LangSmith Complete Tutorial 2026

What is LangSmith?

LangSmith is a powerful LLM observability that enables you to debug, test, and monitor LLM applications. It has become one of the most popular tools in the AI developer toolkit in 2026.

Why Use LangSmith?

Productivity: Dramatically reduces time spent on devops tasks

Integration: Connects seamlessly with major AI providers

Reliability: Production-tested by thousands of teams

Community: Large ecosystem of plugins and examples

Getting Started

Installation

bash
npm/yarn (Node.js projects)
npm install langsmith
pip (Python projects)  
pip install langsmith
Or use the hosted version at langsmith.com

Configuration

yaml
config.yml
name: my-langsmith-app
version: 1.0.0
integrations:
  openai:
    api_key: 1897628437146480647
  anthropic:
    api_key: undefined
settings:
  timeout: 30
  retry_attempts: 3
  log_level: info

Core Concepts

Basic Workflow

python
Python example
from langsmith import Client, Workflow
Initialize
client = Client(api_key="your-key")
Create a workflow
workflow = Workflow()
workflow.add_step("input", type="user_message")
workflow.add_step("ai_process", model="gpt-4o-mini", type="llm_call")
workflow.add_step("output", type="response")
Execute
result = client.run(workflow, input="Your prompt here")
print(result.output)

JavaScript/TypeScript Example

typescript
import { LangSmithClient } from 'langsmith';
const client = new LangSmithClient({
  apiKey: process.env.LANGSMITH_API_KEY,
});
async function main() {
  const result = await client.run({
    workflow: 'my-workflow',
    input: { message: 'Hello, AI!' }
  });
  
  console.log(result.output);
}
main();

Real-World Use Cases

Use Case 1: debug, test, and monitor LLM applications

python
Complete example: debug, test, and monitor LLM applications
import os
from openai import OpenAI
openai_client = OpenAI()
def create_devops_pipeline(input_data: dict) -> dict:
    """
    Pipeline for debug, test, and monitor LLM applications using LangSmith.
    """
    # Step 1: Process input
    processed = preprocess(input_data)
    
    # Step 2: AI analysis
    response = openai_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "system",
                "content": f"You are an expert in {t.category}. Help with debug, test, and monitor LLM applications."
            },
            {
                "role": "user",
                "content": str(processed)
            }
        ]
    )
    
    # Step 3: Post-process
    result = {
        "input": input_data,
        "analysis": response.choices[0].message.content,
        "timestamp": datetime.now().isoformat()
    }
    
    return result
Run it
result = create_devops_pipeline({
    "topic": "debug, test, and monitor LLM applications",
    "context": "Building modern AI applications"
})
print(result["analysis"])

Use Case 2: Integration with Other Tools

python
Integrate LangSmith with your existing stack
import httpx
import json
class LangSmithIntegration:
    def __init__(self, api_key: str):
        self.client = httpx.AsyncClient(
            base_url="https://api.langsmith.com",
            headers={"Authorization": f"Bearer {api_key}"}
        )
    
    async def process(self, data: dict) -> dict:
        response = await self.client.post("/process", json=data)
        response.raise_for_status()
        return response.json()
    
    async def batch_process(self, items: list) -> list:
        import asyncio
        tasks = [self.process(item) for item in items]
        return await asyncio.gather(*tasks)
Usage
import asyncio
async def main():
    integration = LangSmithIntegration(
        api_key=os.environ["LANGSMITH_KEY"]
    )
    
    results = await integration.batch_process([
        {"input": "Item 1"},
        {"input": "Item 2"},
        {"input": "Item 3"},
    ])
    
    for r in results:
        print(r)
asyncio.run(main())

Advanced Features

Monitoring and Logging

python
import logging
from functools import wraps
import time
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("langsmith")
def with_logging(func):
    @wraps(func)
    async def wrapper(*args, **kwargs):
        start = time.time()
        logger.info(f"Starting {func.__name__}")
        
        try:
            result = await func(*args, **kwargs)
            duration = time.time() - start
            logger.info(f"Completed {func.__name__} in {duration:.2f}s")
            return result
        except Exception as e:
            logger.error(f"Error in {func.__name__}: {e}")
            raise
    
    return wrapper
@with_logging
async def my_workflow(data: dict):
    # Your LangSmith workflow here
    pass

Error Handling

python
from tenacity import retry, stop_after_attempt, wait_exponential
@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=10)
)
def reliable_api_call(data: dict) -> dict:
    """Retry on failure with exponential backoff."""
    try:
        return process(data)
    except RateLimitError:
        logger.warning("Rate limit hit, retrying...")
        raise
    except APIError as e:
        if e.status_code >= 500:
            raise  # Retry on server errors
        raise  # Don't retry on client errors

Pricing and Plans

PlanPriceFeatures

Free$0Limited usage, community support Pro$20-50/monthFull features, priority support EnterpriseCustomSLA, custom integrations, SSO

Comparison with Alternatives

ToolLangSmithAlternative 1Alternative 2

Ease of use⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Features⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Cost⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Community⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐

Conclusion

LangSmith is an excellent LLM observability that makes it easy to debug, test, and monitor LLM applications. Its combination of power and usability makes it a top choice for AI developers in 2026.

Whether you're building your first AI application or scaling an enterprise system, LangSmith provides the tools you need to succeed.

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*Tutorial for LangSmith latest version | May 2026*