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langsmith

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LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

When to use

• Debugging LLM application issues (prompts, chains, agents)
• Evaluating model outputs systematically against datasets
• Monitoring production LLM systems
• Building regression testing for AI features
• Analyzing latency, token usage, and costs
• Collaborating on prompt engineering
• **Tracing**: Capture inputs, outputs, latency for all LLM calls
• **Evaluation**: Systematic testing with built-in and custom evaluators

When not to use

• Deep learning experiment tracking
• Model training
• General ML lifecycle
• Model registry focus
• ML monitoring
• Data drift detection

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