diff --git a/DOCKER_SERVICES.md b/DOCKER_SERVICES.md
new file mode 100644
index 0000000..0770d77
--- /dev/null
+++ b/DOCKER_SERVICES.md
@@ -0,0 +1,147 @@
+# Docker Compose Services for Arroyopy
+
+This docker-compose setup provides a complete observability stack for development.
+
+## Services
+
+### 1. Kvrocks (Redis-compatible storage)
+- **Port**: 6379
+- **Purpose**: Redis-compatible key-value store for testing
+- **Access**: `redis-cli -h localhost -p 6379`
+
+### 2. Jaeger (Distributed Tracing)
+- **UI Port**: 16686
+- **OTLP gRPC Port**: 4317
+- **OTLP HTTP Port**: 4318
+- **Purpose**: Collect and visualize distributed traces
+- **Access**: http://localhost:16686
+
+### 3. Prometheus (Metrics Collection)
+- **Port**: 9090
+- **Purpose**: Scrape and store metrics from Arroyopy applications
+- **Access**: http://localhost:9090
+- **Configuration**: `prometheus.yml`
+
+### 4. Grafana (Metrics Visualization)
+- **Port**: 3000
+- **Purpose**: Create dashboards to visualize Prometheus metrics
+- **Access**: http://localhost:3000
+- **Default Credentials**: admin/admin
+
+## Quick Start
+
+### Start all services:
+```bash
+docker-compose up -d
+```
+
+### Start specific services:
+```bash
+# Just Jaeger for tracing
+docker-compose up -d jaeger
+
+# Full observability stack
+docker-compose up -d jaeger prometheus grafana
+
+# Just Redis
+docker-compose up -d kvrocks
+```
+
+### View logs:
+```bash
+docker-compose logs -f [service_name]
+```
+
+### Stop services:
+```bash
+docker-compose down
+```
+
+### Stop and remove volumes:
+```bash
+docker-compose down -v
+```
+
+## Configuration
+
+### Prometheus
+Edit `prometheus.yml` to configure scrape targets. By default, it scrapes:
+- Arroyopy app on `host.docker.internal:8000`
+- Prometheus itself on `localhost:9090`
+
+**Linux Note**: Replace `host.docker.internal` with your Docker bridge IP (usually `172.17.0.1`). Find it with:
+```bash
+ip addr show docker0 | grep inet
+```
+
+### Grafana
+Datasources are automatically configured via `grafana-datasources.yml`.
+
+To create dashboards:
+1. Go to http://localhost:3000
+2. Login (admin/admin)
+3. Create → Dashboard
+4. Add panels with PromQL queries:
+   - `arroyopy_messages_per_second`
+   - `arroyopy_avg_processing_seconds`
+   - `rate(arroyopy_messages_received_total[5m])`
+
+**Quick Start Dashboard:**
+Import the pre-configured dashboard:
+1. Go to Dashboards → Import
+2. Upload `grafana-dashboard.json`
+3. Select the Prometheus datasource
+4. Click Import
+
+This dashboard includes:
+- Messages per second by listener type
+- Average processing time by operator type
+- Total messages received
+- Processing time percentiles (p50, p95, p99)
+
+## Using with Arroyopy
+
+### 1. Start your Arroyopy application with telemetry:
+```python
+from arroyopy import init_telemetry
+from prometheus_client import start_http_server
+
+# Initialize OpenTelemetry
+init_telemetry(service_name="my-app")
+
+# Start metrics server
+start_http_server(8000)
+```
+
+### 2. View traces in Jaeger:
+- Open http://localhost:16686
+- Select service "my-app"
+- Click "Find Traces"
+
+### 3. View metrics in Prometheus:
+- Open http://localhost:9090
+- Go to Graph
+- Enter query: `arroyopy_avg_processing_seconds`
+
+### 4. Create dashboards in Grafana:
+- Open http://localhost:3000
+- Create new dashboard
+- Add Prometheus queries for visualization
+
+## Data Persistence
+
+Metrics and dashboard data are persisted in Docker volumes:
+- `prometheus-data`: Prometheus time-series database
+- `grafana-data`: Grafana dashboards and settings
+
+## Ports Summary
+
+| Service    | Port  | Purpose           |
+|------------|-------|-------------------|
+| Kvrocks    | 6379  | Redis API         |
+| Jaeger UI  | 16686 | Web Interface     |
+| Jaeger OTLP| 4317  | Trace Collection  |
+| Prometheus | 9090  | Metrics & UI      |
+| Grafana    | 3000  | Dashboards & UI   |
+
+All ports are bound to `127.0.0.1` for security on development machines.
diff --git a/README.md b/README.md
index f68767f..f016e11 100644
--- a/README.md
+++ b/README.md
@@ -249,6 +249,35 @@ pixi run -e dev pre-commit-install
 pixi run -e dev clean
 ```
 
+### Docker Services for Development
+
+The repository includes a docker-compose setup with services for testing and observability:
+
+```bash
+# Start all services (Redis, Jaeger, Prometheus, Grafana)
+docker-compose up -d
+
+# Start specific services
+docker-compose up -d kvrocks     # Redis-compatible storage
+docker-compose up -d jaeger      # Distributed tracing
+docker-compose up -d prometheus  # Metrics collection
+docker-compose up -d grafana     # Metrics visualization
+
+# View logs
+docker-compose logs -f
+
+# Stop services
+docker-compose down
+```
+
+**Service Access:**
+- Jaeger UI: http://localhost:16686
+- Prometheus: http://localhost:9090
+- Grafana: http://localhost:3000 (admin/admin)
+- Redis: localhost:6379
+
+See [DOCKER_SERVICES.md](DOCKER_SERVICES.md) for detailed documentation.
+
 ### Using Different Environments
 
 ```bash
diff --git a/TELEMETRY_QUICKSTART.md b/TELEMETRY_QUICKSTART.md
new file mode 100644
index 0000000..884c599
--- /dev/null
+++ b/TELEMETRY_QUICKSTART.md
@@ -0,0 +1,153 @@
+# OpenTelemetry Integration - Quick Reference
+
+## What was added:
+
+### 1. New Module: `telemetry.py`
+- **`@traced` decorator**: Creates Jaeger spans for functions
+- **`init_telemetry()`**: Initialize OpenTelemetry with Jaeger
+- **`get_metrics_tracker()`**: Access the global metrics tracker
+- **Automatic Prometheus metrics** for listeners and operators
+
+### 2. Dependencies Added (pyproject.toml)
+- `opentelemetry-api`
+- `opentelemetry-sdk`
+- `opentelemetry-exporter-jaeger`
+- `prometheus-client`
+
+### 3. Automatic Metrics Collection
+
+#### Listeners (RedisListener, ZMQListener)
+- **Messages per second** - Real-time message rate
+- **Total messages received** - Cumulative counter
+- Labeled by listener type (class name)
+
+#### Operators
+- **Average processing time** - Running average
+- **Processing time histogram** - Full distribution
+- Labeled by operator type (class name)
+
+## Quick Start
+
+### Use the @traced decorator:
+
+```python
+from arroyopy import traced, Operator
+
+class MyOperator(Operator):
+    @traced()  # Simple usage
+    async def process(self, message):
+        return message
+
+    @traced(span_name="validate", attributes={"version": "1.0"})
+    async def validate(self, data):
+        return data
+```
+
+### Initialize tracing (optional):
+
+```python
+from arroyopy import init_telemetry
+
+init_telemetry(
+    service_name="my-app",
+    otlp_endpoint="http://localhost:4317"
+)
+```
+
+### Expose Prometheus metrics:
+
+```python
+from prometheus_client import start_http_server
+
+# Start metrics server
+start_http_server(8000)
+# Metrics at http://localhost:8000/metrics
+```
+
+## Prometheus Metrics Available
+
+| Metric | Type | Description | Labels |
+|--------|------|-------------|--------|
+| `arroyopy_messages_received_total` | Counter | Total messages received | listener_type |
+| `arroyopy_messages_per_second` | Gauge | Current message rate | listener_type |
+| `arroyopy_processing_seconds` | Histogram | Processing time distribution | operator_type |
+| `arroyopy_avg_processing_seconds` | Gauge | Average processing time | operator_type |
+
+## Files Created
+
+1. **`src/arroyopy/telemetry.py`** - Main telemetry module
+2. **`src/_test/test_telemetry.py`** - Unit tests
+3. **`examples/telemetry_example.py`** - Usage examples
+4. **`examples/prometheus_example.py`** - Metrics server example
+5. **`docs/telemetry.md`** - Complete documentation
+
+## Files Modified
+
+1. **`pyproject.toml`** - Added dependencies
+2. **`src/arroyopy/__init__.py`** - Exported telemetry functions
+3. **`src/arroyopy/operator.py`** - Added processing time tracking
+4. **`src/arroyopy/redis.py`** - Added message rate tracking
+5. **`src/arroyopy/zmq.py`** - Added message rate tracking
+
+## Next Steps
+
+1. **Install new dependencies:**
+   ```bash
+   pixi install  # or pip install -e .
+   ```
+
+2. **Start observability stack:**
+   ```bash
+   # Option 1: Full stack (recommended) - Jaeger + Prometheus + Grafana
+   docker-compose up -d
+
+   # Option 2: Just Jaeger for tracing
+   docker-compose up -d jaeger
+
+   # Option 3: Using docker directly
+   docker run -d -p 4317:4317 -p 16686:16686 jaegertracing/all-in-one
+   ```
+
+   **Access points:**
+   - Jaeger UI: http://localhost:16686
+   - Prometheus: http://localhost:9090
+   - Grafana: http://localhost:3000 (admin/admin)
+
+3. **Add metrics endpoint to your app:**
+   ```python
+   from prometheus_client import start_http_server
+   start_http_server(8000)
+   ```
+
+4. **Use @traced decorator in your operators:**
+   ```python
+   @traced(span_name="my_process")
+   async def process(self, message):
+       # your code
+   ```
+
+See [DOCKER_SERVICES.md](DOCKER_SERVICES.md) for detailed docker-compose documentation.
+
+## How It Works
+
+### Automatic Collection
+- **No code changes needed** for basic metrics
+- Listeners automatically track message rates when messages are received
+- Operators automatically track processing times when messages are processed
+- All metrics exported to Prometheus automatically
+
+### Manual Tracing
+- Use `@traced` decorator for custom spans
+- Supports nested spans
+- Works with sync and async functions
+- Automatically captures errors and exceptions
+
+## Documentation
+
+See [docs/telemetry.md](../docs/telemetry.md) for complete documentation including:
+- Detailed usage examples
+- Configuration options
+- Prometheus query examples
+- Grafana dashboard setup
+- Troubleshooting guide
+- Best practices
diff --git a/config/grafana-dashboard.json b/config/grafana-dashboard.json
new file mode 100644
index 0000000..1705745
--- /dev/null
+++ b/config/grafana-dashboard.json
@@ -0,0 +1,141 @@
+{
+  "title": "Arroyopy Metrics",
+  "dashboard": {
+    "title": "Arroyopy Metrics",
+    "tags": ["arroyopy", "telemetry"],
+    "timezone": "browser",
+    "schemaVersion": 16,
+    "version": 0,
+    "refresh": "5s",
+    "time": {
+      "from": "now-1h",
+      "to": "now"
+    },
+    "timepicker": {
+      "refresh_intervals": ["5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d"],
+      "time_options": ["5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d"]
+    },
+    "panels": [
+      {
+        "id": 1,
+        "title": "Messages Per Second",
+        "type": "graph",
+        "targets": [
+          {
+            "expr": "rate(arroyopy_processing_seconds_count[1m])",
+            "legendFormat": "rate",
+            "refId": "A"
+          }
+        ],
+        "gridPos": {
+          "h": 8,
+          "w": 12,
+          "x": 0,
+          "y": 0
+        },
+        "yaxes": [
+          {
+            "label": "messages/sec",
+            "show": true
+          },
+          {
+            "show": true
+          }
+        ]
+      },
+      {
+        "id": 2,
+        "title": "Average Processing Time",
+        "type": "graph",
+        "targets": [
+          {
+            "expr": "arroyopy_avg_processing_seconds",
+            "legendFormat": "avg time",
+            "refId": "A"
+          }
+        ],
+        "gridPos": {
+          "h": 8,
+          "w": 12,
+          "x": 12,
+          "y": 0
+        },
+        "yaxes": [
+          {
+            "label": "seconds",
+            "show": true
+          },
+          {
+            "show": true
+          }
+        ]
+      },
+      {
+        "id": 3,
+        "title": "Total Messages Processed",
+        "type": "graph",
+        "targets": [
+          {
+            "expr": "arroyopy_processing_seconds_count",
+            "legendFormat": "total",
+            "refId": "A"
+          }
+        ],
+        "gridPos": {
+          "h": 8,
+          "w": 12,
+          "x": 0,
+          "y": 8
+        },
+        "yaxes": [
+          {
+            "label": "total messages",
+            "show": true
+          },
+          {
+            "show": true
+          }
+        ]
+      },
+      {
+        "id": 4,
+        "title": "Processing Time Percentiles",
+        "type": "graph",
+        "targets": [
+          {
+            "expr": "histogram_quantile(0.50, rate(arroyopy_processing_seconds_bucket[5m]))",
+            "legendFormat": "p50",
+            "refId": "A"
+          },
+          {
+            "expr": "histogram_quantile(0.95, rate(arroyopy_processing_seconds_bucket[5m]))",
+            "legendFormat": "p95",
+            "refId": "B"
+          },
+          {
+            "expr": "histogram_quantile(0.99, rate(arroyopy_processing_seconds_bucket[5m]))",
+            "legendFormat": "p99",
+            "refId": "C"
+          }
+        ],
+        "gridPos": {
+          "h": 8,
+          "w": 12,
+          "x": 12,
+          "y": 8
+        },
+        "yaxes": [
+          {
+            "label": "seconds",
+            "show": true
+          },
+          {
+            "show": true
+          }
+        ]
+      }
+    ]
+  },
+  "folderId": 0,
+  "overwrite": true
+}
diff --git a/config/grafana-dashboards.yml b/config/grafana-dashboards.yml
new file mode 100644
index 0000000..92cdd99
--- /dev/null
+++ b/config/grafana-dashboards.yml
@@ -0,0 +1,12 @@
+apiVersion: 1
+
+providers:
+  - name: 'Arroyopy Dashboards'
+    orgId: 1
+    folder: ''
+    type: file
+    disableDeletion: false
+    updateIntervalSeconds: 10
+    allowUiUpdates: true
+    options:
+      path: /etc/grafana/provisioning/dashboards
diff --git a/config/grafana-datasources.yml b/config/grafana-datasources.yml
new file mode 100644
index 0000000..f9924f8
--- /dev/null
+++ b/config/grafana-datasources.yml
@@ -0,0 +1,15 @@
+# Grafana datasource provisioning configuration
+
+apiVersion: 1
+
+datasources:
+  # Prometheus datasource
+  - name: Prometheus
+    type: prometheus
+    access: proxy
+    url: http://prometheus:9090
+    isDefault: true
+    editable: true
+    jsonData:
+      timeInterval: "5s"
+      queryTimeout: "60s"
diff --git a/config/prometheus.yml b/config/prometheus.yml
new file mode 100644
index 0000000..6aaef1b
--- /dev/null
+++ b/config/prometheus.yml
@@ -0,0 +1,27 @@
+# Prometheus configuration for Arroyopy metrics
+
+global:
+  scrape_interval: 15s  # How frequently to scrape targets
+  evaluation_interval: 15s  # How frequently to evaluate rules
+
+# Scrape configurations
+scrape_configs:
+  # Arroyopy application metrics
+  - job_name: 'arroyopy'
+    static_configs:
+      - targets: ['host.docker.internal:8000']  # Default metrics port
+        labels:
+          service: 'arroyopy'
+          environment: 'development'
+
+    # Scrape more frequently for development
+    scrape_interval: 5s
+
+  # Prometheus itself
+  - job_name: 'prometheus'
+    static_configs:
+      - targets: ['localhost:9090']
+
+# Note: For Linux, you may need to use the host's IP address instead of host.docker.internal
+# Find your IP with: ip addr show docker0 | grep inet
+# Then replace host.docker.internal with that IP (usually 172.17.0.1)
diff --git a/docker-compose.yml b/docker-compose.yml
index 4c82438..a2cfa6b 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -3,3 +3,47 @@ services:
     image: apache/kvrocks
     ports:
       - 127.0.0.1:6379:6379
+
+  jaeger:
+    image: jaegertracing/all-in-one:latest
+    ports:
+      - "127.0.0.1:16686:16686"  # Jaeger UI
+      - "127.0.0.1:4317:4317"    # OTLP gRPC receiver
+      - "127.0.0.1:4318:4318"    # OTLP HTTP receiver
+    environment:
+      - COLLECTOR_OTLP_ENABLED=true
+    restart: unless-stopped
+
+  prometheus:
+    image: prom/prometheus:latest
+    ports:
+      - "127.0.0.1:9090:9090"  # Prometheus UI
+    volumes:
+      - ./config/prometheus.yml:/etc/prometheus/prometheus.yml:ro,z
+      - prometheus-data:/prometheus
+    command:
+      - '--config.file=/etc/prometheus/prometheus.yml'
+      - '--storage.tsdb.path=/prometheus'
+      - '--web.console.libraries=/usr/share/prometheus/console_libraries'
+      - '--web.console.templates=/usr/share/prometheus/consoles'
+    restart: unless-stopped
+
+  grafana:
+    image: grafana/grafana:latest
+    ports:
+      - "127.0.0.1:3000:3000"  # Grafana UI
+    environment:
+      - GF_SECURITY_ADMIN_PASSWORD=${GRAFANA_ADMIN_PASSWORD:-admin}
+      - GF_USERS_ALLOW_SIGN_UP=false
+    volumes:
+      - grafana-data:/var/lib/grafana
+      - ./config/grafana-datasources.yml:/etc/grafana/provisioning/datasources/datasources.yml:ro,z
+      - ./config/grafana-dashboards.yml:/etc/grafana/provisioning/dashboards/dashboards.yml:ro,z
+      - ./config/grafana-dashboard.json:/etc/grafana/provisioning/dashboards/arroyopy-dashboard.json:ro,z
+    depends_on:
+      - prometheus
+    restart: unless-stopped
+
+volumes:
+  prometheus-data:
+  grafana-data:
diff --git a/docs/telemetry.md b/docs/telemetry.md
new file mode 100644
index 0000000..9de8d76
--- /dev/null
+++ b/docs/telemetry.md
@@ -0,0 +1,324 @@
+# OpenTelemetry and Prometheus Monitoring
+
+Arroyopy includes built-in support for distributed tracing with OpenTelemetry/Jaeger and metrics with Prometheus.
+
+## Features
+
+### 1. Jaeger Distributed Tracing
+
+The `@traced` decorator allows you to create Jaeger spans for any function:
+
+```python
+from arroyopy import traced, Operator
+
+class MyOperator(Operator):
+    @traced(span_name="custom_process", attributes={"version": "1.0"})
+    async def process(self, message):
+        # Your processing logic
+        return message
+```
+
+The decorator automatically:
+- Creates a span for the function execution
+- Records function metadata (name, module)
+- Captures exceptions and errors
+- Works with both sync and async functions
+- Supports nested spans for better trace visualization
+
+### 2. Prometheus Metrics
+
+Metrics are automatically collected for all listeners and operators:
+
+#### Listener Metrics
+- **`arroyopy_messages_received_total`** (Counter): Total messages received by each listener
+- **`arroyopy_messages_per_second`** (Gauge): Current message rate per listener type
+
+#### Operator Metrics
+- **`arroyopy_processing_seconds`** (Histogram): Distribution of processing times
+- **`arroyopy_avg_processing_seconds`** (Gauge): Running average of processing time per operator type
+
+All metrics are labeled by type (listener class name or operator class name).
+
+## Setup
+
+### 1. Install Dependencies
+
+The required dependencies are included in the main package:
+
+```bash
+pip install arroyopy
+```
+
+Or with pixi:
+
+```bash
+pixi install
+```
+
+### 2. Initialize Tracing (Optional)
+
+Initialize OpenTelemetry tracing at application startup:
+
+```python
+from arroyopy import init_telemetry
+
+# Initialize with default settings
+init_telemetry()
+
+# Or customize settings
+init_telemetry(
+    service_name="my-app",
+    otlp_endpoint="http://jaeger.example.com:4317"
+)
+```
+
+If you don't call `init_telemetry()`, it will be automatically initialized with default settings when the first `@traced` decorator is used.
+
+### 3. Start Observability Stack
+
+The repository includes a docker-compose setup with Jaeger, Prometheus, and Grafana:
+
+```bash
+# Start full observability stack (recommended)
+docker-compose up -d
+
+# Or start services individually
+docker-compose up -d jaeger      # Just tracing
+docker-compose up -d prometheus  # Just metrics collection
+docker-compose up -d grafana     # Just visualization
+
+# Access points:
+# - Jaeger UI:    http://localhost:16686
+# - Prometheus:   http://localhost:9090
+# - Grafana:      http://localhost:3000 (admin/admin)
+```
+
+Alternatively, run Jaeger directly with Docker:
+
+```bash
+docker run -d \
+  -p 4317:4317 \
+  -p 16686:16686 \
+  jaegertracing/all-in-one:latest
+```
+
+See [DOCKER_SERVICES.md](../DOCKER_SERVICES.md) for full documentation on the observability stack.
+
+### 4. Expose Prometheus Metrics
+
+Add a metrics endpoint to your application:
+
+```python
+from prometheus_client import start_http_server
+
+# Start metrics server on port 8000
+start_http_server(8000)
+
+# Metrics available at http://localhost:8000/metrics
+```
+
+## Usage Examples
+
+### Basic Tracing
+
+```python
+from arroyopy import traced
+
+@traced()  # Use function name as span name
+async def process_data(data):
+    return transform(data)
+
+@traced(span_name="validate_input")  # Custom span name
+def validate(data):
+    return is_valid(data)
+```
+
+### Custom Attributes
+
+```python
+@traced(
+    span_name="complex_operation",
+    attributes={
+        "version": "2.0",
+        "environment": "production"
+    }
+)
+async def complex_operation(data):
+    # Your logic here
+    return result
+```
+
+### Nested Spans
+
+```python
+class DataProcessor(Operator):
+    @traced(span_name="process_pipeline")
+    async def process(self, message):
+        # These create child spans
+        data = await self._fetch_data(message)
+        result = await self._transform_data(data)
+        return result
+
+    @traced(span_name="fetch_data")
+    async def _fetch_data(self, message):
+        # Fetch logic
+        return data
+
+    @traced(span_name="transform_data")
+    async def _transform_data(self, data):
+        # Transform logic
+        return result
+```
+
+### Monitoring with Prometheus
+
+```python
+from prometheus_client import start_http_server
+from arroyopy import get_metrics_tracker
+
+# Start metrics server
+start_http_server(8000)
+
+# Metrics are automatically collected by listeners and operators
+# No additional code needed!
+
+# Access metrics at http://localhost:8000/metrics
+```
+
+## Querying Metrics in Prometheus
+
+Example PromQL queries:
+
+```promql
+# Message rate by listener type
+arroyopy_messages_per_second{listener_type="RedisListener"}
+
+# Average processing time by operator
+arroyopy_avg_processing_seconds{operator_type="MyOperator"}
+
+# 95th percentile processing time
+histogram_quantile(0.95, arroyopy_processing_seconds_bucket)
+
+# Total messages received
+sum(arroyopy_messages_received_total)
+```
+
+## Architecture
+
+### Automatic Metrics Collection
+
+Metrics are collected automatically:
+
+1. **Listeners**: Each time a message is received, the `MetricsTracker` records:
+   - Message count (incremented)
+   - Message rate (calculated every second)
+
+2. **Operators**: Each time `process()` is called, timing is captured:
+   - Start time before processing
+   - End time after processing
+   - Duration recorded in histogram and used for average calculation
+
+### Tracing Flow
+
+1. `@traced` decorator wraps functions
+2. On function call, a span is started
+3. Function executes (can call other traced functions, creating nested spans)
+4. On completion/error, span is ended with status and attributes
+5. Spans are batched and exported to Jaeger
+
+## Configuration
+
+### Environment Variables
+
+You can configure tracing via environment variables:
+
+```bash
+# OTLP configuration
+export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
+export OTEL_SERVICE_NAME=my-arroyopy-app
+```
+
+### Programmatic Configuration
+
+```python
+from arroyopy import init_telemetry
+
+init_telemetry(
+    service_name="my-app",
+    otlp_endpoint="http://localhost:4317"
+)
+```
+
+## Best Practices
+
+1. **Initialize once**: Call `init_telemetry()` at application startup
+2. **Use meaningful span names**: Make traces easier to understand
+3. **Add context with attributes**: Include relevant metadata in spans
+4. **Don't over-trace**: Focus on important operations, not every function
+5. **Monitor metrics**: Set up alerts on processing time and message rates
+6. **Use labels wisely**: Avoid high-cardinality labels in Prometheus
+
+## Troubleshooting
+
+### Traces not appearing in Jaeger
+
+- Verify Jaeger is running: `curl http://localhost:16686`
+- Check OTLP endpoint is accessible: `curl http://localhost:4317`
+- Ensure `init_telemetry()` was called with correct endpoint
+- Look for OpenTelemetry errors in logs
+
+### Metrics not updating
+
+- Verify Prometheus scrape endpoint is accessible
+- Check that listeners/operators are actually processing messages
+- Confirm `start_http_server()` was called
+- Check for errors in application logs
+
+### Performance Impact
+
+- Tracing has minimal overhead (~1-5% in most cases)
+- Metrics collection is very lightweight
+- Batched exports reduce network overhead
+- Consider sampling in high-throughput scenarios
+
+## Integration with Monitoring Systems
+
+### Grafana Dashboards
+
+Create dashboards using Prometheus data source:
+
+```promql
+# Panel: Message Rate
+arroyopy_messages_per_second
+
+# Panel: Processing Time
+arroyopy_avg_processing_seconds
+
+# Panel: Throughput
+rate(arroyopy_messages_received_total[5m])
+```
+
+### Alerts
+
+Example Prometheus alerts:
+
+```yaml
+groups:
+  - name: arroyopy
+    rules:
+      - alert: HighProcessingTime
+        expr: arroyopy_avg_processing_seconds > 1.0
+        for: 5m
+        annotations:
+          summary: "High processing time detected"
+
+      - alert: LowMessageRate
+        expr: arroyopy_messages_per_second < 10
+        for: 5m
+        annotations:
+          summary: "Message rate dropped"
+```
+
+## Examples
+
+See [telemetry_example.py](../examples/telemetry_example.py) for complete working examples.
diff --git a/examples/prometheus_example.py b/examples/prometheus_example.py
new file mode 100644
index 0000000..ce81804
--- /dev/null
+++ b/examples/prometheus_example.py
@@ -0,0 +1,88 @@
+"""Example showing how to expose Prometheus metrics endpoint.
+
+This example demonstrates:
+1. Starting a Prometheus metrics HTTP server
+2. Running an operator with automatic metrics collection
+3. Querying metrics from the /metrics endpoint
+"""
+
+import asyncio
+import logging
+import random
+from typing import Any
+
+from prometheus_client import start_http_server
+
+from arroyopy import Operator, init_telemetry, traced
+
+logging.basicConfig(level=logging.INFO)
+
+
+class MetricsExampleOperator(Operator):
+    """Example operator that processes messages with metrics collection."""
+
+    @traced(span_name="process_message")
+    async def process(self, message: Any) -> Any:
+        """Process message with variable timing to demonstrate metrics."""
+        # Simulate variable processing time
+        processing_time = random.uniform(0.01, 0.5)
+        await asyncio.sleep(processing_time)
+        return message
+
+
+async def simulate_message_processing():
+    """Simulate processing messages to generate metrics."""
+    operator = MetricsExampleOperator()
+
+    # Simulate processing 100 messages
+    for i in range(100):
+        message = {"id": i, "value": f"message_{i}"}
+        await operator.listener_queue.put(message)
+
+        # Start operator processing in background
+        if i == 0:
+            asyncio.create_task(operator.start())
+
+        # Small delay between messages to show rate variation
+        await asyncio.sleep(random.uniform(0.05, 0.2))
+
+    print("Finished processing messages")
+
+
+async def main():
+    """Main function to demonstrate Prometheus metrics."""
+    # Initialize OpenTelemetry (optional, but good for complete observability)
+    init_telemetry(service_name="prometheus-example")
+
+    # Start Prometheus HTTP server on port 8000
+    # Metrics will be available at http://localhost:8000/metrics
+    print("Starting Prometheus metrics server on port 8000...")
+    start_http_server(8000)
+    print("Metrics available at: http://localhost:8000/metrics")
+
+    print("\nAvailable metrics:")
+    print("- arroyopy_messages_received_total: Total messages received")
+    print("- arroyopy_messages_per_second: Current message rate")
+    print("- arroyopy_processing_seconds: Processing time histogram")
+    print("- arroyopy_avg_processing_seconds: Average processing time")
+
+    print("\nStarting message processing...")
+    print("(Open http://localhost:8000/metrics in a browser to see metrics)\n")
+
+    # Simulate message processing
+    await simulate_message_processing()
+
+    # Keep server running for a bit to allow metric inspection
+    print("\nMetrics server still running. Check http://localhost:8000/metrics")
+    print("Press Ctrl+C to stop...")
+
+    try:
+        # Keep running
+        while True:
+            await asyncio.sleep(1)
+    except KeyboardInterrupt:
+        print("\nShutting down...")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/examples/telemetry_example.py b/examples/telemetry_example.py
new file mode 100644
index 0000000..cf721f5
--- /dev/null
+++ b/examples/telemetry_example.py
@@ -0,0 +1,206 @@
+"""Example demonstrating OpenTelemetry and Prometheus metrics in Arroyopy.
+
+This example shows:
+1. How to use the @traced decorator for Jaeger spans
+2. How metrics are automatically collected for listeners and operators
+3. How to initialize OpenTelemetry/Jaeger tracing
+4. Running operators with actual message processing
+"""
+
+import asyncio
+import logging
+import random
+from typing import Any
+
+from prometheus_client import start_http_server
+
+from arroyopy import Operator, init_telemetry, traced
+
+logging.basicConfig(level=logging.INFO)
+
+
+class ExampleOperator(Operator):
+    """Example operator that uses the @traced decorator."""
+
+    def __init__(self):
+        super().__init__()
+        self.processed_count = 0
+
+    @traced(span_name="process_message", attributes={"component": "example"})
+    async def process(self, message: Any) -> Any:
+        """Process a message with automatic tracing and metrics.
+
+        The @traced decorator automatically:
+        - Creates a Jaeger span for this function
+        - Adds custom attributes to the span
+        - Records exceptions and errors
+        - Tracks function execution time
+
+        The Operator base class automatically:
+        - Tracks processing time metrics
+        - Calculates average processing time
+        - Exports metrics to Prometheus
+        """
+        # Simulate some processing with variable time
+        await asyncio.sleep(random.uniform(0.05, 0.15))
+
+        self.processed_count += 1
+        logger = logging.getLogger(__name__)
+        logger.info(f"Processed message #{self.processed_count}: {message}")
+
+        return message
+
+
+class ComplexOperator(Operator):
+    """Operator with multiple traced methods showing nested spans."""
+
+    def __init__(self):
+        super().__init__()
+        self.processed_count = 0
+
+    @traced(span_name="process_message")
+    async def process(self, message: Any) -> Any:
+        """Main processing method that calls other traced methods."""
+        # These will create nested spans in Jaeger
+        validated = await self._validate_message(message)
+        transformed = await self._transform_message(validated)
+        enriched = await self._enrich_message(transformed)
+
+        self.processed_count += 1
+        logger = logging.getLogger(__name__)
+        logger.info(f"Complex operator processed message #{self.processed_count}")
+
+        return enriched
+
+    @traced(span_name="validate")
+    async def _validate_message(self, message: Any) -> Any:
+        """Validate the message structure."""
+        # Validation logic
+        await asyncio.sleep(0.01)
+        return message
+
+    @traced(span_name="transform")
+    async def _transform_message(self, message: Any) -> Any:
+        """Transform the message data."""
+        # Transformation logic
+        await asyncio.sleep(0.02)
+        return message
+
+    @traced(span_name="enrich")
+    async def _enrich_message(self, message: Any) -> Any:
+        """Enrich the message with additional data."""
+        # Enrichment logic
+        await asyncio.sleep(0.03)
+        return message
+
+
+async def simulate_messages(operator: Operator, num_messages: int = 20):
+    """Simulate sending messages to an operator."""
+    logger = logging.getLogger(__name__)
+
+    for i in range(num_messages):
+        # Just use a simple dict as the message data
+        message = {
+            "id": i,
+            "value": f"test_message_{i}",
+            "timestamp": asyncio.get_event_loop().time(),
+        }
+        await operator.notify(message)
+        # Small delay between messages
+        await asyncio.sleep(random.uniform(0.1, 0.3))
+
+    logger.info(f"Sent {num_messages} messages to operator")
+
+
+async def run_demo():
+    """Run the telemetry demonstration."""
+    logger = logging.getLogger(__name__)
+
+    print("\n" + "=" * 70)
+    print("Arroyopy Telemetry Demo")
+    print("=" * 70)
+
+    # Initialize OpenTelemetry
+    init_telemetry(
+        service_name="arroyopy-demo",
+        otlp_endpoint="http://localhost:4317",
+    )
+    print("✓ OpenTelemetry initialized")
+
+    # Start Prometheus metrics server
+    print("✓ Starting Prometheus metrics server on port 8000")
+    start_http_server(8000)
+    print("  → Metrics available at: http://localhost:8000/metrics")
+
+    print("\n" + "-" * 70)
+    print("Running operators with message processing...")
+    print("-" * 70 + "\n")
+
+    # Create operators
+    simple_operator = ExampleOperator()
+    complex_operator = ComplexOperator()
+
+    # Start operators in background
+    simple_task = asyncio.create_task(simple_operator.start())
+    complex_task = asyncio.create_task(complex_operator.start())
+
+    # Simulate messages for simple operator
+    logger.info("Sending messages to ExampleOperator...")
+    await simulate_messages(simple_operator, num_messages=10)
+
+    # Simulate messages for complex operator
+    logger.info("Sending messages to ComplexOperator...")
+    await simulate_messages(complex_operator, num_messages=10)
+
+    # Let operators finish processing
+    await asyncio.sleep(2)
+
+    # Stop operators
+    simple_operator.stop_requested = True
+    complex_operator.stop_requested = True
+
+    # Send final messages to trigger stop
+    await simple_operator.notify({"stop": True})
+    await complex_operator.notify({"stop": True})
+
+    await asyncio.gather(simple_task, complex_task, return_exceptions=True)
+
+    print("\n" + "=" * 70)
+    print("Demo completed!")
+    print("=" * 70)
+    print("\nWhat just happened:")
+    print("1. ✓ Processed 20 messages through 2 operators")
+    print("2. ✓ Created Jaeger spans with @traced decorator")
+    print("3. ✓ Collected Prometheus metrics automatically")
+    print("\nView the results:")
+    print("• Prometheus metrics: http://localhost:8000/metrics")
+    print("  - Look for arroyopy_avg_processing_seconds")
+    print("  - Look for arroyopy_processing_seconds_* (histogram buckets)")
+    print("\n• Jaeger traces (if running):")
+    print("  1. Start Jaeger: docker-compose up -d jaeger")
+    print(
+        "     (or: docker run -d -p 4317:4317 -p 16686:16686 jaegertracing/all-in-one)"
+    )
+    print("  2. Open: http://localhost:16686")
+    print("  3. Select service: arroyopy-demo")
+    print("  4. Click 'Find Traces'")
+    print("\nPress Ctrl+C to exit (metrics server will keep running)")
+
+    try:
+        # Keep server running
+        while True:
+            await asyncio.sleep(1)
+    except KeyboardInterrupt:
+        print("\n\nShutting down...")
+
+
+def main():
+    """Entry point for the demo."""
+    try:
+        asyncio.run(run_demo())
+    except KeyboardInterrupt:
+        print("\nBye!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pixi.lock b/pixi.lock
index d6d8e6d..774ae77 100644
--- a/pixi.lock
+++ b/pixi.lock
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       - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.11-pyhd8ed1ab_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/yaml-0.2.5-h925e9cb_3.conda
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@@ -1558,8 +1786,8 @@ packages:
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 - pypi: ./
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-  version: 0.3.0a7
-  sha256: 76dc3179c409495f9e06776fc63c6c8572405029c82a82b1368b9ce9282c9500
+  version: 0.3.0a8
+  sha256: b5c24b64018a6eb709668b8d6a07402fda7e240da524fa9f3db8711bc3af9ae1
   requires_dist:
   - python-dotenv
   - pandas
@@ -1567,6 +1795,10 @@ packages:
   - pydantic>=2.0
   - pyyaml
   - typer
+  - opentelemetry-api
+  - opentelemetry-sdk
+  - opentelemetry-exporter-otlp-proto-grpc
+  - prometheus-client
   - fakeredis ; extra == 'dev'
   - flake8 ; extra == 'dev'
   - pre-commit ; extra == 'dev'
@@ -2269,6 +2501,14 @@ packages:
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+  requires_dist:
+  - protobuf>=4.25.8,<8.0.0
+  - grpcio>=1.44.0,<2.0.0 ; extra == 'grpc'
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@@ -2339,6 +2579,30 @@ packages:
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   size: 117162
   timestamp: 1771834183594
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+  name: grpcio
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+  requires_dist:
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+  requires_python: '>=3.9'
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+  name: grpcio
+  version: 1.80.0
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+  requires_dist:
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+  - grpcio-tools>=1.80.0 ; extra == 'protobuf'
+  requires_python: '>=3.9'
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+  name: grpcio
+  version: 1.80.0
+  sha256: 4e78c4ac0d97dc2e569b2f4bcbbb447491167cb358d1a389fc4af71ab6f70411
+  requires_dist:
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+  requires_python: '>=3.9'
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@@ -3476,6 +3740,63 @@ packages:
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   timestamp: 1769556384749
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+  version: 1.41.0
+  sha256: 7a99177bf61f85f4f9ed2072f54d676364719c066f6d11f515acc6c745c7acf0
+  requires_dist:
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+  sha256: 3a1a86bd24806ccf136ec9737dbfa4c09b069f9130ff66b0acb014f9c5255fd1
+  requires_dist:
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+  - grpcio>=1.63.2,<2.0.0 ; python_full_version < '3.13'
+  - grpcio>=1.66.2,<2.0.0 ; python_full_version == '3.13.*'
+  - grpcio>=1.75.1,<2.0.0 ; python_full_version >= '3.14'
+  - opentelemetry-api~=1.15
+  - opentelemetry-exporter-otlp-proto-common==1.41.0
+  - opentelemetry-proto==1.41.0
+  - opentelemetry-sdk~=1.41.0
+  - typing-extensions>=4.6.0
+  - opentelemetry-exporter-credential-provider-gcp>=0.59b0 ; extra == 'gcp-auth'
+  requires_python: '>=3.9'
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+  name: opentelemetry-sdk
+  version: 1.41.0
+  sha256: a596f5687964a3e0d7f8edfdcf5b79cbca9c93c7025ebf5fb00f398a9443b0bd
+  requires_dist:
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+  - opentelemetry-semantic-conventions==0.62b0
+  - typing-extensions>=4.5.0
+  - jsonschema>=4.0 ; extra == 'file-configuration'
+  - pyyaml>=6.0 ; extra == 'file-configuration'
+  requires_python: '>=3.9'
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+  name: opentelemetry-semantic-conventions
+  version: 0.62b0
+  sha256: 0ddac1ce59eaf1a827d9987ab60d9315fb27aea23304144242d1fcad9e16b489
+  requires_dist:
+  - opentelemetry-api==1.41.0
+  - typing-extensions>=4.5.0
+  requires_python: '>=3.9'
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@@ -3953,6 +4274,21 @@ packages:
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   - django ; extra == 'django'
   requires_python: '>=3.9'
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+  name: protobuf
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+  requires_python: '>=3.9'
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+  name: protobuf
+  version: 6.33.6
+  sha256: 9720e6961b251bde64edfdab7d500725a2af5280f3f4c87e57c0208376aa8c3a
+  requires_python: '>=3.9'
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+  name: protobuf
+  version: 6.33.6
+  sha256: e2afbae9b8e1825e3529f88d514754e094278bb95eadc0e199751cdd9a2e82a2
+  requires_python: '>=3.9'
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   name: pyasn1
   version: 0.6.3
diff --git a/pixi.toml b/pixi.toml
index 49c4b40..f74ec62 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -9,6 +9,8 @@ format-check = "black --check src/"
 format = "black src/"
 pre-commit = "pre-commit run --all-files"
 pre-commit-install = "pre-commit install"
+example-telemetry = "python examples/telemetry_example.py"
+example-prometheus = "python examples/prometheus_example.py"
 
 # Build tasks
 build = "pip install build && python -m build"
diff --git a/pyproject.toml b/pyproject.toml
index d905cd8..eec1cfc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,7 +20,11 @@ dependencies = [
     "numpy",
     "pydantic>=2.0",
     "pyyaml",
-    "typer"
+    "typer",
+    "opentelemetry-api",
+    "opentelemetry-sdk",
+    "opentelemetry-exporter-otlp-proto-grpc",
+    "prometheus-client"
 ]
 
 
diff --git a/src/_test/test_telemetry.py b/src/_test/test_telemetry.py
new file mode 100644
index 0000000..d88bfd5
--- /dev/null
+++ b/src/_test/test_telemetry.py
@@ -0,0 +1,189 @@
+"""Tests for OpenTelemetry and Prometheus telemetry."""
+
+import asyncio
+import time
+
+import pytest
+
+from arroyopy.telemetry import (
+    MetricsTracker,
+    get_metrics_tracker,
+    get_tracer,
+    init_telemetry,
+    traced,
+)
+
+
+class TestTelemetryInit:
+    """Test telemetry initialization."""
+
+    def test_init_telemetry_default(self):
+        """Test initialization with default parameters."""
+        init_telemetry()
+        tracer = get_tracer()
+        assert tracer is not None
+
+    def test_init_telemetry_custom(self):
+        """Test initialization with custom parameters."""
+        init_telemetry(
+            service_name="test-service", otlp_endpoint="http://testhost:4317"
+        )
+        tracer = get_tracer()
+        assert tracer is not None
+
+
+class TestTracedDecorator:
+    """Test the @traced decorator."""
+
+    @pytest.mark.asyncio
+    async def test_traced_async_function(self):
+        """Test tracing an async function."""
+
+        @traced()
+        async def async_function():
+            await asyncio.sleep(0.01)
+            return "result"
+
+        result = await async_function()
+        assert result == "result"
+
+    def test_traced_sync_function(self):
+        """Test tracing a sync function."""
+
+        @traced()
+        def sync_function():
+            return "sync_result"
+
+        result = sync_function()
+        assert result == "sync_result"
+
+    @pytest.mark.asyncio
+    async def test_traced_with_custom_name(self):
+        """Test tracing with custom span name."""
+
+        @traced(span_name="custom_span")
+        async def custom_function():
+            return "custom"
+
+        result = await custom_function()
+        assert result == "custom"
+
+    @pytest.mark.asyncio
+    async def test_traced_with_attributes(self):
+        """Test tracing with custom attributes."""
+
+        @traced(span_name="attr_span", attributes={"key": "value", "number": 42})
+        async def attr_function():
+            return "attrs"
+
+        result = await attr_function()
+        assert result == "attrs"
+
+    @pytest.mark.asyncio
+    async def test_traced_exception_handling(self):
+        """Test that traced decorator handles exceptions."""
+
+        @traced()
+        async def failing_function():
+            raise ValueError("test error")
+
+        with pytest.raises(ValueError, match="test error"):
+            await failing_function()
+
+    def test_traced_sync_exception_handling(self):
+        """Test that traced decorator handles exceptions in sync functions."""
+
+        @traced()
+        def failing_sync_function():
+            raise RuntimeError("sync error")
+
+        with pytest.raises(RuntimeError, match="sync error"):
+            failing_sync_function()
+
+
+class TestMetricsTracker:
+    """Test the MetricsTracker class."""
+
+    def test_metrics_tracker_singleton(self):
+        """Test that get_metrics_tracker returns the same instance."""
+        tracker1 = get_metrics_tracker()
+        tracker2 = get_metrics_tracker()
+        assert tracker1 is tracker2
+
+    def test_record_message(self):
+        """Test recording messages."""
+        tracker = MetricsTracker()
+        tracker.record_message("TestListener")
+        # Should not raise any errors
+
+    def test_record_processing_time(self):
+        """Test recording processing time."""
+        tracker = MetricsTracker()
+        tracker.record_processing_time("TestOperator", 0.123)
+        # Should not raise any errors
+
+    def test_message_rate_calculation(self):
+        """Test that message rate is calculated over time."""
+        tracker = MetricsTracker()
+
+        # Record some messages
+        for _ in range(10):
+            tracker.record_message("TestListener")
+
+        # Should have rate data stored internally
+        assert "TestListener" in tracker._message_counts
+
+    def test_processing_time_average(self):
+        """Test that average processing time is calculated."""
+        tracker = MetricsTracker()
+
+        # Record several processing times
+        tracker.record_processing_time("TestOperator", 0.1)
+        tracker.record_processing_time("TestOperator", 0.2)
+        tracker.record_processing_time("TestOperator", 0.3)
+
+        # Verify internal state
+        assert "TestOperator" in tracker._processing_times
+        assert tracker._processing_counts["TestOperator"] == 3
+        # Average should be (0.1 + 0.2 + 0.3) / 3 = 0.2
+        avg = tracker._processing_times["TestOperator"] / 3
+        assert abs(avg - 0.2) < 0.001
+
+
+class TestIntegration:
+    """Integration tests for telemetry."""
+
+    @pytest.mark.asyncio
+    async def test_nested_spans(self):
+        """Test that nested traced functions create nested spans."""
+
+        @traced(span_name="outer")
+        async def outer_function():
+            result = await inner_function()
+            return f"outer-{result}"
+
+        @traced(span_name="inner")
+        async def inner_function():
+            return "inner"
+
+        result = await outer_function()
+        assert result == "outer-inner"
+
+    @pytest.mark.asyncio
+    async def test_metrics_and_tracing_together(self):
+        """Test that metrics and tracing work together."""
+        tracker = get_metrics_tracker()
+
+        @traced(span_name="combined_test")
+        async def traced_with_metrics():
+            tracker.record_message("CombinedListener")
+            start = time.perf_counter()
+            await asyncio.sleep(0.01)
+            duration = time.perf_counter() - start
+            tracker.record_processing_time("CombinedOperator", duration)
+            return "combined"
+
+        result = await traced_with_metrics()
+        assert result == "combined"
+        assert "CombinedListener" in tracker._message_counts
+        assert "CombinedOperator" in tracker._processing_times
diff --git a/src/arroyopy/__init__.py b/src/arroyopy/__init__.py
index b0c2745..5312029 100644
--- a/src/arroyopy/__init__.py
+++ b/src/arroyopy/__init__.py
@@ -8,6 +8,7 @@
 from .listener import Listener
 from .operator import Operator
 from .publisher import Publisher
+from .telemetry import get_metrics_tracker, init_telemetry, traced
 
 __all__ = [
     "Listener",
@@ -18,6 +19,9 @@
     "load_blocks_from_yaml",
     "load_block_from_yaml",
     "ConfigurationError",
+    "init_telemetry",
+    "traced",
+    "get_metrics_tracker",
 ]
 
 # Make flake8 happy by using the names
diff --git a/src/arroyopy/operator.py b/src/arroyopy/operator.py
index 9444151..79c7d60 100644
--- a/src/arroyopy/operator.py
+++ b/src/arroyopy/operator.py
@@ -1,11 +1,13 @@
 import asyncio
 import logging
+import time
 from abc import ABC, abstractmethod
 from typing import List
 
 from .listener import Listener
 from .publisher import Publisher
 from .schemas import Message
+from .telemetry import get_metrics_tracker
 
 logger = logging.getLogger(__name__)
 
@@ -44,6 +46,9 @@ async def publish(self, message: Message) -> None:
 
     async def start(self):
         # Process messages from the queue
+        metrics_tracker = get_metrics_tracker()
+        operator_type = self.__class__.__name__
+
         while True:
             if self.stop_requested:
                 logger.info("Stopping operator...")
@@ -51,5 +56,13 @@ async def start(self):
                     await listener.stop()
                 break
             message = await self.listener_queue.get()
+
+            # Track processing time
+            start_time = time.perf_counter()
             processed_message = await self.process(message)
+            duration = time.perf_counter() - start_time
+
+            # Record metrics
+            metrics_tracker.record_processing_time(operator_type, duration)
+
             await self.publish(processed_message)
diff --git a/src/arroyopy/redis.py b/src/arroyopy/redis.py
index 426fc7b..3756115 100644
--- a/src/arroyopy/redis.py
+++ b/src/arroyopy/redis.py
@@ -5,6 +5,7 @@
 from .listener import Listener
 from .operator import Operator
 from .publisher import Publisher
+from .telemetry import get_metrics_tracker
 
 logger = logging.getLogger("arroyo.zmq")
 
@@ -26,6 +27,9 @@ async def from_client(
 
     async def start(self):
         logger.info("Listener started")
+        metrics_tracker = get_metrics_tracker()
+        listener_type = self.__class__.__name__
+
         pubsub = self.redis_client.pubsub()
         await pubsub.subscribe(self.redis_channel_name)
         # Listen for messages in the subscribed channel
@@ -41,6 +45,10 @@ async def start(self):
             msg = raw_msg["data"]
             if logger.getEffectiveLevel() == logging.DEBUG:
                 logger.debug(f"{msg=}")
+
+            # Record message metrics
+            metrics_tracker.record_message(listener_type)
+
             await self.operator.process(msg)
 
     async def stop(self):
diff --git a/src/arroyopy/telemetry.py b/src/arroyopy/telemetry.py
new file mode 100644
index 0000000..6bad228
--- /dev/null
+++ b/src/arroyopy/telemetry.py
@@ -0,0 +1,223 @@
+"""OpenTelemetry and Prometheus monitoring for Arroyopy."""
+
+import functools
+import logging
+import time
+from typing import Any, Callable, Optional
+
+from opentelemetry import trace
+from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
+from opentelemetry.sdk.resources import Resource
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import BatchSpanProcessor
+from prometheus_client import Counter, Gauge, Histogram
+
+logger = logging.getLogger(__name__)
+
+# Prometheus metrics
+MESSAGE_COUNTER = Counter(
+    "arroyopy_messages_received_total",
+    "Total number of messages received by listeners",
+    ["listener_type"],
+)
+
+MESSAGE_RATE = Gauge(
+    "arroyopy_messages_per_second",
+    "Messages per second processed by listeners",
+    ["listener_type"],
+)
+
+PROCESSING_TIME = Histogram(
+    "arroyopy_processing_seconds",
+    "Time spent processing messages in operators",
+    ["operator_type"],
+    buckets=(0.001, 0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0),
+)
+
+PROCESSING_TIME_SUMMARY = Gauge(
+    "arroyopy_avg_processing_seconds",
+    "Average time to process messages in operators",
+    ["operator_type"],
+)
+
+# OpenTelemetry setup
+_tracer_provider: Optional[TracerProvider] = None
+_tracer: Optional[trace.Tracer] = None
+
+
+def init_telemetry(
+    service_name: str = "arroyopy",
+    otlp_endpoint: str = "http://localhost:4317",
+) -> None:
+    """Initialize OpenTelemetry tracing with OTLP exporter.
+
+    Parameters
+    ----------
+    service_name : str
+        Name of the service for tracing
+    otlp_endpoint : str
+        OTLP endpoint URL (default: http://localhost:4317 for Jaeger)
+        For Jaeger: use http://localhost:4317
+        Can also be configured via OTEL_EXPORTER_OTLP_ENDPOINT env var
+    """
+    global _tracer_provider, _tracer
+
+    resource = Resource(attributes={"service.name": service_name})
+    _tracer_provider = TracerProvider(resource=resource)
+
+    otlp_exporter = OTLPSpanExporter(
+        endpoint=otlp_endpoint,
+        insecure=True,  # Use insecure connection for local development
+    )
+
+    _tracer_provider.add_span_processor(BatchSpanProcessor(otlp_exporter))
+    trace.set_tracer_provider(_tracer_provider)
+    _tracer = trace.get_tracer(__name__)
+
+    logger.info(f"OpenTelemetry initialized with OTLP exporter at {otlp_endpoint}")
+
+
+def get_tracer() -> trace.Tracer:
+    """Get the configured tracer, initializing if needed."""
+    if _tracer is None:
+        init_telemetry()
+    return _tracer
+
+
+def traced(span_name: Optional[str] = None, attributes: Optional[dict] = None):
+    """Decorator to create a Jaeger span for a function.
+
+    Parameters
+    ----------
+    span_name : str, optional
+        Name for the span. If not provided, uses the function name.
+    attributes : dict, optional
+        Additional attributes to add to the span.
+
+    Examples
+    --------
+    >>> @traced()
+    ... async def process_message(msg):
+    ...     # Processing logic
+    ...     pass
+
+    >>> @traced(span_name="custom_operation", attributes={"version": "1.0"})
+    ... def custom_function():
+    ...     pass
+    """
+
+    def decorator(func: Callable) -> Callable:
+        @functools.wraps(func)
+        async def async_wrapper(*args, **kwargs) -> Any:
+            tracer = get_tracer()
+            name = span_name or func.__name__
+            with tracer.start_as_current_span(name) as span:
+                if attributes:
+                    for key, value in attributes.items():
+                        span.set_attribute(key, value)
+
+                # Add function info
+                span.set_attribute("function.name", func.__name__)
+                span.set_attribute("function.module", func.__module__)
+
+                try:
+                    result = await func(*args, **kwargs)
+                    span.set_attribute("status", "success")
+                    return result
+                except Exception as e:
+                    span.set_attribute("status", "error")
+                    span.set_attribute("error.type", type(e).__name__)
+                    span.set_attribute("error.message", str(e))
+                    span.record_exception(e)
+                    raise
+
+        @functools.wraps(func)
+        def sync_wrapper(*args, **kwargs) -> Any:
+            tracer = get_tracer()
+            name = span_name or func.__name__
+            with tracer.start_as_current_span(name) as span:
+                if attributes:
+                    for key, value in attributes.items():
+                        span.set_attribute(key, value)
+
+                # Add function info
+                span.set_attribute("function.name", func.__name__)
+                span.set_attribute("function.module", func.__module__)
+
+                try:
+                    result = func(*args, **kwargs)
+                    span.set_attribute("status", "success")
+                    return result
+                except Exception as e:
+                    span.set_attribute("status", "error")
+                    span.set_attribute("error.type", type(e).__name__)
+                    span.set_attribute("error.message", str(e))
+                    span.record_exception(e)
+                    raise
+
+        # Return appropriate wrapper based on function type
+        import asyncio
+
+        if asyncio.iscoroutinefunction(func):
+            return async_wrapper
+        else:
+            return sync_wrapper
+
+    return decorator
+
+
+class MetricsTracker:
+    """Helper class to track metrics for listeners and operators."""
+
+    def __init__(self):
+        self._message_counts = {}
+        self._last_update_time = {}
+        self._processing_times = {}
+        self._processing_counts = {}
+
+    def record_message(self, listener_type: str) -> None:
+        """Record a message received by a listener."""
+        MESSAGE_COUNTER.labels(listener_type=listener_type).inc()
+
+        # Update message rate calculation
+        current_time = time.time()
+        if listener_type not in self._message_counts:
+            self._message_counts[listener_type] = 0
+            self._last_update_time[listener_type] = current_time
+
+        self._message_counts[listener_type] += 1
+
+        # Update rate every second
+        time_diff = current_time - self._last_update_time[listener_type]
+        if time_diff >= 1.0:
+            rate = self._message_counts[listener_type] / time_diff
+            MESSAGE_RATE.labels(listener_type=listener_type).set(rate)
+            self._message_counts[listener_type] = 0
+            self._last_update_time[listener_type] = current_time
+
+    def record_processing_time(self, operator_type: str, duration: float) -> None:
+        """Record processing time for an operator."""
+        PROCESSING_TIME.labels(operator_type=operator_type).observe(duration)
+
+        # Calculate running average
+        if operator_type not in self._processing_times:
+            self._processing_times[operator_type] = 0.0
+            self._processing_counts[operator_type] = 0
+
+        self._processing_times[operator_type] += duration
+        self._processing_counts[operator_type] += 1
+
+        avg_time = (
+            self._processing_times[operator_type]
+            / self._processing_counts[operator_type]
+        )
+        PROCESSING_TIME_SUMMARY.labels(operator_type=operator_type).set(avg_time)
+
+
+# Global metrics tracker instance
+_metrics_tracker = MetricsTracker()
+
+
+def get_metrics_tracker() -> MetricsTracker:
+    """Get the global metrics tracker instance."""
+    return _metrics_tracker
diff --git a/src/arroyopy/zmq.py b/src/arroyopy/zmq.py
index 419d44e..ed8071a 100644
--- a/src/arroyopy/zmq.py
+++ b/src/arroyopy/zmq.py
@@ -6,6 +6,7 @@
 
 from .listener import Listener
 from .operator import Operator
+from .telemetry import get_metrics_tracker
 
 logger = logging.getLogger("arroyo.zmq")
 
@@ -37,6 +38,9 @@ def from_socket(cls, zmq_socket: zmq.Socket):
 
     async def start(self):
         logger.info("Listener started")
+        metrics_tracker = get_metrics_tracker()
+        listener_type = self.__class__.__name__
+
         # timeout after 100 milliseconds so we can be stopped if requested
         self.zmq_socket.setsockopt(zmq.RCVTIMEO, 100)
         while True:
@@ -46,6 +50,10 @@ async def start(self):
                 msg = await self.zmq_socket.recv()
                 if logger.getEffectiveLevel() == logging.DEBUG:
                     logger.debug(f"{msg=}")
+
+                # Record message metrics
+                metrics_tracker.record_message(listener_type)
+
                 await self.operator.process(msg)
             except zmq.Again:
                 # no message occured within the timeout period