1. What is LangSmith used for?

LangSmith is used to observe, debug, evaluate, and monitor applications built on large language models. It captures detailed traces of every step an LLM agent takes, from the initial input to the final output, so developers can understand exactly what their AI system did and why.

2. Is LangSmith free to use?

Yes, LangSmith has a free Developer plan that includes 5,000 traces per month with no credit card required. Paid plans has different price per month for teams and offer higher trace limits, collaboration features, and enterprise options.

3. Does LangSmith only work with LangChain?

No. LangSmith is framework-agnostic and works with any LLM application. You can use it with LangChain, LangGraph, OpenAI SDK, Anthropic SDK, LlamaIndex, or a fully custom stack.

4. What is a trace in LangSmith?

A trace in LangSmith is a complete, hierarchical record of a single execution of your LLM app. It includes every LLM call, tool invocation, retrieval step, and model output, along with timing and token usage for each step. You can click into any node in the trace to inspect its exact inputs and outputs.

5. How does LangSmith help with debugging hallucinations?

When your model halluculates, LangSmith lets you trace exactly what documents were retrieved and fed into the prompt, what the model received, and what it returned. This lets you identify whether the hallucination came from bad retrieval, a poorly worded prompt, or the model itself, so you can fix the right thing.

6. What is the difference between tracing and evaluation in LangSmith?

Tracing records what your agent did during a run. Evaluation scores whether what the agent did was correct or high quality. Tracing gives you visibility; evaluation gives you measurable quality signals.

7. Can I use LangSmith without writing any code?

For basic use, yes. You can create datasets, run experiments, and review traces entirely from the LangSmith web UI. No coding is needed for simple evaluation setups. For custom evaluators, prompt configurations, or programmatic tracing.

8. How does LangSmith handle data privacy and security?

LangSmith cloud stores data in GCP us-central1 by default. For teams that cannot send data to the cloud, LangSmith offers self-hosted and bring-your-own-cloud (BYOC) options on AWS, GCP, or Azure, so trace data never leaves your environment.

9. What is an LLM evaluator in LangSmith?

An evaluator in LangSmith is a function or model that scores your agent's outputs. You can use a custom Python function for rule-based checks, an LLM-as-judge that rates responses on criteria like accuracy or helpfulness, or human annotators who review outputs directly in the LangSmith UI.

10. How is LangSmith different from traditional application monitoring tools like Datadog?

Traditional monitoring tools track server metrics like CPU usage, memory, and request latency. LangSmith tracks AI-specific signals like prompt quality, token usage, hallucination rates, tool call success, and agent reasoning steps. The two tools are complementary.

11. What LangSmith pricing plan suits a solo developer or small team?

Solo developers can start on the free Developer plan, which gives 5,000 traces per month and access to most core features including tracing, evaluation, and the prompt playground. Higher limits, team collaboration, and priority support. Both plans have no upfront commitment.

Real-world LangChain use cases and workflows

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LangSmith: Guide to Debugging and Monitoring LLM Apps

Infographic showing the core architecture of LangGraph with workflow nodes, edges, state memory, tool execution, retry logic, multi-agent coordination, and AI response flow for building stateful AI agent workflows.

Frequently Asked Questions

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LangSmith: Guide to Debugging and Monitoring LLM Apps

Frequently Asked Questions

1. What is LangSmith used for?

2. Is LangSmith free to use?

3. Does LangSmith only work with LangChain?

4. What is a trace in LangSmith?

5. How does LangSmith help with debugging hallucinations?

6. What is the difference between tracing and evaluation in LangSmith?

7. Can I use LangSmith without writing any code?

8. How does LangSmith handle data privacy and security?

9. What is an LLM evaluator in LangSmith?

10. How is LangSmith different from traditional application monitoring tools like Datadog?

11. What LangSmith pricing plan suits a solo developer or small team?

Popular Blogs

LangChain LLM: Guide to Building AI Applications

LangChain Hugging Face: How to Integrate, Use & Troubleshoot

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21 LangChain Alternatives in 2026

LangSmith: Guide to Debugging and Monitoring LLM Apps

Frequently Asked Questions

1. What is LangSmith used for?

2. Is LangSmith free to use?

3. Does LangSmith only work with LangChain?

4. What is a trace in LangSmith?

5. How does LangSmith help with debugging hallucinations?

6. What is the difference between tracing and evaluation in LangSmith?

7. Can I use LangSmith without writing any code?

8. How does LangSmith handle data privacy and security?

9. What is an LLM evaluator in LangSmith?

10. How is LangSmith different from traditional application monitoring tools like Datadog?

11. What LangSmith pricing plan suits a solo developer or small team?

Popular Blogs

LangChain LLM: Guide to Building AI Applications

LangChain Hugging Face: How to Integrate, Use & Troubleshoot

LangChain Embeddings: A Complete Guide to Setup and Models

21 LangChain Alternatives in 2026