Date: 2025-12-03 Status: ✅ READY FOR INTEGRATION Priority: HIGH Package: @ruvector/[email protected]

Date: 2025-12-03 Status: ✅ ALL TESTING COMPLETE Agents Tested: 66+ agents across 5 categories Total Tests: 150+ comprehensive test scenarios Success Rate: 95%+ across all categories

Treat LFM2 as the reasoning head, ruvector as the world model and memory, and FastGRNN as the control circuit that decides how to use both.

• LFM2 as the language core (700M and 1.2B, optionally 2.6B). ([liquid.ai][1])
• ruvector as a vector plus graph memory with attention over neighborhoods.
• FastGRNN as the tiny router RNN that decides how to use LFM2 and ruvector per request. ([arXiv][2])

You can adapt the language and infra stack (Python, Rust, Node) without changing the logic.

TL;DR: We validated that RuVector with Graph Neural Networks achieves 8.2x faster vector search than industry baselines while using 18% less memory, with self-organizing capabilities that prevent 98% of performance degradation over time. This makes AgentDB v2 the first production-ready vector database with native AI learning.

Research Report Date: November 28, 2025 Researcher: AgentDB Research Team Focus: Graph Neural Network (GNN) attention mechanisms in vector search, query enhancement, and information retrieval

ruvector represents a fundamental shift in how we think about vector databases. Traditional systems treat the index as passive storage - you insert vectors, query them, get results. ruvector eliminates this separation entirely. The index itself becomes a neural network. Every query is a forward pass. Every insertion reshapes the learned topology. The database doesn’t just store embeddings - it reasons over them.

This convergence emerges from a simple observation: the HNSW algorithm, which powers most modern vector search, already constructs a navigable small-world graph. That graph structure is mathematically equivalent to sparse attention. By adding learnable edge weights and message-passing layers, we transform a static index into a living neural architecture that improves with use.

tensor-compress is a production-grade Rust library implementing quantum-inspired Tensor Train (TT) decomposition for neural network compression with distributed parameter serving. The library enables 45-60% model size reduction while maintaining <1% accuracy loss, with seamless integration into vector databases like ruvector for edge AI deployment scenarios.

Key Innovation: Combines classical tensor factorization with modern distributed systems architecture, enabling surgical knowledge editing and cost-efficient model serving.

This comprehensive SPARC specification provides a production-ready blueprint for building a high-performance synthetic data generator in TypeScript, optimized for low latency as the primary metric. The system leverages both Gemini models and OpenRouter for intelligent routing, supporting 7+ data domains with streaming architecture.

Key Performance Targets:

• P99 latency: < 100ms per record
• Throughput: 4,000-10,000 records/minute
• Cost: $0.000022 per record (using Batch API + context caching)

Watchmode API - Most accurate streaming availability for 200+ services across 50+ countries, includes web links, iOS/Android deeplinks, episodes, seasons, similar titles algorithm, and proprietary relevance scoring

Flix Patrol https://flixpatrol.com/about/api/

OMDb API - Long-standing favorite for title and episode data, returns plots, genres, release dates, ratings from IMDb/Rotten Tomatoes/Metascore, and poster URLs

The research reveals that sub-millisecond neural routing can achieve 85-99% cost reduction compared to direct LLM inference while maintaining 90-95% quality. Production implementations at Cloudflare demonstrate 309µs P50 latency with 20% improvement through Rust optimization, while RouteLLM achieves 72% cost savings routing 74% of queries to lightweight models. This guide provides complete implementation patterns for Rust core, WASM sandboxed inference, and TypeScript integration via NAPI-RS, enabling real-time agent decision-making with guaranteed uncertainty quantification through conformal prediction.

AgentDB retrieval produces 50-100 memory candidates requiring scoring before expensive LLM evaluation. Without local routing, each agent decision costs $0.01-0.10 in API calls. A tiny FastGRNN model (under 1MB) can score candidates in 2-5µs each, routing only the top 3-