从零开始构建AI Agent评估体系：12种LangSmith评估方法详解

AI Agent 的评估需要全面考虑其完整的生命周期，从开发阶段到生产部署。评估过程应当涵盖多个关键维度：最终输出的事实准确性和实用价值、推理过程中工具选择的合理性和路径效率、结构化响应生成能力（如 JSON 格式）、多轮对话的上下文维持能力，以及在真实用户流量下的持续性能表现和错误监控能力。为了有效监控和评估 Agent 生命周期的各个组件，LangSmith 作为最具影响力和广泛应用的工具平台

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45人浏览 · 2026-03-26 22:05:01

srtfhdf · 2026-03-26 22:05:01 发布

AI Agent 的评估需要全面考虑其完整的生命周期，从开发阶段到生产部署。评估过程应当涵盖多个关键维度：最终输出的事实准确性和实用价值、推理过程中工具选择的合理性和路径效率、结构化响应生成能力（如 JSON 格式）、多轮对话的上下文维持能力，以及在真实用户流量下的持续性能表现和错误监控能力。

为了有效监控和评估 Agent 生命周期的各个组件，LangSmith 作为最具影响力和广泛应用的工具平台之一，提供了强大的评估框架。本文将深入探讨十二种不同的智能体评估技术，详细阐述每种技术的适用场景和实施方法。这些技术涵盖了从传统的预测答案与标准答案比较，到先进的实时反馈评估等多个层面，其中标准答案会随时间动态变化。

评估技术体系架构

基于标准答案的评估方法

这类方法包括环境配置、精确匹配评估、非结构化问答评估、结构化数据比较以及动态标准答案等技术。

程序性评估（过程分析）

此类别涵盖轨迹评估、工具选择精度分析、组件级 RAG 评估、基于 RAGAS 的 RAG 评估以及实时反馈机制。

观察性与自主评估

包括成对比较、基于仿真的评估、算法反馈以及技术总结等方法。

环境配置与初始化

评估工作的首要步骤是建立 LangSmith 环境。需要从官方仪表板获取 API 密钥，这是后续通过仪表板跟踪 Agent 进度的关键步骤。以下为 API 密钥的初始化配置：

 import os  
 from langchain_openai import ChatOpenAI  
 import langsmith  
   
 # 设置 LangSmith 端点（使用云版本时请勿修改）  
 os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"  
   
 # 配置 LangSmith API 密钥  
 os.environ["LANGCHAIN_API_KEY"] = "YOUR_LANGSMITH_API_KEY"  
   
 # 配置 OpenAI API 密钥  
 os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

虽然本文使用 OpenAI 模型作为示例，但 LangChain 支持广泛的开源和闭源大语言模型。开发者可以根据需求切换到其他模型 API 提供商或本地部署的 Hugging Face 模型。

LangSmith API 端点负责在 Web 仪表板中存储所有评估指标，这将在后续分析中发挥重要作用。接下来需要初始化 LangSmith 客户端，作为整个评估流程的核心组件：

 # 初始化 LangSmith 客户端  
 client = langsmith.Client()

1、精确匹配评估方法

精确匹配评估是最基础但至关重要的评估方法之一。该方法通过比较模型输出与预定义的正确答案来验证一致性。

该评估流程的核心机制包括：建立标准答案作为预期响应的基准；向模型提供输入并获取基于该输入的输出；执行逐字符精确匹配检验，若匹配则赋值 1 分，不匹配则为 0 分；最后对所有样本的得分进行平均计算，得出模型的整体精确匹配性能指标。

为了在 LangSmith 中有效实施这种方法，首先需要构建评估数据集。在 LangSmith 框架中，数据集是由示例集合构成，每个示例包含输入和相应的预期输出（参考或标签）。这些数据集构成了模型测试和评估的基础架构。

以下示例创建了包含两个问题的数据集，并为每个问题提供了期望的精确输出：

 # 创建数据集作为问答示例的容器  
 ds = client.create_dataset(  
     dataset_name=dataset_name,  
     description="A dataset for simple exact match questions."  
 )  
   
 # 每个示例由输入字典和相应的输出字典组成
 # 输入和输出在独立列表中提供，维持相同的顺序  
 client.create_examples(  
     # 输入列表，每个输入都是字典格式  
     inputs=[  
         {  
             "prompt_template": "State the year of the declaration of independence. Respond with just the year in digits, nothing else"  
         },  
         {  
             "prompt_template": "What's the average speed of an unladen swallow?"  
         },  
     ],  
     # 对应的输出列表  
     outputs=[  
         {"output": "1776"},  # 第一个提示的预期输出  
         {"output": "5"}      # 第二个提示的预期输出（陷阱问题）  
     ],  
     # 示例将被添加到的数据集 ID  
     dataset_id=ds.id,  
 )

数据准备完成后，需要定义评估组件。首要组件是待评估的模型或链。本示例中创建了

predict_result

函数，该函数接收提示，将其发送至 OpenAI 的

gpt-3.5-turbo

模型，并返回模型响应：

 # 定义待测试模型  
 model = "gpt-3.5-turbo"  
   
 # 被测试系统：接收输入字典，调用指定的 ChatOpenAI 模型，返回字典格式的输出  
 def predict_result(input_: dict) -> dict:  
     # 输入字典包含 "prompt_template" 键，与数据集输入中定义的键一致  
     prompt = input_["prompt_template"]  
       
     # 初始化并调用模型  
     response = ChatOpenAI(model=model, temperature=0).invoke(prompt)  
       
     # 输出键 "output" 与数据集输出中的键匹配，用于比较  
     return {"output": response.content}

评估器是对系统性能进行评分的核心函数。LangSmith 提供多种内置评估器，同时支持自定义评估器的创建。内置的

exact_match

评估器是预构建的字符串评估器，用于检查预测与参考输出之间的完全字符匹配。自定义

compare_label

评估器则演示了如何实现自定义逻辑，通过

@run_evaluator

装饰器使 LangSmith 能够在评估过程中识别和使用该函数。

 from langsmith.evaluation import EvaluationResult, run_evaluator  
   
 # @run_evaluator 装饰器将函数注册为自定义评估器  
 @run_evaluator  
 def compare_label(run, example) -> EvaluationResult:  
     """
     用于检查精确匹配的自定义评估器
       
     Args:  
         run: LangSmith 运行对象，包含模型输出  
         example: LangSmith 示例对象，包含参考数据  
       
     Returns:  
         包含键和分数的 EvaluationResult 对象  
     """  
     # 从运行输出字典获取模型预测
     # 键 'output' 必须与 `predict_result` 函数返回内容匹配  
     prediction = run.outputs.get("output") or ""  
       
     # 从示例输出字典获取参考答案
     # 键 'output' 必须与数据集中定义内容匹配  
     target = example.outputs.get("output") or ""  
       
     # 执行比较操作  
     match = prediction == target  
       
     # 返回结果，键值为结果中分数的命名方式
     # 精确匹配分数通常为二进制（匹配为 1，不匹配为 0）  
     return EvaluationResult(key="matches_label", score=int(match))

配置完成所有组件后，可以执行评估。

RunEvalConfig

用于配置评估测试套件，指定内置的

"exact_match"

评估器和自定义的

compare_label

评估器，确保每个模型运行都被两个评估器评分。

client.run_on_dataset

作为主要协调函数，遍历指定数据集中的每个示例，对输入运行

predict_result

函数，然后应用

RunEvalConfig

中的评估器对结果进行评分。

 from langchain.smith import RunEvalConfig  
   
 # 定义评估运行的配置  
 eval_config = RunEvalConfig(  
     # 通过字符串名称指定内置评估器  
     evaluators=["exact_match"],   
       
     # 在列表中直接传递自定义评估器函数  
     custom_evaluators=[compare_label],  
 )  
   
 # 触发评估执行
 # 对数据集中每个示例运行 `predict_result` 函数
 # 然后使用 `eval_config` 中的评估器对结果评分  
 client.run_on_dataset(  
     dataset_name=dataset_name,  
     llm_or_chain_factory=predict_result,  
     evaluation=eval_config,  
     verbose=True, # 打印进度条和链接  
     project_metadata={"version": "1.0.1", "model": model}, # 项目可选元数据  
 )

评估执行将在样本数据上启动基于精确匹配方法的评估并显示进度：

 View the evaluation results for project 'gregarious-doctor-77' at:  
 https://smith.langchain.com/o/your-org-id/datasets/some-dataset-uuid/compare?selectedSessions=some-session-uuid  
   
 View all tests for Dataset Oracle of Exactness at:  
 https://smith.langchain.com/o/your-org-id/datasets/some-dataset-uuid  
 [------------------------------------------------->] 2/2

结果展示了多种统计信息，包括评估数据中实体数量的

count

、正确预测实体比例的

mean

（0.5 表示一半实体被正确识别），以及其他统计信息。

LangSmith 精确匹配评估主要适用于需要确定性输出的 RAG 或 AI Agent 任务场景。基于事实的问答需要从上下文中获取单一正确事实答案；封闭式问题需要精确的是非判断或选择匹配；工具使用输出需要验证精确的工具调用结果；结构化输出需要检查精确的格式和键值对。

2、非结构化问答评估

由于大语言模型响应为非结构化文本，简单的字符串匹配往往无法满足评估需求。模型可以用多种不同表述方式提供事实正确的答案。为解决这一挑战，可以采用 LLM 辅助评估器来评估系统响应的语义和事实准确性。

该评估流程始于创建包含问题和参考答案（黄金标准）的数据集。基于 RAG 的问答系统随后使用检索到的文档回答每个问题。独立的 LLM（充当"评判者"）将预测答案与参考答案进行比较。若答案在事实上正确，评判者给予 1 分；若答案错误或存在幻觉，则给予 0 分。评判者提供推理过程来解释评分依据。最终，开发者可以审查错误案例，改进系统并重新执行评估。

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https://github．com/usuzifo-pie/ou/issues/311
https://github．com/usuzifo-pie/ou/issues/312
https://github．com/usuzifo-pie/ou/issues/313
https://github．com/usuzifo-pie/ou/issues/314
https://github．com/usuzifo-pie/ou/issues/315
https://github．com/usuzifo-pie/ou/issues/316
https://github．com/usuzifo-pie/ou/issues/317
https://github．com/usuzifo-pie/ou/issues/318
https://github．com/usuzifo-pie/ou/issues/319
https://github．com/usuzifo-pie/ou/issues/320
https://github．com/usuzifo-pie/ou/issues/321
https://github．com/usuzifo-pie/ou/issues/322
https://github．com/usuzifo-pie/ou/issues/323
https://github．com/usuzifo-pie/ou/issues/324
https://github．com/usuzifo-pie/ou/issues/325
https://github．com/usuzifo-pie/ou/issues/326
https://github．com/usuzifo-pie/ou/issues/327
https://github．com/usuzifo-pie/ou/issues/328
https://github．com/usuzifo-pie/ou/issues/329
https://github．com/usuzifo-pie/ou/issues/330
https://github．com/usuzifo-pie/ou/issues/331
https://github．com/usuzifo-pie/ou/issues/332
https://github．com/usuzifo-pie/ou/issues/333
https://github．com/usuzifo-pie/ou/issues/334
https://github．com/usuzifo-pie/ou/issues/335
https://github．com/usuzifo-pie/ou/issues/336
https://github．com/usuzifo-pie/ou/issues/337
https://github．com/usuzifo-pie/ou/issues/338
https://github．com/usuzifo-pie/ou/issues/339
https://github．com/usuzifo-pie/ou/issues/340
https://github．com/usuzifo-pie/ou/issues/341
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https://github．com/usuzifo-pie/ou/issues/343
https://github．com/usuzifo-pie/ou/issues/344
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https://github．com/usuzifo-pie/ou/issues/348
https://github．com/usuzifo-pie/ou/issues/349
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https://github．com/usuzifo-pie/ou/issues/351
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https://github．com/usuzifo-pie/ou/issues/360
https://github．com/usuzifo-pie/ou/issues/361
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https://github．com/usuzifo-pie/ou/issues/363
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https://github．com/usuzifo-pie/ou/issues/365
https://github．com/usuzifo-pie/ou/issues/366
https://github．com/usuzifo-pie/ou/issues/367
https://github．com/usuzifo-pie/ou/issues/368
https://github．com/usuzifo-pie/ou/issues/369
https://github．com/usuzifo-pie/ou/issues/370
https://github．com/usuzifo-pie/ou/issues/371
https://github．com/usuzifo-pie/ou/issues/372
https://github．com/usuzifo-pie/ou/issues/373
https://github．com/usuzifo-pie/ou/issues/374
https://github．com/usuzifo-pie/ou/issues/375
https://github．com/usuzifo-pie/ou/issues/376
https://github．com/usuzifo-pie/ou/issues/377
https://github．com/usuzifo-pie/ou/issues/378
https://github．com/usuzifo-pie/ou/issues/379
https://github．com/usuzifo-pie/ou/issues/380
https://github．com/usuzifo-pie/ou/issues/381
https://github．com/usuzifo-pie/ou/issues/382
https://github．com/usuzifo-pie/ou/issues/383
https://github．com/usuzifo-pie/ou/issues/384
https://github．com/usuzifo-pie/ou/issues/385
https://github．com/usuzifo-pie/ou/issues/386
https://github．com/usuzifo-pie/ou/issues/387
https://github．com/usuzifo-pie/ou/issues/388
https://github．com/usuzifo-pie/ou/issues/389
https://github．com/usuzifo-pie/ou/issues/390
https://github．com/usuzifo-pie/ou/issues/391
https://github．com/usuzifo-pie/ou/issues/392
https://github．com/usuzifo-pie/ou/issues/393
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https://github．com/usuzifo-pie/ou/issues/395
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https://github．com/usuzifo-pie/ou/issues/397
https://github．com/usuzifo-pie/ou/issues/398
https://github．com/usuzifo-pie/ou/issues/399
https://github．com/usuzifo-pie/ou/issues/400

相比精确匹配方法，非结构化场景的关键区别在于"标准答案"现在是正确性的参考基准，而非精确匹配的模板。

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