Introduction to Error Handling in LangGraph

When working with language models and complex workflows, error handling becomes crucial for building robust and reliable applications. LangGraph, an orchestration framework for Python, provides several mechanisms to handle errors gracefully and maintain the stability of your system.

In this blog post, we'll explore various error handling techniques in LangGraph and how they can be implemented to create more resilient applications.

Basic Exception Handling

Let's start with the basics. Python's try-except blocks are the foundation of error handling, and they work seamlessly with LangGraph:

from langgraph import Graph
from langgraph.prebuilt.llm import LLMNode

def process_text(text):
    try:
        llm_node = LLMNode()
        result = llm_node(text)
        return result
    except Exception as e:
        print(f"An error occurred: {str(e)}")
        return None

graph = Graph()
graph.add_node("process", process_text)

In this example, we wrap the LLMNode processing in a try-except block to catch any exceptions that might occur during the language model interaction.

Custom Error Types

Creating custom error types can help you handle specific scenarios more effectively:

class LLMProcessingError(Exception):
    def __init__(self, message, original_error):
        super().__init__(message)
        self.original_error = original_error

def process_text_with_custom_error(text):
    try:
        llm_node = LLMNode()
        result = llm_node(text)
        return result
    except Exception as e:
        raise LLMProcessingError("Failed to process text with LLM", e)

graph = Graph()
graph.add_node("process", process_text_with_custom_error)

By defining a custom LLMProcessingError, we can provide more context about the error and wrap the original exception for further analysis if needed.

Error Handling in Workflows

LangGraph allows you to create complex workflows with multiple nodes. Here's how you can implement error handling in a multi-node scenario:

from langgraph.prebuilt.llm import LLMNode
from langgraph.prebuilt.text import TextSplitterNode

def safe_text_processing(text):
    try:
        splitter = TextSplitterNode(chunk_size=100)
        chunks = splitter(text)
        
        llm = LLMNode()
        results = []
        for chunk in chunks:
            try:
                result = llm(chunk)
                results.append(result)
            except Exception as e:
                print(f"Error processing chunk: {str(e)}")
        
        return results
    except Exception as e:
        print(f"Fatal error in text processing: {str(e)}")
        return None

graph = Graph()
graph.add_node("process", safe_text_processing)

In this example, we handle errors at multiple levels:

1. The outer try-except block catches any fatal errors in the overall process.
2. The inner try-except block handles errors for individual chunk processing, allowing the workflow to continue even if one chunk fails.

Retry Mechanisms

For transient errors, implementing a retry mechanism can be helpful:

import time
from langgraph.prebuilt.llm import LLMNode

def retry_llm_call(text, max_retries=3, delay=1):
    llm = LLMNode()
    for attempt in range(max_retries):
        try:
            return llm(text)
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            print(f"Attempt {attempt + 1} failed. Retrying in {delay} seconds...")
            time.sleep(delay)

graph = Graph()
graph.add_node("process", retry_llm_call)

This retry mechanism will attempt to call the LLM up to three times before giving up, with a delay between attempts.

Logging and Monitoring

Incorporating logging into your error handling strategy can provide valuable insights:

import logging
from langgraph.prebuilt.llm import LLMNode

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def process_with_logging(text):
    try:
        llm = LLMNode()
        result = llm(text)
        logger.info(f"Successfully processed text: {text[:50]}...")
        return result
    except Exception as e:
        logger.error(f"Error processing text: {str(e)}")
        return None

graph = Graph()
graph.add_node("process", process_with_logging)

By using Python's logging module, you can track errors and successful operations, making it easier to monitor your LangGraph application's performance.

Graceful Degradation

Sometimes, it's better to provide a partial result than to fail completely. Here's an example of graceful degradation:

from langgraph.prebuilt.llm import LLMNode

def process_with_fallback(text):
    llm = LLMNode()
    try:
        result = llm(text)
        return result
    except Exception as e:
        print(f"Error in main processing: {str(e)}")
        try:
            # Fallback to a simpler model or processing method
            fallback_result = simple_text_summary(text)
            return fallback_result
        except Exception as fe:
            print(f"Fallback also failed: {str(fe)}")
            return "Unable to process text"

def simple_text_summary(text):
    # A simple function to summarize text without using LLM
    return text[:100] + "..."

graph = Graph()
graph.add_node("process", process_with_fallback)

In this example, if the main LLM processing fails, we fall back to a simpler text summarization method. If that also fails, we return a default message.

By implementing these error handling techniques in your LangGraph applications, you'll create more robust and reliable systems that can gracefully handle unexpected situations and provide a better user experience.