1. Symptoms
The clw-llm-oom error manifests when OpenClaw attempts to process large volumes of CloudWatch log data through its LLM integration pipeline. When this error occurs, users typically observe the following indicators:
Shell Output:
[ERROR] OpenClaw LLM processing failed: clw-llm-oom
[ERROR] Context window exhausted. The log batch exceeds the model's maximum token limit.
[ERROR] Attempted to process 47,832 tokens against a 8,192 token context window.
Behavioral Symptoms:
- The CLI command terminates immediately after initiating LLM analysis
- Processing jobs fail consistently regardless of retry attempts
- The error appears even when processing logs that previously succeeded
- Memory usage on the host system spikes before the error occurs
- The error message includes token count statistics showing the excess
Additional Console Messages:
[INFO] Fetched 15,847 log events from CloudWatch
[INFO] Compiling context payload for LLM (gpt-4)
[INFO] Token estimate: 48,291 tokens
[WARN] Token count exceeds model context window (8,192 tokens)
[FATAL] clw-llm-oom: Unable to allocate sufficient context space
The error occurs specifically during the context compilation phase, after log data has been fetched but before any LLM inference takes place. This distinguishes it from inference-time errors which would show different messaging.
2. Root Cause
The clw-llm-oom error arises when the cumulative token count of the compiled prompt—including system instructions, log entries, historical context, and user queries—exceeds the maximum context window supported by the underlying language model. OpenClaw’s architecture relies on embedding substantial log context into each LLM request to enable accurate analysis and pattern detection across log streams.
Modern language models impose strict context window limitations that vary by model provider and tier. The most common constraints encountered are: GPT-4 models typically support 8,192 or 32,768 tokens depending on the variant, Claude models offer 100,000 tokens but still require efficient chunking, and open-source models often have more restrictive windows ranging from 2,048 to 4,096 tokens.
The root cause can be attributed to several interrelated factors. First, unbounded log ingestion occurs when users attempt to analyze large time ranges or high-volume log streams without implementing pagination or chunking strategies. Second, verbose log formatting contributes excessive token overhead when individual log entries contain detailed metadata, stack traces, or multi-line text that inflates the token count. Third, inefficient prompt construction happens when OpenClaw’s internal prompt templates include redundant context or when system instructions are duplicated across requests. Fourth, inadequate batch sizing means the default batch sizes may be inappropriate for the selected model, causing accumulated batches to exceed available context space.
The error specifically indicates that the OpenClaw token estimation and batching layer detected an impossible allocation scenario—one where even the most aggressive compression would fail to fit the requested context within the model’s limits.
3. Step-by-Step Fix
Addressing the clw-llm-oom error requires implementing one or more of the following strategies to reduce the token footprint of each LLM request. Begin with the first solution and progress through subsequent options based on your specific use case.
Solution 1: Reduce Time Range for Log Queries
Before:
openclaw analyze --log-group "/aws/lambda/production-api" \
--start-time "2024-01-01T00:00:00Z" \
--end-time "2024-01-15T23:59:59Z" \
--query "Identify error patterns"
After:
openclaw analyze --log-group "/aws/lambda/production-api" \
--start-time "2024-01-14T00:00:00Z" \
--end-time "2024-01-14T23:59:59Z" \
--query "Identify error patterns"
Narrowing the time range from a two-week span to a single day dramatically reduces the volume of log entries fetched and subsequently compiled into the LLM context. This approach works well when investigating recent incidents or conducting targeted analysis.
Solution 2: Apply Log Filtering Before Analysis
Before:
openclaw analyze --log-group "/aws/lambda/production-api" \
--start-time "2024-01-14T00:00:00Z" \
--end-time "2024-01-14T23:59:59Z" \
--query "Identify error patterns"
After:
openclaw analyze --log-group "/aws/lambda/production-api" \
--start-time "2024-01-14T00:00:00Z" \
--end-time "2024-01-14T23:59:59Z" \
--filter "ERROR|WARN" \
--query "Identify error patterns"
Applying CloudWatch filter patterns using the --filter flag reduces the dataset before it reaches OpenClaw’s processing layer. Only log entries matching the specified pattern are retrieved, substantially decreasing token consumption.
Solution 3: Configure Chunk Size and Batch Settings
Modify your OpenClaw configuration to enable intelligent chunking:
Before:
# Using default settings (potentially unlimited batch sizes)
openclaw analyze --log-group "/aws/lambda/production-api"
After:
# Explicitly set chunk and batch parameters
openclaw analyze --log-group "/aws/lambda/production-api" \
--chunk-size 2000 \
--max-batches 4 \
--model gpt-4-32k
Update your global configuration file at ~/.openclaw/config.yaml:
llm:
provider: openai
model: gpt-4-32k
chunk_size: 2000
max_batches: 4
context_strategy: sliding_window
analysis:
overlap_tokens: 500
compression_enabled: true
The chunk_size parameter limits the number of log entries included in each LLM request, while max_batches caps the number of sequential requests made for a single analysis operation. The sliding_window context strategy enables overlapping analysis across chunks, maintaining continuity without overwhelming any single context window.
Solution 4: Upgrade to Extended Context Model
If your analysis requires processing large log volumes, consider switching to a model variant with an extended context window:
Before:
openclaw analyze --log-group "/aws/lambda/production-api" \
--model gpt-4
After:
openclaw analyze --log-group "/aws/lambda/production-api" \
--model gpt-4-32k
The GPT-4-32k model provides a 32,768 token context window—four times the capacity of standard GPT-4. This change requires API access to the appropriate model tier and may incur higher per-token costs.
Solution 5: Enable Log Entry Trimming
Configure OpenClaw to strip unnecessary metadata from log entries before context compilation:
processing:
trim_fields:
- "@timestamp"
- "aws_region"
- "request_id"
max_entry_length: 500
remove_null_fields: true
This configuration removes specified fields from each log entry, limiting individual entries to 500 characters and eliminating null values. The combined effect significantly reduces token usage per request.
4. Verification
After implementing any of the fixes described above, verify that the clw-llm-oom error has been resolved by executing the following validation steps.
First, run a dry-run analysis to confirm token estimates fall within acceptable limits:
openclaw analyze --log-group "/aws/lambda/production-api" \
--start-time "2024-01-14T00:00:00Z" \
--end-time "2024-01-14T23:59:59Z" \
--filter "ERROR|WARN" \
--dry-run
A successful dry-run produces output indicating that the token count is within bounds:
[INFO] Dry run mode: no LLM inference will occur
[INFO] Fetched 1,247 log events matching filter
[INFO] Estimated tokens: 6,842 / 8,192 context window
[INFO] Context utilization: 83.5%
[PASS] Request is within model context limits
Second, execute a full analysis with your configured settings:
openclaw analyze --log-group "/aws/lambda/production-api" \
--start-time "2024-01-14T00:00:00Z" \
--end-time "2024-01-14T23:59:59Z" \
--filter "ERROR|WARN" \
--query "Identify error patterns"
Successful execution produces LLM-generated analysis output without any clw-llm-oom error messages. The output should include structured analysis results and any identified patterns in the log data.
Third, monitor system resource usage during execution to confirm memory consumption remains stable. The clw-llm-oom error often correlates with excessive memory allocation, so resolution should eliminate memory spikes.
Finally, verify that repeated executions with varying time ranges consistently succeed. The error would have previously manifested intermittently based on log volume; successful resolution ensures consistent behavior across different analysis windows.
5. Common Pitfalls
Several recurring mistakes cause the clw-llm-oom error to persist despite attempted fixes. Awareness of these pitfalls enables more effective troubleshooting and prevents frustration during resolution efforts.
The most frequent error involves assuming the issue stems solely from model selection. Users often upgrade to GPT-4-32k without implementing chunking strategies, only to encounter the error again when log volumes exceed even the larger context window. The combined approach of appropriate chunking and model selection provides the most robust solution.
Another common mistake involves neglecting the --filter parameter’s impact. Even with chunking enabled, users frequently run queries against unfiltered log streams containing predominantly informational entries that consume context space without contributing to analysis goals. Applying targeted filters focused on ERROR and WARN levels dramatically improves context efficiency.
Configuration file precedence creates confusion when users set parameters via command-line flags but have contradictory values in their configuration file. OpenClaw’s behavior regarding flag versus config file precedence can lead to unexpected results. Always verify active configuration using openclaw config show to confirm which values are actually in effect.
Insufficient validation before production use represents a significant risk. Users implement changes in test environments with smaller log volumes, then deploy to production where larger datasets trigger the error again. Always validate fixes against datasets matching or exceeding production-scale volumes.
Ignoring token budget implications when using extended context models leads to unexpectedly high API costs. The clw-llm-oom error often serves as a natural budget control; removing that constraint without implementing alternative controls can result in substantial billing surprises.
Finally, failure to implement sliding window overlap when chunking creates analysis gaps where patterns spanning chunk boundaries are missed entirely. The overlap_tokens configuration parameter exists specifically to address this issue, but users frequently leave it at default or zero values, resulting in incomplete analysis.
6. Related Errors
clw-context-limit-exceeded
This error shares the same root cause as clw-llm-oom but occurs at a different architectural layer. While clw-llm-oom reflects OpenClaw’s internal token estimation exceeding limits, clw-context-limit-exceeded indicates that the API call itself was rejected by the LLM provider due to context window violations. Resolution strategies overlap substantially, but this error may also indicate API-specific issues such as malformed request payloads or provider-side rate limiting.
clw-llm-timeout
This error occurs when LLM inference operations exceed the configured timeout threshold, often as a secondary effect of attempting to process oversized contexts. Models require additional time to process longer contexts, and timeout thresholds configured for standard-length requests may be insufficient for extended context operations. Addressing the underlying context size issues typically resolves timeout failures.
clw-token-overflow
The clw-token-overflow error specifically indicates that the token counting mechanism detected an overflow condition in its arithmetic operations, suggesting that the compiled prompt exceeded not just the model’s context window but also the token estimation algorithm’s handling capacity. This typically occurs with extremely large log batches and requires immediate chunking implementation to resolve.