How do you handle errors in chains?

Error handling in LangChain chains operates at several levels: Python exception handling around .invoke(), chain-level fallbacks, parser-level retry, and output validation with Pydantic.

Basic try/except — handles transient API errors or rate limits:

from openai import RateLimitError
try:
    result = chain.invoke({"question": user_input})
except RateLimitError as e:
    result = "Service busy, please retry."
except Exception as e:
    logger.error(f"Chain failed: {e}")
    result = fallback_response

Chain fallbacks — declaratively try a backup chain if the primary fails:

# If gpt4_chain raises any exception, gpt35_chain is tried automatically
robust_chain = gpt4_chain.with_fallbacks([gpt35_chain])

Output parser errors — OutputFixingParser wraps another parser and uses a second LLM call to fix malformed output if parsing fails:

from langchain.output_parsers import OutputFixingParser
fixing_parser = OutputFixingParser.from_llm(
    parser=json_parser, llm=ChatOpenAI()
)
chain = prompt | llm | fixing_parser

For structured output validation, using llm.with_structured_output(MyModel) raises a ValidationError if the model's response doesn't match the schema, making it easy to catch and handle type mismatches.

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