Fix clw-memory-oom: OpenClaw Out of Memory Error Resolution

1. Symptoms

The clw-memory-oom error occurs when an OpenClaw-managed workload exhausts its allocated memory resources. This error manifests through several distinct symptoms that help identify the root cause.

Primary Symptoms:

When the memory limit is reached, the OpenClaw runtime generates a specific error event:

[ERROR] clw-memory-oom: Container workload-xyz exceeded memory limit [ERROR] Current usage: 2048Mi, Limit: 2048Mi [ERROR] Memory allocation request rejected


**Observable Symptoms:**

- **Container restart loops**: The workload container enters an `OOMKilled` state and restarts repeatedly
- **Application crashes**: The process terminates unexpectedly without graceful shutdown
- **Memory metric spikes**: In monitoring dashboards, memory usage graphs show a sharp vertical line at the limit threshold
- **Health check failures**: Liveness probes fail after the container is killed
- **Pod status changes**: In Kubernetes environments, the pod status shows `OOMKilled` or `CrashLoopBackOff`

**Log Output Examples:**

```bash
# OpenClaw runtime logs
2024-01-15T10:23:45.123Z ERROR [runtime] clw-memory-oom detected
2024-01-15T10:23:45.124Z ERROR [runtime] Workload: payment-processor
2024-01-15T10:23:45.125Z ERROR [runtime] Memory stats: used=2147483648 bytes, limit=2147483648 bytes
2024-01-15T10:23:45.126Z INFO [runtime] Initiating graceful termination (timeout: 30s)
2024-01-15T10:23:45.200Z INFO [runtime] Container terminated with signal SIGKILL

# kubectl describe pod output
Name:             payment-processor
Namespace:        production
Status:           OOMKilled
Containers:
  payment-svc:
    Memory:       2148Mi / 2048Mi  # Usage exceeds limit
    Last State:   Terminated
      Reason:     OOMKilled
      Exit Code:  137

2. Root Cause

The clw-memory-oom error stems from a fundamental mismatch between memory consumption and memory allocation. Understanding the underlying causes is essential for implementing an effective solution.

Primary Root Causes:

Insufficient Memory Limits: The configured memory limit is too low for the workload’s actual requirements. This commonly occurs when limits are set based on ideal conditions rather than production load patterns.
Memory Leaks: Application code contains memory leaks that gradually consume available memory until the limit is reached. Memory leaks are particularly insidious because they cause failures only after extended runtime periods.
Unexpected Traffic Spikes: Sudden increases in request volume or data processing can cause memory usage to spike beyond configured limits.
Incorrect Memory Requests: Memory requests in OpenClaw manifests are misconfigured, leading to scheduling on nodes with insufficient available memory.
Shared Memory Misconfiguration: Multiple containers sharing memory segments without proper accounting causes aggregate memory to exceed individual limits.

Technical Deep Dive:

┌─────────────────────────────────────────────────────────────┐
│                    Memory Allocation Model                  │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│   Container Memory Limit      │    2048 Mi                  │
│   ┌───────────────────────────┴───────────────────────────┐ │
│   │                                                        │ │
│   │   Heap Memory       │   Stack    │   Shared Memory   │ │
│   │   ┌────────────┐     │   ┌────┐   │   ┌───────────┐  │ │
│   │   │ 512 Mi     │     │   │64Mi│   │   │ 256 Mi    │  │ │
│   │   └────────────┘     │   └────┘   │   └───────────┘  │ │
│   │                                                        │ │
│   │   Application Memory │ File Cache   │ OS Overhead    │ │
│   │   ┌────────────┐      │   ┌──────┐   │   ┌─────────┐  │ │
│   │   │ 896 Mi     │      │   │128Mi │   │   │ 192 Mi  │  │ │
│   │   └────────────┘      │   └──────┘   │   └─────────┘  │ │
│   └───────────────────────────────────────────────────────┘ │
│                                                             │
│   Total Requested: 2048 Mi                                  │
│   Total Used: 2148 Mi (100 Mi OVER LIMIT)                  │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Common Scenarios Leading to OOM:

Scenario	Description	Impact
Unbounded Caches	Application caches grow without eviction policies	Progressive memory growth
Connection Pool Leaks	Database or HTTP connections not properly released	Connection objects accumulate
Large Batch Processing	Processing large datasets without streaming	Entire dataset loaded into memory
Worker Thread Accumulation	Background workers spawning without limits	Thread stacks consume memory
Memory Fragmentation	Long-running processes with fragmented heap	Effective available memory decreases

3. Step-by-Step Fix

Resolving the clw-memory-oom error requires systematic investigation and targeted remediation. Follow these steps in order to identify and fix the root cause.

Step 1: Identify Current Memory Configuration

First, examine the current memory settings for the affected workload:

# View OpenClaw workload configuration
clw get workload payment-processor -o yaml

# Query memory metrics for the workload
clw metrics workload payment-processor --window 1h

# Check memory allocation across all workloads in namespace
clw get workloads -n production --format table

Step 2: Analyze Memory Usage Patterns

Examine the memory consumption pattern to determine if the issue is a leak or a capacity issue:

# Enable detailed memory profiling
clw debug workload payment-processor --memory-profile --duration 5m

# Export memory trace for analysis
clw debug export --type memory --output /tmp/memory-trace.json

# Check for memory leak patterns
clw analyze memory-trace /tmp/memory-trace.json --leak-detection

Step 3: Increase Memory Limits (Quick Fix)

If the workload legitimately requires more memory, update the resource limits:

Before:

# workload-config.yaml
apiVersion: openclaw.io/v1
kind: Workload
metadata:
  name: payment-processor
  namespace: production
spec:
  containers:
    - name: payment-svc
      image: registry.example.com/payment-service:v2.3.1
      resources:
        limits:
          memory: "2048Mi"
        requests:
          memory: "1024Mi"

After:

# workload-config.yaml
apiVersion: openclaw.io/v1
kind: Workload
metadata:
  name: payment-processor
  namespace: production
spec:
  containers:
    - name: payment-svc
      image: registry.example.com/payment-service:v2.3.1
      resources:
        limits:
          memory: "4096Mi"
        requests:
          memory: "2048Mi"

Apply the changes:

# Apply the updated configuration
clw apply -f workload-config.yaml

# Verify the new limits are active
clw get workload payment-processor -o jsonpath='{.spec.containers[0].resources.limits.memory}'

Step 4: Fix Memory Leaks in Application Code

If a memory leak is identified, address it in the application code. Common patterns include:

Before:

# payment_service.py (leaky implementation)
import asyncio

class PaymentCache:
    def __init__(self):
        self._cache = {}  # Unbounded dictionary
    
    async def store_transaction(self, txn_id, data):
        # Never removes old entries - memory leak
        self._cache[txn_id] = data
        return await self.process_payment(data)
    
    async def get_transaction(self, txn_id):
        return self._cache.get(txn_id)

cache = PaymentCache()

async def process_payment_request(request):
    data = await fetch_payment_data(request)
    # Each call adds to cache forever
    result = await cache.store_transaction(request.txn_id, data)
    return result

After:

# payment_service.py (fixed implementation)
import asyncio
from collections import OrderedDict
from functools import wraps

class BoundedCache:
    def __init__(self, max_size=1000):
        self._cache = OrderedDict()
        self._max_size = max_size
    
    async def store_transaction(self, txn_id, data):
        # Evict oldest entries when cache is full
        if len(self._cache) >= self._max_size:
            self._cache.popitem(last=False)
        
        self._cache[txn_id] = data
        return await self.process_payment(data)
    
    async def get_transaction(self, txn_id):
        return self._cache.get(txn_id)
    
    def clear_expired(self, ttl_seconds=3600):
        """Clear entries older than TTL."""
        current_time = asyncio.get_event_loop().time()
        expired = [
            k for k, v in self._cache.items()
            if current_time - v['timestamp'] > ttl_seconds
        ]
        for key in expired:
            self._cache.pop(key, None)

cache = BoundedCache(max_size=1000)

async def process_payment_request(request):
    data = await fetch_payment_data(request)
    result = await cache.store_transaction(request.txn_id, data)
    return result

Step 5: Implement Memory-Aware Processing

For workloads processing large datasets, implement streaming or batching:

Before:

# batch_processor.py (loads all data in memory)
import pandas as pd

def process_transactions_batch(csv_file):
    # Loads entire CSV into memory - causes OOM for large files
    df = pd.read_csv(csv_file)
    
    results = []
    for _, row in df.iterrows():
        result = process_single_transaction(row)
        results.append(result)
    
    return pd.DataFrame(results)

After:

# batch_processor.py (streaming implementation)
import pandas as pd

def process_transactions_streaming(csv_file, batch_size=1000):
    """Process CSV in chunks to limit memory usage."""
    results = []
    
    # Process file in chunks
    for chunk in pd.read_csv(csv_file, chunksize=batch_size):
        # Process each chunk
        for _, row in chunk.iterrows():
            result = process_single_transaction(row)
            results.append(result)
        
        # Yield intermediate results to prevent memory buildup
        if len(results) >= batch_size * 2:
            yield from results
            results = []
    
    # Yield remaining results
    yield from results

# Usage with OpenClaw workload
def handle_batch_processing(request):
    csv_path = request.file_path
    all_results = []
    
    for partial_results in process_transactions_streaming(csv_path):
        all_results.extend(partial_results)
    
    return {"processed": len(all_results), "results": all_results}

Step 6: Configure Memory Limits with Buffers

When setting memory limits, include a safety buffer for unexpected spikes:

# Calculate recommended limit based on observed usage
clw analyze workload payment-processor --recommend-resources

# Output suggests:
# Current P95 Memory: 1843Mi
# Recommended Limit: 3072Mi (P95 * 1.5 safety factor)
# Recommended Request: 2048Mi (P95 * 1.1)

Update the configuration with recommended values:

spec:
  containers:
    - name: payment-svc
      resources:
        limits:
          memory: "3072Mi"  # 50% buffer over P95
        requests:
          memory: "2048Mi"  # P95 + 10% buffer

4. Verification

After implementing the fix, verify that the clw-memory-oom error no longer occurs by following these verification steps.

Immediate Verification

Check that the workload starts and remains stable:

# Watch workload status for 5 minutes
clw logs workload payment-processor --follow --duration 5m

# Check for OOM errors in recent logs
clw logs workload payment-processor --since 1h | grep -i "clw-memory-oom"
# Expected: No output (no OOM errors)

# Verify container is not in CrashLoopBackOff
clw get workload payment-processor -o jsonpath='{.status.phase}'
# Expected: Running

Memory Metrics Verification

Confirm memory usage is within acceptable bounds:

# Get current memory metrics
clw metrics workload payment-processor --window 5m

# Sample output showing healthy memory usage
# METRIC                    VALUE       LIMIT        STATUS
# memory.usage.bytes        1843245056  3221225472   OK (57%)
# memory.working_set.bytes  1811939328  3221225472   OK (56%)
# memory.rss.bytes          524288000   -            -

Load Testing Verification

Apply realistic load to confirm the fix handles production traffic:

# Run load test against the workload
clw test workload payment-processor \
    --rps 1000 \
    --duration 10m \
    --check-memory

# Verify memory remains stable during load
clw test verify payment-processor-load-test --show-memory-trend

# Expected: Memory usage plateaus, does not continuously grow

Long-term Monitoring Setup

Configure monitoring to catch future memory issues early:

# Enable memory alerting for the workload
clw alert create \
    --workload payment-processor \
    --metric memory.usage.percentage \
    --threshold 80 \
    --severity warning \
    --action notify

clw alert create \
    --workload payment-processor \
    --metric memory.usage.percentage \
    --threshold 90 \
    --severity critical \
    --action page-oncall

# Verify alerts are active
clw alert list --workload payment-processor

5. Common Pitfalls

When fixing clw-memory-oom errors, developers frequently encounter these pitfalls that can worsen the situation or fail to address the underlying issue.

Pitfall 1: Setting Identical Requests and Limits

Setting memory requests equal to limits eliminates the node’s ability to schedule pods efficiently and can cause unnecessary evictions:

# PROBLEMATIC: Identical request and limit
resources:
  limits:
    memory: "2048Mi"
  requests:
    memory: "2048Mi"  # No flexibility for scheduling

Instead, use requests based on normal usage and limits based on maximum acceptable usage with buffers.

Pitfall 2: Ignoring Native Memory Overhead

Container memory accounting includes not just application heap but also:

Kernel buffers and page cache
Memory used by the runtime (JVM heap, Python interpreter)
Shared libraries loaded into memory
Memory-mapped files

# Check total process memory (not just heap)
clw debug workload payment-processor --show-vmmap

# Sample output showing native overhead
# Type           Size     Count
# Heap           512 Mi   -
# Stack          64 Mi    32 threads
# Anonymous      256 Mi   -
# Shared         128 Mi   8 regions
# Page Cache     192 Mi   -
# System Overhead 48 Mi   -
# ─────────────────────────────────
# TOTAL          1200 Mi

Pitfall 3: Not Implementing Graceful Degradation

When memory is constrained, applications should degrade gracefully rather than crash:

# PROBLEMATIC: No memory-aware fallback
def get_recommendations(user_id):
    # Loads all recommendations into memory
    recommendations = load_all_recommendations()
    return recommendations

# BETTER: Memory-aware implementation
def get_recommendations(user_id, max_items=100):
    # Limit memory usage with streaming
    recommendations = []
    for item in stream_recommendations(user_id):
        if len(recommendations) >= max_items:
            break
        recommendations.append(item)
    return recommendations

Pitfall 4: Using Absolute Limits Without Scaling Strategy

A fixed memory limit does not scale with workload demand. Implement auto-scaling based on memory utilization:

# OpenClaw Horizontal Pod Autoscaler configuration
apiVersion: openclaw.io/v1
kind: HorizontalPodAutoscaler
metadata:
  name: payment-processor-hpa
spec:
  scaleTargetRef:
    apiVersion: openclaw.io/v1
    kind: Workload
    name: payment-processor
  minReplicas: 2
  maxReplicas: 10
  metrics:
    - type: Resource
      resource:
        name: memory
        target:
          type: Utilization
          averageUtilization: 70  # Scale when memory exceeds 70%

Pitfall 5: Not Monitoring for Regression

After fixing the immediate OOM error, continue monitoring to catch regressions:

# Set up comprehensive memory monitoring
clw monitor create \
    --workload payment-processor \
    --metrics memory.usage,memory.working_set,memory.rss \
    --export-to prometheus \
    --retention 30d

# Create dashboard for memory visibility
clw dashboard create memory-overview \
    --workloads payment-processor,order-service,inventory-service \
    --metrics memory.usage.percentage,memory.failures.total

The clw-memory-oom error is often accompanied by or confused with these related errors. Understanding their relationships helps with comprehensive troubleshooting.

clw-memory-pressure

This warning indicates the system is approaching memory limits before an OOM condition occurs:

[WARNING] clw-memory-pressure: Memory usage at 85% of limit
[WARNING] Workload: payment-processor, Usage: 1740Mi / 2048Mi

Relationship: clw-memory-pressure is a leading indicator of potential clw-memory-oom failures. Addressing pressure warnings prevents OOM errors.

clw-container-kill

This error confirms the container was terminated due to resource exhaustion:

[ERROR] clw-container-kill: Container payment-svc was killed
[ERROR] Reason: OOMKilled (Exit code 137)
[ERROR] Signal: SIGKILL received

Relationship: clw-container-kill is the immediate consequence of clw-memory-oom. The OOM error triggers the container kill.

clw-resource-limit

A general resource limit error that encompasses memory, CPU, and other resource constraints:

[ERROR] clw-resource-limit: Resource limit exceeded
[ERROR] Resource: memory, Workload: payment-processor
[ERROR] Limit: 2048Mi, Usage: 2096Mi

Relationship: clw-resource-limit is the parent error category; clw-memory-oom is a specific instance of this category.

k8s-oom-killed

Kubernetes-native OOMKilled status that may occur alongside or independently of OpenClaw errors:

Status: OOMKilled
Last State: Terminated
  Reason: OOMKilled
  Exit Code: 137

Relationship: When OpenClaw workloads run on Kubernetes, k8s-oom-killed and clw-memory-oom often occur together. Both must be addressed for complete resolution.

docker-out-of-memory

Docker-level memory exhaustion error when running containers outside OpenClaw management:

docker: Error response from daemon: Cannot kill container:
Memory cgroup out of memory.

Relationship: Docker OOM is the底层 container runtime error that OpenClaw surfaces as clw-memory-oom. Fixing one typically fixes both.

Troubleshooting Flow

┌─────────────────────┐
│  clw-memory-oom     │
└─────────┬───────────┘
          │
          ▼
┌─────────────────────┐     ┌──────────────────────────┐
│ Check memory limits │────▶│ Increase limits if needed │
└─────────┬───────────┘     └──────────────────────────┘
          │
          │ If limits are adequate
          ▼
┌─────────────────────┐     ┌──────────────────────────┐
│ Check for leaks     │────▶│ Fix application code     │
└─────────┬───────────┘     └──────────────────────────┘
          │
          │ If no leaks found
          ▼
┌─────────────────────┐     ┌──────────────────────────┐
│ Check traffic spikes│────▶│ Implement auto-scaling   │
└─────────────────────┘     └──────────────────────────┘

1. Symptoms

When the memory limit is reached, the OpenClaw runtime generates a specific error event:

2. Root Cause

3. Step-by-Step Fix

Step 1: Identify Current Memory Configuration

Step 2: Analyze Memory Usage Patterns

Step 3: Increase Memory Limits (Quick Fix)

Step 4: Fix Memory Leaks in Application Code

Step 5: Implement Memory-Aware Processing

Step 6: Configure Memory Limits with Buffers

4. Verification

Immediate Verification

Memory Metrics Verification

Load Testing Verification

Long-term Monitoring Setup

5. Common Pitfalls

Pitfall 1: Setting Identical Requests and Limits

Pitfall 2: Ignoring Native Memory Overhead

Pitfall 3: Not Implementing Graceful Degradation

Pitfall 4: Using Absolute Limits Without Scaling Strategy

Pitfall 5: Not Monitoring for Regression

6. Related Errors

clw-memory-pressure

clw-container-kill

clw-resource-limit

k8s-oom-killed

docker-out-of-memory

Troubleshooting Flow