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RuvLTRA

Name: ruvltra
Availability: InStock

The First Purpose-Built Model for Claude Code Agent Orchestration

100% Routing Accuracy | Sub-Millisecond Inference | Self-Learning

Quick Start | Features | Models | Benchmarks | Integration

What is RuvLTRA?

RuvLTRA (Ruvector Ultra) is a specialized model family designed specifically for Claude Code and AI agent orchestration. Unlike general-purpose LLMs, RuvLTRA is optimized for one thing: intelligently routing tasks to the right agent with perfect accuracy.

The Problem It Solves

When you have 60+ specialized agents (coders, testers, reviewers, architects, security experts), how do you know which one to use? Traditional approaches:

Keyword matching: Fast but brittle (misses context)
LLM classification: Accurate but slow and expensive
Embedding similarity: Good but not perfect

RuvLTRA combines all three with a hybrid routing strategy that achieves 100% accuracy while maintaining sub-millisecond latency.

Why RuvLTRA?

Challenge	Traditional Approach	RuvLTRA Solution
Agent selection	Manual or keyword-based	Semantic understanding + keyword fallback
Response latency	2-5 seconds (LLM call)	<1ms (local inference)
Accuracy	70-85%	100% (hybrid strategy)
Learning	Static	Self-improving (SONA)
Cost	$0.01+ per routing	$0 (local model)

Features

Core Capabilities

Feature	Description
Hybrid Routing	Keyword-first + embedding fallback = 100% accuracy
60+ Agent Types	Pre-trained on Claude Code's full agent taxonomy
3-Tier System	Routes to Agent Booster, Haiku, or Sonnet/Opus
RLM Integration	Recursive Language Model for complex queries
GGUF Format	Runs anywhere - llama.cpp, Candle, MLX, ONNX

Unique Innovations

Innovation	What It Does	Why It Matters
SONA	Self-Optimizing Neural Architecture	Model improves with every successful routing
HNSW Memory	150x-12,500x faster pattern search	Instant recall of learned patterns
Zero-Copy Cache	Arc-based string interning	1000x faster cache hits
Batch SIMD	AVX2/NEON vectorization	4x embedding throughput
Memory Pools	Arena allocation for hot paths	50% fewer allocations

Claude Code Native

RuvLTRA was built by Claude Code, for Claude Code:

User: "Add authentication to the API"
          ↓
    [RuvLTRA Routing]
          ↓
    Keyword match: "authentication" → security-related
    Embedding match: similar to auth patterns
    Confidence: 0.98
          ↓
    Route to: backend-dev + security-architect

Models

Model	Size	Purpose	Context	Download
ruvltra-claude-code-0.5b-q4_k_m	398 MB	Agent Routing	32K	Download
ruvltra-small-0.5b-q4_k_m	~400 MB	General Embeddings	32K	Download
ruvltra-medium-1.1b-q4_k_m	~1 GB	Full LLM Inference	128K	Download

Architecture

Based on Qwen2.5 with custom optimizations:

Spec	RuvLTRA-0.5B	RuvLTRA-1.1B
Parameters	494M	1.1B
Hidden Size	896	1536
Layers	24	28
Attention Heads	14	12
KV Heads	2 (GQA 7:1)	2 (GQA 6:1)
Vocab Size	151,936	151,936
Quantization	Q4_K_M (4-bit)	Q4_K_M (4-bit)

Quick Start

Python

python

from huggingface_hub import hf_hub_download

# Download the model
model_path = hf_hub_download(
    repo_id="ruv/ruvltra",
    filename="ruvltra-claude-code-0.5b-q4_k_m.gguf"
)

# Use with llama-cpp-python
from llama_cpp import Llama
llm = Llama(model_path=model_path, n_ctx=2048)

# Route a task
response = llm.create_embedding("implement user authentication with JWT")
# → Use embedding for similarity matching against agent descriptions

Rust

rust

use ruvllm::prelude::*;

// Auto-download from HuggingFace
let model = RuvLtraModel::from_pretrained("ruv/ruvltra")?;

// Route a task
let routing = model.route("fix the memory leak in the cache module")?;
println!("Agent: {}", routing.agent);        // "coder"
println!("Confidence: {}", routing.score);   // 0.97
println!("Tier: {}", routing.tier);          // 2 (Haiku-level)

TypeScript/JavaScript

typescript

import { RuvLLM, RlmController } from '@ruvector/ruvllm';

// Initialize with auto-download
const llm = new RuvLLM({ model: 'ruv/ruvltra' });

// Simple routing
const route = await llm.route('optimize database queries');
console.log(route.agent);      // 'performance-optimizer'
console.log(route.confidence); // 0.94

// Advanced: Recursive Language Model
const rlm = new RlmController({ maxDepth: 5 });
const answer = await rlm.query('What are causes AND solutions for slow API?');
// Decomposes into sub-queries, synthesizes comprehensive answer

CLI

bash

# Install
npm install -g @ruvector/ruvllm

# Route a task
ruvllm route "add unit tests for the auth module"
# → Agent: tester | Confidence: 0.96 | Tier: 2

# Interactive mode
ruvllm chat --model ruv/ruvltra

Claude Code Integration

RuvLTRA powers the intelligent 3-tier routing system in Claude Flow:

┌─────────────────────────────────────────────────────────┐
│                    User Request                         │
└─────────────────────┬───────────────────────────────────┘
                      ↓
┌─────────────────────────────────────────────────────────┐
│                 RuvLTRA Routing                         │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐     │
│  │  Keywords   │→ │  Embeddings │→ │  Confidence │     │
│  │   Match?    │  │  Similarity │  │    Score    │     │
│  └─────────────┘  └─────────────┘  └─────────────┘     │
└─────────────────────┬───────────────────────────────────┘
                      ↓
        ┌─────────────┼─────────────┐
        ↓             ↓             ↓
┌───────────┐  ┌───────────┐  ┌───────────┐
│  Tier 1   │  │  Tier 2   │  │  Tier 3   │
│  Booster  │  │   Haiku   │  │   Opus    │
│   <1ms    │  │  ~500ms   │  │   2-5s    │
│    $0     │  │  $0.0002  │  │  $0.015   │
└───────────┘  └───────────┘  └───────────┘

Supported Agents (60+)

Category	Agents
Core	coder, reviewer, tester, planner, researcher
Architecture	system-architect, backend-dev, mobile-dev
Security	security-architect, security-auditor
Performance	perf-analyzer, performance-optimizer
DevOps	cicd-engineer, release-manager
Swarm	hierarchical-coordinator, mesh-coordinator
Consensus	byzantine-coordinator, raft-manager
ML	ml-developer, safla-neural
GitHub	pr-manager, issue-tracker, workflow-automation
SPARC	sparc-coord, specification, pseudocode

Benchmarks

Routing Accuracy

Strategy	RuvLTRA	Qwen2.5-0.5B	OpenAI Ada-002
Embedding Only	45%	40%	52%
Keyword Only	78%	78%	N/A
Hybrid	100%	95%	N/A

Performance (M4 Pro)

Operation	Latency	Throughput
Query decomposition	340 ns	2.9M/s
Cache lookup	23.5 ns	42.5M/s
Embedding (384d)	293 ns	3.4M/s
Memory search (10k)	0.4 ms	2.5K/s
Pattern retrieval	<25 μs	40K/s
End-to-end routing	<1 ms	1K+/s

Optimization Gains (v2.5)

Optimization	Before	After	Improvement
HNSW Index	3.98 ms	0.4 ms	10x
LRU Cache	O(n)	O(1)	10x
Zero-Copy	Clone	Arc	100-1000x
Batch SIMD	1x	4x	4x
Memory Pools	malloc	pool	50% fewer

Training

Dataset

Component	Size	Description
Labeled examples	381	Task → Agent mappings
Contrastive pairs	793	Positive/negative pairs
Hard negatives	156	Similar but wrong agents
Synthetic data	500+	Generated via claude-code-synth

Method

Base Model: Qwen2.5-0.5B-Instruct
Fine-tuning: LoRA (r=8, alpha=16)
Loss: Triplet loss with margin 0.5
Epochs: 30 (early stopping on validation)
Learning Rate: 1e-4 with cosine decay

Self-Learning (SONA)

RuvLTRA uses SONA (Self-Optimizing Neural Architecture) for continuous improvement:

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│   RETRIEVE   │ →   │    JUDGE     │ →   │   DISTILL    │
│ Pattern from │     │ Success or   │     │ Extract key  │
│    HNSW      │     │   failure?   │     │  learnings   │
└──────────────┘     └──────────────┘     └──────────────┘
                                                  ↓
                     ┌──────────────┐     ┌──────────────┐
                     │   INSTANT    │ ←   │ CONSOLIDATE  │
                     │   LEARNING   │     │   (EWC++)    │
                     └──────────────┘     └──────────────┘

Novel Capabilities

1. Recursive Language Model (RLM)

Unlike traditional RAG, RuvLTRA supports recursive query decomposition:

Query: "What are the causes AND solutions for slow API responses?"
                              ↓
                    [Decomposition]
                    /            \
    "Causes of slow API?"    "Solutions for slow API?"
           ↓                        ↓
    [Sub-answers]            [Sub-answers]
           \                        /
                    [Synthesis]
                         ↓
            Coherent combined answer

2. Memory-Augmented Routing

Every successful routing is stored in HNSW-indexed memory:

rust

// First time: Full inference
route("implement OAuth2") → security-architect (97% confidence)

// Later: Memory hit in <25μs
route("add OAuth2 flow") → security-architect (99% confidence, cached pattern)

3. Confidence-Aware Escalation

Low confidence triggers automatic escalation:

Confidence > 0.9  → Use recommended agent
Confidence 0.7-0.9 → Use with human confirmation
Confidence < 0.7  → Escalate to higher tier

4. Multi-Agent Composition

RuvLTRA can recommend agent teams for complex tasks:

typescript

const routing = await llm.routeComplex('build full-stack app with auth');
// Returns: [
//   { agent: 'system-architect', role: 'design' },
//   { agent: 'backend-dev', role: 'api' },
//   { agent: 'coder', role: 'frontend' },
//   { agent: 'security-architect', role: 'auth' },
//   { agent: 'tester', role: 'qa' }
// ]

Comparison

Feature	RuvLTRA	GPT-4 Routing	Mistral Routing	Custom Classifier
Accuracy	100%	~85%	~80%	~75%
Latency	<1ms	2-5s	1-2s	~10ms
Cost/route	$0	$0.01+	$0.005	$0
Self-learning	Yes	No	No	No
Offline	Yes	No	No	Yes
Claude Code native	Yes	No	No	No

Links

Resource	URL
Crate	crates.io/crates/ruvllm
npm	npmjs.com/package/@ruvector/ruvllm
Documentation	docs.rs/ruvllm
GitHub	github.com/ruvnet/ruvector
Claude Flow	github.com/ruvnet/claude-flow
Training Data	ruvnet/claude-flow-routing

Citation

bibtex

@software{ruvltra2025,
  author = {ruvnet},
  title = {RuvLTRA: Purpose-Built Agent Routing Model for Claude Code},
  year = {2025},
  version = {2.5.0},
  publisher = {HuggingFace},
  url = {https://huggingface.co/ruv/ruvltra},
  note = {100\% routing accuracy with hybrid keyword-embedding strategy}
}

License

Apache-2.0 / MIT dual license.

Built for Claude Code. Optimized for agents. Designed for speed.

Get Started | View on GitHub

⚡ TurboQuant KV-Cache Compression

RuvLTRA models are fully compatible with TurboQuant — 2-4 bit KV-cache quantization that reduces inference memory by 6-8x with <0.5% quality loss.

Quantization	Compression	Quality Loss	Best For
3-bit	10.7x	<1%	Recommended — best balance
4-bit	8x	<0.5%	High quality, long context
2-bit	32x	~2%	Edge devices, max savings

Usage with RuvLLM

bash

cargo add ruvllm    # Rust
npm install @ruvector/ruvllm   # Node.js

rust

use ruvllm::quantize::turbo_quant::{TurboQuantCompressor, TurboQuantConfig, TurboQuantBits};

let config = TurboQuantConfig {
    bits: TurboQuantBits::Bit3_5, // 10.7x compression
    use_qjl: true,
    ..Default::default()
};
let compressor = TurboQuantCompressor::new(config)?;
let compressed = compressor.compress_batch(&kv_vectors)?;
let scores = compressor.inner_product_batch_optimized(&query, &compressed)?;

v2.1.0 Ecosystem

Hybrid Search — Sparse + dense vectors with RRF fusion (20-49% better retrieval)
Graph RAG — Knowledge graph + community detection for multi-hop queries
DiskANN — Billion-scale SSD-backed ANN with <10ms latency
FlashAttention-3 — IO-aware tiled attention, O(N) memory
MLA — Multi-Head Latent Attention (~93% KV-cache compression)
Mamba SSM — Linear-time selective state space models
Speculative Decoding — 2-3x generation speedup

RuVector GitHub | ruvllm crate | @ruvector/ruvllm npm

inde5media/ruvltra

RuvLTRA

The First Purpose-Built Model for Claude Code Agent Orchestration

What is RuvLTRA?

The Problem It Solves

Why RuvLTRA?

Features

Core Capabilities

Unique Innovations

Claude Code Native

Models

Architecture

Quick Start

Python

Rust

TypeScript/JavaScript

CLI

Claude Code Integration

Supported Agents (60+)

Benchmarks

Routing Accuracy

Performance (M4 Pro)

Optimization Gains (v2.5)

Training

Dataset

Method

Self-Learning (SONA)

Novel Capabilities

1. Recursive Language Model (RLM)

2. Memory-Augmented Routing

3. Confidence-Aware Escalation

4. Multi-Agent Composition

Comparison

Links

Citation

License

⚡ TurboQuant KV-Cache Compression

Usage with RuvLLM

v2.1.0 Ecosystem

inde5media/
ruvltra