1. Symptoms
The clw-api-oom error manifests when the OpenClaw API service exhausts available memory resources during operation. This memory exhaustion produces several distinct observable symptoms that help identify the root cause and distinguish it from other runtime failures.
Primary Indicators:
- API requests begin returning HTTP 503 Service Unavailable responses with extended latency
- Process termination events in system logs showing
exit code 137(128 + SIGKILL signal) - Progressive degradation in API response times as memory pressure increases
- Container or process restart cycles triggered by the orchestrator’s health checks
- Memory usage metrics showing consumption approaching or exceeding configured limits
Shell Output Examples:
When the error occurs in containerized environments, you may observe the following system-level messages:
[FATAL] clw-api-oom: Memory allocation failed. Requested 2.4GB, available 512MB
[ERROR] RuntimeError: Cannot allocate memory for request payload processing
[CRITICAL] Process clw-api (PID 8472) killed by OOM killer
In application logs, the error typically appears as:
clw.api.exceptions.MemoryExhaustedError: clw-api-oom
Message: API worker exceeded maximum memory threshold (3.2GB / 4GB allocated)
Request ID: req_7f8a9b2c3d4e5f6g
Timestamp: 2024-12-18T14:32:07.891Z
Behavioral Symptoms:
The API service exhibits characteristic behavior patterns before complete failure, including garbage collection running more frequently, increased CPU utilization from memory management operations, and connection pool exhaustion as pending requests accumulate in memory. These symptoms typically develop over a period of minutes to hours depending on request volume and payload sizes.
2. Root Cause
The clw-api-oom error occurs when the OpenClaw API process requests memory allocation that exceeds the available system resources or configured memory limits. Understanding the underlying mechanisms requires examining both the application architecture and the memory management lifecycle within the OpenClaw platform.
Primary Causes:
The most frequent trigger for this error is inadequate memory limit configuration relative to actual workload requirements. OpenClaw API workers process incoming requests by loading request payloads, executing business logic, and maintaining connection pools—all operations that consume heap memory. When the volume of concurrent requests or the size of individual request payloads exceeds expectations, memory consumption grows proportionally until the configured limit is reached.
Secondary Causes:
Memory leaks within custom business logic or third-party extensions represent another significant cause. OpenClaw’s plugin architecture allows developers to extend API functionality, but improper resource management in plugins—such as failing to release database connections, closing file handles, or clearing cached objects—causes memory usage to grow monotonically over time. The leak becomes catastrophic only after extended operation, making diagnosis challenging.
Third-Party Integration Issues:
External service integrations frequently contribute to memory pressure. When the OpenClaw API communicates with downstream services, response data must be buffered in memory. Undersized buffer configurations or malformed responses from external APIs can cause excessive memory allocation within the integration layer. Additionally, deserialization of large JSON or XML payloads without streaming parsers forces the entire response into memory simultaneously.
Resource Contention:
In containerized deployments, memory contention between co-located services introduces another failure mode. When multiple containers share a host system’s memory resources without proper limits, one container’s memory exhaustion triggers the OOM killer, which may affect unrelated processes depending on system configuration. The OpenClaw API becomes collateral damage when system-wide memory pressure activates aggressive page reclamation.
Configuration Deficiencies:
Default memory configurations in OpenClaw assume development-scale workloads. Production deployments handling substantial request volumes, large payloads, or complex computations frequently require explicit memory allocation increases. The default JVM heap size, Node.js memory limit, or container memory allocation often proves insufficient for sustained production traffic patterns.
3. Step-by-Step Fix
Resolving the clw-api-oom error requires systematic diagnosis followed by targeted intervention. The following procedure addresses each root cause category in order of implementation complexity and impact.
Step 1: Assess Current Memory Utilization
Before implementing fixes, establish a baseline understanding of current memory consumption patterns.
Before:
# Check current memory usage of clw-api process
ps aux | grep clw-api
# View will show process with current RSS and VSZ values
# For containerized deployments
docker stats $(docker ps --filter name=clw-api --format "{{.Names}}")
After:
# Obtain precise memory metrics
free -h
cat /sys/fs/cgroup/memory/memory.usage_in_bytes 2>/dev/null || cat /sys/fs/cgroup/memory.max 2>/dev/null
# Monitor memory over time to identify growth patterns
while true; do date; docker stats --no-stream clw-api; sleep 10; done
Step 2: Increase Memory Limits
For containerized OpenClaw deployments, adjust memory limits in the container orchestration configuration.
Before:
# docker-compose.yml (inadequate configuration)
services:
clw-api:
image: openclaw/api:latest
deploy:
resources:
limits:
memory: 512M
After:
# docker-compose.yml (optimized configuration)
services:
clw-api:
image: openclaw/api:latest
deploy:
resources:
limits:
memory: 4G
reservations:
memory: 2G
environment:
- CLW_MAX_HEAP_SIZE=3G
- CLW_REQUEST_BUFFER_SIZE=10M
Step 3: Configure JVM Memory for Java-based OpenClaw Deployments
When OpenClaw API runs on the JVM, proper heap configuration is critical.
Before:
# Environment variable or startup script
export JAVA_OPTS="-Xms256m -Xmx512m"
After:
# Optimized JVM heap settings for production workloads
export JAVA_OPTS="-Xms2g -Xmx3g -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -XX:+HeapDumpOnOutOfMemoryError"
export CLW_JVM_OPTS="-XX:+UnlockExperimentalVMOptions -XX:+UseCGroupMemoryLimitForHeap"
Step 4: Implement Request Payload Limits
Configure maximum payload sizes to prevent individual requests from exhausting memory.
Before:
# openclaw-api.yml (default, unlimited payload)
server:
maxRequestSize: unlimited
After:
# openclaw-api.yml (bounded configuration)
server:
maxRequestSize: 50MB
maxConcurrentRequests: 100
requestTimeout: 30000
api:
maxPayloadSize: 52428800 # 50MB in bytes
maxResponseBufferSize: 10485760 # 10MB
streamLargeResponses: true
Step 5: Enable Memory-Monitoring and Auto-Restart Policies
Configure health checks and restart policies to handle temporary memory spikes gracefully.
Before:
# kubernetes deployment (minimal configuration)
spec:
containers:
- name: clw-api
image: openclaw/api:latest
resources:
limits:
memory: "1Gi"
After:
# kubernetes deployment (production configuration)
spec:
containers:
- name: clw-api
image: openclaw/api:latest
resources:
limits:
memory: "4Gi"
requests:
memory: "2Gi"
livenessProbe:
httpGet:
path: /health
port: 8080
initialDelaySeconds: 60
periodSeconds: 10
failureThreshold: 3
readinessProbe:
httpGet:
path: /ready
port: 8080
initialDelaySeconds: 30
periodSeconds: 5
env:
- name: CLW_ENABLE_MEMORY_MONITORING
value: "true"
- name: CLW_MEMORY_THRESHOLD_PCT
value: "85"
Step 6: Audit for Memory Leaks in Custom Code
Review custom plugins and business logic for memory management issues.
// Common leak pattern to avoid in OpenClaw plugins
// BEFORE (leaky implementation)
function processRequest(req, res) {
const data = req.body;
const result = expensiveOperation(data);
// BUG: Cache grows indefinitely
requestCache[req.id] = result;
res.json(result);
}
// AFTER (corrected implementation)
function processRequest(req, res) {
const data = req.body;
const result = expensiveOperation(data);
// Correct: Cache with TTL and size limits
const cacheKey = req.id;
if (requestCache.size >= MAX_CACHE_SIZE) {
const firstKey = requestCache.keys().next().value;
requestCache.delete(firstKey);
}
requestCache.set(cacheKey, result);
// Set expiration
setTimeout(() => requestCache.delete(cacheKey), CACHE_TTL_MS);
res.json(result);
// Explicit cleanup for complex objects
if (data && data.cleanup) data.cleanup();
}
4. Verification
After implementing fixes, thorough verification ensures the clw-api-oom error has been resolved and the system operates reliably under production load conditions.
Immediate Verification Steps:
Execute these commands within 5 minutes of deploying fixes to confirm immediate stability:
# Check API health endpoint
curl -f http://localhost:8080/health || echo "Health check failed"
# Verify process memory consumption
ps -o pid,vsz,rss,pmem,comm -p $(pgrep -f clw-api)
# Confirm no OOM events in kernel logs
dmesg | grep -i "oom\|killed process" | tail -20
# For containers, verify resource constraints
docker inspect clw-api --format '{{json .HostConfig.Memory}}'
docker stats --no-stream clw-api
Load Testing Verification:
Generate realistic production traffic to confirm the fix handles expected workloads:
# Using Apache Bench for basic load testing
ab -n 1000 -c 50 -p payload.json -T application/json http://localhost:8080/api/v1/process
# Using wrk for sustained load testing
wrk -t12 -c400 -d60s --latency http://localhost:8080/api/v1/endpoint
# Monitor memory during load test (in separate terminal)
watch -n 1 'docker stats --no-stream clw-api --format "{{.Name}}\t{{.MemUsage}}\t{{.MemPerc}}"'
Long-Term Monitoring:
Deploy monitoring to track memory patterns over extended periods:
# Set up Prometheus scraping for memory metrics
curl -X POST http://localhost:9090/api/v1/rules \
-H "Content-Type: application/json" \
-d '{
"name": "clw_api_memory_alerts",
"rules": [{
"alert": "ClwApiHighMemory",
"expr": "container_memory_usage_bytes{name=\"clw-api\"} / container_spec_memory_limit_bytes{name=\"clw-api\"} > 0.85",
"for": "5m",
"labels": {"severity": "warning"},
"annotations": {"summary": "clw-api memory usage exceeds 85% of limit"}
}]
}'
Success Criteria:
The fix is verified successful when all of the following conditions are met simultaneously: API health endpoints return 200 OK consistently under load, memory utilization remains below 80% of configured limits during sustained traffic, no OOM events appear in kernel logs, API response times maintain acceptable latency (<500ms p95), and no container or process restarts occur due to memory exhaustion.
5. Common Pitfalls
Avoiding these common mistakes prevents recurring clw-api-oom errors and ensures stable production deployments.
Pitfall 1: Setting Memory Limits Too Close to Actual Usage
Many administrators configure memory limits based on current consumption rather than accounting for traffic spikes and growth. A limit set at 1.2x current usage provides insufficient headroom. Always calculate limits based on peak consumption plus 50% minimum headroom, accounting for seasonal traffic variations and future growth projections.
Pitfall 2: Ignoring Container-Level Memory Limits
Even when JVM or Node.js heap limits are configured correctly, container-level memory constraints can still trigger OOM conditions. The operating system’s memory accounting includes not just heap allocations but also native memory, stack space, memory-mapped files, and kernel buffers. Always ensure container memory limits exceed application heap settings by at least 20-30% to accommodate non-heap memory consumption.
Pitfall 3: Misconfiguring Garbage Collection
Aggressive garbage collection settings can paradoxically increase memory pressure. Overly frequent GC cycles consume CPU and may prevent efficient memory compaction. Conversely, GC configurations too lenient for the workload cause memory to fill before collection triggers. Profile your specific workload to determine optimal GC settings rather than copying configurations from different applications.
Pitfall 4: Neglecting External Dependency Memory Behavior
OpenClaw API integrates with databases, caches, and external services. These dependencies consume memory on the API host—for connection buffers, query result caching, and response buffering. A configuration that appears adequate when tested in isolation may fail in production where external dependencies introduce additional memory pressure. Test with realistic integration loads.
Pitfall 5: Assuming Fixed Limits Solve Memory Leaks
Increasing memory limits addresses symptoms but not the underlying cause if a memory leak exists. The leak continues consuming memory at the same rate, and the application will eventually exhaust even larger limits. Always investigate whether memory growth is bounded (expected behavior with caching) or unbounded (indicating a leak requiring code fixes).
Pitfall 6: Overlooking ulimit Settings
Container processes are also subject to system-level resource limits. The ulimit settings for virtual memory, open file descriptors, and process memory mappings can restrict the API even when other configurations are correct. Verify ulimit -v, ulimit -n, and ulimit -m settings in the deployment environment.
Pitfall 7: Inadequate Health Check Configuration
When health checks are too aggressive or too lenient, they can either cause unnecessary restart cycles or fail to detect memory exhaustion. A health check that triggers restart at 90% memory usage may cause instability under legitimate peak load. Configure thresholds based on measured production patterns rather than arbitrary percentages.
6. Related Errors
The following related errors share common root causes or occur in similar contexts to clw-api-oom:
clw-api-timeout
The clw-api-timeout error occurs when API requests exceed configured timeout thresholds rather than memory limits. While distinct in their primary trigger, both errors frequently appear together because memory pressure causes processing slowdowns that eventually exceed timeouts. Addressing clw-api-oom often resolves timeout issues as a secondary benefit, and both errors benefit from similar load management strategies.
clw-svc-oom
The clw-svc-oom error affects OpenClaw service workers rather than the API component specifically. Service workers typically perform background processing, batch operations, or streaming data transformations. Like the API variant, this error results from memory exhaustion but occurs in a different component of the OpenClaw architecture. The fix procedures are similar—increase memory limits and audit for leaks—but target the worker process configuration.
clw-worker-crash
The clw-worker-crash error encompasses process termination events that may result from various causes including OOM conditions. Not all worker crashes stem from memory exhaustion, but the error code should be evaluated for OOM indicators when investigating. Crash dumps and exit codes (137 specifically indicates SIGKILL from OOM killer) help distinguish memory-related crashes from other failure modes.