langchain_chain_error

LangChain LLM requests timeout after periods of inactivity due to connection pool staleness or dropped connections, requiring server restart to resolve.

Tool executions that timeout continue chain processing with missing data, causing downstream errors or poor quality outputs without clear failure signals.

LLM provider rate limits cause request failures that aren't retried with appropriate backoff, leading to cascading failures during usage spikes.

First embedding requests can timeout waiting for tiktoken encoding files to download from external CDNs (e.g., openaipublic.blob.core.windows.net), causing initial request failures.

High langchain_request_error or langchain_chain_error rates can suppress latency metrics (fast-failing requests skew averages downward), hiding underlying performance issues that affect successful requests.