azure-ai-agents-persistent-dotnet
microsoft/skills
使用 Azure AI Agents SDK for .NET,通过线程、消息、运行和工具来创建和管理持久性 AI 代理。
...展开全部Azure.AI.Agents.Persistent (.NET)
用于创建和管理具有线程、消息、运行和工具的持久化 AI 代理的低级 SDK。
安装
dotnet add package Azure.AI.Agents.Persistent --prerelease
dotnet add package Azure.Identity
当前版本:稳定版 v1.1.0,预览版 v1.2.0-beta.8
环境变量
PROJECT_ENDPOINT=https://.services.ai.azure.com/api/projects/ # 必填:Azure AI 项目端点
MODEL_DEPLOYMENT_NAME=gpt-4o-mini # 必填:模型部署名称
AZURE_BING_CONNECTION_ID= # 必填:Bing 连接资源 ID
AZURE_AI_SEARCH_CONNECTION_ID= # 必填:Azure AI Search 连接资源 ID
AZURE_TOKEN_CREDENTIALS=prod # 仅当在生产环境中使用 DefaultAzureCredential 时才为必填项
身份验证
using Azure.AI.Agents.Persistent;
using Azure.Identity;
var projectEndpoint = Environment.GetEnvironmentVariable("PROJECT_ENDPOINT");
// 本地开发:DefaultAzureCredential。 生产环境:将 AZURE_TOKEN_CREDENTIALS 设置为 prod 或 AZURE_TOKEN_CREDENTIALS=
var credential = new DefaultAzureCredential(
DefaultAzureCredential.DefaultEnvironmentVariableName
);
// 或者在生产环境中直接使用特定凭据:
// 请参阅 https://learn.microsoft.com/dotnet/api/overview/azure/identity-readme?view=azure-dotnet#credential-classes
// var credential = new ManagedIdentityCredential();
PersistentAgentsClient client = new(projectEndpoint, credential);
客户端层次结构
PersistentAgentsClient
├── 管理 → 代理 CRUD 操作
├── 线程 → 线程管理
├── 消息 → 消息操作
├── 运行 → 运行执行与流式传输
├── 文件 → 文件上传/下载
└── 向量存储 → 向量存储管理
核心工作流
1. 创建代理
var modelDeploymentName = Environment.GetEnvironmentVariable("MODEL_DEPLOYMENT_NAME");
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "数学辅导老师",
instructions: "您是一名私人数学辅导老师。编写并运行代码来回答数学问题。",
tools: [new CodeInterpreterToolDefinition()]
);
2. 创建线程和消息
// 创建线程
PersistentAgentThread thread = await client.Threads.CreateThreadAsync();
// 创建消息
await client.Messages.CreateMessageAsync(
thread.Id,
MessageRole.User,
"我需要解方程 `3x + 11 = 14`。你能帮我吗?"
);
3. 运行代理(轮询)
// 创建运行实例
ThreadRun run = await client.Runs.CreateRunAsync(
thread.Id,
agent.Id,
additionalInstructions: "请称呼该用户为 Jane Doe。"
);
// 轮询完成状态
do
{
await Task.Delay(TimeSpan.FromMilliseconds(500));
run = await client.Runs.GetRunAsync(thread.Id, run.Id);
}
while (run.Status == RunStatus.Queued || run.Status == RunStatus.InProgress);
// 检索消息
await foreach (PersistentThreadMessage message in client.Messages.GetMessagesAsync(
threadId: thread.Id,
order: ListSortOrder.Ascending))
{
Console.Write($"{message.Role}: ");
foreach (MessageContent content in message.ContentItems)
{
if (content is MessageTextContent textContent)
Console.WriteLine(textContent.Text);
}
}
4. 流式响应
AsyncCollectionResult stream = client.Runs.CreateRunStreamingAsync(
thread.Id,
agent.Id
);
await foreach (StreamingUpdate update in stream)
{
if (update.UpdateKind == StreamingUpdateReason.RunCreated)
{
Console.WriteLine("--- 运行已启动! ---");
}
else if (update is MessageContentUpdate contentUpdate)
{
Console.Write(contentUpdate.Text);
}
else if (update.UpdateKind == StreamingUpdateReason.RunCompleted)
{
Console.WriteLine("\n--- 运行已完成! ---");
}
}
5. 函数调用
// 定义工具函数
FunctionToolDefinition weatherTool = new(
name: "getCurrentWeather",
description: "获取某地的当前天气。",
parameters: BinaryData.FromObjectAsJson(new
{
Type = "object",
Properties = new
{
Location = new { Type = "string", Description = "城市和州,例如:San Francisco, CA" },
Unit = new { Type = "string", Enum = new[] { "c", "f" } }
},
Required = new[] { "location" }
}, new JsonSerializerOptions { PropertyNamingPolicy = JsonNamingPolicy.CamelCase })
);
// 使用函数创建代理
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "天气机器人",
instructions: "你是一个天气机器人。",
tools: [weatherTool]
);
// 在轮询期间处理函数调用
do
{
await Task.Delay(500);
run = await client.Runs.GetRunAsync(thread.Id, run.Id);
if (run.Status == RunStatus.RequiresAction
&& run.RequiredAction 是 SubmitToolOutputsAction 且 submitAction)
{
List outputs = [];
foreach (RequiredToolCall toolCall in submitAction.ToolCalls)
{
if (toolCall 是 RequiredFunctionToolCall funcCall)
{
// 执行函数并获取结果
string result = ExecuteFunction(funcCall.Name, funcCall.Arguments);
outputs.Add(new ToolOutput(toolCall, result));
}
}
run = await client.Runs.SubmitToolOutputsToRunAsync(run, outputs, toolApprovals: null);
}
}
while (run.Status == RunStatus.Queued || run.Status == RunStatus.InProgress);
6. 使用向量存储进行文件搜索
// 上传文件
PersistentAgentFileInfo file = await client.Files.UploadFileAsync(
filePath: "document.txt",
purpose: PersistentAgentFilePurpose.Agents
);
// 创建向量存储
PersistentAgentsVectorStore vectorStore = await client.VectorStores.CreateVectorStoreAsync(
fileIds: [file.Id],
name: "my_vector_store"
);
// 创建文件搜索资源
FileSearchToolResource fileSearchResource = new();
fileSearchResource.VectorStoreIds.Add(vectorStore.Id);
// 创建带文件搜索功能的代理
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "文档助手",
instructions: "您帮助用户在文档中查找信息。",
tools: [new FileSearchToolDefinition()],
toolResources: new ToolResources { FileSearch = fileSearchResource }
);
7. Bing 基于上下文搜索
var bingConnectionId = Environment.GetEnvironmentVariable("AZURE_BING_CONNECTION_ID");
BingGroundingToolDefinition bingTool = new(
new BingGroundingSearchToolParameters(
[new BingGroundingSearchConfiguration(bingConnectionId)]
)
);
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "搜索代理",
instructions: "使用 Bing 回答有关时事的问题。",
tools: [bingTool]
);
8. Azure AI Search
AzureAISearchToolResource searchResource = new(
connectionId: searchConnectionId,
indexName: "my_index",
topK: 5,
filter: "category eq 'documentation'",
queryType: AzureAISearchQueryType.Simple
);
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "搜索代理",
instructions: "搜索文档索引以回答问题。",
tools: [new AzureAISearchToolDefinition()],
toolResources: new ToolResources { AzureAISearch = searchResource }
);
9. 清理
await client.Threads.DeleteThreadAsync(thread.Id);
await client.Administration.DeleteAgentAsync(agent.Id);
await client.VectorStores.DeleteVectorStoreAsync(vectorStore.Id);
await client.Files.DeleteFileAsync(file.Id);
可用工具
| 工具 | 类 | 用途 |
|---|---|---|
| 代码解释器 | CodeInterpreterToolDefinition |
执行 Python 代码,生成可视化效果 |
| 文件搜索 | 文件搜索工具定义 |
通过向量存储库搜索上传的文件 |
| 函数调用 | 函数工具定义 |
调用自定义函数 |
| Bing 定位 | BingGroundingToolDefinition |
通过 Bing 进行网络搜索 |
| Azure AI 搜索 | AzureAISearchToolDefinition |
搜索 Azure AI Search 索引 |
| OpenAPI | OpenApiToolDefinition |
通过 OpenAPI 规范调用外部 API |
| Azure Functions | AzureFunctionToolDefinition |
调用 Azure Functions |
| MCP | MCPToolDefinition |
模型上下文协议工具 |
| SharePoint | SharePoint 工具定义 |
访问 SharePoint 内容 |
| Microsoft Fabric | MicrosoftFabricToolDefinition |
访问 Fabric 数据 |
流式更新类型
| 更新类型 | 描述 |
|---|---|
StreamingUpdateReason.RunCreated |
运行已启动 |
StreamingUpdateReason.RunInProgress |
正在运行 |
StreamingUpdateReason.RunCompleted |
运行已完成 |
StreamingUpdateReason.RunFailed |
运行出错 |
MessageContentUpdate |
文本内容块 |
RunStepUpdate |
步骤状态变更 |
键类型参考
| 类型 | 目的 |
|---|---|
PersistentAgentsClient |
主要入口点 |
PersistentAgent |
包含模型、指令和工具的代理 |
持久代理线程 |
对话线程 |
持久线程消息 |
线程中的消息 |
线程运行 |
代理在线程上的执行 |
运行状态 |
已排队、正在进行、需要操作、已完成、失败 |
工具资源 |
组合工具资源 |
工具输出 |
函数调用响应 |
最佳实践
- 务必释放客户端资源— 使用
using语句或显式释放 - 以适当的延迟进行轮询— 建议状态检查间隔为 500 毫秒
- 清理资源— 完成后删除线程和代理
- 处理所有运行状态— 检查是否处于
“需要操作”、“失败”或“已取消”状态 - 使用流式处理实现实时用户体验— 比轮询提供更好的用户体验
- 存储 ID 而非对象— 通过 ID 引用代理/线程
- 使用异步方法——所有操作都应采用异步方式
错误处理
using Azure;
try
{
var agent = await client.Administration.CreateAgentAsync(...);
}
catch (RequestFailedException ex) when (ex.Status == 404)
{
Console.WriteLine("资源未找到");
}
catch (RequestFailedException ex)
{
Console.WriteLine($"错误:{ex.Status} - {ex.ErrorCode}: {ex.Message}");
}
相关 SDK
| SDK | 用途 | 安装 |
|---|---|---|
Azure.AI.Agents.Persistent |
低级代理(此 SDK) | dotnet add package Azure.AI.Agents.Persistent |
Azure.AI.Projects |
高级项目客户端 | dotnet add package Azure.AI.Projects |
参考链接
| 资源 | URL |
|---|---|
| NuGet 包 | https://www.nuget.org/packages/Azure.AI.Agents.Persistent |
| API 参考 | https://learn.microsoft.com/dotnet/api/azure.ai.agents.persistent |
| GitHub 源代码 | https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Agents.Persistent |
| 示例 | https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Agents.Persistent/samples |
---
name: azure-ai-agents-persistent-dotnet
description: Create and manage persistent AI agents with threads, messages, runs, and tools using the Azure AI Agents SDK for .NET.
license: MIT
---
# Azure.AI.Agents.Persistent (.NET)
Low-level SDK for creating and managing persistent AI agents with threads, messages, runs, and tools.
## Installation
```bash
dotnet add package Azure.AI.Agents.Persistent --prerelease
dotnet add package Azure.Identity
```
**Current Versions**: Stable v1.1.0, Preview v1.2.0-beta.8
## Environment Variables
```bash
PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project> # Required: Azure AI project endpoint
MODEL_DEPLOYMENT_NAME=gpt-4o-mini # Required: model deployment name
AZURE_BING_CONNECTION_ID=<bing-connection-resource-id> # Required: Bing connection resource ID
AZURE_AI_SEARCH_CONNECTION_ID=<search-connection-resource-id> # Required: Azure AI Search connection resource ID
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
```
## Authentication
```csharp
using Azure.AI.Agents.Persistent;
using Azure.Identity;
var projectEndpoint = Environment.GetEnvironmentVariable("PROJECT_ENDPOINT");
// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
var credential = new DefaultAzureCredential(
DefaultAzureCredential.DefaultEnvironmentVariableName
);
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/dotnet/api/overview/azure/identity-readme?view=azure-dotnet#credential-classes
// var credential = new ManagedIdentityCredential();
PersistentAgentsClient client = new(projectEndpoint, credential);
```
## Client Hierarchy
```
PersistentAgentsClient
├── Administration → Agent CRUD operations
├── Threads → Thread management
├── Messages → Message operations
├── Runs → Run execution and streaming
├── Files → File upload/download
└── VectorStores → Vector store management
```
## Core Workflow
### 1. Create Agent
```csharp
var modelDeploymentName = Environment.GetEnvironmentVariable("MODEL_DEPLOYMENT_NAME");
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Math Tutor",
instructions: "You are a personal math tutor. Write and run code to answer math questions.",
tools: [new CodeInterpreterToolDefinition()]
);
```
### 2. Create Thread and Message
```csharp
// Create thread
PersistentAgentThread thread = await client.Threads.CreateThreadAsync();
// Create message
await client.Messages.CreateMessageAsync(
thread.Id,
MessageRole.User,
"I need to solve the equation `3x + 11 = 14`. Can you help me?"
);
```
### 3. Run Agent (Polling)
```csharp
// Create run
ThreadRun run = await client.Runs.CreateRunAsync(
thread.Id,
agent.Id,
additionalInstructions: "Please address the user as Jane Doe."
);
// Poll for completion
do
{
await Task.Delay(TimeSpan.FromMilliseconds(500));
run = await client.Runs.GetRunAsync(thread.Id, run.Id);
}
while (run.Status == RunStatus.Queued || run.Status == RunStatus.InProgress);
// Retrieve messages
await foreach (PersistentThreadMessage message in client.Messages.GetMessagesAsync(
threadId: thread.Id,
order: ListSortOrder.Ascending))
{
Console.Write($"{message.Role}: ");
foreach (MessageContent content in message.ContentItems)
{
if (content is MessageTextContent textContent)
Console.WriteLine(textContent.Text);
}
}
```
### 4. Streaming Response
```csharp
AsyncCollectionResult<StreamingUpdate> stream = client.Runs.CreateRunStreamingAsync(
thread.Id,
agent.Id
);
await foreach (StreamingUpdate update in stream)
{
if (update.UpdateKind == StreamingUpdateReason.RunCreated)
{
Console.WriteLine("--- Run started! ---");
}
else if (update is MessageContentUpdate contentUpdate)
{
Console.Write(contentUpdate.Text);
}
else if (update.UpdateKind == StreamingUpdateReason.RunCompleted)
{
Console.WriteLine("\n--- Run completed! ---");
}
}
```
### 5. Function Calling
```csharp
// Define function tool
FunctionToolDefinition weatherTool = new(
name: "getCurrentWeather",
description: "Gets the current weather at a location.",
parameters: BinaryData.FromObjectAsJson(new
{
Type = "object",
Properties = new
{
Location = new { Type = "string", Description = "City and state, e.g. San Francisco, CA" },
Unit = new { Type = "string", Enum = new[] { "c", "f" } }
},
Required = new[] { "location" }
}, new JsonSerializerOptions { PropertyNamingPolicy = JsonNamingPolicy.CamelCase })
);
// Create agent with function
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Weather Bot",
instructions: "You are a weather bot.",
tools: [weatherTool]
);
// Handle function calls during polling
do
{
await Task.Delay(500);
run = await client.Runs.GetRunAsync(thread.Id, run.Id);
if (run.Status == RunStatus.RequiresAction
&& run.RequiredAction is SubmitToolOutputsAction submitAction)
{
List<ToolOutput> outputs = [];
foreach (RequiredToolCall toolCall in submitAction.ToolCalls)
{
if (toolCall is RequiredFunctionToolCall funcCall)
{
// Execute function and get result
string result = ExecuteFunction(funcCall.Name, funcCall.Arguments);
outputs.Add(new ToolOutput(toolCall, result));
}
}
run = await client.Runs.SubmitToolOutputsToRunAsync(run, outputs, toolApprovals: null);
}
}
while (run.Status == RunStatus.Queued || run.Status == RunStatus.InProgress);
```
### 6. File Search with Vector Store
```csharp
// Upload file
PersistentAgentFileInfo file = await client.Files.UploadFileAsync(
filePath: "document.txt",
purpose: PersistentAgentFilePurpose.Agents
);
// Create vector store
PersistentAgentsVectorStore vectorStore = await client.VectorStores.CreateVectorStoreAsync(
fileIds: [file.Id],
name: "my_vector_store"
);
// Create file search resource
FileSearchToolResource fileSearchResource = new();
fileSearchResource.VectorStoreIds.Add(vectorStore.Id);
// Create agent with file search
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Document Assistant",
instructions: "You help users find information in documents.",
tools: [new FileSearchToolDefinition()],
toolResources: new ToolResources { FileSearch = fileSearchResource }
);
```
### 7. Bing Grounding
```csharp
var bingConnectionId = Environment.GetEnvironmentVariable("AZURE_BING_CONNECTION_ID");
BingGroundingToolDefinition bingTool = new(
new BingGroundingSearchToolParameters(
[new BingGroundingSearchConfiguration(bingConnectionId)]
)
);
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Search Agent",
instructions: "Use Bing to answer questions about current events.",
tools: [bingTool]
);
```
### 8. Azure AI Search
```csharp
AzureAISearchToolResource searchResource = new(
connectionId: searchConnectionId,
indexName: "my_index",
topK: 5,
filter: "category eq 'documentation'",
queryType: AzureAISearchQueryType.Simple
);
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Search Agent",
instructions: "Search the documentation index to answer questions.",
tools: [new AzureAISearchToolDefinition()],
toolResources: new ToolResources { AzureAISearch = searchResource }
);
```
### 9. Cleanup
```csharp
await client.Threads.DeleteThreadAsync(thread.Id);
await client.Administration.DeleteAgentAsync(agent.Id);
await client.VectorStores.DeleteVectorStoreAsync(vectorStore.Id);
await client.Files.DeleteFileAsync(file.Id);
```
## Available Tools
| Tool | Class | Purpose |
|------|-------|---------|
| Code Interpreter | `CodeInterpreterToolDefinition` | Execute Python code, generate visualizations |
| File Search | `FileSearchToolDefinition` | Search uploaded files via vector stores |
| Function Calling | `FunctionToolDefinition` | Call custom functions |
| Bing Grounding | `BingGroundingToolDefinition` | Web search via Bing |
| Azure AI Search | `AzureAISearchToolDefinition` | Search Azure AI Search indexes |
| OpenAPI | `OpenApiToolDefinition` | Call external APIs via OpenAPI spec |
| Azure Functions | `AzureFunctionToolDefinition` | Invoke Azure Functions |
| MCP | `MCPToolDefinition` | Model Context Protocol tools |
| SharePoint | `SharepointToolDefinition` | Access SharePoint content |
| Microsoft Fabric | `MicrosoftFabricToolDefinition` | Access Fabric data |
## Streaming Update Types
| Update Type | Description |
|-------------|-------------|
| `StreamingUpdateReason.RunCreated` | Run started |
| `StreamingUpdateReason.RunInProgress` | Run processing |
| `StreamingUpdateReason.RunCompleted` | Run finished |
| `StreamingUpdateReason.RunFailed` | Run errored |
| `MessageContentUpdate` | Text content chunk |
| `RunStepUpdate` | Step status change |
## Key Types Reference
| Type | Purpose |
|------|---------|
| `PersistentAgentsClient` | Main entry point |
| `PersistentAgent` | Agent with model, instructions, tools |
| `PersistentAgentThread` | Conversation thread |
| `PersistentThreadMessage` | Message in thread |
| `ThreadRun` | Execution of agent against thread |
| `RunStatus` | Queued, InProgress, RequiresAction, Completed, Failed |
| `ToolResources` | Combined tool resources |
| `ToolOutput` | Function call response |
## Best Practices
1. **Always dispose clients** — Use `using` statements or explicit disposal
2. **Poll with appropriate delays** — 500ms recommended between status checks
3. **Clean up resources** — Delete threads and agents when done
4. **Handle all run statuses** — Check for `RequiresAction`, `Failed`, `Cancelled`
5. **Use streaming for real-time UX** — Better user experience than polling
6. **Store IDs not objects** — Reference agents/threads by ID
7. **Use async methods** — All operations should be async
## Error Handling
```csharp
using Azure;
try
{
var agent = await client.Administration.CreateAgentAsync(...);
}
catch (RequestFailedException ex) when (ex.Status == 404)
{
Console.WriteLine("Resource not found");
}
catch (RequestFailedException ex)
{
Console.WriteLine($"Error: {ex.Status} - {ex.ErrorCode}: {ex.Message}");
}
```
## Related SDKs
| SDK | Purpose | Install |
|-----|---------|---------|
| `Azure.AI.Agents.Persistent` | Low-level agents (this SDK) | `dotnet add package Azure.AI.Agents.Persistent` |
| `Azure.AI.Projects` | High-level project client | `dotnet add package Azure.AI.Projects` |
## Reference Links
| Resource | URL |
|----------|-----|
| NuGet Package | https://www.nuget.org/packages/Azure.AI.Agents.Persistent |
| API Reference | https://learn.microsoft.com/dotnet/api/azure.ai.agents.persistent |
| GitHub Source | https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Agents.Persistent |
| Samples | https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Agents.Persistent/samples |
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