Callback Patterns - LangChain in Production

Learn advanced callback strategies for LangChain applications, including event hooks, monitoring, and extensibility

🔄 Callback Patterns Overview

Callbacks enable extensibility, monitoring, and custom logic in LangChain systems. This guide covers event hooks, callback design, and best practices for production.

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🏛️ Event Hooks & Callbacks

• Use callbacks for logging, monitoring, and custom actions
• Hook into chain execution, LLM calls, and retrieval events
• Design callbacks for reliability and performance

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🧑‍💻 Callback Design Patterns

• Use observer or pub/sub patterns for extensibility
• Pass context and metadata to callbacks
• Handle errors gracefully in callback logic

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🛠️ Example: LangChain Callback Implementation

from langchain_core.callbacks import CallbackManager, BaseCallbackHandler

class LoggingCallback(BaseCallbackHandler):
    def on_chain_start(self, *args, **kwargs):
        print("Chain started")
    def on_chain_end(self, *args, **kwargs):
        print("Chain ended")

callback_manager = CallbackManager([LoggingCallback()])
# Pass callback_manager to chains

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🔗 Next Steps

• Review all previous patterns for production readiness
• Integrate callbacks with monitoring and logging
• Extend callbacks for custom business logic

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Key Callback Takeaways:

• Use callbacks for extensibility and monitoring
• Design reliable and performant event hooks
• Integrate with logging and alerting systems
• Continuously improve callback coverage