langsmith-observability

What it does

This skill enables AI agents to integrate LangSmith for comprehensive observability of language model operations. It allows agents to trace, monitor, and debug interactions with LLMs in real-time, ensuring reliable performance across complex workflows.

When to use it

• Debugging latency issues: Identify bottlenecks when API calls to models take longer than expected.
• Quality assurance: Monitor evaluation metrics to ensure generated outputs meet specific accuracy standards.
• Cost tracking: Analyze token usage patterns to optimize spending on expensive model endpoints.
• Production monitoring: Maintain visibility into agent behavior after deployment to catch regressions early.

Key capabilities

• End-to-end tracing of LLM requests and responses
• Real-time performance metrics collection
• Automated evaluation framework integration
• Debugging support for complex multi-step agent chains

Example prompts

• "Set up LangSmith tracing for my current RAG pipeline to visualize token usage per step."
• "Create an evaluation run comparing the latest model version against baseline accuracy scores."
• "Generate a trace report showing where latency spikes occurred during yesterday's production logs."

Tips & gotchas

Ensure your LangSmith project is properly configured with API keys before attempting integration. This skill requires active network access to the LangSmith backend; offline agents will not be able to send telemetry data.