azure-ai-agents-persistent-dotnet
microsoft/skills
使用 Azure AI Agents SDK for .NET,透過執行緒、訊息、執行程序及工具來建立並管理持久性 AI 代理程式。
...展開全部Azure.AI.Agents.Persistent (.NET)
用於建立和管理具執行緒、訊息、執行次數及工具之持久化 AI 代理程式的低階 SDK。
安裝
dotnet add package Azure.AI.Agents.Persistent --prerelease
dotnet add package Azure.Identity
當前版本:穩定版 v1.1.0、預覽版 v1.2.0-beta.8
環境變數
PROJECT_ENDPOINT=https://.services.ai.azure.com/api/projects/ # 必填:Azure AI 專案端點
MODEL_DEPLOYMENT_NAME=gpt-4o-mini # 必填:模型部署名稱
AZURE_BING_CONNECTION_ID= # 必填:Bing 連線資源 ID
AZURE_AI_SEARCH_CONNECTION_ID= # 必填:Azure AI Search 連線資源 ID
AZURE_TOKEN_CREDENTIALS=prod # 僅當在生產環境中使用 DefaultAzureCredential 時才為必填
驗證
using Azure.AI.Agents.Persistent;
using Azure.Identity;
var projectEndpoint = Environment.GetEnvironmentVariable("PROJECT_ENDPOINT");
// 本地開發環境:使用 DefaultAzureCredential。 生產環境:請設定 AZURE_TOKEN_CREDENTIALS=prod 或 AZURE_TOKEN_CREDENTIALS=
var credential = new DefaultAzureCredential(
DefaultAzureCredential.DefaultEnvironmentVariableName
);
// 或者在生產環境中直接使用特定憑證:
// 請參閱 https://learn.microsoft.com/dotnet/api/overview/azure/identity-readme?view=azure-dotnet#credential-classes
// var credential = new ManagedIdentityCredential();
PersistentAgentsClient client = new(projectEndpoint, credential);
客戶端層級結構
PersistentAgentsClient
├── 管理 → 代理程式 CRUD 操作
├── 執行緒 → 執行緒管理
├── 訊息 → 訊息操作
├── 執行任務 → 執行任務與串流處理
├── 檔案 → 檔案上傳/下載
└── 向量儲存庫 → 向量儲存庫管理
核心工作流程
1. 建立代理程式
var modelDeploymentName = Environment.GetEnvironmentVariable("MODEL_DEPLOYMENT_NAME");
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "數學家教",
instructions: "您是一位個人數學家教。請編寫並執行程式碼來解答數學問題。",
tools: [new CodeInterpreterToolDefinition()]
);
2. 建立執行緒與訊息
// 建立執行緒
PersistentAgentThread thread = await client.Threads.CreateThreadAsync();
// 建立訊息
await client.Messages.CreateMessageAsync(
thread.Id,
MessageRole.User,
"我需要解方程式 `3x + 11 = 14`。你能幫我嗎?"
);
3. 執行代理程式(輪詢)
// 建立執行實例
ThreadRun run = await client.Runs.CreateRunAsync(
thread.Id,
agent.Id,
additionalInstructions: "請稱呼該使用者為 Jane Doe。"
);
// 輪詢完成狀況
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);
// 擷取訊息
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. 串流回應
AsyncCollectionResult stream = client.Runs.CreateRunStreamingAsync(
thread.Id,
agent.Id
);
await foreach (StreamingUpdate update in stream)
{
if (update.UpdateKind == StreamingUpdateReason.RunCreated)
{
Console.WriteLine("--- 執行程序已啟動! ---");
}
else if (update is MessageContentUpdate contentUpdate)
{
Console.Write(contentUpdate.Text);
}
else if (update.UpdateKind == StreamingUpdateReason.RunCompleted)
{
Console.WriteLine("\n--- 執行完成! ---");
}
}
5. 函式呼叫
// 定義工具函式
FunctionToolDefinition weatherTool = new(
name: "getCurrentWeather",
description: "取得某地點的當前天氣。",
參數: BinaryData.FromObjectAsJson(new
{
Type = "object",
Properties = new
{
Location = new { Type = "string", Description = "城市與州,例如:San Francisco, CA" },
單位 = new { 類型 = "字串", 枚舉 = new[] { "c", "f" } }
},
必填 = new[] { "location" }
}, new JsonSerializerOptions { 屬性命名規則 = JsonNamingPolicy.CamelCase })
);
// 使用函式建立代理程式
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Weather Bot",
instructions: "您是天氣機器人。",
tools: [weatherTool]
);
// 在輪詢期間處理函式呼叫
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)
{
// 執行函式並取得結果
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. 使用向量儲存庫進行檔案搜尋
// 上傳檔案
PersistentAgentFileInfo file = await client.Files.UploadFileAsync(
filePath: "document.txt",
purpose: PersistentAgentFilePurpose.Agents
);
// 建立向量儲存庫
PersistentAgentsVectorStore vectorStore = await client.VectorStores.CreateVectorStoreAsync(
fileIds: [file.Id],
name: "my_vector_store"
);
// 建立檔案搜尋資源
FileSearchToolResource fileSearchResource = new();
fileSearchResource.VectorStoreIds.Add(vectorStore.Id);
// 建立具備檔案搜尋功能的代理程式
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "文件助理",
instructions: "您協助使用者在文件中查找資訊。",
tools: [new FileSearchToolDefinition()],
toolResources: new ToolResources { FileSearch = fileSearchResource }
);
7. Bing 基礎知識
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: "使用 Bing 回答關於時事的提問。",
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: "Search Agent",
instructions: "搜尋文件索引以回答問題。",
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 | 類別 | 用途 |
|---|---|---|
| 程式碼解釋器 | CodeInterpreterToolDefinition |
執行 Python 程式碼,產生視覺化圖表 |
| 檔案搜尋 | 檔案搜尋工具定義 |
透過向量儲存庫搜尋上傳的檔案 |
| 函式呼叫 | 函式工具定義 |
呼叫自訂函式 |
| Bing 定位 | BingGroundingToolDefinition |
透過 Bing 進行網路搜尋 |
| Azure AI 搜尋 | AzureAISearchToolDefinition |
搜尋 Azure AI Search 索引 |
| OpenAPI | OpenApiToolDefinition |
透過 OpenAPI 規範呼叫外部 API |
| Azure Functions | AzureFunctionToolDefinition |
呼叫 Azure Functions |
| MCP | MCPToolDefinition |
模型上下文協定工具 |
| SharePoint | SharePoint 工具定義 |
存取 SharePoint 內容 |
| Microsoft Fabric | MicrosoftFabricToolDefinition |
存取 Fabric 資料 |
串流更新類型
| 更新類型 | 說明 |
|---|---|
StreamingUpdateReason.RunCreated |
執行已啟動 |
StreamingUpdateReason.RunInProgress |
執行中 |
StreamingUpdateReason.RunCompleted |
執行已完成 |
StreamingUpdateReason.RunFailed |
執行發生錯誤 |
訊息內容更新 |
文字內容區塊 |
執行步驟更新 |
步驟狀態變更 |
關鍵字類型參考
| 類型 | 目的 |
|---|---|
PersistentAgentsClient |
主要入口點 |
PersistentAgent |
具備模型、指令及工具的代理程式 |
PersistentAgentThread |
對話執行緒 |
PersistentThreadMessage |
執行緒中的訊息 |
執行緒執行 |
針對該執行緒的代理程式執行 |
執行狀態 |
已排入佇列、進行中、需採取行動、已完成、失敗 |
工具資源 |
整合式工具資源 |
工具輸出 |
函式呼叫回應 |
最佳實務
- 務必釋放客戶端資源— 使用
`using` 語句或明確的釋放操作 - 以適當的延遲進行輪詢— 建議狀態檢查間隔為 500 毫秒
- 清理資源— 完成後刪除執行緒和代理程式
- 處理所有執行狀態— 檢查是否為
「需採取行動」、「失敗」或「已取消」 - 使用串流技術實現即時使用者體驗— 相較於輪詢,能提供更佳的使用者體驗
- 儲存 ID 而非物件— 透過 ID 引用代理程式/執行緒
- 使用非同步方法— 所有操作都應採用非同步方式
錯誤處理
using Azure;
try
{
var agent = await client.Administration.CreateAgentAsync(...);
}
catch (RequestFailedException ex) when (ex.Status == 404)
{
Console.WriteLine("找不到資源");
}
catch (RequestFailedException ex)
{
Console.WriteLine($"錯誤:{ex.Status} - {ex.ErrorCode}: {ex.Message}");
}
相關 SDK
| SDK | 用途 | 安裝 |
|---|---|---|
Azure.AI.Agents.Persistent |
低階代理程式(此 SDK) | dotnet add package Azure.AI.Agents.Persistent |
Azure.AI.Projects |
高階專案客戶端 | dotnet add package Azure.AI.Projects |
參考連結
| 資源 | URL |
|---|---|
| NuGet 套件 | https://www.nuget.org/packages/Azure.AI.Agents.Persistent |
| API 參考 | https://learn.microsoft.com/dotnet/api/azure.ai.agents.persistent |
| GitHub 原始碼 | https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Agents.Persistent |
| 範例 | 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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