "category": "vector-database",
      "example_prompts": [
        "Build high-performance vector search",
        "Store millions of vectors efficiently",
        "Query similar embeddings at scale",
        "Create AI retrieval systems",
        "Implement production vector database"
      ]
    },
    {
      "name": "@ruvector/sona",
      "version": "0.1.4",
      "description": "Self-Optimizing Neural Architecture (SONA) - Runtime-adaptive learning with LoRA, EWC++, and ReasoningBank",
      "keywords": ["sona", "neural-network", "adaptive-learning", "lora", "ewc", "reasoningbank", "continual-learning"],
      "category": "machine-learning",
      "example_prompts": [
        "Implement SONA for adaptive AI",
        "Use LoRA fine-tuning in Node.js",
        "Apply EWC++ to prevent forgetting",
        "Build reasoning pattern banks",
        "Create self-improving AI agents"
      ]
    },
    {
      "name": "@ruvector/router",
      "version": "0.1.25",
      "description": "Semantic router for AI agents - vector-based intent matching with HNSW indexing and SIMD acceleration",
      "keywords": ["semantic-router", "intent-matching", "ai-routing", "hnsw", "similarity-search", "simd"],
      "category": "routing",
      "example_prompts": [
        "Build semantic routing for chatbots",
        "Match user intents to handlers",
        "Create AI agent dispatcher",
        "Route queries by semantic similarity",
        "Implement multi-agent coordination"
      ]
    },
    {
      "name": "@ruvector/tiny-dancer",
      "version": "0.1.15",
      "description": "Neural router for AI agent orchestration - FastGRNN-based routing with circuit breaker and uncertainty estimation",
      "keywords": ["neural-router", "fastgrnn", "circuit-breaker", "uncertainty-estimation", "agent-orchestration"],
      "category": "routing",
      "example_prompts": [
        "Build neural routing for AI agents",
        "Implement circuit breakers for reliability",
        "Estimate confidence in routing decisions",
        "Create hot-reload capable routers",
        "Orchestrate multi-model inference"
      ]
    },
    {
      "name": "@ruvector/graph-node",
      "version": "0.1.25",
      "description": "Native Node.js bindings for RuVector Graph Database with hypergraph support and Cypher queries",
      "keywords": ["graph-database", "hypergraph", "cypher", "neo4j", "vector-database", "knowledge-graph"],
      "category": "database",
      "example_prompts": [
        "Build knowledge graphs in Node.js",
        "Execute Cypher queries",
        "Store hypergraph relationships",
        "Create Neo4j-compatible databases",
        "Combine vectors with graph structure"
      ]
    },
    {
      "name": "@ruvector/rudag",
      "version": "0.1.0",
      "description": "Fast DAG library with Rust/WASM - topological sort, critical path, task scheduling, and self-learning attention",
      "keywords": ["dag", "topological-sort", "critical-path", "task-scheduler", "workflow", "wasm"],
      "category": "data-structures",
      "example_prompts": [
        "Build workflow engines with DAGs",
        "Compute critical paths in projects",
        "Schedule tasks with dependencies",
        "Implement topological sorting",
        "Create data pipelines with DAGs"
      ]
    },
    {
      "name": "rvlite",
      "version": "0.2.0",
      "description": "Lightweight vector database with SQL, SPARQL, and Cypher - runs everywhere (Node.js, Browser, Edge)",
      "keywords": ["vector-database", "sql", "sparql", "cypher", "wasm", "lightweight", "graph-database"],
      "category": "database",
      "example_prompts": [
        "Run vector database in browser",
        "Query vectors with SQL",
        "Use SPARQL for semantic queries",
        "Execute Cypher in JavaScript",
        "Deploy on edge devices"
      ]
    },
    {
      "name": "@ruvector/agentic-synth",
      "version": "0.1.6",
      "description": "High-performance synthetic data generator for AI/ML training, RAG systems, and agentic workflows with DSPy.ts",
      "keywords": ["synthetic-data", "data-generation", "ai-training", "rag", "dspy", "gemini", "openrouter"],
      "category": "data-generation",
      "example_prompts": [
        "Generate synthetic training data",
        "Create datasets for AI models",
        "Build RAG test collections",
        "Augment training data programmatically",
        "Generate edge cases for testing"
      ]
    },
    {
      "name": "@ruvector/spiking-neural",
      "version": "1.0.1",
      "description": "High-performance Spiking Neural Network (SNN) with SIMD optimization - CLI and SDK",
      "keywords": ["spiking-neural-network", "snn", "neuromorphic", "simd", "stdp", "lif-neuron"],
      "category": "neuromorphic",
      "example_prompts": [
        "Build spiking neural networks in JS",
        "Implement STDP learning rules",
        "Create neuromorphic computing systems",
        "Simulate LIF neurons",
        "Apply bio-inspired pattern recognition"
      ]
    },
    {
      "name": "@ruvector/agentic-integration",
      "version": "1.0.0",
      "description": "Distributed agent coordination for ruvector with claude-flow integration and swarm management",
      "keywords": ["distributed-systems", "agent-coordination", "claude-flow", "swarm", "mesh-coordination"],
      "category": "coordination",
      "example_prompts": [
        "Coordinate distributed AI agents",
        "Integrate with Claude Flow swarms",
        "Build multi-region agent systems",
        "Implement agent mesh topologies",
        "Create fault-tolerant AI coordination"
      ]
    }
  ],
  "cli_commands": [
    {
      "name": "ruvector",
      "description": "Main CLI for RuVector vector database operations",
      "category": "vector-database",
      "subcommands": ["index", "search", "insert", "delete", "info", "mcp"],
      "example_prompts": [
        "Create vector index with ruvector CLI",
        "Search vectors from command line",
        "Insert vectors into database",
        "Start MCP server for ruvector"
      ]
    },
    {
      "name": "ruvllm",
      "description": "CLI for LLM model management and inference",
      "category": "llm-inference",
      "subcommands": ["download", "list", "run", "serve", "benchmark", "quantize"],
      "example_prompts": [
        "Download GGUF models from HuggingFace",
        "List available local models",
        "Run LLM inference from CLI",
        "Start inference server",
        "Benchmark model performance"
      ]
    },
    {
      "name": "rudag",
      "description": "CLI for DAG operations and workflow management",
      "category": "workflow",
      "subcommands": ["create", "topo-sort", "critical-path", "schedule", "visualize"],
      "example_prompts": [
        "Create DAG workflows",
        "Compute topological sort",
        "Find critical paths",
        "Schedule tasks with dependencies"
      ]
    },
    {
      "name": "rvlite",
      "description": "CLI for lightweight vector database with SQL/SPARQL/Cypher",
      "category": "database",
      "subcommands": ["query", "insert", "index", "export", "import"],
      "example_prompts": [
        "Query vectors with SQL syntax",
        "Execute SPARQL queries",
        "Run Cypher on embedded database"
      ]
    },
    {
      "name": "agentic-synth",
      "description": "CLI for synthetic data generation",
      "category": "data-generation",
      "subcommands": ["generate", "config", "validate", "export"],
      "example_prompts": [
        "Generate synthetic training data",
        "Configure data generation pipelines",
        "Validate generated datasets"
      ]
    },
    {
      "name": "spiking-neural",
      "description": "CLI for spiking neural network simulation",
      "category": "neuromorphic",
      "subcommands": ["simulate", "train", "test", "benchmark", "demo"],
      "example_prompts": [
        "Simulate spiking neural networks",
        "Train SNN with STDP",
        "Run pattern recognition demos"
      ]
    }
  ],
  "capabilities": {
    "vector_search": {
      "description": "High-performance vector similarity search with multiple algorithms and optimizations",
      "features": [
        {
          "name": "HNSW Indexing",
          "description": "Hierarchical Navigable Small World graphs for approximate nearest neighbor search",
          "performance": "O(log n) search complexity, 2.5K queries/sec on 10K vectors",
          "keywords": ["hnsw", "ann", "approximate-nearest-neighbor"]
        },
        {
          "name": "SIMD Distance",
          "description": "SimSIMD-powered distance calculations with AVX2/AVX-512/NEON acceleration",
          "performance": "16M+ ops/sec for 512-dimensional vectors",
          "keywords": ["simd", "avx", "neon", "distance"]
        },
        {
          "name": "Hyperbolic Search",
          "description": "Poincare ball model for hierarchy-aware similarity search",
          "keywords": ["hyperbolic", "poincare", "hierarchy"]
        },
        {
          "name": "Quantization",
          "description": "Multiple compression strategies: Scalar (4x), Int4 (8x), Product (8-16x), Binary (32x)",
          "keywords": ["quantization", "compression", "memory-efficient"]
        }
      ]
    },
    "llm_inference": {
      "description": "Production-grade LLM serving with multiple acceleration backends",
      "features": [
        {
          "name": "Paged Attention",
          "description": "Memory-efficient attention with page tables for long contexts",
          "keywords": ["paged-attention", "memory-efficient", "long-context"]
        },
        {
          "name": "KV Cache",
          "description": "Two-tier FP16 tail + quantized store for optimal memory/quality tradeoff",
          "keywords": ["kv-cache", "inference", "memory"]
        },
        {
          "name": "Metal Acceleration",
          "description": "Apple Silicon GPU acceleration via Candle and native Metal shaders",
          "keywords": ["metal", "apple-silicon", "gpu", "m1", "m2", "m3", "m4"]
        },
        {
          "name": "CUDA Acceleration",
          "description": "NVIDIA GPU acceleration for datacenter deployment",
          "keywords": ["cuda", "nvidia", "gpu"]
        },
        {
          "name": "GGUF Support",
          "description": "Load and run GGUF quantized models with memory mapping",
          "keywords": ["gguf", "quantized", "llama", "mistral"]
        },
        {
          "name": "Speculative Decoding",
          "description": "Fast inference with draft models and tree-based speculation",
          "keywords": ["speculative-decoding", "fast-inference"]
        }
      ]
    },
    "adaptive_learning": {
      "description": "Self-optimizing neural architectures for continuous improvement",
      "features": [
        {
          "name": "SONA Engine",
          "description": "Self-Optimizing Neural Architecture with three-tier learning loops",
          "keywords": ["sona", "self-optimizing", "adaptive"]
        },
        {
          "name": "Micro-LoRA",
          "description": "Ultra-low rank (1-2) LoRA for instant learning adaptation",
          "performance": "<0.05ms adaptation latency",
          "keywords": ["lora", "micro-lora", "fine-tuning"]
        },
        {
          "name": "EWC++",
          "description": "Elastic Weight Consolidation to prevent catastrophic forgetting",
          "keywords": ["ewc", "continual-learning", "forgetting"]
        },
        {
          "name": "ReasoningBank",
          "description": "Pattern extraction and similarity search for learned strategies",
          "keywords": ["reasoning-bank", "patterns", "learning"]
        }
      ]
    },
    "agent_routing": {
      "description": "Intelligent routing and orchestration for AI agents",
      "features": [
        {
          "name": "FastGRNN Router",
          "description": "Neural routing with FastGRNN for sub-millisecond decisions",
          "keywords": ["fastgrnn", "neural-router", "fast"]
        },
        {
          "name": "Semantic Router",
          "description": "Vector-based intent matching with HNSW indexing",
          "keywords": ["semantic-router", "intent-matching"]
        },
        {
          "name": "Circuit Breaker",
          "description": "Reliability patterns for fault-tolerant routing",
          "keywords": ["circuit-breaker", "reliability", "fault-tolerant"]
        },
        {
          "name": "Uncertainty Estimation",
          "description": "Confidence scoring for routing decisions",
          "keywords": ["uncertainty", "confidence", "calibration"]
        }
      ]
    },
    "graph_database": {
      "description": "Neo4j-compatible graph database with vector embeddings",
      "features": [
        {
          "name": "Hypergraph Support",
          "description": "Store and query hyperedges connecting multiple nodes",
          "keywords": ["hypergraph", "graph", "edges"]
        },
        {
          "name": "Cypher Queries",
          "description": "Execute Neo4j-compatible Cypher queries",
          "keywords": ["cypher", "query", "neo4j"]
        },
        {
          "name": "Distributed Storage",
          "description": "RAFT-based distributed graph with federation",
          "keywords": ["distributed", "raft", "federation"]
        },
        {
          "name": "Vector+Graph",
          "description": "Combine vector embeddings with graph relationships",
          "keywords": ["vector-graph", "hybrid", "knowledge-graph"]
        }
      ]
    },
    "neuromorphic": {
      "description": "Bio-inspired neural computing with spiking networks",
      "features": [
        {
          "name": "Spiking Neural Networks",
          "description": "LIF neurons with STDP learning rules",
          "keywords": ["snn", "spiking", "lif", "stdp"]
        },
        {
          "name": "BTSP Learning",
          "description": "Biological-plausible temporal spike patterns",
          "keywords": ["btsp", "temporal", "biological"]
        },
        {
          "name": "Pattern Separation",
          "description": "Hippocampal-inspired pattern separation",
          "keywords": ["pattern-separation", "hippocampus"]
        }
      ]
    },
    "hardware_acceleration": {
      "description": "Multi-platform hardware acceleration",
      "features": [
        {
          "name": "Apple Silicon (Metal)",
          "description": "Native Metal acceleration for M1/M2/M3/M4",
          "keywords": ["metal", "apple-silicon", "m1", "m2", "m3", "m4"]
        },
        {
          "name": "Apple Neural Engine",
          "description": "Core ML integration for ANE acceleration",
          "keywords": ["ane", "coreml", "neural-engine"]
        },
        {
          "name": "NVIDIA CUDA",
          "description": "CUDA acceleration for NVIDIA GPUs",
          "keywords": ["cuda", "nvidia", "gpu"]
        },
        {
          "name": "FPGA Backend",
          "description": "Deterministic latency transformer inference on FPGA",
          "keywords": ["fpga", "deterministic", "low-latency"]
        },
        {
          "name": "ARM NEON",
          "description": "SIMD acceleration for ARM processors",
          "keywords": ["neon", "arm", "simd"]
        }
      ]
    },
    "quantum_integration": {
      "description": "Classical nervous system for quantum machines",
      "features": [
        {
          "name": "Coherence Assessment",
          "description": "Real-time quantum gate coherence monitoring",
          "keywords": ["coherence", "quantum", "gate"]
        },
        {
          "name": "Min-Cut Decoding",
          "description": "Dynamic min-cut for quantum error correction",
          "keywords": ["min-cut", "error-correction", "decoding"]
        }
      ]
    }
  },
  "integrations": {
    "claude_flow": {
      "description": "Deep integration with Claude Flow for AI agent orchestration",
      "features": ["agent-routing", "swarm-coordination", "hooks-integration", "memory-bridge"]
    },
    "huggingface": {
      "description": "Model download and upload with HuggingFace Hub",
      "features": ["model-download", "model-upload", "model-cards", "datasets"]
    },
    "mcp": {
      "description": "Model Context Protocol server for AI assistants",
      "features": ["tool-execution", "resource-access", "prompt-templates"]
    },
    "onnx": {
      "description": "ONNX runtime for cross-platform embeddings",
      "features": ["embedding-generation", "model-inference"]
    }
  },
  "performance_benchmarks": {
    "vector_search": {
      "insertions": "50,000+ vectors/sec",
      "queries": "2,500 queries/sec on 10K vectors",
      "simd_distance": "16M+ ops/sec for 512-dim"
    },
    "learning": {
      "sona_adaptation": "<0.05ms latency",
      "pattern_search": "150x-12,500x faster with HNSW"
    },
    "inference": {
      "flash_attention": "2.49x-7.47x speedup",
      "memory_reduction": "50-75% with quantization"
    }
  }
}