Observability

• LangSmith

  • A developer tool/platform for managing and debugging LLM (Large Language Model) applications.

  • Helps with tracing, monitoring, and evaluation of prompts and responses.

• LangFuse

  • An open-source observability and analytics platform for LLM applications.

  • Provides monitoring, logging, and user feedback integration.

Key Difference Example:

• LangSmith → Often used for prompt debugging and evaluation.

• LangFuse → Often used for observability and logging across the lifecycle.

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Implementation Notes

LangSmith Environment Setup

Add the following to .env file:

LANGSMITH_TRACING=true
LANGSMITH_ENDPOINT=https://api.smith.langchain.com
LANGSMITH_API_KEY=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
LANGSMITH_PROJECT=calender-ai-project

Installation Command

Run:

npm install @langchain/langgraph @langchain/core

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LangFuse Setup

Self-Hosting with Docker Compose

• Official deployment guide: LangFuse Docker Compose Deployment

• Provides steps to set up LangFuse locally or on a server using Docker Compose.

Integration with LangChain

• Official integration guide: LangFuse x LangChain

• Explains how to integrate LangFuse observability directly into LangChain apps.

Comparison