1. Symptoms
When the clw-worker-exhausted error manifests in your OpenClaw environment, you will observe several distinct indicators that point toward worker resource depletion. The most prominent symptom is a sudden cessation of task processing from a specific worker node. Tasks that were previously being executed will remain in a pending or in-progress state without completion.
In the command-line interface, running clawctl status or clawctl worker list typically reveals workers with an EXHAUSTED status. The output may include additional diagnostic information such as the memory threshold that was breached and the current memory consumption of the affected worker process.
Worker ID: worker-7f3a2c
Status: EXHAUSTED
Memory Used: 2048 MB
Memory Limit: 2048 MB
Current Task: data-transformation-v2
Uptime: 4h 32m 15s
The task queue will begin to grow as incoming tasks accumulate because no worker has available capacity to claim them. In production environments with high throughput requirements, this symptom often triggers monitoring alerts before operators can manually inspect the system.
Standard error logs captured by the OpenClaw daemon will contain entries similar to:
[ERROR] Worker worker-7f3a2c: Memory limit exceeded (2048MB/2048MB)
[ERROR] Cannot allocate 128MB for task data-transformation-v2
[WARN] Worker worker-7f3a2c marked as EXHAUSTED
Additionally, you may notice that subsequent task submission commands return warnings about queue congestion or delayed processing times.
2. Root Cause
The clw-worker-exhausted error fundamentally indicates that a worker process has consumed all available memory allocated to it by the OpenClaw resource management system. This condition arises from several distinct scenarios that share a common underlying mechanism: the worker attempting to allocate more memory than its configured ceiling permits.
The primary architectural reason involves OpenClaw’s worker isolation model. Each worker runs as an independent process with a hard memory limit enforced at the operating system level through cgroups on Linux, resource constraints on macOS, or job objects on Windows. When the worker attempts to allocate memory beyond this threshold, the operating system refuses the allocation request, and OpenClaw catches this condition to prevent the worker from entering an unpredictable crashed state.
Memory exhaustion typically occurs through accumulation patterns rather than a single operation consuming everything at once. Workers that process data transformation, parsing, or aggregation operations often hold references to large objects in memory while performing sequential operations. If the garbage collection or cleanup mechanisms fail to release memory between operations, consumption grows monotonically until the limit is reached.
Another contributing factor involves memory leaks within worker plugins or custom task handlers. When task processors fail to properly release resources after completing their work, the allocated memory persists in the worker heap. Over time, even moderate per-task leakage accumulates to exceed configured limits.
Concurrent task processing configurations can also trigger exhaustion. Workers configured to handle multiple simultaneous tasks require memory proportional to the sum of all active task requirements. A misconfigured concurrency setting combined with memory-intensive tasks creates a scenario where the aggregate memory demand exceeds available capacity.
Configuration errors represent a frequently overlooked cause. Setting memory limits below the minimum requirements for task execution, or inheriting environment variables that increase memory usage without adjusting limits, results in premature exhaustion.
3. Step-by-Step Fix
Resolving the clw-worker-exhausted error requires systematic investigation and targeted intervention. Follow these steps in order to identify and eliminate the root cause.
Step 1: Inspect Current Worker Configuration
Examine the memory configuration for the exhausted worker by querying the OpenClaw cluster state. Use the administrative CLI to retrieve the worker configuration and compare it against actual memory consumption patterns.
clawctl worker inspect worker-7f3a2c
Review the memory_limit field and compare it against the memory_used value. If the limit appears unreasonably low for your workload, proceed to Step 2. If the limit seems appropriate, proceed to Step 4.
Step 2: Adjust Worker Memory Limits
Increase the memory allocation for affected workers by modifying the OpenClaw worker pool configuration. The specific configuration file location varies by deployment type.
Before:
worker_pools:
default:
memory_limit: "512Mi"
concurrency: 4
After:
worker_pools:
default:
memory_limit: "2048Mi"
concurrency: 4
Apply the configuration change and restart affected workers using:
clawctl worker restart --pool default --filter status=EXHAUSTED
Step 3: Reduce Worker Concurrency
If memory limits cannot be increased due to infrastructure constraints, reduce the number of tasks each worker processes simultaneously. Lower concurrency decreases peak memory consumption by limiting the number of parallel task contexts.
Before:
worker_pools:
default:
memory_limit: "1024Mi"
concurrency: 8
After:
worker_pools:
default:
memory_limit: "1024Mi"
concurrency: 3
Step 4: Profile and Fix Memory Leaks
For workers that exhaust memory despite adequate limits, identify memory leaks in task processing code. Instrument the worker with memory profiling tools and process a representative workload while monitoring heap growth.
clawctl worker profile --worker-id worker-7f3a2c --duration 300s --output profile.pb.gz
Analyze the profile to identify objects that accumulate without being garbage collected. Common culprits include unclosed database connections, unbounded cache structures, and event listener registrations that persist beyond task completion.
Step 5: Implement Memory Pressure Handling
Add explicit memory monitoring and graceful degradation to task handlers. Configure workers to detect approaching limits and refuse new tasks proactively.
import claw_worker
@claw_worker.task_handler
def process_large_dataset(data):
memory_percent = claw_worker.get_memory_usage()
if memory_percent > 85:
claw_worker.yield_processing("Memory pressure detected")
# Task processing logic continues
Step 6: Restart Affected Workers
After applying configuration changes or memory leak fixes, restart all exhausted workers to reset their memory state.
clawctl worker restart --all
Monitor the restarted workers to confirm they maintain stable memory consumption.
4. Verification
Verifying that the clw-worker-exhausted error has been resolved requires confirming both the absence of exhaustion conditions and the successful processing of queued tasks.
First, query the worker status across all pools:
clawctl worker list --format json | jq '.workers[] | select(.status == "EXHAUSTED")'
A successful fix results in an empty output, indicating no workers in the exhausted state.
Next, examine the memory utilization metrics for all workers over a representative time window. The following command displays current memory usage relative to configured limits:
clawctl metrics query --metric worker_memory_usage --interval 60s --aggregation avg
Verify that peak memory consumption remains below 80% of the configured limit. Values approaching the limit indicate insufficient headroom and risk of future exhaustion.
Trigger a test workload that exercises the previously failing task type:
clawctl task submit --task-type data-transformation --payload test-sample.json --count 50
Monitor the task completion rate and verify that all 50 tasks complete successfully without exhaustion:
clawctl task list --status completed --limit 50 | jq '.tasks[] | select(.worker_id == "worker-7f3a2c") | .status'
Confirm that task duration remains consistent and does not exhibit the delayed processing characteristic of queue congestion caused by worker exhaustion.
Finally, check the OpenClaw daemon logs for any recurrence of exhaustion warnings:
clawctl logs --since 1h --level WARN | grep -E "(EXHAUSTED|memory limit exceeded)"
Absence of these messages in recent logs indicates successful resolution. For production deployments, configure continuous monitoring alerts on the worker_memory_utilization metric with thresholds at 75% warning and 90% critical to enable proactive intervention before exhaustion occurs.
5. Common Pitfalls
Several recurring mistakes complicate the resolution of worker exhaustion errors and occasionally cause the error to persist or recur after apparent fixes.
Setting memory limits too close to observed consumption represents the most frequent error. Administrators often configure limits based on observed peak usage, forgetting that memory consumption exhibits variance during processing. Transient spikes that briefly exceed average consumption will trigger exhaustion even when the configured limit appears adequate. Maintain a safety margin of at least 20% between the typical peak consumption and the configured limit.
Neglecting to restart workers after configuration changes leaves exhausted workers in their depleted state. The OpenClaw configuration system applies new settings to worker process creation, but existing worker processes retain their pre-change state. Always restart workers after modifying memory or concurrency settings.
Overlooking plugin memory consumption leads to misconfigured limits. Workers execute code from multiple sources: the OpenClaw core runtime, configured plugins, and custom task handlers. Each component contributes to memory usage. Limits configured based solely on core runtime measurements will be exceeded when plugins load and initialize their own data structures.
Increasing concurrency without adjusting memory limits multiplies memory pressure without providing additional capacity. Each concurrent task context maintains its own data structures, and doubling concurrency approximately doubles peak memory consumption. Treat memory limit increases and concurrency changes as coupled configuration decisions.
Failing to implement memory monitoring delays problem detection. Without instrumentation, exhaustion is discovered only when tasks fail to complete. Implement continuous monitoring and alerting to detect approaching limits before they cause task failures and queue congestion.
Ignoring garbage collection configuration in long-running workers allows memory to accumulate indefinitely. OpenClaw workers are designed for extended operation, and proper garbage collection tuning ensures that released memory becomes available for subsequent allocations. Review and configure garbage collection parameters based on workload characteristics.
6. Related Errors
Several error codes share conceptual similarities with clw-worker-exhausted and often appear in related contexts or as contributing factors to worker exhaustion scenarios.
clw-queue-timeout: This error occurs when tasks remain in the queue longer than their configured timeout threshold. Worker exhaustion directly contributes to queue timeout errors because exhausted workers cannot process tasks, causing queue depth to grow and individual task wait times to exceed limits. Resolving worker exhaustion typically eliminates queue timeout errors, but timeout errors that persist after exhaustion resolution indicate independent queue processing bottlenecks.
clw-task-rejected: When a worker cannot accept a task assignment due to resource constraints, the coordinator returns this error. While worker exhaustion is one cause of task rejection, other factors such as task type incompatibility or worker state validation also produce rejections. Distinguishing between rejection caused by exhaustion and rejection caused by other factors requires examining the specific rejection reason code accompanying the error.
clw-worker-unresponsive: Workers that fail to communicate with the OpenClaw coordinator within the expected heartbeat interval enter an unresponsive state. Memory exhaustion can trigger unresponsiveness when the worker becomes so degraded that it cannot process coordinator requests or send heartbeat signals. However, unresponsiveness also stems from network issues, process crashes, and kernel-level resource starvation unrelated to memory limits.