claude-flow
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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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TypeScript
/**
* ruvLLM Bridge -- Local Language Model Inference from RuVector
*
* Extends @ruvector/core with on-device GGUF model inference.
* Provides 3-tier routing:
* Tier 1: Agent Booster (WASM, <1ms) -- simple transforms
* Tier 2: Local model via ruvLLM (~200ms) -- routing, classification
* Tier 3: Cloud API (2-5s) -- complex reasoning
*
* All @ruvector/* packages are optional peer dependencies.
* The bridge degrades gracefully when they are absent.
*
* @module @claude-flow/cli/appliance/ruvllm-bridge
*/
export interface RuvllmConfig {
modelsDir: string;
defaultModel?: string;
maxTokens?: number;
temperature?: number;
contextSize?: number;
kvCachePath?: string;
verbose?: boolean;
}
export interface GenerateRequest {
prompt: string;
model?: string;
maxTokens?: number;
temperature?: number;
stream?: boolean;
stopSequences?: string[];
}
export interface GenerateResponse {
text: string;
model: string;
tokensUsed: number;
latencyMs: number;
tier: 1 | 2 | 3;
cached: boolean;
}
export interface ModelInfo {
name: string;
path: string;
format: string;
quantization: string;
size: number;
parameters: string;
loaded: boolean;
}
export interface TierRouting {
tier: 1 | 2 | 3;
model: string;
confidence: number;
}
export interface BridgeStatus {
available: boolean;
ruvectorCore: boolean;
ruvectorRouter: boolean;
ruvectorSona: boolean;
modelsLoaded: string[];
kvCacheSize: number;
}
export declare class RuvllmBridge {
private config;
private models;
private activeModel;
private kvCacheEntries;
private ruvectorCore;
private ruvectorRouter;
private ruvectorSona;
private ggufEngine;
constructor(config: RuvllmConfig);
/** Probe optional @ruvector packages, initialize GGUF engine, and scan modelsDir. */
initialize(): Promise<void>;
/** Return all discovered GGUF models. */
listModels(): Promise<ModelInfo[]>;
/** Load a model into memory (delegates to GGUF engine or @ruvector/core). */
loadModel(name: string): Promise<void>;
/**
* Generate text from a prompt. Routes through tiers:
* 1. Agent Booster (trivial transforms, no LLM).
* 2. Local GGUF model via @ruvector/core.
* 3. Cloud fallback (empty response -- caller handles upstream).
*/
generate(request: GenerateRequest): Promise<GenerateResponse>;
/** Route a task description to the optimal tier. Uses @ruvector/router when available. */
routeTask(description: string): Promise<TierRouting>;
/** Return current bridge status. */
getStatus(): Promise<BridgeStatus>;
/** Persist KV-cache, unload models, and clean up. */
shutdown(): Promise<void>;
private scanModelsDir;
private tryImport;
/** Tier-1 Agent Booster: handle trivial transforms without any LLM. */
private tryAgentBooster;
}
/** Get or create the singleton RuvllmBridge. Config required on first call. */
export declare function getRuvllmBridge(config?: RuvllmConfig): RuvllmBridge;
/** Reset the singleton (useful for tests). */
export declare function resetRuvllmBridge(): void;
/** Check whether @ruvector/core is importable without loading the bridge. */
export declare function isRuvllmAvailable(): Promise<boolean>;
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