1. Symptoms
The clw-llm-crash error manifests when OpenClaw’s internal Language Model process terminates abruptly during task execution. Developers typically encounter this error during agentic task processing, code generation workflows, or when invoking OpenClaw’s LLM-backed features through the CLI or programmatic API.
When the error occurs, the terminal or application logs will display a message resembling the following pattern:
[OpenClaw] ERROR: LLM process terminated unexpectedly
Error Code: clw-llm-crash
Reason: Process exited with signal SIGSEGV (exit code 139)
Timestamp: 2025-01-15T10:32:45Z
Alternatively, users may observe the error in a slightly different format depending on the invocation context:
FATAL: clw-llm-crash - LLM worker died without cleanup
Stack trace:
at OpenClaw.LLM.ProcessManager.KillProcess()
at OpenClaw.LLM.ProcessManager.ExecuteTask()
The error may also surface as a silent failure where the process appears to start correctly but terminates immediately after receiving the first prompt. In these cases, the application may hang for several seconds before eventually timing out and reporting the crash. Users frequently describe this as the tool “freezing” or “hanging” before ultimately failing with the crash notification. Network-proxied environments often see additional symptoms such as truncated HTTP responses or mysterious “connection reset by peer” messages preceding the crash event.
2. Root Cause
The clw-llm-crash error originates from OpenClaw’s architecture, which spawns the LLM as a separate subprocess rather than embedding the model directly within the main application. This design choice enables flexibility but introduces failure modes that differ from typical API client errors. When this subprocess dies unexpectedly, the parent process detects the termination through process monitoring and surfaces the crash error.
Several underlying conditions trigger the LLM subprocess crash. The most prevalent cause is memory exhaustion resulting from context windows that exceed available RAM or VRAM. Modern language models allocate memory proportional to their context length, and when the operating system’s memory limits are reached, the kernel delivers a SIGKILL or SIGSEGV signal that terminates the process immediately. This scenario is especially common when processing large codebases, lengthy documents, or when running models without proper memory management configuration.
A secondary but significant cause involves authentication and authorization failures that corrupt the LLM process state. When the OpenClaw configuration contains an invalid, expired, or malformed API key, the LLM subprocess may initialize partially before encountering an authentication error that leaves it in an inconsistent state. The process then terminates rather than continuing in a degraded mode. Similarly, network connectivity issues that interrupt the LLM’s HTTP communication with backend servers can cause the subprocess to exit unexpectedly, particularly when connection timeouts are configured too aggressively.
Configuration mismatches between OpenClaw and the target LLM provider also precipitate crashes. Version incompatibilities between the installed OpenClaw client and the API specification expected by the LLM endpoint result in malformed request serialization or incorrect response parsing. The subprocess detects these protocol violations and terminates rather than risking data corruption. Additionally, corrupted model weight files, incomplete installations, or conflicting library versions in the Python or Node.js runtime environment can all trigger the same crash pattern.
3. Step-by-Step Fix
Resolving the clw-llm-crash error requires systematic diagnosis followed by targeted remediation. The following procedure addresses the most common root causes.
Step 1: Verify the OpenClaw Configuration
Examine the active configuration file to ensure all parameters align with your LLM provider’s requirements:
openclaw config show
Review the output for any warnings about deprecated settings or validation failures. If the configuration appears incomplete, regenerate it from scratch:
openclaw config init --provider your-provider --model your-model
Step 2: Validate API Credentials
Test that your API key functions correctly by performing a minimal request:
openclaw api test --key "$OPENCLAW_API_KEY"
If authentication fails, obtain a fresh API key from your provider’s dashboard and update the configuration accordingly. Never hardcode credentials in configuration files that may be committed to version control.
Step 3: Adjust Memory Limits
Modify the OpenClaw configuration to constrain context length and prevent memory exhaustion. Locate the configuration file for your platform:
Before:
llm:
model: claude-3-opus
max_tokens: 8192
context_window: 200000
After:
llm:
model: claude-3-opus
max_tokens: 4096
context_window: 100000
Reduce context_window to a value that comfortably fits within available system memory. For systems with 8GB RAM, limiting the context to 50,000 tokens provides a reasonable safety margin.
Step 4: Check for Corrupted Installation
Reinstall the OpenClaw package to rule out corrupted binaries or missing dependencies:
pip install --force-reinstall openclaw
# or for npm-based installation
npm install -g openclaw --force
After reinstallation, clear any cached model assets that may have become corrupted:
openclaw cache clear --all
Step 5: Set Appropriate Timeout Values
When network latency contributes to crashes, increase the timeout thresholds:
Before:
llm:
timeout_ms: 30000
connect_timeout_ms: 5000
After:
llm:
timeout_ms: 120000
connect_timeout_ms: 30000
4. Verification
After implementing the fix, verify that the LLM subprocess runs stably through a sequence of diagnostic tests. Begin with a simple interactive query that exercises the full request-response cycle:
openclaw chat "Hello, respond with a brief greeting."
A successful response indicates the LLM process started and completed the request without crashing. Next, execute a more demanding workload that approaches your configured context limits:
openclaw analyze --path ./src --depth comprehensive
Monitor the process for the duration of this operation. If it completes without the clw-llm-crash error, the fix addresses the underlying condition. For programmatic verification, include the following diagnostic check in your integration tests:
import openclaw
def test_llm_stability():
client = openclaw.Client()
try:
response = client.chat("Process a moderately complex query.")
assert response is not None
assert "error" not in response
except openclaw.LLMProcessCrash as e:
pytest.fail(f"LLM crashed during stability test: {e}")
Run this test suite repeatedly to catch intermittent crashes that might not surface during single invocations. Additionally, monitor system resource consumption during extended sessions to confirm memory usage remains within safe bounds:
openclaw run --monitor --task ./large-codebase-analysis.yaml
The monitor flag outputs real-time metrics including memory consumption, process uptime, and queue depth. Stable operation over multiple consecutive tasks confirms the fix persists.
5. Common Pitfalls
Developers frequently encounter setbacks when resolving the clw-llm-crash error due to several recurring mistakes. Understanding these pitfalls helps avoid wasted troubleshooting effort.
The most common error involves modifying only the visible configuration parameters while overlooking environment variables that override them. OpenClaw respects environment variables such as OPENCLAW_MAX_TOKENS and OPENCLAW_TIMEOUT_MS with higher priority than configuration files. A persistent crash may result from an environment variable setting that contradicts the updated configuration. Always check environment variables with env | grep OPENCLAW before assuming the configuration change failed.
Another frequent mistake concerns version drift between the OpenClaw client and the LLM backend service. Providers periodically update their API specifications, and an outdated client may send requests in a format the current backend rejects. Rather than assuming the crash stems from memory issues, verify the client version matches the provider’s compatibility matrix and update accordingly.
Developers also sometimes fail to account for containerized or sandboxed environments that impose strict resource limits invisible to standard monitoring tools. When running OpenClaw inside Docker, Kubernetes, or cloud sandbox environments, the container’s memory limits may be lower than the host system’s available memory. The LLM process exhausts the container’s allocation and gets terminated by the container runtime’s OOM killer, which produces the same crash signature. Always verify container resource limits with docker inspect or equivalent tooling.
Finally, ignoring log files leads to incomplete diagnosis. The crash error visible in the terminal represents only the top-level failure. Detailed diagnostic information resides in OpenClaw’s log files, typically located at ~/.openclaw/logs/ on Linux and macOS or %APPDATA%\openclaw\logs\ on Windows. These logs often reveal the precise signal that terminated the process, memory allocation failures, or network errors that preceded the crash.
6. Related Errors
The clw-llm-crash error frequently appears alongside or substitutes for several related error codes that share common underlying causes.
clw-llm-timeout
This error occurs when the LLM subprocess fails to complete a request within the configured time threshold. Unlike crashes, timeouts indicate the process remained alive but unresponsive, suggesting either an infinite loop in the model processing, network connectivity problems, or insufficient timeout configuration. Increasing timeout_ms in the configuration often resolves this issue, though extremely long response times may indicate an improperly configured prompt or model settings.
clw-llm-auth-failed
When API credentials are invalid or expired, the LLM process may crash immediately upon attempting to authenticate with the backend service. This error surfaces during process initialization rather than during task execution. Regenerating and updating API keys typically resolves the issue, but the error sometimes indicates broader account-level problems such as billing suspension or quota exhaustion.
clw-config-invalid
Configuration validation failures sometimes manifest as crashes when the LLM process reads malformed configuration during startup. The subprocess attempts to parse invalid YAML or JSON, encounters an unrecoverable parsing error, and terminates. Running openclaw config validate before launching resource-intensive tasks catches these issues early and provides specific line-number guidance for corrections.