LangSmith

Shoutout for all the observability and prompt management capabilities!

LangSmith gives me visibility into what my LLMs are actually doing — tracing, evaluating, and debugging end-to-end. Alternatives feel piecemeal, but LangSmith is built for production from day one. I can test, monitor, and improve models in the same place, which saves hours and makes iteration faster.

For evals and developing agents.

LangSmith has made it so much easier to take my LLM projects from idea to production. The debugging and iteration tools save me tons of trial and error.

Shoutout to LangSmith — the best tool we've found for debugging, evaluating, and improving LLM apps in production.

We’re building on OpenAI, Claude, and some custom RAG pipelines. Prompt engineering used to feel like trying to tune a car engine blindfolded. LangSmith gave us eyes, logs, metrics, and iteration velocity.

Why LangSmith stood out We considered:

Traceloop – beautiful UI but lacks first-class support for LangChain agents/tools

PromptLayer – good for tracking OpenAI usage but not built for structured evals or agents

Weights & Biases – amazing for ML ops but too heavyweight for LLM chains

Manual logging + BigQuery – flexible but high overhead and no evaluation layer

LangSmith hit the sweet spot of observability, evaluation, and dev UX.

Why we use it in production:

• Structured logs across complex chains, agents, tools, retrievers
• Real-time debugging of streaming outputs and nested calls
• Trace replay to see exactly what happened inside a failed agent run
• Built-in evals for regressions, hallucinations, quality benchmarks
• Clean Python SDK that doesn't force a rewrite of your stack

Tips from our experience

• Use dataset runs + evals early, even on toy prompts — you'll thank yourself later
• When deploying an agent, wrap it in LangSmith tracing from day one — the logs alone are worth it
• Pair LangSmith with a feature flag system (like GrowthBook or LaunchDarkly) to ship LLM prompt updates safely
• We ship a lot faster now by building A/B evals around new prompts or RAG logic before rollout

LangSmith makes LLM dev feel like real software engineering. Observability and testability were missing from this space — until now.

I've been using LangSmith for a couple of months at our startup and it's been incredibly useful for running ongoing LLM evaluations and for evaluating new features. The Python SDK is handy and we've automated LangSmith evals as part of CI/CD on GitHub to spot regressions.

My main struggle with LangSmith has been that the UI can be tricky to work with for larger datasets or datasets with a large experiment history. I would also love for the filters to be persisted in the URL. That way I can send a filtered set of failing examples to someone without having to provide instructions for how to reconstruct it.

We use LangSmith to streamline and manage our conversational agents, helping us build and refine the AI-driven coach within our app. It allows us to evaluate and improve LLM performance quickly, optimizing for user engagement and ensuring the chatbot provides accurate feedback.

I've loved using LangSmith! It's efficient and user-friendly, making it a joy to work with. The platform's comprehensive visibility into the chain sequence of calls simplifies debugging and enhances the development process.