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
/**
* SONA Learning Modes Index
*
* Exports all learning mode implementations and the common interface.
*/
import type { SONAModeConfig, ModeOptimizations, Trajectory, Pattern, PatternMatch, LoRAWeights, EWCState } from '../types.js';
/**
* Common interface for all mode implementations
*/
export interface ModeImplementation {
/** Mode identifier */
readonly mode: string;
/** Initialize the mode */
initialize(): Promise<void>;
/** Cleanup resources */
cleanup(): Promise<void>;
/** Find similar patterns (k-nearest) */
findPatterns(embedding: Float32Array, k: number, patterns: Pattern[]): Promise<PatternMatch[]>;
/** Perform a learning step */
learn(trajectories: Trajectory[], config: SONAModeConfig, ewcState: EWCState): Promise<number>;
/** Apply LoRA adaptations */
applyLoRA(input: Float32Array, weights?: LoRAWeights): Promise<Float32Array>;
/** Get mode-specific stats */
getStats(): Record<string, number>;
}
/**
* Base class for mode implementations
*/
export declare abstract class BaseModeImplementation implements ModeImplementation {
abstract readonly mode: string;
protected config: SONAModeConfig;
protected optimizations: ModeOptimizations;
protected isInitialized: boolean;
constructor(config: SONAModeConfig, optimizations: ModeOptimizations);
initialize(): Promise<void>;
cleanup(): Promise<void>;
/**
* Compute cosine similarity between two vectors (SIMD-optimized)
*/
protected cosineSimilarity(a: Float32Array, b: Float32Array): number;
/**
* Apply LoRA: output = input + BA * input (simplified)
*/
protected applyLoRATransform(input: Float32Array, A: Float32Array, B: Float32Array, rank: number): Float32Array;
abstract findPatterns(embedding: Float32Array, k: number, patterns: Pattern[]): Promise<PatternMatch[]>;
abstract learn(trajectories: Trajectory[], config: SONAModeConfig, ewcState: EWCState): Promise<number>;
abstract applyLoRA(input: Float32Array, weights?: LoRAWeights): Promise<Float32Array>;
abstract getStats(): Record<string, number>;
}
export { RealTimeMode } from './real-time.js';
export { BalancedMode } from './balanced.js';
export { ResearchMode } from './research.js';
export { EdgeMode } from './edge.js';
export { BatchMode } from './batch.js';
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