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.
Try LangSmith free. No credit card required.
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.
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Leading AI teams trust LangSmith for evaluation and quality assurance
Define, execute, and scale evaluations for continuous quality improvement
LangSmith captures complete execution traces from your LLM applications. These traces become the source of truth for your evaluation datasets, so you're always testing against real production patterns.
Connect with our team to see how
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.
Create custom evaluators with code, LLM-as-judge, or human feedback. Measure accuracy, latency, cost, safety—whatever your product requires.
Run evals offline on test datasets or online against live traffic. Surface issues automatically so you ship with confidence.
LangSmith evaluation works with OpenAI, Anthropic, open-source models, or custom implementations. Bring any LLM stack.
"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.