Langsmith

LangSmith Observability is for teams running AI agents or LLM-powered apps who need visibility into what those agents do in production. If you’re seeing slow responses, unexpected tool behavior, or quality issues, it helps you inspect execution end-to-end, then track costs and latency alongside those traces.

You can use it with different agent setups because it supports tracing through popular frameworks and OpenTelemetry, plus SDKs for Python, TypeScript, Go, and Java.

• Agent tracing that shows step-by-step execution, including latency and cost drivers, across multi-turn conversations (message threading)
• Real-time monitoring with cost tracking and quality scoring using online LLM-as-judge and code evals
• Trajectory and tool monitoring to understand how agent behavior unfolds over time
• Alerts via webhooks and PagerDuty for issues that impact performance
• Trace analysis and clustering that groups traces to surface common behaviors and failure modes, with templates for error analysis and an executive summary
• Search and debugging in SmithDB with random access to runs, full-text search, JSON key-path filtering, and trajectory queries

What sets it apart is SmithDB, which is purpose-built for deeply nested agent traces and the way teams query them. It targets fast retrieval and filtering even as trace volume grows, so you can move from “something went wrong” to a concrete trace and tool path.