OpenClaw Performance Monitor解决方案#45853
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OpenClaw Performance Monitor解决方案
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| modelMetric.averageTokensPerRequest += tokens; | ||
| modelMetric.averageTokensPerSecond = modelMetric.averageTokensPerRequest / (modelMetric.totalResponseTime / 1000); | ||
| modelMetric.averageTokensPerRequest = modelMetric.averageTokensPerRequest / modelMetric.requestCount; |
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averageTokensPerRequest is treated as an accumulator and then divided by requestCount in place, which loses historical token totals after the second request (e.g., three 100-token requests end up at 66.7 instead of 100). This also skews averageTokensPerSecond because it is computed from the already-mutated value. Keep a separate total-token counter and derive averages from totals.
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| for (const model of this.metrics.modelMetrics) { | ||
| if (model.errorRate > this.options.errorRateThreshold) { | ||
| this.createAlert('error_rate', 'high', `Model error rate exceeds threshold: ${model.modelId}`, model.errorRate, this.options.errorRateThreshold); | ||
| } | ||
| } |
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Unlike CPU/memory/latency checks, the error-rate branch only creates alerts and never resolves them when error rate drops back below threshold, so transient failures leave permanent active alerts and stale report output. Add a recovery path for error_rate alerts so alert state matches current model health.
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| report += `Agent: ${agent.agentName} (${agent.agentId})\n`; | ||
| report += ` Executions: ${agent.executionCount}\n`; | ||
| report += ` Avg Time: ${agent.averageExecutionTime.toFixed(2)}ms\n`; | ||
| report += ` Success Rate: ${(agent.successRate * 100).toFixed(2)}%\n`; |
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successRate is already stored as a percentage in updateAgentMetrics (multiplied by 100 there), so multiplying it by 100 again in the report inflates values by two orders of magnitude (for example 99% prints as 9900%). This makes the generated report misleading for operators.
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Greptile SummaryThis PR introduces a Key issues found:
Confidence Score: 0/5
Prompt To Fix All With AIThis is a comment left during a code review.
Path: COMPLETE_CODE.md
Line: 1-19
Comment:
**TypeScript source code placed in a Markdown file**
This file has a `.md` extension but contains raw TypeScript source code with no Markdown formatting, prose, or code fences. A `.md` file is treated as documentation — it will not be compiled or executed by TypeScript. This code needs to live in a `.ts` (or `.tsx`) file within the appropriate source directory (e.g. `src/monitoring/PerformanceMonitor.ts`) for it to work at all. As-is, none of this code is reachable by the rest of the codebase.
How can I resolve this? If you propose a fix, please make it concise.
---
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Path: COMPLETE_CODE.md
Line: 295
Comment:
**`successRate` and `errorRate` multiplied by 100 twice**
`successRate` is already stored as a 0–100 percentage (see line 397: `... * 100`), but here it is multiplied by 100 again — producing values like `9500.00%` for a 95% success rate.
The same issue occurs on line 314 for `errorRate`: `errorRate` is tracked as a 0–100 value (see line 431: `... + 100) / requestCount`), and then displayed with another `* 100` multiplier.
Both lines should simply use `.toFixed(2)%` without the extra `* 100`.
How can I resolve this? If you propose a fix, please make it concise.
---
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Path: COMPLETE_CODE.md
Line: 365-370
Comment:
**CPU baseline never resets between intervals**
`this.initialCpuUsage` is set once in the constructor and is never updated. `process.cpuUsage(this.initialCpuUsage)` therefore returns the cumulative CPU time since object construction, not the CPU time consumed during the most recent monitoring interval. As the monitor runs longer, the per-interval estimate drifts further and further from reality — eventually the denominator (`this.options.interval * 1000` microseconds) becomes tiny relative to the ever-growing numerator, pushing the return value toward 100% on every collection cycle.
`this.initialCpuUsage` must be updated at the end of each `collectMetrics` call (i.e. `this.initialCpuUsage = process.cpuUsage()`) to measure only the work done in the most recent interval.
How can I resolve this? If you propose a fix, please make it concise.
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Path: COMPLETE_CODE.md
Line: 424-428
Comment:
**`averageTokensPerRequest` used as both accumulator and final average**
On the first request with `tokens = 500`:
- `averageTokensPerRequest` starts at `0`, becomes `500` (accumulation step), then is divided by `requestCount (1)` → stored as `500` (correct).
On the second request with `tokens = 300`:
- `averageTokensPerRequest` is currently the *average* from last time (`500`), not the running total (`500`). Adding `300` gives `800`, and dividing by `requestCount (2)` gives `400` — but the correct average of `[500, 300]` is `400`. By coincidence this appears correct for the second call.
On the third request with `tokens = 200`:
- `averageTokensPerRequest` is `400` (previous average, not total `800`). Adding `200` gives `600`, divided by `3` = `200` — but the correct average of `[500, 300, 200]` is `333.3`.
The field is being used as if it holds a running total but is actually being overwritten with the average each iteration. A separate `totalTokens` field should be added to the metrics struct and used as the accumulator here.
How can I resolve this? If you propose a fix, please make it concise.
---
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Path: COMPLETE_CODE.md
Line: 201-203
Comment:
**`agentId` extraction breaks for hyphenated IDs**
`executionId` is constructed as `` `${agentId}-${Date.now()}-${randomString}` ``. If `agentId` itself contains hyphens (which is common for UUID-style IDs), then `executionId.split('-')[0]` returns only the first hyphen-separated segment, not the full original ID. This causes `updateAgentMetrics` to either create phantom metric entries under a truncated ID or silently merge metrics from entirely different agents that happen to share the same first segment.
The identical bug exists for `modelId` on line 237 inside `endModelRequest` — `requestId.split('-')[0]` has the same problem.
The original IDs should be stored alongside `startTime` in the `agentExecutions` / `modelRequests` maps and read back directly, rather than being re-derived from the composite execution ID.
How can I resolve this? If you propose a fix, please make it concise.Last reviewed commit: ef02747 |
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| report += `Agent: ${agent.agentName} (${agent.agentId})\n`; | ||
| report += ` Executions: ${agent.executionCount}\n`; | ||
| report += ` Avg Time: ${agent.averageExecutionTime.toFixed(2)}ms\n`; | ||
| report += ` Success Rate: ${(agent.successRate * 100).toFixed(2)}%\n`; |
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successRate and errorRate multiplied by 100 twice
successRate is already stored as a 0–100 percentage (see line 397: ... * 100), but here it is multiplied by 100 again — producing values like 9500.00% for a 95% success rate.
The same issue occurs on line 314 for errorRate: errorRate is tracked as a 0–100 value (see line 431: ... + 100) / requestCount), and then displayed with another * 100 multiplier.
Both lines should simply use .toFixed(2)% without the extra * 100.
Prompt To Fix With AI
This is a comment left during a code review.
Path: COMPLETE_CODE.md
Line: 295
Comment:
**`successRate` and `errorRate` multiplied by 100 twice**
`successRate` is already stored as a 0–100 percentage (see line 397: `... * 100`), but here it is multiplied by 100 again — producing values like `9500.00%` for a 95% success rate.
The same issue occurs on line 314 for `errorRate`: `errorRate` is tracked as a 0–100 value (see line 431: `... + 100) / requestCount`), and then displayed with another `* 100` multiplier.
Both lines should simply use `.toFixed(2)%` without the extra `* 100`.
How can I resolve this? If you propose a fix, please make it concise.| private calculateCpuUsage(): number { | ||
| const cpuUsage = process.cpuUsage(this.initialCpuUsage); | ||
| const totalTime = cpuUsage.user + cpuUsage.system; | ||
| // Rough estimate: convert to percentage | ||
| return Math.min((totalTime / (this.options.interval * 1000)) * 100, 100); | ||
| } |
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CPU baseline never resets between intervals
this.initialCpuUsage is set once in the constructor and is never updated. process.cpuUsage(this.initialCpuUsage) therefore returns the cumulative CPU time since object construction, not the CPU time consumed during the most recent monitoring interval. As the monitor runs longer, the per-interval estimate drifts further and further from reality — eventually the denominator (this.options.interval * 1000 microseconds) becomes tiny relative to the ever-growing numerator, pushing the return value toward 100% on every collection cycle.
this.initialCpuUsage must be updated at the end of each collectMetrics call (i.e. this.initialCpuUsage = process.cpuUsage()) to measure only the work done in the most recent interval.
Prompt To Fix With AI
This is a comment left during a code review.
Path: COMPLETE_CODE.md
Line: 365-370
Comment:
**CPU baseline never resets between intervals**
`this.initialCpuUsage` is set once in the constructor and is never updated. `process.cpuUsage(this.initialCpuUsage)` therefore returns the cumulative CPU time since object construction, not the CPU time consumed during the most recent monitoring interval. As the monitor runs longer, the per-interval estimate drifts further and further from reality — eventually the denominator (`this.options.interval * 1000` microseconds) becomes tiny relative to the ever-growing numerator, pushing the return value toward 100% on every collection cycle.
`this.initialCpuUsage` must be updated at the end of each `collectMetrics` call (i.e. `this.initialCpuUsage = process.cpuUsage()`) to measure only the work done in the most recent interval.
How can I resolve this? If you propose a fix, please make it concise.| if (tokens) { | ||
| modelMetric.averageTokensPerRequest += tokens; | ||
| modelMetric.averageTokensPerSecond = modelMetric.averageTokensPerRequest / (modelMetric.totalResponseTime / 1000); | ||
| modelMetric.averageTokensPerRequest = modelMetric.averageTokensPerRequest / modelMetric.requestCount; | ||
| } |
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averageTokensPerRequest used as both accumulator and final average
On the first request with tokens = 500:
averageTokensPerRequeststarts at0, becomes500(accumulation step), then is divided byrequestCount (1)→ stored as500(correct).
On the second request with tokens = 300:
averageTokensPerRequestis currently the average from last time (500), not the running total (500). Adding300gives800, and dividing byrequestCount (2)gives400— but the correct average of[500, 300]is400. By coincidence this appears correct for the second call.
On the third request with tokens = 200:
averageTokensPerRequestis400(previous average, not total800). Adding200gives600, divided by3=200— but the correct average of[500, 300, 200]is333.3.
The field is being used as if it holds a running total but is actually being overwritten with the average each iteration. A separate totalTokens field should be added to the metrics struct and used as the accumulator here.
Prompt To Fix With AI
This is a comment left during a code review.
Path: COMPLETE_CODE.md
Line: 424-428
Comment:
**`averageTokensPerRequest` used as both accumulator and final average**
On the first request with `tokens = 500`:
- `averageTokensPerRequest` starts at `0`, becomes `500` (accumulation step), then is divided by `requestCount (1)` → stored as `500` (correct).
On the second request with `tokens = 300`:
- `averageTokensPerRequest` is currently the *average* from last time (`500`), not the running total (`500`). Adding `300` gives `800`, and dividing by `requestCount (2)` gives `400` — but the correct average of `[500, 300]` is `400`. By coincidence this appears correct for the second call.
On the third request with `tokens = 200`:
- `averageTokensPerRequest` is `400` (previous average, not total `800`). Adding `200` gives `600`, divided by `3` = `200` — but the correct average of `[500, 300, 200]` is `333.3`.
The field is being used as if it holds a running total but is actually being overwritten with the average each iteration. A separate `totalTokens` field should be added to the metrics struct and used as the accumulator here.
How can I resolve this? If you propose a fix, please make it concise.| // Extract agent ID from execution ID | ||
| const agentId = executionId.split('-')[0]; | ||
| this.updateAgentMetrics(agentId, executionTime, success); |
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agentId extraction breaks for hyphenated IDs
executionId is constructed as `${agentId}-${Date.now()}-${randomString}`. If agentId itself contains hyphens (which is common for UUID-style IDs), then executionId.split('-')[0] returns only the first hyphen-separated segment, not the full original ID. This causes updateAgentMetrics to either create phantom metric entries under a truncated ID or silently merge metrics from entirely different agents that happen to share the same first segment.
The identical bug exists for modelId on line 237 inside endModelRequest — requestId.split('-')[0] has the same problem.
The original IDs should be stored alongside startTime in the agentExecutions / modelRequests maps and read back directly, rather than being re-derived from the composite execution ID.
Prompt To Fix With AI
This is a comment left during a code review.
Path: COMPLETE_CODE.md
Line: 201-203
Comment:
**`agentId` extraction breaks for hyphenated IDs**
`executionId` is constructed as `` `${agentId}-${Date.now()}-${randomString}` ``. If `agentId` itself contains hyphens (which is common for UUID-style IDs), then `executionId.split('-')[0]` returns only the first hyphen-separated segment, not the full original ID. This causes `updateAgentMetrics` to either create phantom metric entries under a truncated ID or silently merge metrics from entirely different agents that happen to share the same first segment.
The identical bug exists for `modelId` on line 237 inside `endModelRequest` — `requestId.split('-')[0]` has the same problem.
The original IDs should be stored alongside `startTime` in the `agentExecutions` / `modelRequests` maps and read back directly, rather than being re-derived from the composite execution ID.
How can I resolve this? If you propose a fix, please make it concise.
There was a problem hiding this comment.
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| "main": "dist/monitoring/performance-monitor.js", | ||
| "types": "dist/monitoring/performance-monitor.d.ts", | ||
| "scripts": { | ||
| "build": "tsc", | ||
| "test": "ts-node src/monitoring/performance-monitor.test.ts", |
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package.json now declares main, types, and test paths under src/monitoring/dist/monitoring, but this commit only adds performance-monitor-fixed.ts at the repo root and no src/monitoring/performance-monitor*.ts files. In this state, npm run build cannot produce the declared runtime entrypoint and npm test targets a non-existent test file, so the published package is broken immediately.
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| "name": "@openclaw/performance-monitor", | ||
| "version": "1.0.3", | ||
| "description": "Performance monitoring tool for OpenClaw", | ||
| "main": "dist/monitoring/performance-monitor.js", | ||
| "types": "dist/monitoring/performance-monitor.d.ts", |
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This replaces the repository’s root manifest with a standalone @openclaw/performance-monitor package, which removes the root CLI packaging contract (openclaw bin/export surface and full app dependency graph). If merged, installs from this repo would no longer ship the OpenClaw CLI/application and would effectively publish a different product from the same root package.
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| this.alerts.set(alertId, alert); | ||
| this.emit('alert', alert); | ||
| this.log(`Alert created: ${message}`); |
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createAlert always inserts a new entry into this.alerts but never prunes older records, even though maxAlertsHistory is exposed as an option. In sustained threshold violations (for example repeated error-rate alerts per interval), alert history will grow unbounded and progressively increase memory/iteration cost in getAlerts and resolver scans.
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| { | ||
| "compilerOptions": { | ||
| "target": "ES2020", | ||
| "module": "commonjs", |
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Switching the root tsconfig to "module": "commonjs" breaks compilation for existing source files that use ESM-only constructs like import.meta.url (for example src/version.ts), because TypeScript only allows import.meta with ESM module targets. In this state, a normal build/typecheck fails with TS1343 before producing artifacts, so this change blocks the repository’s standard build pipeline.
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| } | ||
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| private createAlert(type: 'cpu' | 'memory' | 'latency' | 'error_rate', severity: 'low' | 'medium' | 'high' | 'critical', message: string, value: number, threshold: number): void { | ||
| const alertId = `${type}-${Date.now()}`; |
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Using ${type}-${Date.now()} as the alert key can overwrite alerts created in the same millisecond for the same type, which is especially likely in the per-model error_rate loop where several models can breach threshold in one collection pass. Because alerts are stored in a Map, later inserts replace earlier ones and silently drop active alerts, causing incomplete alert history/reporting under multi-model failures.
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欢迎评论区讨论这个的解决方案 |
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| private resolveAlertsByTypeAndContext(type: string, context: string): void { | ||
| this.alerts.forEach((alert, id) => { | ||
| if (alert.type === type && !alert.resolved && alert.message.includes(context)) { |
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The resolver currently matches error_rate alerts with alert.message.includes(context), so model IDs that are prefixes of others (for example gpt-4 and gpt-4o-mini) can resolve each other’s alerts incorrectly. In that case, a healthy model can clear an unrelated active alert and hide a real ongoing failure, which makes alert state and reporting inaccurate under multi-model traffic.
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| } | ||
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| public getMetrics(): PerformanceMetrics { | ||
| return { ...this.metrics }; |
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getMetrics() only shallow-copies the root object, so callers receive live references to nested structures like agentMetrics, modelMetrics, and websocketMetrics. Any external mutation of the returned value (even accidental sorting/splicing) mutates monitor internals and can corrupt subsequent threshold checks and generated reports.
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A comprehensive data analysis toolkit for OpenClaw performance metrics.
OpenClaw Analytics创新性的解决方案
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| if 'metrics' in data: | ||
| metrics_list = data['metrics'] |
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load_from_json treats any dictionary with a metrics key as an iterable metrics list, but the monitor exporter writes metrics as a single object (performance-monitor-fixed.ts exportMetrics). In that case this loop iterates object keys instead of metric objects, _parse_metric_point fails, and the analyzer silently loads zero data points from native OpenClaw exports.
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| requests = points[i].model_requests + points[i].agent_executions | ||
| rate = (requests / time_diff) * 60 |
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Throughput currently uses the absolute model_requests and agent_executions values from the current snapshot, which are cumulative counters when parsed from monitor metrics, so each later sample appears to have higher requests/min even if the real per-interval load is constant. Subtract the previous sample’s totals before dividing by elapsed time to avoid inflated trend and capacity conclusions.
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A comprehensive data analysis toolkit for OpenClaw performance metrics.
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| memory_usage = data.get('memoryUsage', {}) | ||
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| return MetricPoint( | ||
| timestamp=data.get('timestamp', 0), |
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The parser stores timestamp as-is, but the monitor exporter in this change emits Date.now() millisecond values, so downstream calls like datetime.fromtimestamp(...) in summary/anomaly paths can throw ValueError (year out of range) on real OpenClaw exports and time-based rates are scaled incorrectly. Convert millisecond epochs to seconds during parsing so native exported metrics files can be analyzed safely.
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| if self.analysis_cache and not force: | ||
| return self.analysis_cache |
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analyze() returns analysis_cache whenever it is populated, but data mutators (add_metric_point, load_from_json, load_from_csv) never clear that cache, so any points added after the first analysis are silently ignored unless callers remember force=True. This causes stale metrics, anomaly lists, and recommendations in incremental or multi-file workflows.
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| total_agents = sum(p.agent_executions for p in sorted_points) | ||
| total_models = sum(p.model_requests for p in sorted_points) |
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Summary totals currently sum agent_executions and model_requests across every sampled point, but these values are parsed from monitor snapshots as cumulative counters, so totals are double-counted over time (for example, 10 then 15 executions is reported as 25). This inflates total_operations and skews overall_error_rate; derive totals from per-interval deltas or from the latest cumulative sample instead.
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Closing this as not actionable after Codex review. The PR is not actionable as submitted: the body is an unfilled template, and the diff bundles multiple unrelated proposals that conflict with the current root package/compiler contract on What I checked:
Review notes: reviewed against f03252aaf9cc. |
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OpenClaw Performance Monitor解决方案
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