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AI Agents with LangChain & Autonomous Workflows · 课时

使用 LangSmith 进行追踪与监控

利用 LangSmith 深入了解智能体的思考过程、工具使用情况和整体执行流程

使用 LangSmith 进行追踪与监控 是 CoddyKit 上的免费 AI Agents with LangChain & Autonomous Workflows 课时。 这是第 1 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 AI Agents with LangChain & Autonomous Workflows 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 AI Agents with LangChain & Autonomous Workflows 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

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.

常见问题解答

「使用 LangSmith 进行追踪与监控」课时是免费的吗？

是的 — 「使用 LangSmith 进行追踪与监控」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 AI Agents with LangChain & Autonomous Workflows 课程的其余内容，请升级到 CoddyKit PRO。 AI Agents with LangChain & Autonomous Workflows 课程共包含 4 节课。

「使用 LangSmith 进行追踪与监控」这节课中我会学到什么？

利用 LangSmith 深入了解智能体的思考过程、工具使用情况和整体执行流程 你通过在浏览器中直接运行的动手代码来练习 AI Agents with LangChain & Autonomous Workflows，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 AI Agents with LangChain & Autonomous Workflows 需要有经验吗？

无需任何先前经验。CoddyKit 上的 AI Agents with LangChain & Autonomous Workflows 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 1 节课，共 4 节。

「使用 LangSmith 进行追踪与监控」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 AI Agents with LangChain & Autonomous Workflows 课中编写并运行代码吗？

能。每节 AI Agents with LangChain & Autonomous Workflows 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

  1. 使用 LangSmith 进行追踪与监控
  2. 调试智能体的思考过程
  3. 评估智能体性能
  4. 令牌使用量与成本监控
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