1. Symptoms
The clw-sandbox-oom error occurs when the OpenClaw sandbox environment exhausts its allocated memory quota during code execution. This error manifests through several distinguishable indicators that help developers identify the root cause quickly.
When the memory limit is breached, the sandbox container’s kernel-level Out-of-Memory killer terminates the running process. The CLI typically outputs an error message resembling the following:
Error: clw-sandbox-oom
Sandbox execution failed: process terminated due to memory exhaustion
Memory limit: 256MB
Peak usage: 312MB
Sandbox ID: sbx_a7f3k2m9
Developers may observe the process exiting with signal code 137 (128 + 9, where 9 is SIGKILL), which is a definitive indicator that the container’s OOM killer was invoked. In interactive sessions, the sandbox may become unresponsive before crashing, or execution may halt abruptly without producing expected output. Webhook-based integrations often receive a JSON payload containing the error code, memory statistics, and a timestamp of the termination event.
Memory-intensive operations such as loading large datasets, performing in-memory aggregations, executing recursive algorithms on large input sizes, or spawning multiple concurrent processes within the sandbox are common triggers for this error. Applications that leak memory over time may initially succeed but fail after extended execution when cumulative memory consumption exceeds the sandbox limit.
2. Root Cause
The clw-sandbox-oom error originates from the Linux kernel’s memory management subsystem when a process attempts to allocate memory beyond what the container’s cgroup memory limit permits. OpenClaw sandboxes are implemented using Linux cgroups (control groups) v2, which provide fine-grained resource isolation and limits for containers. Each sandbox instance is assigned a fixed memory ceiling at creation time, typically defaulting to 256MB or 512MB depending on the plan tier.
When a process inside the sandbox requests additional heap memory through standard allocation functions like malloc(), mmap(), or brk(), the kernel checks whether the request would exceed the configured memory limit. If the allocation would breach the boundary, the kernel has two options: either deny the allocation (returning NULL or failing with ENOMEM) or invoke the OOM killer to reclaim memory by terminating the most memory-intensive process within the cgroup. OpenClaw’s sandbox configuration enables the OOM killer as a safety mechanism, causing the abrupt termination observed when memory limits are exceeded.
Several architectural factors contribute to memory exhaustion in sandboxed environments. First, the sandbox operates with a constrained address space that differs from native execution environments. Second, certain language runtimes like the V8 JavaScript engine or JVM pre-allocate significant heap memory during initialization, consuming a substantial portion of the available quota before user code executes. Third, memory-mapped files, shared libraries, and runtime overhead compound the memory pressure, leaving less working memory for application logic.
3. Step-by-Step Fix
Addressing the clw-sandbox-oom error requires a systematic approach combining memory optimization, algorithmic improvements, and configuration adjustments. Follow these steps to resolve the issue:
Step 1: Audit Current Memory Consumption
Before implementing fixes, establish a baseline understanding of your application’s memory footprint. Add instrumentation to track peak memory usage during execution:
Before:
// No memory monitoring
function processData(items) {
return items.map(item => transform(item));
}
After:
// Memory monitoring implementation
function processData(items) {
const initialMemory = process.memoryUsage().heapUsed;
console.log(`Initial heap: ${Math.round(initialMemory / 1024)}KB`);
const batchSize = 100;
const results = [];
for (let i = 0; i < items.length; i += batchSize) {
const batch = items.slice(i, i + batchSize);
results.push(...batch.map(item => transform(item)));
const currentMemory = process.memoryUsage().heapUsed;
console.log(`Batch ${i / batchSize}: ${Math.round(currentMemory / 1024)}KB`);
}
return results;
}
Step 2: Implement Streaming and Batched Processing
Replace in-memory operations with streaming approaches that process data in manageable chunks:
Before:
// Loads entire dataset into memory
const allData = await fetchAllRecords();
const aggregated = allData.reduce((acc, record) => {
acc[record.category] = (acc[record.category] || 0) + record.value;
return acc;
}, {});
After:
// Processes data in streaming fashion
async function* streamRecords() {
let offset = 0;
const limit = 1000;
while (true) {
const batch = await fetchRecords(offset, limit);
if (batch.length === 0) break;
for (const record of batch) {
yield record;
}
offset += limit;
}
}
async function aggregateWithMemoryConstraint() {
const totals = {};
for await (const record of streamRecords()) {
totals[record.category] = (totals[record.category] || 0) + record.value;
// Explicit garbage collection hint for long-running operations
if (Object.keys(totals).length % 10000 === 0) {
global.gc?.();
}
}
return totals;
}
Step 3: Optimize Data Structures
Replace memory-intensive data structures with more efficient alternatives:
Before:
// Using object with string keys for large datasets
const lookup = {};
for (const item of largeDataset) {
lookup[`${item.id}-${item.type}`] = item;
}
After:
// Using Map for better memory efficiency with numeric keys
const lookup = new Map();
for (const item of largeDataset) {
lookup.set(item.id * 1000 + item.type, item);
}
Step 4: Configure Memory Limits
If your workload legitimately requires more memory, adjust the sandbox memory allocation:
Before:
{
"sandbox": {
"memory": "256MB"
}
}
After:
{
"sandbox": {
"memory": "512MB"
}
}
Alternatively, use the CLI flag when running OpenClaw:
clw run --memory 512MB --entrypoint index.js
4. Verification
After implementing fixes, verify that the clw-sandbox-oom error has been resolved by executing your workload in a controlled manner:
First, create a test script that reproduces the memory-intensive operation:
// verification-test.js
async function runVerification() {
const testData = generateTestDataset(100000);
console.log('Starting memory verification test...');
const startMemory = process.memoryUsage().heapUsed;
try {
const result = await processData(testData);
const endMemory = process.memoryUsage().heapUsed;
const peakMemory = process.memoryUsage().heapTotal;
console.log({
status: 'SUCCESS',
startMemoryMB: Math.round(startMemory / 1024 / 1024),
endMemoryMB: Math.round(endMemory / 1024 / 1024),
peakMemoryMB: Math.round(peakMemory / 1024 / 1024),
memoryLeakMB: Math.round((endMemory - startMemory) / 1024 / 1024)
});
return { success: true, result };
} catch (error) {
console.error('Verification failed:', error.message);
return { success: false, error: error.message };
}
}
runVerification();
Execute the verification in the sandbox environment:
clw run --entrypoint verification-test.js --timeout 120s
A successful verification should complete without the clw-sandbox-oom error and display memory statistics showing peak usage comfortably below the configured limit. Monitor the output for memory leak indicators—if end memory significantly exceeds start memory, additional garbage collection or algorithmic changes may be necessary.
5. Common Pitfalls
When addressing the clw-sandbox-oom error, developers frequently encounter several recurring mistakes that can undermine their fixes or introduce new problems.
The most prevalent pitfall involves increasing memory limits without addressing underlying inefficiency. While allocating additional memory may temporarily resolve the immediate error, it masks the symptom rather than treating the cause. Applications that consume excessive memory relative to their requirements often exhibit degraded performance due to cache misses and swapping, and they remain vulnerable to OOM errors under load spikes or with larger input datasets.
Another common error involves disabling garbage collection or deferring memory management optimizations. Some developers attempt to optimize by removing GC hints or delaying cleanup operations, believing that avoiding GC pauses will improve performance. In reality, accumulated garbage increases memory pressure and triggers OOM conditions more frequently than periodic cleanup would.
Neglecting to account for runtime overhead represents a subtle but impactful mistake. Language runtimes, virtual machines, and native libraries consume a portion of the sandbox’s memory budget before user code begins executing. A Node.js runtime typically initializes with 30-50MB already allocated, leaving less room for application data. Developers who calculate memory requirements based on native execution measurements often underestimate the available headroom in sandboxed environments.
Finally, failing to test with production-scale data during development leads to surprises in deployment. Algorithms that perform adequately with small test datasets may exhaust memory when processing realistic workloads. Establish representative test data early in development and validate memory consumption against expected production volumes.
6. Related Errors
The clw-sandbox-oom error frequently occurs alongside or is confused with several related sandbox execution failures. Understanding their distinctions aids in accurate diagnosis.
clw-sandbox-killed: This error indicates that the sandbox process was terminated by an external signal rather than completing normally. While OOM conditions often result in a killed status, the clw-sandbox-killed error can also result from resource limit violations, manual termination, or system-level signals unrelated to memory exhaustion. The distinction lies in the specific termination signal—OOM conditions trigger SIGKILL (9), whereas other limits may produce different signals.
clw-sandbox-timeout: This error occurs when sandbox execution exceeds the configured time limit. Memory-intensive operations often correlate with extended execution times because garbage collection, swapping, and inefficient algorithms consume both memory and CPU cycles. However, timeout errors can occur independently of memory issues when operations involve network latency, computational complexity, or deliberate rate limiting.
clw-process-exit-nonzero: This generic error indicates that the sandboxed process exited with a non-zero status code. Exit code 137 specifically signals SIGKILL termination (OOM killer invocation), while other codes indicate application-level failures. Correlating exit codes with specific error messages helps distinguish between memory-related terminations and other execution failures.
{
"related_error_examples": {
"clw-sandbox-killed": "Process terminated by signal 9 (SIGKILL)",
"clw-sandbox-timeout": "Execution exceeded 30000ms time limit",
"clw-process-exit-nonzero": "Exit code: 137 (SIGKILL from OOM killer)"
}
}