LangChain Launches LangSmith 2.0 for AI Agent Observability
LangChain has released LangSmith 2.0, a comprehensive observability platform for AI agents featuring real-time monitoring, automated evaluation, cost tracking, and production debugging tools.
LangChain has launched LangSmith 2.0, a major upgrade to its observability and debugging platform for AI applications and agents. The release addresses one of the biggest challenges in production AI: understanding why agents behave the way they do and catching issues before they affect users.
LangSmith 2.0 provides real-time monitoring dashboards that show agent performance metrics including success rates, latency distributions, cost per interaction, and error patterns. Teams can set alerts for anomalies such as sudden increases in failure rates or unexpected cost spikes.
The platform introduces automated evaluation pipelines that continuously test agent outputs against customizable quality criteria. Teams can define evaluation rubrics for accuracy, helpfulness, safety, and adherence to instructions, with LangSmith running these evaluations on a configurable sample of production traffic.
A new debugging interface allows developers to replay any agent interaction step by step, seeing exactly which tools were called, what context was available, and where the agent's reasoning diverged from expected behavior. This trace-based debugging has dramatically reduced the time to diagnose and fix agent issues.
LangSmith 2.0 is available in both cloud and self-hosted deployments. The cloud version starts at $39/month for small teams, with enterprise pricing for larger deployments. LangChain reports that over 5,000 companies use LangSmith in production, monitoring billions of LLM calls per month.
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