Langsmith

LangSmith Observability helps you see what your AI agents actually do in real runs, not just what they’re supposed to do. It’s for teams building LLM-powered agents who need to debug failures, understand performance in production, and track cost and latency.

• Agent tracing that shows step-by-step execution, including multi-turn thread activity, so you can pinpoint what’s driving issues
• Real-time monitoring to spot production problems early and understand their impact
• Cost tracking so you can break down expense alongside performance
• Online evals (LLM-as-judge and code evals) to score quality using the characteristics that matter to your application
• Tool and trajectory monitoring to follow what the agent does across actions and decisions
• Alerts via webhooks and PagerDuty when key signals cross thresholds
• Trace analysis and clustering to automatically find usage patterns, common behaviors, and failure modes
• Search and debug with SmithDB for fast trace querying, full-text search, JSON key-path filtering, and trajectory queries

A key reason to pick it is its focus on agent observability data. Agent traces can be deeply nested and large, and SmithDB is built for how teams query conversations and runs, including sub-second performance as trace volume grows.