LangSmith

Why It Matters

Building an LLM application that works in a notebook is easy. Building one that works reliably in production is hard. LangSmith addresses the observability gap. When something goes wrong, you need to see exactly what the LLM received, what it returned, and why.

The key insight: LLM applications are non-deterministic and complex. A single user request might involve multiple LLM calls, tool invocations, and retrieval steps. Without tracing, debugging is guesswork. With tracing, you see the full execution path.

For AI engineers, LangSmith (or similar tools) becomes essential once you move beyond prototypes. You need to answer questions like: Why did this answer fail? Which prompt version performs better? How much are we spending per request? LangSmith provides these answers.

How It Works

LangSmith instruments your LLM application to capture execution traces:

1. Automatic Tracing When enabled, LangSmith captures every LLM call, tool invocation, and chain execution. Each trace includes inputs, outputs, latency, and token counts.

2. Trace Visualization View execution as a tree structure to see exactly which steps ran, in what order, with what data. Identify where failures or slow-downs occur.

3. Datasets and Evaluation Create evaluation datasets from production traces or synthetic examples. Run your application against these datasets to measure quality changes.

4. Monitoring and Alerts Track metrics over time: latency percentiles, error rates, costs, user feedback. Set up alerts for regressions.

Implementation Basics

Getting started with LangSmith:

SDK Integration Add the LangSmith SDK and set your API key. For LangChain applications, tracing enables automatically. For other frameworks, use the decorator or context manager.

Framework Agnostic Despite the name, LangSmith works with any LLM framework, including OpenAI SDK, Anthropic SDK, and custom code. The tracing API is independent.

Trace Organization Use projects to separate environments (dev, staging, prod). Tag traces with metadata for filtering, including user IDs, feature flags, and experiment groups.

Evaluation Runs Define evaluators (LLM-as-judge, heuristic checks, human review) and run them against datasets. Compare runs to measure improvement.

Cost Tracking LangSmith calculates token costs automatically based on model pricing. See per-request costs and aggregate spending.

Privacy Considerations Traces contain actual prompts and responses. Configure data retention policies and consider anonymization for sensitive data.

LangSmith is free for development usage with paid tiers for production volume. Alternatives include Langfuse (open-source), Weights & Biases Prompts, and Helicone.