azure-ai-agents-persistent-dotnet
microsoft/skills
Crie e gerencie agentes de IA persistentes com threads, mensagens, execuções e ferramentas usando o SDK do Azure AI Agents para .NET.
...Expandir tudoAzure.AI.Agents.Persistent (.NET)
SDK de baixo nível para criar e gerenciar agentes de IA persistentes com threads, mensagens, execuções e ferramentas.
Instalação
dotnet add package Azure.AI.Agents.Persistent --prerelease
dotnet add package Azure.Identity
Versões atuais: Estável v1.1.0, Pré-visualização v1.2.0-beta.8
Variáveis de ambiente
PROJECT_ENDPOINT=https://.services.ai.azure.com/api/projects/ # Obrigatório: endpoint do projeto do Azure AI
MODEL_DEPLOYMENT_NAME=gpt-4o-mini # Obrigatório: nome da implantação do modelo
AZURE_BING_CONNECTION_ID= # Obrigatório: ID do recurso de conexão do Bing
AZURE_AI_SEARCH_CONNECTION_ID= # Obrigatório: ID do recurso de conexão do Azure AI Search
AZURE_TOKEN_CREDENTIALS=prod # Obrigatório apenas se DefaultAzureCredential for usado em produção
Autenticação
using Azure.AI.Agents.Persistent;
using Azure.Identity;
var projectEndpoint = Environment.GetEnvironmentVariable("PROJECT_ENDPOINT");
// Desenvolvimento local: DefaultAzureCredential. Produção: defina AZURE_TOKEN_CREDENTIALS=prod ou AZURE_TOKEN_CREDENTIALS=
var credential = new DefaultAzureCredential(
DefaultAzureCredential.DefaultEnvironmentVariableName
);
// Ou use uma credencial específica diretamente na produção:
// Consulte https://learn.microsoft.com/dotnet/api/overview/azure/identity-readme?view=azure-dotnet#credential-classes
// var credential = new ManagedIdentityCredential();
PersistentAgentsClient client = new(projectEndpoint, credential);
Hierarquia do cliente
PersistentAgentsClient
├── Administração → Operações CRUD do agente
├── Threads → Gerenciamento de threads
├── Mensagens → Operações com mensagens
├── Execuções → Execução e streaming de execuções
├── Arquivos → Upload/download de arquivos
└── VectorStores → Gerenciamento de armazenamentos vetoriais
Fluxo de trabalho principal
1. Criar agente
var modelDeploymentName = Environment.GetEnvironmentVariable("MODEL_DEPLOYMENT_NAME");
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Tutor de Matemática",
instructions: "Você é um professor particular de matemática. Escreva e execute código para responder a questões de matemática.",
tools: [new CodeInterpreterToolDefinition()]
);
2. Criar thread e mensagem
// Criar thread
PersistentAgentThread thread = await client.Threads.CreateThreadAsync();
// Criar mensagem
await client.Messages.CreateMessageAsync(
thread.Id,
MessageRole.User,
"Preciso resolver a equação `3x + 11 = 14`. Você pode me ajudar?"
);
3. Executar o agente (polling)
// Criar execução
ThreadRun run = await client.Runs.CreateRunAsync(
thread.Id,
agent.Id,
additionalInstructions: "Por favor, dirija-se à usuária como Jane Doe."
);
// Verificar se a execução foi concluída
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);
// Recuperar mensagens
await foreach (PersistentThreadMessage mensagem 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. Resposta em streaming
AsyncCollectionResult stream = client.Runs.CreateRunStreamingAsync(
thread.Id,
agent.Id
);
await foreach (StreamingUpdate update in stream)
{
if (update.UpdateKind == StreamingUpdateReason.RunCreated)
{
Console.WriteLine("--- Execução iniciada! ---");
}
else if (update is MessageContentUpdate contentUpdate)
{
Console.Write(contentUpdate.Text);
}
else if (update.UpdateKind == StreamingUpdateReason.RunCompleted)
{
Console.WriteLine("\n--- Execução concluída! ---");
}
}
5. Chamada de função
// Definir a ferramenta de função
FunctionToolDefinition weatherTool = new(
name: "getCurrentWeather",
description: "Obtém as condições meteorológicas atuais em um local.",
parâmetros: BinaryData.FromObjectAsJson(new
{
Type = "object",
Properties = new
{
Location = new { Type = "string", Description = "Cidade e estado, por exemplo, São Francisco, CA" },
Unit = new { Type = "string", Enum = new[] { "c", "f" } }
},
Required = new[] { "location" }
}, new JsonSerializerOptions { PropertyNamingPolicy = JsonNamingPolicy.CamelCase })
);
// Criar agente com função
PersistentAgent agente = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Bots de previsão do tempo",
instructions: "Você é um bot de previsão do tempo.",
tools: [weatherTool]
);
// Tratar chamadas de função durante a sondagem
do
{
await Task.Delay(500);
run = await client.Runs.GetRunAsync(thread.Id, run.Id);
if (run.Status == RunStatus.RequiresAction
&& run.RequiredAction é SubmitToolOutputsAction submitAction)
{
List outputs = [];
foreach (RequiredToolCall toolCall in submitAction.ToolCalls)
{
if (toolCall é RequiredFunctionToolCall funcCall)
{
// Executar função e obter resultado
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. Pesquisa de arquivos com o Vector Store
// Carregar arquivo
PersistentAgentFileInfo file = await client.Files.UploadFileAsync(
filePath: "document.txt",
purpose: PersistentAgentFilePurpose.Agents
);
// Criar armazenamento vetorial
PersistentAgentsVectorStore vectorStore = await client.VectorStores.CreateVectorStoreAsync(
fileIds: [file.Id],
name: "my_vector_store"
);
// Criar recurso de busca de arquivos
FileSearchToolResource fileSearchResource = new();
fileSearchResource.VectorStoreIds.Add(vectorStore.Id);
// Criar agente com pesquisa de arquivos
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Assistente de Documentos",
instructions: "Você ajuda os usuários a encontrar informações em documentos.",
ferramentas: [new FileSearchToolDefinition()],
recursosDeFerramentas: new ToolResources { FileSearch = fileSearchResource }
);
7. Bing Grounding
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 o Bing para responder a perguntas sobre eventos atuais.",
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: "Pesquise no índice de documentação para responder às perguntas.",
ferramentas: [new AzureAISearchToolDefinition()],
recursosDeFerramentas: new ToolResources { AzureAISearch = searchResource }
);
9. Limpeza
await client.Threads.DeleteThreadAsync(thread.Id);
await client.Administration.DeleteAgentAsync(agent.Id);
await client.VectorStores.DeleteVectorStoreAsync(vectorStore.Id);
await client.Files.DeleteFileAsync(file.Id);
Ferramentas disponíveis
| Ferramenta | Classe | Finalidade |
|---|---|---|
| Interpretador de código | CodeInterpreterToolDefinition |
Executar código Python, gerar visualizações |
| Pesquisa de arquivos | FileSearchToolDefinition |
Pesquisar arquivos enviados por meio de armazenamentos vetoriais |
| Chamada de função | Definição da Ferramenta de Função |
Chamar funções personalizadas |
| Bing Grounding | BingGroundingToolDefinition |
Pesquisa na Web via Bing |
| Pesquisa com IA do Azure | AzureAISearchToolDefinition |
Pesquisar nos índices do Azure AI Search |
| OpenAPI | OpenApiToolDefinition |
Chamar APIs externas por meio da especificação OpenAPI |
| Azure Functions | AzureFunctionToolDefinition |
Chamar o Azure Functions |
| MCP | MCPToolDefinition |
Ferramentas de Protocolo de Contexto de Modelo |
| SharePoint | SharepointToolDefinition |
Acessar conteúdo do SharePoint |
| Microsoft Fabric | MicrosoftFabricToolDefinition |
Acessar dados do Fabric |
Tipos de atualização de streaming
| Tipo de atualização | Descrição |
|---|---|
StreamingUpdateReason.RunCreated |
Execução iniciada |
StreamingUpdateReason.RunInProgress |
Processamento em andamento |
StreamingUpdateReason.RunCompleted |
Execução concluída |
StreamingUpdateReason.ExecuçãoFalhou |
Erro na execução |
MessageContentUpdate |
Bloco de conteúdo de texto |
Atualização da etapa de execução |
Alteração no status da etapa |
Referência de tipos de chaves
| Tipo | Finalidade |
|---|---|
PersistentAgentsClient |
Ponto de entrada principal |
PersistentAgent |
Agente com modelo, instruções e ferramentas |
PersistentAgentThread |
Tópico de conversa |
PersistentThreadMessage |
Mensagem na thread |
ThreadRun |
Execução do agente na thread |
Status da execução |
Em fila, Em andamento, Requer ação, Concluído, Falha |
Recursos da ferramenta |
Recursos combinados da ferramenta |
Saída da ferramenta |
Resposta à chamada de função |
Melhores práticas
- Sempre libere os clientes — Use instruções
`using` ou liberação explícita - Faça a sondagem com intervalos adequados — recomenda-se 500 ms entre as verificações de status
- Limpe os recursos — Exclua threads e agentes ao concluir
- Lide com todos os status de execução — Verifique se há
“RequiresAction”,“Failed”ou“Cancelled” - Use streaming para uma experiência do usuário em tempo real — Melhor experiência do usuário do que a sondagem
- Armazene IDs, não objetos — Faça referência a agentes/threads por ID
- Use métodos assíncronos — Todas as operações devem ser assíncronas
Tratamento de erros
using Azure;
try
{
var agent = await client.Administration.CreateAgentAsync(...);
}
catch (RequestFailedException ex) when (ex.Status == 404)
{
Console.WriteLine("Recurso não encontrado");
}
catch (RequestFailedException ex)
{
Console.WriteLine($"Erro: {ex.Status} - {ex.ErrorCode}: {ex.Message}");
}
SDKs relacionados
| SDK | Finalidade | Instalar |
|---|---|---|
Azure.AI.Agents.Persistent |
Agentes de baixo nível (este SDK) | dotnet add package Azure.AI.Agents.Persistent |
Azure.AI.Projects |
Cliente de projeto de alto nível | dotnet add package Azure.AI.Projects |
Links de referência
| Recurso | URL |
|---|---|
| Pacote NuGet | https://www.nuget.org/packages/Azure.AI.Agents.Persistent |
| Referência da API | https://learn.microsoft.com/dotnet/api/azure.ai.agents.persistent |
| Código-fonte no GitHub | https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Agents.Persistent |
| Exemplos | 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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