WorkflowRetryExhaustedError
langchain_core.exceptions.WorkflowRetryExhaustedError
Stack trace
langchain_core.exceptions.WorkflowRetryExhaustedError: All retry attempts for the LLM call have been exhausted without success.
File "/app/workflow.py", line 45, in run_workflow
response = llm_chain.invoke_with_retries()
File "/app/llm_chain.py", line 78, in invoke_with_retries
raise WorkflowRetryExhaustedError("Retry limit reached") Why it happens
This error happens when the LLM repeatedly fails to produce a valid response within the configured retry limit. Causes include persistent API errors, invalid prompt formatting, or network issues that prevent successful completion.
Detection
Monitor retry counters and catch WorkflowRetryExhaustedError exceptions to log the last LLM response and error details before the workflow aborts.
Causes & fixes
LLM API returns repeated transient errors like rate limits or timeouts
Implement exponential backoff and increase retry limits or switch to a less rate-limited API key.
Prompt or input format causes the LLM to return invalid or unparsable responses repeatedly
Validate and sanitize prompts before sending; add clearer instructions or use structured output formats.
Network connectivity issues causing repeated request failures
Check network stability and add network error handling with retries at the HTTP client level.
Retry limit configured too low for the complexity or instability of the LLM calls
Increase the maximum retry count in the workflow configuration to allow more attempts.
Code: broken vs fixed
from langchain_core import Workflow
workflow = Workflow(retry_limit=3)
response = workflow.run_llm_call(prompt) # This line raises WorkflowRetryExhaustedError after retriesimport os
from langchain_core import Workflow
workflow = Workflow(retry_limit=5, backoff_strategy='exponential') # Increased retries with backoff
response = workflow.run_llm_call(prompt) # Now retries more times before failing
print(response)Workaround
Wrap the LLM call in a try/except block catching WorkflowRetryExhaustedError, then log the last error and fallback to a cached or default response to keep the workflow running.
Prevention
Design workflows with robust retry strategies including backoff, validate prompts thoroughly, and monitor API usage to avoid hitting rate limits that cause repeated failures.