Define custom evaluation metrics tailored to your LLM application. Run evals on production data, catch regressions early, and iterate confidently with structured testing instead of ship-and-see.
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Connect production traces, custom evaluators, datasets, and deployment gates into a repeatable quality system.
Leading AI teams trust LangSmith for evaluation and quality assurance
Observe, evaluate, and deploy agents with LangSmith. LangSmith is framework-agnostic: trace your preferred framework or integrate LangSmith with any agent stack using our Python, TypeScript, Go, or Java SDKs.
Surface and diagnose undetected issues autonomously to improve agents faster. LangSmith Engine clusters production failures into prioritized issues, finds the root cause in your traces and code, and proposes the fix for your review.
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LangSmith helps teams define, run, monitor, and operationalize evaluations throughout the agent lifecycle.
Build custom evals with Python, LLM-as-judge, or human feedback. Target the metrics your product actually cares about—accuracy, latency, cost, safety, or domain-specific requirements.
Test offline on datasets or online against production traffic. Automatically surface regressions and compare performance across prompt versions and model changes.
Gate deployments on evaluation thresholds. Close the feedback loop from production data to training datasets, turning real usage into continuous improvement.
Built for Enterprise
LangSmith meets the demanding security, performance, and collaboration requirements of large organizations building AI applications at scale.
Role-based access control with org-level permissions and project isolation to meet your security and compliance requirements.
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Self-hosting options to maintain full control over your AI data and meet strict compliance requirements.
Connect traces, evals, human feedback, and experiments into a repeatable process for improving agent quality.
Learn how to give agents the tools to work autonomously while keeping execution isolated, observable, and under control.
Learn how leading teams are building and scaling AI agents in production.
Explore the runtime primitives needed to build long-horizon agents that are reliable, observable, and production-grade.
Join the LangChain community at conferences, meetups, and webinars to learn how teams are advancing AI evaluation frameworks in production.
"Working with LangSmith on the Elastic AI Assistant had a significant positive impact on the overall pace and quality of our development and shipping experience. We couldn't have delivered the product experience our customers now have without LangSmith—and we couldn't have done it at the same pace without it."
"What we really needed was a more structured way to test new approaches, something better than just shipping and seeing what happened. LangSmith gave us a scientific, structured way to understand what was actually working. We could run pairwise evaluations and understand why accuracy jumped from 70% to 80%. Our engineers love the intuitive debugging experience."
Learn how to build evaluation frameworks that catch issues early and keep your LLM applications performing at their best.