LangSmith untuk Pelacakan dan Pemantauan
Manfaatkan LangSmith untuk memperoleh visibilitas terhadap proses berpikir agen, penggunaan alat, dan keseluruhan alur eksekusinya.
LangSmith untuk Pelacakan dan Pemantauan adalah pelajaran AI Agents with LangChain & Autonomous Workflows gratis di CoddyKit. Ini adalah pelajaran 1 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar AI Agents with LangChain & Autonomous Workflows, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus AI Agents with LangChain & Autonomous Workflows mencakup 4 pelajaran total.
Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.
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.
Pertanyaan yang Sering Diajukan
Apakah pelajaran “LangSmith untuk Pelacakan dan Pemantauan” gratis?
Ya — teks lengkap “LangSmith untuk Pelacakan dan Pemantauan” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus AI Agents with LangChain & Autonomous Workflows, upgrade ke CoddyKit PRO. Kursus AI Agents with LangChain & Autonomous Workflows mencakup 4 pelajaran total.
Apa yang akan aku pelajari di “LangSmith untuk Pelacakan dan Pemantauan”?
Manfaatkan LangSmith untuk memperoleh visibilitas terhadap proses berpikir agen, penggunaan alat, dan keseluruhan alur eksekusinya. Kamu berlatih AI Agents with LangChain & Autonomous Workflows dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.
Apakah aku perlu pengalaman untuk memulai AI Agents with LangChain & Autonomous Workflows?
Tidak diperlukan pengalaman sebelumnya. AI Agents with LangChain & Autonomous Workflows di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 1 dari 4.
Berapa lama pelajaran “LangSmith untuk Pelacakan dan Pemantauan” memakan waktu?
Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.
Bisakah aku menulis dan menjalankan kode dalam pelajaran AI Agents with LangChain & Autonomous Workflows ini?
Ya. Setiap pelajaran AI Agents with LangChain & Autonomous Workflows menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.
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