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What is @ruvector/ruvllm?

@ruvector/ruvllm is a TypeScript/JavaScript SDK for intelligent LLM orchestration, specifically designed for Claude Code and multi-agent systems. It provides:

- RLM (Recursive Language Model) - Break complex queries into sub-queries, synthesize coherent answers
- 100% Routing Accuracy - Hybrid keyword + embedding strategy for perfect agent selection
- SONA Self-Learning - Model improves with every successful interaction
- SIMD Acceleration - AVX2/NEON optimized inference

$3

| Challenge | Traditional Approach | @ruvector/ruvllm Solution |
|-----------|---------------------|---------------------------|
| Agent selection | Manual or keyword-based | Semantic + keyword hybrid = 100% |
| Complex queries | Single-shot RAG | Recursive decomposition + synthesis |
| Response latency | 2-5 seconds | <1ms cache, 50-200ms full |
| Learning | Static models | Self-improving (SONA) |
| Cost per route | $0.01+ (API call) | $0 (local inference) |

---

Installation

``bash
npm install @ruvector/ruvllm
`

Quick Start

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

// Simple LLM inference
const llm = new RuvLLM({
modelPath: '~/.ruvllm/models/ruvltra-claude-code-0.5b-q4_k_m.gguf',
sonaEnabled: true,
});

const response = await llm.query('Explain quantum computing');
console.log(response.text);

// Recursive Language Model for complex queries
const rlm = new RlmController({ maxDepth: 5 });
const answer = await rlm.query('What are the causes AND solutions for slow API responses?');
// Automatically decomposes into sub-queries, retrieves context, synthesizes answer
`

---

Core Features

$3

Built by Claude Code, for Claude Code. Routes tasks to 60+ agent types:

`typescript
import { RuvLLM } from '@ruvector/ruvllm';

const llm = new RuvLLM({ model: 'ruv/ruvltra' });

// Intelligent routing
const route = await llm.route('implement OAuth2 authentication');
console.log(route.agent); // 'security-architect'
console.log(route.confidence); // 0.98
console.log(route.tier); // 2 (Haiku-level complexity)

// Multi-agent teams for complex tasks
const team = await llm.routeComplex('build full-stack app with auth');
// Returns: [system-architect, backend-dev, coder, security-architect, tester]
`

$3

`
┌─────────────────────────────────────────────────────────┐
│ User Request │
└─────────────────────┬───────────────────────────────────┘
↓
[RuvLTRA Routing]
↓
┌─────────────┼─────────────┐
↓ ↓ ↓
┌───────────┐ ┌───────────┐ ┌───────────┐
│ Tier 1 │ │ Tier 2 │ │ Tier 3 │
│ Booster │ │ Haiku │ │ Opus │
│ <1ms │ │ ~500ms │ │ 2-5s │
│ $0 │ │ $0.0002 │ │ $0.015 │
└───────────┘ └───────────┘ └───────────┘
`

$3

Every successful interaction improves the model:

`typescript
// First routing: Full inference
llm.route('implement OAuth2') → security-architect (97%)

// Later: Pattern hit in <25μs (learned from success)
llm.route('add OAuth2 flow') → security-architect (99%, cached pattern)
`

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RLM (Recursive Language Model)

RLM provides recursive query decomposition - unlike traditional RAG that retrieves once, RLM breaks complex questions into sub-queries and synthesizes coherent answers.

$3

`
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 with sources
`

$3

`typescript
import { RlmController } from '@ruvector/ruvllm';

const rlm = new RlmController({
maxDepth: 5,
retrievalTopK: 10,
enableCache: true,
});

// Add knowledge to memory
await rlm.addMemory('TypeScript adds static typing to JavaScript.');
await rlm.addMemory('React is a library for building user interfaces.');

// Query with recursive retrieval
const answer = await rlm.query('What are causes and solutions for type errors in React?');
console.log(answer.text); // Comprehensive synthesized answer
console.log(answer.sources); // Source attributions
console.log(answer.qualityScore); // 0.0-1.0
console.log(answer.confidence); // Routing confidence
`

$3

`typescript
for await (const event of rlm.queryStream('Explain machine learning')) {
if (event.type === 'token') {
process.stdout.write(event.text);
} else {
console.log('\n\nQuality:', event.answer.qualityScore);
}
}
`

$3

`typescript
const rlm = new RlmController({
enableReflection: true,
maxReflectionIterations: 2,
minQualityScore: 0.8,
});

// Answers are iteratively refined until quality >= 0.8
const answer = await rlm.query('Complex multi-part technical question...');
`

$3

`typescript
interface RlmConfig {
maxDepth?: number; // Max recursion depth (default: 3)
maxSubQueries?: number; // Max sub-queries per level (default: 5)
tokenBudget?: number; // Token budget (default: 4096)
enableCache?: boolean; // Enable caching (default: true)
cacheTtl?: number; // Cache TTL in ms (default: 300000)
retrievalTopK?: number; // Memory spans to retrieve (default: 10)
minQualityScore?: number; // Min quality threshold (default: 0.7)
enableReflection?: boolean; // Enable self-reflection (default: false)
maxReflectionIterations?: number; // Max reflection loops (default: 2)
}
`

---

Unique Capabilities

$3

Every successful routing is stored in HNSW-indexed memory for instant recall:

`typescript
// First time: Full inference (~50ms)
route("implement OAuth2") → security-architect (97% confidence)

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

$3

`typescript
// Low confidence automatically escalates
Confidence > 0.9 → Use recommended agent
Confidence 0.7-0.9 → Use with human confirmation
Confidence < 0.7 → Escalate to higher tier
`

$3

`typescript
import { simd } from '@ruvector/ruvllm/simd';

// 4x faster vector operations with AVX2/NEON
const similarity = simd.batchCosineSimilarity(query, targets);
const attended = simd.flashAttention(q, k, v, scale);
`

$3

Arc-based string interning for 100-1000x faster cache hits on large responses.

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Performance

$3

| 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 |
| End-to-end routing | <1 ms | 1K+/s |
| Full RLM query | 50-200 ms | 5-20/s |

$3

| Strategy | RuvLTRA | Qwen Base | OpenAI |
|----------|---------|-----------|--------|
| Embedding Only | 45% | 40% | 52% |
| Keyword Only | 78% | 78% | N/A |
| Hybrid | 100% | 95% | N/A |

$3

`
145 tests passing
- RLM Controller: 24 tests
- Routing Accuracy: 18 tests
- Contrastive Training: 15 tests
- SIMD Operations: 22 tests
- SONA Learning: 19 tests
- Memory/HNSW: 21 tests
- Benchmarks: 26 tests
`

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Models

$3

URL: https://huggingface.co/ruv/ruvltra

$3

| Model | Size | Purpose | Accuracy |
|-------|------|---------|----------|
| ruvltra-claude-code-0.5b-q4_k_m | 398 MB | Agent routing | 100% (hybrid) |
| ruvltra-small-0.5b-q4_k_m | ~400 MB | Embeddings | - |
| ruvltra-medium-1.1b-q4_k_m | ~1 GB | Full inference | - |

$3

`typescript
// Programmatic
import { downloadModel } from '@ruvector/ruvllm';
await downloadModel('ruv/ruvltra', { quantization: 'q4_k_m' });

// CLI
ruvllm download ruv/ruvltra
`

$3

Models are automatically downloaded on first use:

`typescript
const llm = new RuvLLM({ model: 'ruv/ruvltra' });
// Downloads to ~/.ruvllm/models/ if not present
`

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Training

$3

`bash
node scripts/training/routing-dataset.js


Output: 381 examples, 793 contrastive pairs, 156 hard negatives


`

$3

`typescript
import { ContrastiveTrainer } from '@ruvector/ruvllm';

const trainer = new ContrastiveTrainer({
modelPath: './models/base.gguf',
loraRank: 8,
loraAlpha: 16,
learningRate: 1e-4,
});

const pairs = [
{ anchor: 'Fix auth bug', positive: 'coder', negative: 'researcher' },
// ... more pairs
];

await trainer.train(pairs, { epochs: 10 });
await trainer.save('./adapters/routing-lora');
`

$3

| Script | Description |
|--------|-------------|
| routing-dataset.js | Generate 381 routing examples |
| claude-code-synth.js | Synthetic data generation |
| contrastive-finetune.js | LoRA fine-tuning pipeline |
| rlm-dataset.js | RLM training data (500 examples) |

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API Reference

$3

`typescript
class RuvLLM {
constructor(config?: RuvLLMConfig);

query(prompt: string, params?: GenerateParams): Promise;
stream(prompt: string, params?: GenerateParams): AsyncIterable;
route(task: string): Promise;
routeComplex(task: string): Promise;

loadModel(path: string): Promise;
addMemory(text: string, metadata?: object): number;
searchMemory(query: string, topK?: number): MemoryResult[];

sonaStats(): SonaStats | null;
adapt(input: Float32Array, quality: number): void;
}
`

$3

`typescript
class RlmController {
constructor(config?: RlmConfig, engine?: RuvLLM);

query(input: string): Promise;
queryStream(input: string): AsyncGenerator;

addMemory(text: string, metadata?: object): Promise;
searchMemory(query: string, topK?: number): Promise;

clearCache(): void;
getCacheStats(): { size: number; entries: number };

updateConfig(config: Partial): void;
getConfig(): Required;
}
`

$3

`typescript
import {
// Core
RuvLLM, RuvLLMConfig,

// RLM
RlmController, RlmConfig, RlmAnswer, MemorySpan, StreamToken,

// Training
RlmTrainer, ContrastiveTrainer, createRlmTrainer,
DEFAULT_RLM_CONFIG, FAST_RLM_CONFIG, THOROUGH_RLM_CONFIG,

// SONA Learning
SonaCoordinator, TrajectoryBuilder,

// LoRA
LoraAdapter, LoraManager,

// Benchmarks
ModelComparisonBenchmark, RoutingBenchmark, EmbeddingBenchmark,
} from '@ruvector/ruvllm';
`

---

CLI

`bash


Route a task

ruvllm route "add unit tests for auth module"

→ Agent: tester | Confidence: 0.96 | Tier: 2

Query with streaming

ruvllm query --stream "Explain machine learning"

Download models

ruvllm download ruv/ruvltra

Run benchmarks

ruvllm bench ./models/model.gguf

Evaluate (SWE-Bench)

ruvllm eval --model ./models/model.gguf --subset lite
``

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Platform Support

| Platform | Architecture | Status |
|----------|--------------|--------|
| macOS | arm64 (M1-M4) | Full support |
| macOS | x64 | Supported |
| Linux | x64 | Supported |
| Linux | arm64 | Supported |
| Windows | x64 | Supported |

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Links

| Resource | URL |
|----------|-----|
| npm | npmjs.com/package/@ruvector/ruvllm |
| HuggingFace | huggingface.co/ruv/ruvltra |
| Crate (Rust) | crates.io/crates/ruvllm |
| Documentation | docs.rs/ruvllm |
| GitHub | github.com/ruvnet/ruvector |
| Claude Flow | github.com/ruvnet/claude-flow |

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License

MIT OR Apache-2.0

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