ThoughtMCP
by keyurgolani
- ThoughtMcp
- docs
- performance
benchmarking-guide.md•33.6 kB
# Performance Benchmarking and Optimization Guide
## Overview
This guide provides comprehensive benchmarking methodologies and optimization strategies for the ThoughtMCP cognitive architecture. It covers performance metrics, benchmarking tools, optimization techniques, and scaling considerations.
## Performance Metrics
### Core Performance Indicators
#### 1. Latency Metrics
- **Total Response Time**: End-to-end processing time
- **Component Processing Time**: Time spent in each cognitive component
- **Memory Operation Time**: Time for memory storage and retrieval
- **Network Overhead**: MCP protocol communication time
#### 2. Throughput Metrics
- **Requests Per Second (RPS)**: Maximum sustainable request rate
- **Concurrent Sessions**: Number of simultaneous client sessions
- **Memory Operations Per Second**: Rate of memory storage/retrieval
- **Consolidation Throughput**: Rate of memory consolidation processing
#### 3. Resource Utilization
- **CPU Usage**: Processor utilization during cognitive processing
- **Memory Usage**: RAM consumption per session and total
- **Storage I/O**: Disk read/write operations for memory persistence
- **Network Bandwidth**: Data transfer rates for MCP communication
#### 4. Quality Metrics
- **Confidence Scores**: Average confidence in reasoning outputs
- **Coherence Ratings**: Logical consistency of reasoning chains
- **Bias Detection Rate**: Frequency of cognitive bias identification
- **Memory Retrieval Accuracy**: Precision of memory recall operations
## Benchmarking Framework
### Automated Benchmarking Suite
```typescript
import { ThoughtMCPClient } from "../examples/cognitive-client.js";
import { performance } from "perf_hooks";
interface BenchmarkResult {
metric: string;
value: number;
unit: string;
timestamp: number;
metadata?: any;
}
class CognitiveBenchmark {
private client: ThoughtMCPClient;
private results: BenchmarkResult[] = [];
constructor() {
this.client = new ThoughtMCPClient();
}
async runComprehensiveBenchmark(): Promise<BenchmarkReport> {
console.log("🚀 Starting ce cognitive benchmark...");
await this.client.connect();
try {
// Latency benchmarks
await this.benchmarkLatency();
// Throughput benchmarks
await this.benchmarkThroughput();
// Memory performance benchmarks
await this.benchmarkMemoryPerformance();
// Quality benchmarks
await this.benchmarkQuality();
// Resource utilization benchmarks
await this.benchmarkResourceUsage();
return this.generateReport();
} finally {
await this.client.disconnect();
}
}
private async benchmarkLatency(): Promise<void> {
console.log("⏱️ Benchmarking latency across processing modes...");
const testCases = [
{
name: "Simple Intuitive",
input: "What is 2+2?",
mode: "intuitive",
expectedLatency: 100,
},
{
name: "Complex Deliberative",
input:
"Analyze the ethical implications of artificial general intelligence",
mode: "deliberative",
expectedLatency: 800,
},
{
name: "Creative Problem Solving",
input: "Design an innovative solution for urban transportation",
mode: "creative",
expectedLatency: 600,
},
{
name: "Analytical Reasoning",
input:
"Evaluate the statistical significance of this dataset: [1,2,3,4,5,6,7,8,9,10]",
mode: "analytical",
expectedLatency: 500,
},
];
for (const testCase of testCases) {
const iterations = 10;
const latencies: number[] = [];
for (let i = 0; i < iterations; i++) {
const startTime = performance.now();
try {
await (this.client as any).cognitiveClient.client.request({
method: "tools/call",
params: {
name: "think",
arguments: {
input: testCase.input,
mode: testCase.mode,
},
},
});
const latency = performance.now() - startTime;
latencies.push(latency);
} catch (error) {
console.error(`❌ Error in ${testCase.name}: ${error}`);
}
}
const avgLatency =
latencies.reduce((a, b) => a + b, 0) / latencies.length;
const p95Latency = this.percentile(latencies, 95);
const p99Latency = this.percentile(latencies, 99);
this.results.push({
metric: `${testCase.name}_avg_latency`,
value: avgLatency,
unit: "ms",
timestamp: Date.now(),
metadata: { expected: testCase.expectedLatency, mode: testCase.mode },
});
this.results.push({
metric: `${testCase.name}_p95_latency`,
value: p95Latency,
unit: "ms",
timestamp: Date.now(),
});
this.results.push({
metric: `${testCase.name}_p99_latency`,
value: p99Latency,
unit: "ms",
timestamp: Date.now(),
});
console.log(
` ${testCase.name}: ${avgLatency.toFixed(
2
)}ms avg, ${p95Latency.toFixed(2)}ms p95`
);
}
}
private async benchmarkThroughput(): Promise<void> {
console.log("🔄 Benchmarking throughput and concurrency...");
const concurrencyLevels = [1, 5, 10, 20, 50];
const testDuration = 30000; // 30 ss
for (const concurrency of concurrencyLevels) {
console.log(` Testing concurrency level: ${concurrency}`);
const startTime = Date.now();
let completedRequests = 0;
let errors = 0;
const workers = Array.from({ length: concurrency }, async () => {
while (Date.now() - startTime < testDuration) {
try {
await (this.client as any).cognitiveClient.client.request({
method: "tools/call",
params: {
name: "think",
arguments: {
input: "Quick test question",
mode: "intuitive",
},
},
});
completedRequests++;
} catch (error) {
errors++;
}
}
});
await Promise.all(workers);
const actualDuration = Date.now() - startTime;
const rps = (completedRequests / actualDuration) * 1000;
const errorRate = errors / (completedRequests + errors);
this.results.push({
metric: `throughput_concurrency_${concurrency}`,
value: rps,
unit: "rps",
timestamp: Date.now(),
metadata: { concurrency, errors, errorRate },
});
console.log(
` RPS: ${rps.toFixed(2)}, Error Rate: ${(errorRate * 100).toFixed(
2
)}%`
);
}
}
private async benchmarkMemoryPerformance(): Promise<void> {
console.log("🧠 Benchmarking memory system performance...");
// Memory storage benchmark
const memoryItems = 1000;
const startTime = performance.now();
for (let i = 0; i < memoryItems; i++) {
try {
await (this.client as any).cognitiveClient.client.request({
method: "tools/call",
params: {
name: "remember",
arguments: {
content: `Test memory item ${i}: This is sample content for benchmarking`,
type: i % 2 === 0 ? "episodic" : "semantic",
importance: Math.random(),
},
},
});
} catch (error) {
console.error(`Memory storage error: ${error}`);
}
}
const storageTime = performance.now() - startTime;
const storageRate = (memoryItems / storageTime) * 1000;
this.results.push({
metric: "memory_storage_rate",
value: storageRate,
unit: "items/sec",
timestamp: Date.now(),
});
// Memory retrieval benchmark
const retrievalQueries = [
"test memory",
"sample content",
"benchmarking",
"episodic",
"semantic",
];
const retrievalStartTime = performance.now();
let totalRetrievals = 0;
for (const query of retrievalQueries) {
for (let i = 0; i < 100; i++) {
try {
await (this.client as any).cognitiveClient.client.request({
method: "tools/call",
params: {
name: "recall",
arguments: {
cue: query,
max_results: 10,
},
},
});
totalRetrievals++;
} catch (error) {
console.error(`Memory retrieval error: ${error}`);
}
}
}
const retrievalTime = performance.now() - retrievalStartTime;
const retrievalRate = (totalRetrievals / retrievalTime) * 1000;
this.results.push({
metric: "memory_retrieval_rate",
value: retrievalRate,
unit: "queries/sec",
timestamp: Date.now(),
});
console.log(` Storage Rate: ${storageRate.toFixed(2)} items/sec`);
console.log(` Retrieval Rate: ${retrievalRate.toFixed(2)} queries/sec`);
}
private async benchmarkQuality(): Promise<void> {
console.log("🎯 Benchmarking reasoning quality...");
const qualityTestCases = [
{
input:
"All birds can fly. Penguins are birds. Therefore, penguins can fly.",
expectedBiases: ["hasty_generalization"],
expectedCoherence: 0.3,
},
{
input:
"If it rains, the ground gets wet. The ground is wet. Therefore, it rained.",
expectedBiases: ["affirming_consequent"],
expectedCoherence: 0.4,
},
{
input:
"All mammals are warm-blooded. Whales are mammals. Therefore, whales are warm-blooded.",
expectedBiases: [],
expectedCoherence: 0.9,
},
];
let totalConfidence = 0;
let totalCoherence = 0;
let biasDetectionAccuracy = 0;
for (const testCase of qualityTestCases) {
try {
const thinkResult = await (
this.client as any
).cognitiveClient.client.request({
method: "tools/call",
params: {
name: "think",
arguments: {
input: testCase.input,
mode: "analytical",
enable_metacognition: true,
},
},
});
const analysisResult = await (
this.client as any
).cognitiveClient.client.request({
method: "tools/call",
params: {
name: "analyze_reasoning",
arguments: {
reasoning_steps: thinkResult.content?.reasoning_path || [],
},
},
});
totalConfidence += thinkResult.content?.confidence || 0;
totalCoherence += analysisResult.content?.coherence_score || 0;
// Check bias detection accuracy
const detectedBiases =
analysisResult.content?.biases?.map((b: any) => b.bias_type) || [];
const expectedBiases = testCase.expectedBiases;
const correctDetections = expectedBiases.filter((bias) =>
detectedBiases.includes(bias)
).length;
const accuracy =
expectedBiases.length > 0
? correctDetections / expectedBiases.length
: 1;
biasDetectionAccuracy += accuracy;
} catch (error) {
console.error(`Quality test error: ${error}`);
}
}
const avgConfidence = totalConfidence / qualityTestCases.length;
const avgCoherence = totalCoherence / qualityTestCases.length;
const avgBiasAccuracy = biasDetectionAccuracy / qualityTestCases.length;
this.results.push({
metric: "average_confidence",
value: avgConfidence,
unit: "score",
timestamp: Date.now(),
});
this.results.push({
metric: "average_coherence",
value: avgCoherence,
unit: "score",
timestamp: Date.now(),
});
this.results.push({
metric: "bias_detection_accuracy",
value: avgBiasAccuracy,
unit: "accuracy",
timestamp: Date.now(),
});
console.log(` Average Confidence: ${avgConfidence.toFixed(3)}`);
console.log(` Average Coherence: ${avgCoherence.toFixed(3)}`);
console.log(` Bias Detection Accuracy: ${avgBiasAccuracy.toFixed(3)}`);
}
private async benchmarkResourceUsage(): Promise<void> {
console.log("💾 Benchmarking resource utilization...");
const initialMemory = process.memoryUsage();
const startTime = Date.now();
// Simulate sustained load
const loadDuration = 60000; // 1 minute
const requestInterval = 100; // 100ms between requests
const loadTest = setInterval(async () => {
try {
await (this.client as any).cognitiveClient.client.request({
method: "tools/call",
params: {
name: "think",
arguments: {
input: "Resource usage test query",
mode: "balanced",
},
},
});
} catch (error) {
// Ignore errors during load test
}
}, requestInterval);
// Monitor resource usage
const resourceSamples: any[] = [];
const monitorInterval = setInterval(() => {
const memUsage = process.memoryUsage();
const cpuUsage = process.cpuUsage();
resourceSamples.push({
timestamp: Date.now(),
memory: memUsage,
cpu: cpuUsage,
});
}, 1000);
// Wait for load test to complete
await new Promise((resolve) => setTimeout(resolve, loadDuration));
clearInterval(loadTest);
clearInterval(monitorInterval);
// Calculate resource metrics
const finalMemory = process.memoryUsage();
const memoryGrowth = finalMemory.heapUsed - initialMemory.heapUsed;
const avgMemoryUsage =
resourceSamples.reduce((sum, sample) => sum + sample.memory.heapUsed, 0) /
resourceSamples.length;
this.results.push({
metric: "memory_growth",
value: memoryGrowth / 1024 / 1024, // MB
unit: "MB",
timestamp: Date.now(),
});
this.results.push({
metric: "average_memory_usage",
value: avgMemoryUsage / 1024 / 1024, // MB
unit: "MB",
timestamp: Date.now(),
});
console.log(
` Memory Growth: ${(memoryGrowth / 1024 / 1024).toFixed(2)} MB`
);
console.log(
` Average Memory Usage: ${(avgMemoryUsage / 1024 / 1024).toFixed(2)} MB`
);
}
private percentile(values: number[], p: number): number {
const sorted = values.slice().sort((a, b) => a - b);
const index = Math.ceil((p / 100) * sorted.length) - 1;
return sorted[index];
}
private generateReport(): BenchmarkReport {
return {
timestamp: Date.now(),
results: this.results,
summary: this.generateSummary(),
recommendations: this.generateRecommendations(),
};
}
private generateSummary(): BenchmarkSummary {
const latencyResults = this.results.filter((r) =>
r.metric.includes("latency")
);
const throughputResults = this.results.filter((r) =>
r.metric.includes("throughput")
);
const qualityResults = this.results.filter(
(r) => r.metric.includes("confidence") || r.metric.includes("coherence")
);
return {
avgLatency:
latencyResults.reduce((sum, r) => sum + r.value, 0) /
latencyResults.length,
maxThroughput: Math.max(...throughputResults.map((r) => r.value)),
avgQuality:
qualityResults.reduce((sum, r) => sum + r.value, 0) /
qualityResults.length,
resourceEfficiency: this.calculateResourceEfficiency(),
};
}
private calculateResourceEfficiency(): number {
const memoryResult = this.results.find(
(r) => r.metric === "average_memory_usage"
);
const throughputResult = this.results.find((r) =>
r.metric.includes("throughput")
);
if (!memoryResult || !throughputResult) return 0;
// Efficiency = throughput / memory usage
return throughputResult.value / memoryResult.value;
}
private generateRecommendations(): string[] {
const recommendations: string[] = [];
// Latency recommendations
const avgLatency =
this.results.find((r) => r.metric.includes("avg_latency"))?.value || 0;
if (avgLatency > 500) {
recommendations.push(
"Consider reducing max_depth or using faster processing modes for better latency"
);
}
// Memory recommendations
const memoryGrowth =
this.results.find((r) => r.metric === "memory_growth")?.value || 0;
if (memoryGrowth > 100) {
// 100MB
recommendations.push(
"Memory usage is growing significantly - consider implementing memory cleanup strategies"
);
}
// Quality recommendations
const avgCoherence =
this.results.find((r) => r.metric === "average_coherence")?.value || 0;
if (avgCoherence < 0.7) {
recommendations.push(
"Reasoning coherence is below optimal - consider enabling metacognitive monitoring"
);
}
return recommendations;
}
}
interface BenchmarkReport {
timestamp: number;
results: BenchmarkResult[];
summary: BenchmarkSummary;
recommendations: string[];
}
interface BenchmarkSummary {
avgLatency: number;
maxThroughput: number;
avgQuality: number;
resourceEfficiency: number;
}
export { CognitiveBenchmark };
```
### Benchmark Execution Script
```typescript
// benchmark-runner.ts
import { CognitiveBenchmark } from "./cognitive-benchmark.js";
import fs from "fs/promises";
async function runBenchmarks() {
const benchmark = new CognitiveBenchmark();
console.log("🧠 ThoughtMCP Performance Benchmark Suite");
console.log("==========================================");
try {
const report = await benchmark.runComprehensiveBenchmark();
// Save results
const timestamp = new Date().toISOString().replace(/[:.]/g, "-");
const filename = `benchmark-report-${timestamp}.json`;
await fs.writeFile(filename, JSON.stringify(report, null, 2));
console.log("\n📊 Benchmark Summary:");
console.log(` Average Latency: ${report.summary.avgLatency.toFixed(2)}ms`);
console.log(
` Max Throughput: ${report.summary.maxThroughput.toFixed(2)} RPS`
);
console.log(` Average Quality: ${report.summary.avgQuality.toFixed(3)}`);
console.log(
` Resource Efficiency: ${report.summary.resourceEfficiency.toFixed(2)}`
);
if (report.recommendations.length > 0) {
console.log("\n💡 Recommendations:");
report.recommendations.forEach((rec) => console.log(` • ${rec}`));
}
console.log(`\n📄 Full report saved to: ${filename}`);
} catch (error) {
console.error("❌ Benchmark failed:", error);
process.exit(1);
}
}
if (require.main === module) {
runBenchmarks();
}
```
## Performance Optimization Strategies
### 1. Latency Optimization
#### Component-Level Optimizations
**Sensory Processing**:
```typescript
// Fast configuration for simple inputs
const fastSensoryConfig = {
attention_threshold: 0.5, // Higher threshold = less processing
max_tokens: 500, // Limit input size
pattern_detection: false, // Disable for simple inputs
semantic_chunking: false, // Disable for speed
};
```
**Working Memory**:
```typescript
// Optimized for speed
const fastWorkingMemoryConfig = {
capacity: 5, // Smaller capacity = faster processing
decay_rate: 0.2, // Faster decay = less maintenance
rehearsal_threshold: 0.7, // Higher threshold = less rehearsal
chunking_enabled: false, // Disable for speed
};
```
**Dual Process**:
```typescript
// Bias toward System 1 for speed
const fastDualProcessConfig = {
conflict_threshold: 0.8, // Higher threshold = more System 1
system2_timeout: 1000, // Shorter timeout
mode_bias: "system1", // Prefer fast processing
};
```
#### Pipeline Optimizations
**Early Termination**:
```typescript
class OptimizedCognitiveOrchestrator {
async think(input: string, options: ThinkOptions): Promise<ThoughtResult> {
// Early termination for simple inputs
if (this.isSimpleInput(input)) {
return await this.fastTrack(input);
}
// Progressive processing with confidence checks
let result = await this.system1Process(input);
if (result.confidence > 0.8) {
return result; // Early termination
}
return await this.system2Process(input, result);
}
private isSimpleInput(input: string): boolean {
return (
input.length < 50 &&
!input.includes("?") &&
this.getComplexityScore(input) < 0.3
);
}
}
```
**Parallel Processing**:
```typescript
class ParallelCognitiveProcessor {
async processInParallel(input: CognitiveInput): Promise<ThoughtResult> {
// Process multiple components in parallel
const [sensoryResult, memoryResult, emotionalResult] = await Promise.all([
this.sensoryProcessor.process(input),
this.memorySystem.retrieve(input.cue),
this.emotionalProcessor.assess(input),
]);
// Combine results
return this.combineResults(sensoryResult, memoryResult, emotionalResult);
}
}
```
### 2. Memory Optimization
#### Memory Pool Management
```typescript
class MemoryPoolManager {
private episodicPool: MemoryPool;
private semanticPool: MemoryPool;
private consolidationQueue: ConsolidationQueue;
constructor() {
this.episodicPool = new MemoryPool({
maxSize: 50000,
evictionPolicy: "LRU",
compressionEnabled: true,
});
this.semanticPool = new MemoryPool({
maxSize: 100000,
evictionPolicy: "importance",
compressionEnabled: true,
});
}
async optimizeMemoryUsage(): Promise<void> {
// Compress old memories
await this.compressOldMemories();
// Remove duplicates
await this.deduplicateMemories();
// Consolidate patterns
await this.consolidationEngine.run();
// Garbage collect unused memories
await this.garbageCollect();
}
}
```
#### Efficient Memory Retrieval
```typescript
class OptimizedMemoryRetrieval {
private indexCache: Map<string, MemoryIndex>;
private embeddingCache: Map<string, Float32Array>;
async retrieve(cue: string, options: RetrievalOptions): Promise<Memory[]> {
// Check cache first
const cacheKey = this.generateCacheKey(cue, options);
if (this.indexCache.has(cacheKey)) {
return this.retrieveFromCache(cacheKey);
}
// Use approximate nearest neighbor for speed
const embedding = await this.getOrComputeEmbedding(cue);
const candidates = await this.approximateSearch(
embedding,
options.maxResults * 2
);
// Refine with exact similarity
const results = await this.refineResults(candidates, cue, options);
// Cache results
this.cacheResults(cacheKey, results);
return results;
}
}
```
### 3. Throughput Optimization
#### Connection Pooling
```typescript
class MCPConnectionPool {
private connections: MCPConnection[];
private availableConnections: Queue<MCPConnection>;
private maxConnections: number = 100;
async getConnection(): Promise<MCPConnection> {
if (this.availableConnections.length > 0) {
return this.availableConnections.dequeue();
}
if (this.connections.length < this.maxConnections) {
const connection = await this.createConnection();
this.connections.push(connection);
return connection;
}
// Wait for available connection
return await this.waitForConnection();
}
releaseConnection(connection: MCPConnection): void {
this.availableConnections.enqueue(connection);
}
}
```
#### Request Batching
```typescript
class BatchProcessor {
private batchQueue: CognitiveRequest[] = [];
private batchSize: number = 10;
private batchTimeout: number = 100; // ms
async processBatch(
requests: CognitiveRequest[]
): Promise<CognitiveResponse[]> {
// Group similar requests
const groups = this.groupSimilarRequests(requests);
// Process groups in parallel
const results = await Promise.all(
groups.map((group) => this.processGroup(group))
);
return results.flat();
}
private groupSimilarRequests(
requests: CognitiveRequest[]
): CognitiveRequest[][] {
const groups = new Map<string, CognitiveRequest[]>();
for (const request of requests) {
const key = this.getGroupKey(request);
if (!groups.has(key)) {
groups.set(key, []);
}
groups.get(key)!.push(request);
}
return Array.from(groups.values());
}
}
```
### 4. Resource Optimization
#### CPU Optimization
```typescript
class CPUOptimizedProcessor {
private workerPool: WorkerPool;
constructor() {
this.workerPool = new WorkerPool({
maxWorkers: require("os").cpus().length,
workerScript: "./cognitive-worker.js",
});
}
async processIntensive(task: IntensiveTask): Promise<TaskResult> {
// Offload CPU-intensive tasks to worker threads
return await this.workerPool.execute(task);
}
async processWithPriority(tasks: PrioritizedTask[]): Promise<TaskResult[]> {
// Sort by priority
tasks.sort((a, b) => b.priority - a.priority);
// Process high-priority tasks first
const results: TaskResult[] = [];
for (const task of tasks) {
if (task.priority > 0.8) {
results.push(await this.processImmediately(task));
} else {
results.push(await this.processWhenAvailable(task));
}
}
return results;
}
}
```
#### Memory Optimization
```typescript
class MemoryOptimizedStorage {
private compressionEnabled: boolean = true;
private compressionThreshold: number = 1000; // bytes
async store(data: any): Promise<string> {
const serialized = JSON.stringify(data);
if (
this.compressionEnabled &&
serialized.length > this.compressionThreshold
) {
const compressed = await this.compress(serialized);
return await this.persistCompressed(compressed);
}
return await this.persistRaw(serialized);
}
async retrieve(id: string): Promise<any> {
const stored = await this.getStored(id);
if (stored.compressed) {
const decompressed = await this.decompress(stored.data);
return JSON.parse(decompressed);
}
return JSON.parse(stored.data);
}
}
```
## Scaling Strategies
### Horizontal Scaling
#### Load Balancing
```typescript
class CognitiveLoadBalancer {
private servers: CognitiveServer[];
private currentIndex: number = 0;
async routeRequest(request: CognitiveRequest): Promise<CognitiveResponse> {
const server = this.selectServer(request);
return await server.process(request);
}
private selectServer(request: CognitiveRequest): CognitiveServer {
// Round-robin with health checks
for (let i = 0; i < this.servers.length; i++) {
const serverIndex = (this.currentIndex + i) % this.servers.length;
const server = this.servers[serverIndex];
if (server.isHealthy() && server.canHandle(request)) {
this.currentIndex = (serverIndex + 1) % this.servers.length;
return server;
}
}
throw new Error("No available servers");
}
}
```
#### Distributed Memory
```typescript
class DistributedMemorySystem {
private memoryNodes: MemoryNode[];
private consistentHashing: ConsistentHashRing;
async store(memory: Memory): Promise<void> {
const nodes = this.consistentHashing.getNodes(memory.id, 3); // 3 replicas
await Promise.all(nodes.map((node) => node.store(memory)));
}
async retrieve(cue: string): Promise<Memory[]> {
const nodes = this.consistentHashing.getNodes(cue, 1);
const primaryNode = nodes[0];
try {
return await primaryNode.retrieve(cue);
} catch (error) {
// Fallback to other replicas
const fallbackNodes = this.consistentHashing.getNodes(cue, 3).slice(1);
for (const node of fallbackNodes) {
try {
return await node.retrieve(cue);
} catch (fallbackError) {
continue;
}
}
throw new Error("All memory nodes failed");
}
}
}
```
### Vertical Scaling
#### Resource Allocation
```typescript
class ResourceManager {
private cpuQuota: number;
private memoryQuota: number;
private currentUsage: ResourceUsage;
async allocateResources(task: CognitiveTask): Promise<ResourceAllocation> {
const required = this.estimateRequirements(task);
if (!this.canAllocate(required)) {
await this.freeResources();
}
return this.allocate(required);
}
private async freeResources(): Promise<void> {
// Garbage collect unused memories
await this.memorySystem.garbageCollect();
// Cancel low-priority tasks
await this.taskScheduler.cancelLowPriorityTasks();
// Compress cached data
await this.compressionManager.compressCache();
}
}
```
## Monitoring and Alerting
### Performance Monitoring
```typescript
class PerformanceMonitor {
private metrics: MetricsCollector;
private alerts: AlertManager;
startMonitoring(): void {
// Collect metrics every second
setInterval(() => {
this.collectMetrics();
}, 1000);
// Check alerts every 10 seconds
setInterval(() => {
this.checkAlerts();
}, 10000);
}
private collectMetrics(): void {
const metrics = {
timestamp: Date.now(),
latency: this.measureLatency(),
throughput: this.measureThroughput(),
memory: process.memoryUsage(),
cpu: process.cpuUsage(),
activeConnections: this.getActiveConnections(),
queueLength: this.getQueueLength(),
};
this.metrics.record(metrics);
}
private checkAlerts(): void {
const recentMetrics = this.metrics.getRecent(60000); // Last minute
// High latency alert
const avgLatency = this.calculateAverage(recentMetrics, "latency");
if (avgLatency > 1000) {
this.alerts.trigger("high_latency", { value: avgLatency });
}
// Low throughput alert
const avgThroughput = this.calculateAverage(recentMetrics, "throughput");
if (avgThroughput < 10) {
this.alerts.trigger("low_throughput", { value: avgThroughput });
}
// High memory usage alert
const avgMemory = this.calculateAverage(recentMetrics, "memory.heapUsed");
if (avgMemory > 1024 * 1024 * 1024) {
// 1GB
this.alerts.trigger("high_memory", { value: avgMemory });
}
}
}
```
### Health Checks
```typescript
class HealthChecker {
async checkHealth(): Promise<HealthStatus> {
const checks = await Promise.allSettled([
this.checkCognitiveComponents(),
this.checkMemorySystem(),
this.checkMCPConnection(),
this.checkResourceUsage(),
]);
const results = checks.map((check, index) => ({
component: ["cognitive", "memory", "mcp", "resources"][index],
status: check.status === "fulfilled" ? "healthy" : "unhealthy",
details: check.status === "fulfilled" ? check.value : check.reason,
}));
const overallHealth = results.every((r) => r.status === "healthy")
? "healthy"
: "unhealthy";
return {
status: overallHealth,
timestamp: Date.now(),
components: results,
};
}
private async checkCognitiveComponents(): Promise<ComponentHealth> {
// Test basic thinking functionality
const testResult = await this.orchestrator.think("Health check test", {
mode: "intuitive",
timeout: 5000,
});
return {
responsive: true,
latency: testResult.processingTime,
confidence: testResult.confidence,
};
}
}
```
## Best Practices Summary
### Performance Best Practices
1. **Choose Appropriate Processing Modes**
- Use intuitive mode for simple, familiar tasks
- Use deliberative mode only for complex, important decisions
- Use balanced mode as a safe default
2. **Optimize Memory Usage**
- Set appropriate memory limits and decay rates
- Use importance scoring to prioritize memory retention
- Implement regular memory consolidation
3. **Monitor and Alert**
- Set up comprehensive performance monitoring
- Configure alerts for key metrics (latency, throughput, memory)
- Regularly review and adjust thresholds
4. **Scale Appropriately**
- Start with vertical scaling for simplicity
- Move to horizontal scaling for high throughput requirements
- Use load balancing and distributed memory for large deployments
5. **Optimize for Your Use Case**
- Profile your specific workload patterns
- Tune configuration parameters based on requirements
- Regularly benchmark and optimize
### Configuration Recommendations
#### Development Environment
```typescript
const devConfig = {
mode: "balanced",
max_depth: 8,
temperature: 0.6,
enable_emotion: true,
enable_metacognition: true,
memory: {
episodic_capacity: 1000,
semantic_capacity: 5000,
consolidation_interval: 300000, // 5 minutes
},
};
```
#### Production Environment
```typescript
const prodConfig = {
mode: "balanced",
max_depth: 12,
temperature: 0.7,
enable_emotion: true,
enable_metacognition: true,
memory: {
episodic_capacity: 50000,
semantic_capacity: 100000,
consolidation_interval: 1800000, // 30 minutes
},
performance: {
connection_pool_size: 50,
batch_size: 10,
timeout: 30000,
},
};
```
This comprehensive benchmarking and optimization guide provides the tools and strategies needed to achieve optimal performance with the ThoughtMCP cognitive architecture across different deployment scenarios and use cases.
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