azure-ai-projects-ts
npx machina-cli add skill microsoft/skills/azure-ai-projects-ts --openclawAzure AI Projects SDK for TypeScript
High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.
Installation
npm install @azure/ai-projects @azure/identity
For tracing:
npm install @azure/monitor-opentelemetry @opentelemetry/api
Environment Variables
AZURE_AI_PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
MODEL_DEPLOYMENT_NAME=gpt-4o
Authentication
import { AIProjectClient } from "@azure/ai-projects";
import { DefaultAzureCredential } from "@azure/identity";
const client = new AIProjectClient(
process.env.AZURE_AI_PROJECT_ENDPOINT!,
new DefaultAzureCredential()
);
Operation Groups
| Group | Purpose |
|---|---|
client.agents | Create and manage AI agents |
client.connections | List connected Azure resources |
client.deployments | List model deployments |
client.datasets | Upload and manage datasets |
client.indexes | Create and manage search indexes |
client.evaluators | Manage evaluation metrics |
client.memoryStores | Manage agent memory |
Getting OpenAI Client
const openAIClient = await client.getOpenAIClient();
// Use for responses
const response = await openAIClient.responses.create({
model: "gpt-4o",
input: "What is the capital of France?"
});
// Use for conversations
const conversation = await openAIClient.conversations.create({
items: [{ type: "message", role: "user", content: "Hello!" }]
});
Agents
Create Agent
const agent = await client.agents.createVersion("my-agent", {
kind: "prompt",
model: "gpt-4o",
instructions: "You are a helpful assistant."
});
Agent with Tools
// Code Interpreter
const agent = await client.agents.createVersion("code-agent", {
kind: "prompt",
model: "gpt-4o",
instructions: "You can execute code.",
tools: [{ type: "code_interpreter", container: { type: "auto" } }]
});
// File Search
const agent = await client.agents.createVersion("search-agent", {
kind: "prompt",
model: "gpt-4o",
tools: [{ type: "file_search", vector_store_ids: [vectorStoreId] }]
});
// Web Search
const agent = await client.agents.createVersion("web-agent", {
kind: "prompt",
model: "gpt-4o",
tools: [{
type: "web_search_preview",
user_location: { type: "approximate", country: "US", city: "Seattle" }
}]
});
// Azure AI Search
const agent = await client.agents.createVersion("aisearch-agent", {
kind: "prompt",
model: "gpt-4o",
tools: [{
type: "azure_ai_search",
azure_ai_search: {
indexes: [{
project_connection_id: connectionId,
index_name: "my-index",
query_type: "simple"
}]
}
}]
});
// Function Tool
const agent = await client.agents.createVersion("func-agent", {
kind: "prompt",
model: "gpt-4o",
tools: [{
type: "function",
function: {
name: "get_weather",
description: "Get weather for a location",
strict: true,
parameters: {
type: "object",
properties: { location: { type: "string" } },
required: ["location"]
}
}
}]
});
// MCP Tool
const agent = await client.agents.createVersion("mcp-agent", {
kind: "prompt",
model: "gpt-4o",
tools: [{
type: "mcp",
server_label: "my-mcp",
server_url: "https://mcp-server.example.com",
require_approval: "always"
}]
});
Run Agent
const openAIClient = await client.getOpenAIClient();
// Create conversation
const conversation = await openAIClient.conversations.create({
items: [{ type: "message", role: "user", content: "Hello!" }]
});
// Generate response using agent
const response = await openAIClient.responses.create(
{ conversation: conversation.id },
{ body: { agent: { name: agent.name, type: "agent_reference" } } }
);
// Cleanup
await openAIClient.conversations.delete(conversation.id);
await client.agents.deleteVersion(agent.name, agent.version);
Connections
// List all connections
for await (const conn of client.connections.list()) {
console.log(conn.name, conn.type);
}
// Get connection by name
const conn = await client.connections.get("my-connection");
// Get connection with credentials
const connWithCreds = await client.connections.getWithCredentials("my-connection");
// Get default connection by type
const defaultAzureOpenAI = await client.connections.getDefault("AzureOpenAI", true);
Deployments
// List all deployments
for await (const deployment of client.deployments.list()) {
if (deployment.type === "ModelDeployment") {
console.log(deployment.name, deployment.modelName);
}
}
// Filter by publisher
for await (const d of client.deployments.list({ modelPublisher: "OpenAI" })) {
console.log(d.name);
}
// Get specific deployment
const deployment = await client.deployments.get("gpt-4o");
Datasets
// Upload single file
const dataset = await client.datasets.uploadFile(
"my-dataset",
"1.0",
"./data/training.jsonl"
);
// Upload folder
const dataset = await client.datasets.uploadFolder(
"my-dataset",
"2.0",
"./data/documents/"
);
// Get dataset
const ds = await client.datasets.get("my-dataset", "1.0");
// List versions
for await (const version of client.datasets.listVersions("my-dataset")) {
console.log(version);
}
// Delete
await client.datasets.delete("my-dataset", "1.0");
Indexes
import { AzureAISearchIndex } from "@azure/ai-projects";
const indexConfig: AzureAISearchIndex = {
name: "my-index",
type: "AzureSearch",
version: "1",
indexName: "my-index",
connectionName: "search-connection"
};
// Create index
const index = await client.indexes.createOrUpdate("my-index", "1", indexConfig);
// List indexes
for await (const idx of client.indexes.list()) {
console.log(idx.name);
}
// Delete
await client.indexes.delete("my-index", "1");
Key Types
import {
AIProjectClient,
AIProjectClientOptionalParams,
Connection,
ModelDeployment,
DatasetVersionUnion,
AzureAISearchIndex
} from "@azure/ai-projects";
Best Practices
- Use getOpenAIClient() - For responses, conversations, files, and vector stores
- Version your agents - Use
createVersionfor reproducible agent definitions - Clean up resources - Delete agents, conversations when done
- Use connections - Get credentials from project connections, don't hardcode
- Filter deployments - Use
modelPublisherfilter to find specific models
Source
git clone https://github.com/microsoft/skills/blob/main/.github/plugins/azure-sdk-typescript/skills/azure-ai-projects-ts/SKILL.mdView on GitHub Overview
A high-level TypeScript SDK for Azure AI Foundry projects, enabling management of agents, connections, deployments, datasets, indexes, evaluations, and memory stores. It also provides an OpenAI client to interact with responses and conversations. This kit is essential for building AI apps that orchestrate Foundry resources and OpenAI models.
How This Skill Works
Install the SDKs (@azure/ai-projects and @azure/identity), then instantiate AIProjectClient with the project endpoint and credentials. Use the operation groups (client.agents, client.connections, client.deployments, client.datasets, client.indexes, client.evaluators, client.memoryStores) to manage resources, and call getOpenAIClient() to work with OpenAI endpoints for responses and conversations. Environment variables like AZURE_AI_PROJECT_ENDPOINT and MODEL_DEPLOYMENT_NAME can guide your setup, while authentication uses DefaultAzureCredential.
When to Use It
- Create and customize AI agents (including tools like code_interpreter, file_search, web_search) for Foundry projects.
- Manage datasets, indexes, and evaluations to curate data and measure model performance.
- List and inspect model deployments to understand available capabilities and configurations.
- Obtain an OpenAI client to run responses and conversations against deployed models.
- Configure and connect Azure resources via client.connections to streamline project workflows.
Quick Start
- Step 1: npm install @azure/ai-projects @azure/identity
- Step 2: Create AIProjectClient with AZURE_AI_PROJECT_ENDPOINT and DefaultAzureCredential
- Step 3: Use client.getOpenAIClient() or create a sample agent version to validate setup
Best Practices
- Use DefaultAzureCredential and set AZURE_AI_PROJECT_ENDPOINT to securely authenticate and connect.
- Validate tool configurations for agents (e.g., strict parameter definitions in Function tools) before deployment.
- Leverage client.getOpenAIClient() for both response and conversation flows to keep interactions consistent.
- Keep deployments, datasets, and indexes organized by naming conventions and versioning.
- Regularly monitor telemetry and keep SDKs up to date to align with Foundry and OpenAI updates.
Example Use Cases
- Create a Code Interpreter agent: an agent named 'code-agent' with a code_interpreter tool.
- Build a Web Search agent: an agent named 'web-agent' using a web_search tool with location context.
- Integrate Azure AI Search: an agent 'aisearch-agent' with azure_ai_search indexes and project connections.
- Add a Function tool agent: 'func-agent' that exposes a get_weather function with strict parameters.
- Use MCP tool: an agent 'mcp-agent' configured with an MCP tool and server details.