Eval directory
Evals for LangSmith
15 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for LangSmith AI products.
About LangSmith
LangSmith is LangChain's LLM observability and evaluation platform: tracing, datasets, evaluators (LLM-as-judge, code, and human), experiments, prompt management, and online monitoring used by AI teams to measure and improve LLM apps in production.
Employees
~200
Industry
LLM Observability
Headquarters
San Francisco, CA
Website
www.langchain.com/langsmithAvailable eval packs for LangSmith
15 packs ready to run.
Annotation Queues
LangSmith evals — Annotation Queues (relift v3)
Auth Workspaces Rbac Governance
LangSmith evals — Auth, Workspaces, RBAC & Governance (relift v3 InfraRed)
Datasets
LangSmith evals — Datasets (relift v3)
Datasets And Examples
LangSmith evals — Datasets & Examples (relift v3 InfraRed)
Evaluators
LangSmith evals — Evaluators (relift v3 InfraRed)
Experiments
LangSmith evals — Experiments (relift v3)
Experiments And Comparisons
LangSmith evals — Experiments & Comparisons (relift v3 InfraRed)
Langgraph Platform And Studio
LangSmith evals — LangGraph Platform & Studio (relift v3 InfraRed)
Online Monitoring Alerting
LangSmith evals — Online Monitoring & Alerting (relift v3)
Online Monitoring And Feedback
LangSmith evals — Online Monitoring & Feedback (relift v3 InfraRed)
Prompt Hub
LangSmith evals — Prompt Hub (relift v3)
Prompt Hub And Prompt Management
LangSmith evals — Prompt Hub / Prompt Management (relift v3 InfraRed)
Tracing And Runs Api
LangSmith evals — Tracing & Runs API (relift v3 InfraRed)
Tracing Runs
LangSmith evals — Tracing & Runs (relift v3)
Workspaces Rbac Billing
LangSmith evals — Workspaces, RBAC & Billing (relift v3)
Why eval LangSmith AI
LangSmith's AI features ship behind brand promises about accuracy, safety, and reliability. Buyers and integrators need to know those promises hold up under adversarial prompts, edge-case workflows, and the long tail of real customer inputs — not just the demo path.
The Corsac eval library for LangSmith measures four dimensions teams care about most when deploying ai platform agents:
- Adversarial robustness — does the agent resist prompt injection, jailbreaks, and social-engineering attempts?
- Workflow quality— does it complete the task buyers were shown in the demo, on inputs that don't look like the demo?
- Safety gates — does it escalate or refuse when it should, and only then?
- Operator quality — does it preserve analyst trust by surfacing the right context at the right time?
Every eval pack above is hand-authored against LangSmith's public product surface and runnable in Corsac with your own data.