LangSmith — Prompt Debugging and LLM Observability

What is LangSmith?

LangSmith is an observability and evaluation platform for LLM applications. It captures detailed traces of every LLM call, chain, and agent step — showing latency, token usage, inputs, and outputs. Beyond monitoring, it provides a prompt playground for iteration, dataset management for systematic evaluation, and regression testing to catch prompt regressions before deployment.

Answer-Ready: LangSmith is an LLM observability platform by LangChain. Provides trace visualization, prompt playground, dataset evaluation, and regression testing. Works with any LLM framework (not just LangChain). Free tier available. Used by thousands of AI teams in production.

Best for: AI teams debugging and monitoring LLM applications. Works with: LangChain, OpenAI, Anthropic Claude, any Python/JS LLM app. Setup time: Under 3 minutes.

Core Features

1. Trace Visualization

Every LLM call is captured with:

• Input/output at each step
• Latency breakdown
• Token usage and cost
• Error details and stack traces
• Nested chain/agent visualization

2. Prompt Playground

1. Select a traced LLM call
2. Modify the prompt in the playground
3. Re-run with different models
4. Compare outputs side-by-side
5. Save winning prompt version

3. Dataset & Evaluation

from langsmith import Client
from langsmith.evaluation import evaluate

client = Client()

# Create evaluation dataset
dataset = client.create_dataset("qa-pairs")
client.create_examples(
    inputs=[{"question": "What is RAG?"}],
    outputs=[{"answer": "Retrieval-Augmented Generation"}],
    dataset_id=dataset.id,
)

# Run evaluation
results = evaluate(
    my_llm_call,
    data="qa-pairs",
    evaluators=["correctness", "helpfulness"],
)

4. Online Evaluation (Production)

from langsmith import Client

client = Client()

# Add feedback to production traces
client.create_feedback(
    run_id="...",
    key="user_rating",
    score=1.0,
    comment="Helpful response",
)

5. Regression Testing

# Compare prompt versions on same dataset
results_v1 = evaluate(prompt_v1, data="test-set")
results_v2 = evaluate(prompt_v2, data="test-set")
# Side-by-side comparison in UI

LangSmith vs Alternatives

Pricing

FAQ

Q: Do I need to use LangChain? A: No. LangSmith works with any LLM framework. The @traceable decorator works with plain Python functions.

Q: How much overhead does tracing add? A: Traces are sent asynchronously. Typical overhead is <5ms per trace.

Q: Can I self-host? A: Enterprise plan includes self-hosted deployment. For open-source alternatives, see LangFuse.