/observability-langsmith

name: langsmith-observability
description: LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Observability, LangSmith, Tracing, Evaluation, Monitoring, Debugging, Testing, LLM Ops, Production]
dependencies: [langsmith>=0.2.0]

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"),
        LangChainStringEvaluator("cot_qa")
    ]
)

Advanced tracing

Tracing context

from langsmith import tracing_context

with tracing_context(
    project_name="experiment-1",
    tags=["production", "v2"],
    metadata={"version": "2.0"}
):
    # All traceable calls inherit context
    result = my_function()

Manual runs

from langsmith import trace

with trace(
    name="custom_operation",
    run_type="tool",
    inputs={"query": "test"}
) as run:
    result = do_something()
    run.end(outputs={"result": result})

Process inputs/outputs

def sanitize_inputs(inputs: dict) -> dict:
    if "password" in inputs:
        inputs["password"] = "***"
    return inputs

@traceable(process_inputs=sanitize_inputs)
def login(username: str, password: str):
    return authe