Debug, trace, and evaluate LLM agents with LangSmith. Learn how LangSmith improves the reliability, observability, and performance of AI applications.

DigitalOcean Community's platform is a central hub for developers and sysadmins using DigitalOcean's cloud infrastructure, offering insights into cloud computing, DevOps practices, and open-source technologies. Through tutorials, Q&A, and community forums, DO_Community offers insights into deploying and managing applications on DigitalOcean's cloud platform. Developers can learn about Linux server administration, containerization, and automation tools to build and scale applications in the cloud.

DigitalOcean Community

LangSmith is a framework-agnostic platform for debugging, evaluating, and monitoring LLM-powered applications and agents. It provides end-to-end tracing that captures every step of an agent's execution (LLM calls, tool invocations, prompts), enabling developers to diagnose failures like hallucinations, tool errors, and reasoning issues. The platform supports both offline evaluation using datasets with automated or human evaluators, and online monitoring in production. Key features include trace visualization, prompt playground for testing variants, annotation queues for human review (single-run and pairwise comparisons), and deployment capabilities. The tutorial walks through instrumenting agents with tracing via environment variables or SDK wrappers, creating evaluation datasets, running experiments with evaluators, and iterating on prompts based on results.

LangSmith Explained: Debugging and Evaluating LLM Agents

What is LangSmith (and When It’s Worth Using)

The Agent Debugging Problem LangSmith Solves

Quickstart: Trace an Agent Run (Python Example)

Evaluation Workflow: Datasets, Evaluators, and Experiments

Human-in-the-Loop: Annotation Queues for Quality at Scale

LangSmith vs Alternatives: How It Stacks Up