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AI Agents with LangChain & Autonomous Workflows · Aula

LangSmith para rastreamento e monitoramento

Use LangSmith para obter visibilidade sobre o processo de raciocínio do seu agente, o uso de ferramentas e o fluxo geral de execução.

LangSmith para rastreamento e monitoramento é uma aula grátis de AI Agents with LangChain & Autonomous Workflows no CoddyKit. Esta é a aula 1 de 4. Você pode ler a aula completa abaixo gratuitamente — depois pratica ao vivo no navegador com um editor de código integrado e um tutor de IA 24/7. Faz parte do caminho de aprendizado de AI Agents with LangChain & Autonomous Workflows, e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de AI Agents with LangChain & Autonomous Workflows inclui 4 aulas no total.

Partes desta aula ainda não foram traduzidas e aparecem em inglês.

Seeing Inside Your Agent

When you build an AI agent, it often feels like a black box. You provide an input, and it gives an output, but what happens in between?

  • Did it understand your prompt correctly?
  • Which tools did it decide to use?
  • Why did it choose that particular action?

Understanding these internal workings is crucial for debugging and improving your agents.

Introducing LangSmith

LangSmith is a developer platform by LangChain, designed specifically for building, debugging, and evaluating Large Language Model (LLM) applications.

It provides the visibility you need to understand your agent's behavior, identify issues, and ensure it performs as expected.

Core Concepts: Traces & Runs

In LangSmith, two key concepts are Runs and Traces:

  • A Run represents a single execution of a component, like an LLM call, a tool invocation, or a chain step.
  • A Trace is a collection of related runs that together form a complete execution of your agent or application, showing the full sequence of events.

Imagine a trace as a detailed log of your agent's entire thought process and actions.

Setting Up LangSmith

To use LangSmith, you need to set a few environment variables:

  • LANGCHAIN_TRACING_V2=true: Enables LangSmith tracing.
  • LANGCHAIN_API_KEY: Your unique API key for authentication.
  • LANGCHAIN_PROJECT: A name for your project to group related traces.

You can get your API key from the LangSmith website after signing up.

Enabling Tracing in Code

Let's see how to enable tracing for a simple LangChain application. First, ensure you have your API key and project name set up.

import os
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

# Set these environment variables before running
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "YOUR_LANGSMITH_API_KEY"
os.environ["LANGCHAIN_PROJECT"] = "MyFirstLangSmithProject"

# Replace with your actual OpenAI API key
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# Create a simple LLM chain
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("user", "{question}")
])
chain = prompt | llm

# Invoke the chain
response = chain.invoke({"question": "What is the capital of France?"})
print(response.content)

Viewing Your First Trace

After running the previous code, a trace will be sent to your LangSmith project. Navigate to the LangSmith UI (app.langsmith.com) and select your project.

You'll see a visual representation of the 'chain' execution, including:

  • The input to the prompt.
  • The LLM call itself.
  • The final output from the LLM.

Each step is a 'Run' within the 'Trace'.

Understanding Agent Thought Process

For more complex agents, LangSmith shines by showing the agent's thought process. You can see the sequence of:

  • Thought: The agent's reasoning.
  • Action: The tool it decided to use.
  • Action Input: The arguments passed to the tool.
  • Observation: The result returned by the tool.

This detailed breakdown helps you understand why an agent made certain decisions.

Monitoring Tool Usage

Agents often rely on various tools (e.g., search engines, calculators, custom APIs). LangSmith traces clearly show:

  • Which specific tools were called.
  • The exact inputs provided to each tool.
  • The outputs received from the tools.

This is invaluable for verifying that your agent is using tools correctly and for debugging when a tool returns an unexpected result.

Debugging with LangSmith Traces

Traces are your best friend for debugging. If your agent behaves unexpectedly:

  • Identify the exact step: Pinpoint where the incorrect logic or error occurred.
  • Inspect inputs/outputs: Check if the prompt received the expected input or if the tool returned the right output.
  • Review agent thoughts: Understand if the agent's reasoning led to a bad decision.

This visibility dramatically reduces debugging time compared to traditional print statements.

Check Your Knowledge

Which of the following is NOT a primary benefit of using LangSmith for AI agents?

Recap: Your Observability Ally

LangSmith is an essential tool for anyone building AI agents with LangChain. It transforms the black box of agent execution into a transparent, inspectable process.

By providing detailed traces of runs, agent thoughts, and tool usage, LangSmith empowers you to debug faster, understand your agents better, and build more robust and reliable AI applications.

Perguntas Frequentes

A aula “LangSmith para rastreamento e monitoramento” é grátis?

Sim — o texto completo de “LangSmith para rastreamento e monitoramento” é grátis para ler aqui na web. Para praticá-la interativamente (um editor de código integrado e um tutor de IA 24/7) e desbloquear o restante do curso de AI Agents with LangChain & Autonomous Workflows, atualize para CoddyKit PRO. O curso de AI Agents with LangChain & Autonomous Workflows inclui 4 aulas no total.

O que vou aprender em “LangSmith para rastreamento e monitoramento”?

Use LangSmith para obter visibilidade sobre o processo de raciocínio do seu agente, o uso de ferramentas e o fluxo geral de execução. Você pratica AI Agents with LangChain & Autonomous Workflows com código prático que executa diretamente no navegador, e um tutor de IA 24/7 responde suas dúvidas enquanto trabalha na aula.

Preciso ter experiência prévia para começar AI Agents with LangChain & Autonomous Workflows?

Nenhuma experiência prévia é necessária. AI Agents with LangChain & Autonomous Workflows no CoddyKit é estruturado para alunos iniciantes até avançados, então você pode começar aqui ou desde o início e aprender no seu ritmo. Esta é a aula 1 de 4.

Quanto tempo leva a aula “LangSmith para rastreamento e monitoramento”?

A maioria das aulas CoddyKit leva cerca de 5–10 minutos. Cada uma é compacta e interativa, então você faz progresso constante e retoma exatamente de onde parou entre web e app.

Posso escrever e executar código nesta aula de AI Agents with LangChain & Autonomous Workflows?

Sim. Cada aula de AI Agents with LangChain & Autonomous Workflows inclui um editor de código integrado, então você escreve e executa código real direto no navegador e recebe feedback de IA instantaneamente — nenhuma configuração local necessária.

Todas as aulas deste curso

  1. LangSmith para rastreamento e monitoramento
  2. Depurando processos de raciocínio de agentes
  3. Avaliando o desempenho dos agentes
  4. Uso de tokens e monitoramento de custos
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