ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

2026-08-20 13:12:50 +00:00
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+# Metrics Module
+
+## Overview
+The `aworld.core.metrics` module provides a unified interface for collecting and exporting metrics. It supports various types of metrics (e.g., counters, histograms) and allows exporting data to different monitoring systems (e.g., Prometheus).
+
+## Key Features
+- **Metric Types**:
+  - `Counter`: A cumulative metric that represents a single numerical value that only ever increases.
+  - `UpDownCounter`: A cumulative metric that can increase or decrease.
+  - `Gauge`: A metric that represents a single numerical value that can arbitrarily go up and down.
+  - `Histogram`: A metric that represents the distribution of a set of values.
+
+- **Adapters**:
+  - `PrometheusAdapter`: Exports metrics to Prometheus.
+  - `ConsoleAdapter`: Prints metrics to the console (for debugging purposes).
+
+## Usage Example
+
+```python
+import random
+import time
+from aworld.core.metrics.metric import set_metric_provider, MetricType
+from aworld.core.metrics.prometheus.prometheus_adapter import PrometheusConsoleMetricExporter,
+
+PrometheusMetricProvider
+from aworld.core.metrics.context_manager import MetricContext, ApiMetricTracker
+from aworld.core.metrics.template import MetricTemplate
+
+# Set OpenTelemetry as the metric provider
+# set_metric_provider(OpentelemetryMetricProvider())
+
+# Set Prometheus as the metric provider
+set_metric_provider(PrometheusMetricProvider(PrometheusConsoleMetricExporter(out_interval_secs=2)))
+
+# Define metric templates
+my_counter = MetricTemplate(
+    type=MetricType.COUNTER,
+    name="my_counter",
+    description="My custom counter",
+    unit="1"
+)
+
+my_gauge = MetricTemplate(
+    type=MetricType.GAUGE,
+    name="my_gauge"
+)
+
+my_histogram = MetricTemplate(
+    type=MetricType.HISTOGRAM,
+    name="my_histogram",
+    buckets=[2, 4, 6, 8, 10]
+)
+
+
+# Track API metrics using decorator
+@ApiMetricTracker()
+def test_api():
+    time.sleep(random.uniform(0, 1))
+
+
+# Track custom code block using context manager
+def test_custom_code():
+    with ApiMetricTracker("test_custom_code"):
+        time.sleep(random.uniform(0, 1))
+
+
+# Main loop to generate and record metrics
+while 1:
+    MetricContext.count(my_counter, random.randint(1, 10))
+    MetricContext.gauge_set(my_gauge, random.randint(1, 10))
+    MetricContext.histogram_record(my_histogram, random.randint(1, 10))
+    test_api()
+    test_custom_code()
+    time.sleep(random.random())
+```
+
+## Notes
+- Before using metrics, you must set a metric provider ( set_metric_provider ).
+- Different metric types serve different purposes; choose the appropriate type based on your needs.
+- For production environments, it is recommended to use Prometheus as the exporter.