1. Symptoms
When the clw-llm-exhausted error occurs during OpenClaw workflow execution, you will observe a distinctive set of symptoms that indicate token quota depletion in your language model integration. The workflow typically halts mid-execution, and the CLI outputs a truncated response that abruptly terminates without completing the expected output generation.
The primary indicator manifests in the terminal as a red-highlighted error message containing the error code clw-llm-exhausted followed by a technical description referencing maximum token limits. You may also notice that subsequent workflow stages fail to execute, as the dependency chain breaks when the LLM call does not return valid JSON or structured data as expected.
Additional observable symptoms include intermittent behavior where the same workflow succeeds on initial runs but fails after repeated executions, suggesting accumulated token consumption across multiple invocations. Log files will contain entries showing partial completion of the LLM request before the exhaustion point, with truncation markers visible in the raw output.
In verbose mode, OpenClaw may display supplementary information such as the current token count, the maximum allowed threshold, and the specific model endpoint that was targeted. This metadata proves essential for diagnosing whether the issue stems from a single oversized prompt or accumulated usage across a session.
2. Root Cause
The clw-llm-exhausted error originates from exhausting the token allocation for a language model during OpenClaw workflow processing. This exhaustion can occur through several distinct mechanisms, each representing a different aspect of token consumption that developers must understand to implement effective solutions.
The most common root cause involves prompt inflation where accumulated system prompts, conversation history, and contextual data exceed the model’s context window capacity. When OpenClaw workflows incorporate extensive retrieval-augmented generation patterns or multi-shot prompting techniques, each LLM invocation consumes tokens from the available budget, and the cumulative effect eventually triggers exhaustion.
Another prevalent cause centers on the absence of aggressive context management strategies. OpenClaw’s default behavior may preserve full conversation histories to maintain coherence across workflow stages, but this preservation comes at the cost of rapid token depletion. The accumulation of previous exchanges, intermediate results, and artifact metadata within a single context window creates a ceiling that eventually prevents new content from being processed.
Model-specific limitations also contribute significantly to this error. Different LLM providers enforce varying token limits, and OpenClaw workflows that assume higher limits than the configured model supports will consistently trigger the exhaustion condition. Furthermore, rate limiting implementations by API providers may trigger artificial exhaustion before true context window limits are reached, especially on tiered pricing plans with lower quotas.
Configuration errors in OpenClaw’s token management settings can also precipitate this issue. When the max_tokens parameter is set too conservatively or when streaming responses are not properly segmented, the system may interpret normal operation as quota exhaustion rather than recognizing the actual throughput constraints.
3. Step-by-Step Fix
Resolving the clw-llm-exhausted error requires implementing a combination of context management, configuration adjustments, and workflow restructuring. Follow these systematic steps to restore normal operation.
Step 1: Audit Current Token Consumption
Begin by examining your OpenClaw workflow’s token usage patterns. Enable verbose logging and execute a single workflow run while monitoring the token count progression in the output. Identify which workflow stages consume the most tokens and whether the consumption pattern follows an expected accumulation curve or reveals anomalous spikes.
Step 2: Implement Context Truncation Strategy
Modify your workflow configuration to incorporate intelligent context management:
Before:
llm_config:
provider: openai
model: gpt-4
max_tokens: 8192
preserve_history: true
After:
llm_config:
provider: openai
model: gpt-4
max_tokens: 8192
preserve_history: false
context_truncation:
enabled: true
strategy: sliding_window
window_size: 4096
overlap: 512
Step 3: Optimize Prompt Templates
Reduce token consumption by simplifying system prompts and removing redundant instructions. Streamline your prompt templates to include only essential context:
Before:
{
"system": "You are an expert coding assistant with 20 years of experience in multiple programming languages. You have deep knowledge of system architecture, design patterns, and best practices. You provide detailed explanations with code examples and comprehensive documentation."
}
After:
{
"system": "You are a coding assistant. Provide concise, accurate responses with minimal examples."
}
Step 4: Implement Chunked Processing
For workflows processing large inputs, split the work into smaller batches with explicit state management between chunks:
Before:
def process_large_file(filename):
content = read_file(filename)
result = llm.process(f"Analyze this: {content}")
return result
After:
def process_large_file(filename):
chunks = split_file(filename, chunk_size=2000)
accumulated_insights = []
for i, chunk in enumerate(chunks):
prompt = f"Analyze chunk {i+1}/{len(chunks)}: {chunk}"
if i > 0:
prompt = f"Previous insights: {accumulated_insights[-1]}\n\nCurrent chunk: {chunk}"
insight = llm.process(prompt)
accumulated_insights.append(insight)
# Clear context between chunks
if i % 3 == 0:
llm.clear_session()
return accumulated_insights[-1]
Step 5: Configure Provider-Specific Limits
Align your OpenClaw configuration with actual API provider constraints:
Before:
api_limits:
tokens_per_minute: 100000
After:
api_limits:
tokens_per_minute: 60000
max_retries: 3
retry_delay: 5
Step 6: Enable Token Budget Monitoring
Add proactive monitoring to detect approaching exhaustion before workflow failure:
class TokenBudgetMonitor:
def __init__(self, threshold=0.8):
self.threshold = threshold
self.budget = 0
self.used = 0
def update_budget(self, total_budget):
self.budget = total_budget
def check_exhaustion(self):
if self.budget > 0:
utilization = self.used / self.budget
if utilization > self.threshold:
return True
return False
def record_usage(self, tokens):
self.used += tokens
4. Verification
After implementing the fix, verification ensures that the clw-llm-exhausted error no longer occurs and that workflow execution completes successfully. Begin by running the workflow that previously triggered the error and examining the output for successful completion indicators.
Execute the workflow multiple times in succession to confirm that accumulated usage no longer causes token exhaustion. OpenClaw should now properly manage context windows across invocations, preventing the gradual token buildup that previously triggered failures. Monitor the terminal output for any warning messages indicating approaching limits, which suggests the monitoring infrastructure correctly identifies potential exhaustion before it becomes a blocking error.
Validate that the output quality remains acceptable after implementing aggressive context truncation. Run comparative tests between pre-fix and post-fix results to ensure that necessary context is preserved for accurate task completion. Check that workflow stages maintaining dependencies on LLM outputs receive valid data structures rather than truncated fragments.
Confirm API provider monitoring shows compliant usage patterns. If your provider dashboard provides token consumption metrics, verify that your OpenClaw workflow operates within documented limits. The fix should demonstrate reduced token consumption per workflow run while maintaining equivalent output quality.
Finally, test edge cases involving maximum input sizes. Create workflow inputs approaching your identified limits to confirm that even near-boundary conditions execute without exhaustion errors. Document the new working boundaries for future reference and workflow design guidance.
5. Common Pitfalls
Several common mistakes can undermine efforts to resolve the clw-llm-exhausted error. Understanding these pitfalls helps avoid recurring issues and ensures robust implementation of the fix.
Setting max_tokens to an extremely low value to prevent exhaustion represents a frequent misstep. While this approach technically prevents the error, it frequently results in truncated responses where the LLM output cuts off mid-sentence, rendering the workflow output unusable. Instead of arbitrarily reducing limits, implement intelligent truncation that preserves semantic completeness.
Neglecting to clear session state between workflow invocations causes gradual token accumulation that eventually triggers exhaustion. Developers often assume that each workflow run starts fresh, but OpenClaw’s default behavior preserves conversational context across multiple executions within the same session. Explicitly calling session clearing methods or configuring automatic session reset after each completion prevents this accumulation.
Failing to account for response tokens when calculating available context represents another critical pitfall. Developers frequently consider only input token consumption, but LLM responses also consume from the context window. A prompt that leaves insufficient remaining tokens for response generation will cause incomplete outputs or exhaustion errors even when the input appears appropriately sized.
Ignoring provider-specific rate limits that operate independently of model token limits creates persistent issues. Some providers impose per-minute or per-day token quotas distinct from context window limits, and reaching these quotas produces the same clw-llm-exhausted error despite adequate context window capacity. Ensure your monitoring includes both types of limits.
Finally, implementing fixes without preserving critical context leads to workflow failures where the error disappears but task quality degrades unacceptably. Truncation strategies must intelligently preserve domain-specific context rather than applying generic first-in-first-out removal that discards essential information.
6. Related Errors
The clw-llm-exhausted error frequently appears alongside or is confused with several related errors that share similar manifestations but require distinct resolution approaches.
clw-context-overflow: This error occurs when the input prompt exceeds the model’s absolute context window limit, even before any response generation. Unlike token exhaustion that results from accumulated usage, context overflow represents an immediate capacity violation. The fix involves splitting inputs across multiple calls rather than managing cumulative consumption.
clw-model-timeout: When LLM requests exceed reasonable response time expectations, this error triggers even if tokens remain available. Timeout errors can masquerade as exhaustion errors in certain configurations, requiring careful log analysis to distinguish between the two root causes.
clw-quota-exceeded: This error indicates exhaustion of account-level API quotas imposed by the LLM provider, which differs from model-level token limits. Quota exceeded errors persist regardless of context management improvements and require either upgrading provider plans or implementing request throttling to spread usage across permitted quota windows.