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LangSmith 文档版本 跟随上游 源快照 76919082 更新于 2026-10-18

如何以编程方式创建和管理数据集

你可以使用 Python 和 TypeScript SDK 以编程方式管理数据集。这包括创建、更新和删除数据集，以及向它们添加示例。

创建数据集

从值列表创建数据集

使用 client 制作数据集最灵活的方式，是从一个输入和可选输出的列表创建示例。下面是一个示例。

注意，你可以为每个示例添加任意元数据，例如一条备注或一个来源。元数据以字典形式存储。

TIP

如果你有许多示例要创建，考虑使用 create_examples/createExamples 方法在一次请求中创建多个示例。如果只创建一个示例，你可以使用 create_example/createExample 方法。

python
from langsmith import Client

examples = [
  {
    "inputs": {"question": "What is the largest mammal?"},
    "outputs": {"answer": "The blue whale"},
    "metadata": {"source": "Wikipedia"},
  },
  {
    "inputs": {"question": "What do mammals and birds have in common?"},
    "outputs": {"answer": "They are both warm-blooded"},
    "metadata": {"source": "Wikipedia"},
  },
  {
    "inputs": {"question": "What are reptiles known for?"},
    "outputs": {"answer": "Having scales"},
    "metadata": {"source": "Wikipedia"},
  },
  {
    "inputs": {"question": "What's the main characteristic of amphibians?"},
    "outputs": {"answer": "They live both in water and on land"},
    "metadata": {"source": "Wikipedia"},
  },
]

client = Client()
dataset_name = "Elementary Animal Questions"

# Storing inputs in a dataset lets us
# run chains and LLMs over a shared set of examples.
dataset = client.create_dataset(
  dataset_name=dataset_name, description="Questions and answers about animal phylogenetics.",
)

# Prepare inputs, outputs, and metadata for bulk creation
client.create_examples(
  dataset_id=dataset.id,
  examples=examples
)
typescript
import { Client } from "langsmith";

const client = new Client();

const exampleInputs: [string, string][] = [
  ["What is the largest mammal?", "The blue whale"],
  ["What do mammals and birds have in common?", "They are both warm-blooded"],
  ["What are reptiles known for?", "Having scales"],
  [
    "What's the main characteristic of amphibians?",
    "They live both in water and on land",
  ],
];

const datasetName = "Elementary Animal Questions";

// Storing inputs in a dataset lets us
// run chains and LLMs over a shared set of examples.
const dataset = await client.createDataset(datasetName, {
  description: "Questions and answers about animal phylogenetics",
});

// Prepare inputs, outputs, and metadata for bulk creation
const inputs = exampleInputs.map(([inputPrompt]) => ({ question: inputPrompt }));
const outputs = exampleInputs.map(([, outputAnswer]) => ({ answer: outputAnswer }));
const metadata = exampleInputs.map(() => ({ source: "Wikipedia" }));

// Use the bulk createExamples method
await client.createExamples({
  inputs,
  outputs,
  metadata,
  datasetId: dataset.id,
});
java
import com.langchain.smith.client.LangsmithClient;
import com.langchain.smith.client.okhttp.LangsmithOkHttpClient;
import com.langchain.smith.core.JsonValue;
import com.langchain.smith.errors.UnexpectedStatusCodeException;
import com.langchain.smith.models.datasets.Dataset;
import com.langchain.smith.models.datasets.DatasetCreateParams;
import com.langchain.smith.models.datasets.DatasetListParams;
import com.langchain.smith.models.examples.bulk.BulkCreateParams;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;

public class CreateDatasetExample {
    public static void main(String[] args) {
        LangsmithClient client = LangsmithOkHttpClient.fromEnv();

        List<String[]> exampleInputs = List.of(
            new String[]{"What is the largest mammal?", "The blue whale"},
            new String[]{"What do mammals and birds have in common?", "They are both warm-blooded"},
            new String[]{"What are reptiles known for?", "Having scales"},
            new String[]{"What's the main characteristic of amphibians?", "They live both in water and on land"}
        );

        String datasetName = "Elementary Animal Questions";

        Dataset dataset;
        try {
            dataset = client.datasets().create(
                DatasetCreateParams.builder()
                    .name(datasetName)
                    .description("Questions and answers about animal phylogenetics")
                    .build()
            );
        } catch (UnexpectedStatusCodeException e) {
            // Dataset already exists, get it
            if (e.statusCode() == 409) {
                DatasetListParams listParams = DatasetListParams.builder()
                    .name(datasetName)
                    .build();
                dataset = client.datasets().list(listParams).items().get(0);
            } else {
                throw e;
            }
        }

        // Prepare inputs, outputs, and metadata for bulk creation
        List<Map<String, String>> inputs = exampleInputs.stream()
            .map(pair -> {
                return Maps.of("question", pair[0]);
            })
            .collect(Collectors.toList());

        List<Map<String, String>> outputs = exampleInputs.stream()
            .map(pair -> {
                return Maps.of("answer", pair[1]);
            })
            .collect(Collectors.toList());

        List<Map<String, String>> metadata = exampleInputs.stream()
            .map(pair -> {
                return Maps.of("source", "Wikipedia");
            })
            .collect(Collectors.toList());

        // Use the bulk createExamples method
        BulkCreateParams.Builder bulkParamsBuilder = BulkCreateParams.builder();
        for (int i = 0; i < inputs.size(); i++) {
            bulkParamsBuilder.addBody(
                BulkCreateParams.Body.builder()
                    .datasetId(dataset.id())
                    .inputs(JsonValue.from(inputs.get(i)))
                    .outputs(JsonValue.from(outputs.get(i)))
                    .metadata(JsonValue.from(metadata.get(i)))
                    .build()
            );
        }

        client.examples().bulk().create(bulkParamsBuilder.build());
    }
}

从追踪创建数据集

要从你追踪的运行（span）创建数据集，你可以使用同样的方法。有关获取和过滤运行的更多示例，参阅导出追踪指南。下面是一个示例：

python
from langsmith import Client

client = Client()
dataset_name = "Example Dataset"

# Filter runs to add to the dataset
runs = client.list_runs(
  project_name="my_project",
  is_root=True,
  error=False,
)

dataset = client.create_dataset(dataset_name, description="An example dataset")

# Prepare inputs and outputs for bulk creation
examples = [{"inputs": run.inputs, "outputs": run.outputs} for run in runs]

# Use the bulk create_examples method
client.create_examples(
  dataset_id=dataset.id,
  examples=examples
)
typescript
import { Client, Run } from "langsmith";

const client = new Client();
const datasetName = "Example Dataset";

// Filter runs to add to the dataset
const runs: Run[] = [];
for await (const run of client.listRuns({
  projectName: "my_project",
  isRoot: 1,
  error: false,
})) {
  runs.push(run);
}

const dataset = await client.createDataset(datasetName, {
  description: "An example dataset",
  dataType: "kv",
});

// Prepare inputs and outputs for bulk creation
const inputs = runs.map(run => run.inputs);
const outputs = runs.map(run => run.outputs ?? {});

// Use the bulk createExamples method
await client.createExamples({
  inputs,
  outputs,
  datasetId: dataset.id,
});
java
import com.langchain.smith.client.LangsmithClient;
import com.langchain.smith.client.okhttp.LangsmithOkHttpClient;
import com.langchain.smith.core.JsonValue;
import com.langchain.smith.models.datasets.Dataset;
import com.langchain.smith.models.datasets.DatasetCreateParams;
import com.langchain.smith.models.examples.bulk.BulkCreateParams;
import com.langchain.smith.models.runs.RunQueryParams;
import com.langchain.smith.models.runs.RunQueryResponse;
import java.util.ArrayList;
import java.util.List;

public class CreateDatasetExample {
    public static void main(String[] args) {
        LangsmithClient client = LangsmithOkHttpClient.fromEnv();
        String projectId = System.getenv("LANGSMITH_PROJECT_ID");
        String datasetName = "Example Dataset";

        List<RunQueryResponse.Run> allRuns = new ArrayList<>();
        String cursor = null;
        try {
            do {
                RunQueryParams.Builder paramsBuilder = RunQueryParams.builder()
                    .addSession(projectId)
                    .isRoot(true)
                    .error(false)
                    .limit(10L);

                if (cursor != null) {
                    paramsBuilder.cursor(cursor);
                }

                RunQueryResponse response = client.runs().query(paramsBuilder.build());
                allRuns.addAll(response.runs());

                // Get cursor for next page
                try {
                    Map<String, JsonValue> cursorProps = response.cursors()._additionalProperties();
                    if (cursorProps != null && cursorProps.containsKey("next")) {
                        JsonValue nextValue = cursorProps.get("next");
                        if (nextValue != null && !nextValue.isNull() && !nextValue.isMissing()) {
                            cursor = nextValue.asString().orElse(null);
                        } else {
                            cursor = null;
                        }
                    } else {
                        cursor = null;
                    }
                } catch (Exception e) {
                    cursor = null;
                }
                if (response.runs().size() < 50) {
                    cursor = null;
                }
            } while (cursor != null && !cursor.isEmpty());
        } catch (Exception e) {
            System.err.println("Error querying runs: " + e.getMessage());
            e.printStackTrace();
            System.exit(1);
        }

        System.out.println("Total runs found: " + allRuns.size());

        // Create dataset
        Dataset dataset = client.datasets().create(
            DatasetCreateParams.builder()
                .name(datasetName)
                .description("An example dataset")
                .build()
        );

        // Prepare inputs and outputs for bulk creation
        BulkCreateParams.Builder bulkParamsBuilder = BulkCreateParams.builder();
        int examplesWithData = 0;
        for (RunQueryResponse.Run run : allRuns) {
            if (run.inputs().isPresent() && run.outputs().isPresent()) {
                // Get the additional properties maps which contain the actual data
                Map<String, JsonValue> inputsMap = run.inputs().get()._additionalProperties();
                Map<String, JsonValue> outputsMap = run.outputs().get()._additionalProperties();

                bulkParamsBuilder.addBody(
                    BulkCreateParams.Body.builder()
                        .datasetId(dataset.id())
                        .inputs(JsonValue.from(inputsMap))
                        .outputs(JsonValue.from(outputsMap))
                        .build()
                );
                examplesWithData++;
            }
        }

        System.out.println("Prepared " + examplesWithData + " examples from " + allRuns.size() + " runs");

        if (examplesWithData == 0) {
            System.err.println("No runs have both inputs and outputs. Cannot create examples.");
            System.exit(1);
        }

        client.examples().bulk().create(bulkParamsBuilder.build());
        System.out.println("Created " + examplesWithData + " examples in dataset");
    }
}

从 CSV 文件创建数据集

在本节中，我们将演示你如何通过上传一个 CSV 文件来创建数据集。

首先，确保你的 CSV 文件格式正确，其列代表你的输入和输出键。这些键将在上传期间被用于正确映射你的数据。你可以为你的数据集指定一个可选的名称和描述。否则，文件名将被用作数据集名，且不提供描述。

python
from langsmith import Client
import os

client = Client()
csv_file = 'path/to/your/csvfile.csv'
input_keys = ['column1', 'column2'] # replace with your input column names
output_keys = ['output1', 'output2'] # replace with your output column names

dataset = client.upload_csv(
  csv_file=csv_file,
  input_keys=input_keys,
  output_keys=output_keys,
  name="My CSV Dataset",
  description="Dataset created from a CSV file",
  data_type="kv"
)
typescript
import { Client } from "langsmith";

const client = new Client();
const csvFile = 'path/to/your/csvfile.csv';
const inputKeys = ['column1', 'column2']; // replace with your input column names
const outputKeys = ['output1', 'output2']; // replace with your output column names

const dataset = await client.uploadCsv({
  csvFile: csvFile,
  fileName: "My CSV Dataset",
  inputKeys: inputKeys,
  outputKeys: outputKeys,
  description: "Dataset created from a CSV file",
  dataType: "kv"
});
java
import com.langchain.smith.client.LangsmithClient;
import com.langchain.smith.client.okhttp.LangsmithOkHttpClient;
import com.langchain.smith.models.datasets.Dataset;
import com.langchain.smith.models.datasets.DatasetUploadParams;
import com.langchain.smith.models.datasets.DataType;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.List;

LangsmithClient client = LangsmithOkHttpClient.fromEnv();
Path csvFile = Paths.get("path/to/your/csvfile.csv");
List<String> inputKeys = List.of("column1", "column2");
List<String> outputKeys = List.of("output1", "output2");

Dataset dataset = client.datasets().upload(
    DatasetUploadParams.builder()
        .file(csvFile)
        .inputKeys(inputKeys)
        .outputKeys(outputKeys)
        .name("My CSV Dataset")
        .description("Dataset created from a CSV file")
        .dataType(DataType.KV)
        .build()
);

从 pandas DataFrame 创建数据集（仅 Python）

python client 提供了一个额外的便捷方法，用于从一个 pandas dataframe 上传数据集。

python
from langsmith import Client
import os
import pandas as pd

client = Client()
df = pd.read_parquet('path/to/your/myfile.parquet')
input_keys = ['column1', 'column2'] # replace with your input column names
output_keys = ['output1', 'output2'] # replace with your output column names

dataset = client.upload_dataframe(
    df=df,
    input_keys=input_keys,
    output_keys=output_keys,
    name="My Parquet Dataset",
    description="Dataset created from a parquet file",
    data_type="kv" # The default
)

获取数据集

你可以使用 Python 和 TypeScript SDK 中的 list_datasets/listDatasets 方法以编程方式从 LangSmith 获取数据集。下面是一些常见的调用。

INFO

在运行下面的代码片段之前先初始化客户端。

python
from langsmith import Client

client = Client()
typescript
import { Client } from "langsmith";

const client = new Client();
java
import com.langchain.smith.client.LangsmithClient;
import com.langchain.smith.client.okhttp.LangsmithOkHttpClient;

LangsmithClient client = LangsmithOkHttpClient.fromEnv();

查询所有数据集

python
datasets = client.list_datasets()
typescript
const datasets = await client.listDatasets();
java
import com.langchain.smith.models.datasets.DatasetListParams;

DatasetListParams listParams = DatasetListParams.builder().build();
var datasets = client.datasets().list(listParams);

按名称列出数据集

如果你想按精确名搜索，可以这样做：

python
datasets = client.list_datasets(dataset_name="My Test Dataset 1")
typescript
const datasets = await client.listDatasets({
  datasetName: "My Test Dataset 1"
});
java
import com.langchain.smith.models.datasets.DatasetListParams;

DatasetListParams listParams = DatasetListParams.builder()
    .name("My Test Dataset 1")
    .build();
var datasets = client.datasets().list(listParams);

如果你想做一个大小写无关的子串搜索，试试以下做法：

python
datasets = client.list_datasets(dataset_name_contains="some substring")
typescript
const datasets = await client.listDatasets({
  datasetNameContains: "some substring"
});
java
import com.langchain.smith.models.datasets.DatasetListParams;

DatasetListParams listParams = DatasetListParams.builder()
    .nameContains("some substring")
    .build();
var datasets = client.datasets().list(listParams);

按类型列出数据集

你可以按类型过滤数据集：

python
datasets = client.list_datasets(data_type="kv")
typescript
const datasets = await client.listDatasets({
  dataType: "kv"
});
java
import com.langchain.smith.models.datasets.DatasetListParams;

DatasetListParams listParams = DatasetListParams.builder()
    .datatype(DataType.of("kv"))
    .build();
var datasets = client.datasets().list(listParams);

获取示例

你可以使用 Python 和 TypeScript SDK 中的 list_examples/listExamples 方法以编程方式从 LangSmith 获取示例。下面是一些常见的调用。

INFO

在运行下面的代码片段之前先初始化客户端。

python
from langsmith import Client

client = Client()
typescript
import { Client } from "langsmith";

const client = new Client();
java
import com.langchain.smith.client.LangsmithClient;
import com.langchain.smith.client.okhttp.LangsmithOkHttpClient;

LangsmithClient client = LangsmithOkHttpClient.fromEnv();

列出一个数据集的所有示例

你可以按数据集 ID 过滤：

python
examples = client.list_examples(dataset_id="c9ace0d8-a82c-4b6c-13d2-83401d68e9ab")
typescript
const examples = await client.listExamples({
  datasetId: "c9ace0d8-a82c-4b6c-13d2-83401d68e9ab"
});
java
import com.langchain.smith.models.examples.ExampleListParams;

ExampleListParams listParams = ExampleListParams.builder()
    .dataset("c9ace0d8-a82c-4b6c-13d2-83401d68e9ab")
    .build();
var examples = client.examples().list(listParams);

或者你可以按数据集名过滤（它必须与你想要查询的数据集名完全匹配）

python
examples = client.list_examples(dataset_name="My Test Dataset")
typescript
const examples = await client.listExamples({
  datasetName: "My test Dataset"
});

按 id 列出示例

你也可以一次按 ID 列出多个示例。

python
example_ids = [
  '734fc6a0-c187-4266-9721-90b7a025751a',
  'd6b4c1b9-6160-4d63-9b61-b034c585074f',
  '4d31df4e-f9c3-4a6e-8b6c-65701c2fed13',
]

examples = client.list_examples(example_ids=example_ids)
typescript
const exampleIds = [
  "734fc6a0-c187-4266-9721-90b7a025751a",
  "d6b4c1b9-6160-4d63-9b61-b034c585074f",
  "4d31df4e-f9c3-4a6e-8b6c-65701c2fed13",
];

const examples = await client.listExamples({
  exampleIds: exampleIds
});
java
import com.langchain.smith.models.examples.ExampleListParams;
import java.util.List;

List<String> exampleIds = List.of(
    "734fc6a0-c187-4266-9721-90b7a025751a",
    "d6b4c1b9-6160-4d63-9b61-b034c585074f",
    "4d31df4e-f9c3-4a6e-8b6c-65701c2fed13"
);

ExampleListParams listParams = ExampleListParams.builder()
    .id(exampleIds)
    .build();
var examples = client.examples().list(listParams);

按元数据列出示例

你也可以按元数据过滤示例。下面是一个查询具有特定元数据键值对示例的例子。在底层，我们会检查该示例的元数据是否包含你指定的键值对。

例如，如果你有一个元数据为 {"foo": "bar", "baz": "qux"} 的示例，那么 {foo: bar} 和 {baz: qux} 都会匹配，{foo: bar, baz: qux} 也会匹配。

python
examples = client.list_examples(dataset_name=dataset_name, metadata={"foo": "bar"})
typescript
const examples = await client.listExamples({
  datasetName: datasetName,
  metadata: {foo: "bar"}
});
java
import com.langchain.smith.models.examples.ExampleListParams;

ExampleListParams listParams = ExampleListParams.builder()
    .datasetId(datasetId)
    .metadata("{\"foo\":\"bar\"}")
    .build();
var examples = client.examples().list(listParams);

按结构化过滤器列出示例

就像你可以使用结构化过滤器查询语言来获取运行一样，你可以用它来获取示例。

NOTE

这目前仅在 Python SDK 的 v0.1.83 及更高版本，以及 TypeScript SDK 的 v0.1.35 及更高版本中可用。

此外，结构化过滤器查询语言仅支持 metadata 字段。

你可以使用 has 操作符来获取元数据字段包含特定键/值对的示例，并使用 exists 操作符来获取元数据字段包含特定键的示例。此外，你可以使用 and 操作符串联多个过滤器，并使用 not 操作符取反一个过滤器。

python
examples = client.list_examples(
  dataset_name=dataset_name,
  filter='and(not(has(metadata, \'{"foo": "bar"}\')), exists(metadata, "tenant_id"))'
)
typescript
const examples = await client.listExamples({
  datasetName: datasetName,
  filter: 'and(not(has(metadata, \'{"foo": "bar"}\')), exists(metadata, "tenant_id"))'
});
java
import com.langchain.smith.models.examples.ExampleListParams;

String filter = "and(not(has(metadata, '{\"foo\": \"bar\"}')), exists(metadata, \"tenant_id\"))";

ExampleListParams listParams = ExampleListParams.builder()
    .datasetId(datasetId)
    .filter(filter)
    .build();
var examples = client.examples().list(listParams);

更新示例

更新单个示例

你可以使用 Python 和 TypeScript SDK 中的 update_example/updateExample 方法以编程方式从 LangSmith 更新示例。下面是一个示例。

python
client.update_example(
  example_id=example.id,
  inputs={"input": "updated input"},
  outputs={"output": "updated output"},
  metadata={"foo": "bar"},
  split="train"
)
typescript
await client.updateExample(example.id, {
  inputs: { input: "updated input" },
  outputs: { output: "updated output" },
  metadata: { "foo": "bar" },
  split: "train",
});
java
import com.langchain.smith.core.JsonValue;
import com.langchain.smith.models.examples.ExampleUpdateParams;

 // Create Inputs using the builder
ExampleUpdateParams.Inputs inputsObj = ExampleUpdateParams.Inputs.builder()
    .putAdditionalProperty("input", JsonValue.from("updated input"))
    .build();

// Create Outputs using the builder
ExampleUpdateParams.Outputs outputsObj = ExampleUpdateParams.Outputs.builder()
    .putAdditionalProperty("output", JsonValue.from("updated output"))
    .build();

// Create Metadata using the builder
ExampleUpdateParams.Metadata metadataObj = ExampleUpdateParams.Metadata.builder()
    .putAdditionalProperty("foo", JsonValue.from("bar"))
    .build();

ExampleUpdateParams updateParams = ExampleUpdateParams.builder()
    .inputs(inputsObj)
    .outputs(outputsObj)
    .metadata(metadataObj)
    .split("train")
    .build();

ExampleUpdateResponse updateResponse = client.examples().update(example.id(), updateParams);

批量更新示例

你也可以使用 Python 和 TypeScript SDK 中的 update_examples/updateExamples 方法在一次请求中批量更新多个示例。下面是一个示例。

python
client.update_examples(
  example_ids=[example.id, example_2.id],
  inputs=[{"input": "updated input 1"}, {"input": "updated input 2"}],
  outputs=[
      {"output": "updated output 1"},
      {"output": "updated output 2"},
  ],
  metadata=[{"foo": "baz"}, {"foo": "qux"}],
  splits=[["training", "foo"], "training"] # Splits can be arrays or standalone strings
)
typescript
await client.updateExamples([
  {
    id: example.id,
    inputs: { input: "updated input 1" },
    outputs: { output: "updated output 1" },
    metadata: { foo: "baz" },
    split: ["training", "foo"] // Splits can be arrays or standalone strings
  },
  {
    id: example2.id,
    inputs: { input: "updated input 2" },
    outputs: { output: "updated output 2" },
    metadata: { foo: "qux" },
    split: "training"
  },
]);
java
Map<String, String> inputs1 = Map.of("question", "What is the capital of France?")
Map<String, String> outputs1 = Map.of("answer", "The capital of France is Paris.");
Map<String, String> metadata1 = Map.of(
    "source", "Wikipedia",
    "difficulty", "easy"
);

Map<String, String> inputs2 = Map.of("question", "What is 2 + 2?");
Map<String, String> outputs2 = Map.of("answer", "The answer is 4.");
Map<String, String> metadata2 = Map.of(
    "source", "Math textbook",
    "difficulty", "easy");

BulkPatchAllParams.Builder bulkParamsBuilder = BulkPatchAllParams.builder();

bulkParamsBuilder.addBody(
    BulkPatchAllParams.Body.builder()
        .id(example1.id())
        .inputs(buildInputs(inputs1))
        .outputs(buildOutputs(outputs1))
        .metadata(buildMetadata(metadata1))
        .splitOfStrings(Arrays.asList("training", "validation"))
        .build()
);

bulkParamsBuilder.addBody(
    BulkPatchAllParams.Body.builder()
        .id(example2.id())
        .inputs(buildInputs(inputs2))
        .outputs(buildOutputs(outputs2))
        .metadata(buildMetadata(metadata2))
        .split("test")
        .build()
);

client.examples().bulk().patchAll(bulkParamsBuilder.build());

友情链接

  • Agents.md面向 AI 编码助手的仓库说明文件规范
  • LangChainLangChain 是一个开放的Agent平台
  • LangSmithAgent 工程平台：可观测与评估、部署、Fleet、Engine 等
  • EmbabelEmbabel 是一个JVM平台Agent框架
  • SpringAISpringAI 是一个基于 Spring 的 AI 框架
  • MCPModel Context Protocol，连接 AI 应用与外部数据/工具的开放标准
  • LangChain4jJava 生态的 LLM 应用开发框架
  • LlamaIndex把数据接入 LLM 的框架与文档平台
  • LlamaParse智能体式 OCR 文档解析：把 PDF、扫描件变成 LLM 可用的文本与结构化数据
  • LiteParse本地优先、轻量的文档解析库，不需要 API key
  • CrewAI多智能体编排框架：以 Crew/Flow 组织自主协作的 AI Agent
  • MAFMicrosoft Agent Framework，企业级 .NET/Python 智能体框架

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