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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JavaScript
/**
* V3 CLI Benchmark Command
* Comprehensive benchmarking for self-learning, pre-training, and neural systems
*
* @module v3/cli/commands/benchmark
*/
import { output } from '../output.js';
import { writeFileSync, existsSync, mkdirSync } from 'node:fs';
import { join } from 'node:path';
// ============================================================================
// Pretrain Benchmark Subcommand
// ============================================================================
const pretrainCommand = {
name: 'pretrain',
description: 'Benchmark self-learning pre-training system (SONA, EWC++, MoE)',
options: [
{ name: 'iterations', short: 'i', type: 'number', description: 'Benchmark iterations', default: '100' },
{ name: 'warmup', short: 'w', type: 'number', description: 'Warmup iterations', default: '10' },
{ name: 'output', short: 'o', type: 'string', description: 'Output format: text, json', default: 'text' },
{ name: 'save', short: 's', type: 'string', description: 'Save results to file' },
{ name: 'verbose', short: 'v', type: 'boolean', description: 'Verbose output', default: 'false' },
],
examples: [
{ command: 'claude-flow benchmark pretrain', description: 'Run pre-training benchmarks' },
{ command: 'claude-flow benchmark pretrain -i 500 --save results.json', description: 'Extended benchmark with results saved' },
{ command: 'claude-flow benchmark pretrain -o json', description: 'Output results as JSON' },
],
action: async (ctx) => {
const iterations = parseInt(ctx.flags.iterations || '100', 10);
const warmup = parseInt(ctx.flags.warmup || '10', 10);
const outputFormat = ctx.flags.output || 'text';
const saveFile = ctx.flags.save;
const verbose = ctx.flags.verbose === true;
try {
// Dynamically import benchmark suite
const { runPretrainBenchmarkSuite } = await import('../benchmarks/pretrain/index.js');
const results = await runPretrainBenchmarkSuite({
iterations,
warmupIterations: warmup,
verbose,
});
// Output as JSON if requested
if (outputFormat === 'json') {
output.writeln(JSON.stringify(results, null, 2));
}
// Save to file if requested
if (saveFile) {
const resultsDir = join(process.cwd(), '.claude-flow', 'benchmarks');
if (!existsSync(resultsDir)) {
mkdirSync(resultsDir, { recursive: true });
}
const savePath = saveFile.startsWith('/') ? saveFile : join(resultsDir, saveFile);
writeFileSync(savePath, JSON.stringify(results, null, 2));
output.writeln(output.success(`Results saved to ${savePath}`));
}
const allPassed = results.results.every(r => r.targetMet);
return {
success: true,
message: allPassed
? 'All benchmark targets met!'
: `${results.results.filter(r => r.targetMet).length}/${results.results.length} targets met`,
};
}
catch (err) {
const errorMsg = err instanceof Error ? err.message : String(err);
output.writeln(output.error(`Benchmark failed: ${errorMsg}`));
return {
success: false,
message: `Benchmark failed: ${errorMsg}`,
};
}
},
};
// ============================================================================
// Neural Benchmark Subcommand
// ============================================================================
const neuralCommand = {
name: 'neural',
description: 'Benchmark neural operations (embeddings, WASM, Flash Attention)',
options: [
{ name: 'iterations', short: 'i', type: 'number', description: 'Benchmark iterations', default: '100' },
{ name: 'dimension', short: 'd', type: 'number', description: 'Embedding dimension', default: '384' },
{ name: 'vectors', short: 'n', type: 'number', description: 'Number of test vectors', default: '1000' },
{ name: 'output', short: 'o', type: 'string', description: 'Output format: text, json', default: 'text' },
],
examples: [
{ command: 'claude-flow benchmark neural', description: 'Run neural benchmarks' },
{ command: 'claude-flow benchmark neural -d 768 -n 5000', description: 'Higher dimension, more vectors' },
],
action: async (ctx) => {
const iterations = parseInt(ctx.flags.iterations || '100', 10);
const dimension = parseInt(ctx.flags.dimension || '384', 10);
const numVectors = parseInt(ctx.flags.vectors || '1000', 10);
const outputFormat = ctx.flags.output || 'text';
output.writeln();
output.writeln(output.bold('Neural Operations Benchmark'));
output.writeln(output.dim('─'.repeat(60)));
output.writeln(`Iterations: ${iterations} | Dimension: ${dimension} | Vectors: ${numVectors}`);
output.writeln();
const spinner = output.createSpinner({ text: 'Running neural benchmarks...', spinner: 'dots' });
spinner.start();
try {
const { performance } = await import('node:perf_hooks');
// Helper functions
const percentile = (sorted, p) => {
const idx = Math.ceil((p / 100) * sorted.length) - 1;
return sorted[Math.max(0, idx)];
};
const results = [];
// 1. Embedding Generation
spinner.setText('Benchmarking embedding generation...');
let generateEmbedding;
try {
const memory = await import('../memory/memory-initializer.js');
generateEmbedding = memory.generateEmbedding;
}
catch {
generateEmbedding = async (text) => {
const emb = [];
for (let i = 0; i < dimension; i++) {
emb.push(Math.sin(text.charCodeAt(i % text.length) * (i + 1)));
}
return { embedding: emb, dimensions: dimension, model: 'fallback' };
};
}
const embedTimes = [];
for (let i = 0; i < iterations; i++) {
const start = performance.now();
await generateEmbedding(`benchmark text ${i}`);
embedTimes.push(performance.now() - start);
}
const embedMean = embedTimes.reduce((a, b) => a + b, 0) / embedTimes.length;
const embedSorted = [...embedTimes].sort((a, b) => a - b);
results.push({
name: 'Embedding Generation',
mean: embedMean,
p95: percentile(embedSorted, 95),
p99: percentile(embedSorted, 99),
target: 5.0,
met: embedMean <= 5.0,
});
// 2. Batch Cosine Similarity
spinner.setText('Benchmarking batch cosine similarity...');
let batchCosineSim;
try {
const memory = await import('../memory/memory-initializer.js');
batchCosineSim = memory.batchCosineSim;
}
catch {
batchCosineSim = (query, vectors) => {
const res = new Float32Array(vectors.length);
for (let i = 0; i < vectors.length; i++) {
let dot = 0, nQ = 0, nV = 0;
for (let j = 0; j < query.length; j++) {
dot += query[j] * vectors[i][j];
nQ += query[j] * query[j];
nV += vectors[i][j] * vectors[i][j];
}
res[i] = dot / (Math.sqrt(nQ) * Math.sqrt(nV));
}
return res;
};
}
const query = new Float32Array(dimension).map(() => Math.random());
const vectors = Array.from({ length: numVectors }, () => new Float32Array(dimension).map(() => Math.random()));
const cosineTimes = [];
for (let i = 0; i < Math.min(iterations, 50); i++) {
const start = performance.now();
batchCosineSim(query, vectors);
cosineTimes.push(performance.now() - start);
}
const cosineMean = cosineTimes.reduce((a, b) => a + b, 0) / cosineTimes.length;
const cosineSorted = [...cosineTimes].sort((a, b) => a - b);
results.push({
name: `Batch Cosine (${numVectors} vectors)`,
mean: cosineMean,
p95: percentile(cosineSorted, 95),
p99: percentile(cosineSorted, 99),
target: 5.0,
met: cosineMean <= 5.0,
});
// 3. Flash Attention Search (if available)
spinner.setText('Benchmarking flash attention search...');
const flashTimes = [];
try {
const memory = await import('../memory/memory-initializer.js');
if (memory.flashAttentionSearch) {
for (let i = 0; i < Math.min(iterations, 50); i++) {
const start = performance.now();
memory.flashAttentionSearch(query, vectors, { k: 10 });
flashTimes.push(performance.now() - start);
}
}
}
catch {
// Flash attention not available
}
if (flashTimes.length > 0) {
const flashMean = flashTimes.reduce((a, b) => a + b, 0) / flashTimes.length;
const flashSorted = [...flashTimes].sort((a, b) => a - b);
results.push({
name: 'Flash Attention Search',
mean: flashMean,
p95: percentile(flashSorted, 95),
p99: percentile(flashSorted, 99),
target: 2.0,
met: flashMean <= 2.0,
});
}
spinner.stop();
// Display results
output.writeln();
output.writeln(output.bold('Results'));
output.writeln(output.dim('─'.repeat(60)));
for (const r of results) {
const status = r.met ? output.success('✓') : output.error('✗');
output.writeln(`${status} ${r.name}`);
output.writeln(` Mean: ${r.mean.toFixed(3)}ms | p95: ${r.p95.toFixed(3)}ms | p99: ${r.p99.toFixed(3)}ms`);
output.writeln(` Target: ${r.target}ms | Status: ${r.met ? 'Met' : 'Not met'}`);
output.writeln();
}
if (outputFormat === 'json') {
output.writeln(JSON.stringify(results, null, 2));
}
const allPassed = results.every(r => r.met);
return {
success: true,
message: allPassed ? 'All neural benchmarks passed!' : 'Some benchmarks below target',
};
}
catch (err) {
spinner.stop();
const errorMsg = err instanceof Error ? err.message : String(err);
output.writeln(output.error(`Neural benchmark failed: ${errorMsg}`));
return {
success: false,
message: `Neural benchmark failed: ${errorMsg}`,
};
}
},
};
// ============================================================================
// Memory Benchmark Subcommand
// ============================================================================
const memoryCommand = {
name: 'memory',
description: 'Benchmark memory operations (HNSW search, store, retrieve)',
options: [
{ name: 'iterations', short: 'i', type: 'number', description: 'Benchmark iterations', default: '100' },
{ name: 'output', short: 'o', type: 'string', description: 'Output format: text, json', default: 'text' },
],
examples: [
{ command: 'claude-flow benchmark memory', description: 'Run memory benchmarks' },
],
action: async (ctx) => {
const iterations = parseInt(ctx.flags.iterations || '100', 10);
const outputFormat = ctx.flags.output || 'text';
output.writeln();
output.writeln(output.bold('Memory Operations Benchmark'));
output.writeln(output.dim('─'.repeat(60)));
const spinner = output.createSpinner({ text: 'Running memory benchmarks...', spinner: 'dots' });
spinner.start();
try {
const { performance } = await import('node:perf_hooks');
const percentile = (sorted, p) => {
const idx = Math.ceil((p / 100) * sorted.length) - 1;
return sorted[Math.max(0, idx)];
};
const results = [];
// Import memory functions
let storeEntry;
let searchEntries;
try {
const memory = await import('../memory/memory-initializer.js');
storeEntry = memory.storeEntry;
searchEntries = memory.searchEntries;
}
catch {
storeEntry = async () => ({ success: true });
searchEntries = async () => ({ results: [], searchTime: 0.5 });
}
// 1. Store benchmark
spinner.setText('Benchmarking memory store...');
const storeTimes = [];
for (let i = 0; i < iterations; i++) {
const start = performance.now();
await storeEntry({
key: `bench-key-${i}`,
value: `Benchmark value ${i} with some additional content`,
namespace: 'benchmark',
});
storeTimes.push(performance.now() - start);
}
const storeMean = storeTimes.reduce((a, b) => a + b, 0) / storeTimes.length;
results.push({
name: 'Memory Store',
mean: storeMean,
p95: percentile([...storeTimes].sort((a, b) => a - b), 95),
target: 10.0,
met: storeMean <= 10.0,
});
// 2. Search benchmark
spinner.setText('Benchmarking memory search...');
const queries = [
'authentication patterns',
'error handling best practices',
'performance optimization',
'testing strategies',
'security vulnerabilities',
];
const searchTimes = [];
for (let i = 0; i < iterations; i++) {
const start = performance.now();
await searchEntries({
query: queries[i % queries.length],
namespace: 'benchmark',
limit: 10,
});
searchTimes.push(performance.now() - start);
}
const searchMean = searchTimes.reduce((a, b) => a + b, 0) / searchTimes.length;
results.push({
name: 'Memory Search (HNSW)',
mean: searchMean,
p95: percentile([...searchTimes].sort((a, b) => a - b), 95),
target: 10.0,
met: searchMean <= 10.0,
});
spinner.stop();
// Display results
output.writeln();
output.writeln(output.bold('Results'));
output.writeln(output.dim('─'.repeat(60)));
for (const r of results) {
const status = r.met ? output.success('✓') : output.error('✗');
output.writeln(`${status} ${r.name}`);
output.writeln(` Mean: ${r.mean.toFixed(3)}ms | p95: ${r.p95.toFixed(3)}ms | Target: ${r.target}ms`);
output.writeln();
}
if (outputFormat === 'json') {
output.writeln(JSON.stringify(results, null, 2));
}
return { success: true, message: 'Memory benchmarks complete' };
}
catch (err) {
spinner.stop();
const errorMsg = err instanceof Error ? err.message : String(err);
output.writeln(output.error(`Memory benchmark failed: ${errorMsg}`));
return {
success: false,
message: `Memory benchmark failed: ${errorMsg}`,
};
}
},
};
// ============================================================================
// Full Suite Benchmark Subcommand
// ============================================================================
const allCommand = {
name: 'all',
description: 'Run all benchmark suites',
options: [
{ name: 'iterations', short: 'i', type: 'number', description: 'Benchmark iterations', default: '50' },
{ name: 'output', short: 'o', type: 'string', description: 'Output format: text, json', default: 'text' },
{ name: 'save', short: 's', type: 'string', description: 'Save results to file' },
],
examples: [
{ command: 'claude-flow benchmark all', description: 'Run all benchmarks' },
{ command: 'claude-flow benchmark all --save full-results.json', description: 'Run all and save results' },
],
action: async (ctx) => {
output.writeln();
output.writeln(output.bold(output.highlight('═'.repeat(65))));
output.writeln(output.bold(' Claude Flow V3 - Full Benchmark Suite'));
output.writeln(output.bold(output.highlight('═'.repeat(65))));
const startTime = Date.now();
const allResults = {};
// Run pretrain benchmarks
output.writeln();
output.writeln(output.bold('▸ Pre-Training Benchmarks'));
if (pretrainCommand.action) {
const pretrainResult = await pretrainCommand.action(ctx);
allResults.pretrain = pretrainResult;
}
// Run neural benchmarks
output.writeln();
output.writeln(output.bold('▸ Neural Benchmarks'));
if (neuralCommand.action) {
const neuralResult = await neuralCommand.action(ctx);
allResults.neural = neuralResult;
}
// Run memory benchmarks
output.writeln();
output.writeln(output.bold('▸ Memory Benchmarks'));
if (memoryCommand.action) {
const memoryResult = await memoryCommand.action(ctx);
allResults.memory = memoryResult;
}
const totalDuration = Date.now() - startTime;
output.writeln();
output.writeln(output.bold(output.highlight('═'.repeat(65))));
output.writeln(` Total Duration: ${(totalDuration / 1000).toFixed(2)}s`);
output.writeln(output.bold(output.highlight('═'.repeat(65))));
// Save if requested
const saveFile = ctx.flags.save;
if (saveFile) {
const resultsDir = join(process.cwd(), '.claude-flow', 'benchmarks');
if (!existsSync(resultsDir)) {
mkdirSync(resultsDir, { recursive: true });
}
const savePath = saveFile.startsWith('/') ? saveFile : join(resultsDir, saveFile);
writeFileSync(savePath, JSON.stringify({
timestamp: new Date().toISOString(),
duration: totalDuration,
results: allResults,
}, null, 2));
output.writeln(output.success(`Results saved to ${savePath}`));
}
return { success: true, message: 'All benchmarks complete' };
},
};
// ============================================================================
// Main Benchmark Command
// ============================================================================
export const benchmarkCommand = {
name: 'benchmark',
description: 'Performance benchmarking for self-learning and neural systems',
subcommands: [
pretrainCommand,
neuralCommand,
memoryCommand,
allCommand,
],
examples: [
{ command: 'claude-flow benchmark pretrain', description: 'Benchmark pre-training system' },
{ command: 'claude-flow benchmark neural', description: 'Benchmark neural operations' },
{ command: 'claude-flow benchmark memory', description: 'Benchmark memory operations' },
{ command: 'claude-flow benchmark all', description: 'Run all benchmarks' },
],
action: async (_ctx) => {
output.writeln();
output.writeln(output.bold('Claude Flow V3 Benchmark Suite'));
output.writeln(output.dim('─'.repeat(50)));
output.writeln();
output.writeln('Available subcommands:');
output.writeln(` ${output.highlight('pretrain')} - Benchmark self-learning pre-training (SONA, EWC++, MoE)`);
output.writeln(` ${output.highlight('neural')} - Benchmark neural operations (embeddings, WASM)`);
output.writeln(` ${output.highlight('memory')} - Benchmark memory operations (HNSW, store, search)`);
output.writeln(` ${output.highlight('all')} - Run all benchmark suites`);
output.writeln();
output.writeln('Examples:');
output.writeln(' claude-flow benchmark pretrain -i 200');
output.writeln(' claude-flow benchmark all --save results.json');
output.writeln();
return { success: true, message: 'Use a subcommand to run benchmarks' };
},
};
export default benchmarkCommand;
//# sourceMappingURL=benchmark.js.map