LangSmith - LLM Observability Platform

Development platform for debugging, evaluating, and monitoring language models and AI applications.

When to use LangSmith

Use LangSmith when:

• Debugging LLM application issues (prompts, chains, agents)
• Evaluating model outputs systematically against datasets
• Monitoring production LLM systems
• Building regression testing for AI features
• Analyzing latency, token usage, and costs
• Collaborating on prompt engineering

Key features:

• Tracing: Capture inputs, outputs, latency for all LLM calls
• Evaluation: Systematic testing with built-in and custom evaluators
• Datasets: Create test sets from production traces or manually
• Monitoring: Track metrics, errors, and costs in production
• Integrations: Works with OpenAI, Anthropic, LangChain, LlamaIndex

Use alternatives instead:

• Weights & Biases: Deep learning experiment tracking, model training
• MLflow: General ML lifecycle, model registry focus
• Arize/WhyLabs: ML monitoring, data drift detection

Quick start

Installation

pip install langsmith

# Set environment variables
export LANGSMITH_API_KEY="your-api-key"
export LANGSMITH_TRACING=true

Basic tracing with @traceable

from langsmith import traceable
from openai import OpenAI

client = OpenAI()

@traceable
def generate_response(prompt: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

# Automatically traced to LangSmith
result = generate_response("What is machine learning?")

OpenAI wrapper (automatic tracing)

from langsmith.wrappers import wrap_openai
from openai import OpenAI

# Wrap client for automatic tracing
client = wrap_openai(OpenAI())

# All calls automatically traced
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

Core concepts

Runs and traces

A run is a single execution unit (LLM call, chain, tool). Runs form hierarchical traces showing the full execution flow.

from langsmith import traceable

@traceable(run_type="chain")
def process_query(query: str) -> str:
    # Parent run
    context = retrieve_context(query)  # Child run
    response = generate_answer(query, context)  # Child run
    return response

@traceable(run_type="retriever")
def retrieve_context(query: str) -> list:
    return vector_store.search(query)

@traceable(run_type="llm")
def generate_answer(query: str, context: list) -> str:
    return llm.invoke(f"Context: {context}\n\nQuestion: {query}")

Projects

Projects organize related runs. Set via environment or code:

import os
os.environ["LANGSMITH_PROJECT"] = "my-project"

# Or per-function
@traceable(project_name="my-project")
def my_function():
    pass

Client API

from langsmith import Client

client = Client()

# List runs
runs = list(client.list_runs(
    project_name="my-project",
    filter='eq(status, "success")',
    limit=100
))

# Get run details
run = client.read_run(run_id="...")

# Create feedback
client.create_feedback(
    run_id="...",
    key="correctness",
    score=0.9,
    comment="Good answer"
)

Datasets and evaluation

Create dataset

from langsmith import Client

client = Client()

# Create dataset
dataset = client.create_dataset("qa-test-set", description="QA evaluation")

# Add examples
client.create_examples(
    inputs=[
        {"question": "What is Python?"},
        {"question": "What is ML?"}
    ],
    outputs=[
        {"answer": "A programming language"},
        {"answer": "Machine learning"}
    ],
    dataset_id=dataset.id
)

Run evaluation

from langsmith import evaluate

def my_model(inputs: dict) -> dict:
    # Your model logic
    return {"answer": generate_answer(inputs["question"])}

def correctness_evaluator(run, example):
    prediction = run.outputs["answer"]
    reference = example.outputs["answer"]
    score = 1.0 if reference.lower() in prediction.lower() else 0.0
    return {"key": "correctness", "score": score}

results = evaluate(
    my_model,
    data="qa-test-set",
    evaluators=[correctness_evaluator],
    experiment_prefix="v1"
)

print(f"Average score: {results.aggregate_metrics['correctness']}")

Built-in evaluators

from langsmith.evaluation import LangChainStringEvaluator

# Use LangChain evaluators
results = evaluate(
    my_model,
    data="qa-test-set",
    evaluators=[
        LangChainStringEvaluator("qa")
how to use langsmith-observability
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use langsmith-observability on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add langsmith-observability
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/davila7/claude-code-templates --skill langsmith-observabilitycopy
The skills CLI fetches langsmith-observability from GitHub repository davila7/claude-code-templates and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/langsmith-observability
Reload or restart Cursor to activate langsmith-observability. Access the skill through slash commands (e.g., /langsmith-observability) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?