{"name":"LangSmith","slug":"langsmith","category":"observability","type":"cloud","website":"https://smith.langchain.com","pricing":"freemium","pricing_tiers":["Free (5k traces)","$39/seat/mo Plus","Custom Enterprise"],"open_source":false,"self_hosted":false,"sdk_languages":["python","javascript","typescript"],"frameworks":["langchain"],"agent_features":{"llm_tracing":true,"cost_tracking":true,"evaluation":true,"prompt_management":true,"real_time_monitoring":true},"compliance":["soc2","gdpr"],"best_for":"Deep tracing and evaluation for LangChain-based agents — tightest integration with the LangChain ecosystem","limitations":"Heavily coupled to LangChain; no self-hosted option; closed-source; less useful if you're not using LangChain","verified_by":"editorial","last_verified":"2026-04-28","source_urls":{"docs":"https://docs.smith.langchain.com","pricing":"https://www.langchain.com/pricing"},"feature_labels":{"llm_tracing":"Trace LLM calls, tool invocations, and agent reasoning steps end-to-end","cost_tracking":"Track token usage and cost per request, per agent run, and per model","evaluation":"Score agent outputs against test datasets with automated evaluators","prompt_management":"Version, manage, and A/B test prompts in production","real_time_monitoring":"Live dashboards and alerting for agent performance metrics"},"comparisons":[{"slug":"datadog-vs-langsmith","title":"Datadog vs LangSmith","vs":"datadog"},{"slug":"grafana-vs-langsmith","title":"Grafana vs LangSmith","vs":"grafana"},{"slug":"helicone-vs-langsmith","title":"Helicone vs LangSmith","vs":"helicone"},{"slug":"langfuse-vs-langsmith","title":"Langfuse vs LangSmith","vs":"langfuse"}],"body":"# LangSmith\n\nLangSmith is LangChain's proprietary observability and evaluation platform. If you're building agents with LangChain or LangGraph, LangSmith provides the deepest tracing integration available — every chain step, tool call, and LLM invocation is captured automatically.\n\nThe evaluation suite lets you build test datasets, run agents against them, and score outputs with custom or LLM-based evaluators. The tradeoff is tight coupling to the LangChain ecosystem — if you move away from LangChain, LangSmith becomes significantly less useful."}