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azure-ai-projects-ts
Build AI applications using Azure AI Projects SDK for JavaScript (@azure/ai-projects). Use when working with Foundry project clients, agents, connections, deployments, datasets, indexes, evaluations, or getting OpenAI clients.
231 stars
Installation
Claude Code / Cursor / Codex
$curl -o ~/.claude/skills/azure-ai-projects-ts/SKILL.md --create-dirs "https://raw.githubusercontent.com/aiskillstore/marketplace/main/skills/sickn33/azure-ai-projects-ts/SKILL.md"
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/azure-ai-projects-ts/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How azure-ai-projects-ts Compares
| Feature / Agent | azure-ai-projects-ts | Standard Approach |
|---|---|---|
| Platform Support | multi | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Build AI applications using Azure AI Projects SDK for JavaScript (@azure/ai-projects). Use when working with Foundry project clients, agents, connections, deployments, datasets, indexes, evaluations, or getting OpenAI clients.
Which AI agents support this skill?
This skill is compatible with multi.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Azure AI Projects SDK for TypeScript
High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.
## Installation
```bash
npm install @azure/ai-projects @azure/identity
```
For tracing:
```bash
npm install @azure/monitor-opentelemetry @opentelemetry/api
```
## Environment Variables
```bash
AZURE_AI_PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
MODEL_DEPLOYMENT_NAME=gpt-4o
```
## Authentication
```typescript
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
```typescript
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
```typescript
const agent = await client.agents.createVersion("my-agent", {
kind: "prompt",
model: "gpt-4o",
instructions: "You are a helpful assistant."
});
```
### Agent with Tools
```typescript
// 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
```typescript
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
```typescript
// 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
```typescript
// 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
```typescript
// 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
```typescript
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
```typescript
import {
AIProjectClient,
AIProjectClientOptionalParams,
Connection,
ModelDeployment,
DatasetVersionUnion,
AzureAISearchIndex
} from "@azure/ai-projects";
```
## Best Practices
1. **Use getOpenAIClient()** - For responses, conversations, files, and vector stores
2. **Version your agents** - Use `createVersion` for reproducible agent definitions
3. **Clean up resources** - Delete agents, conversations when done
4. **Use connections** - Get credentials from project connections, don't hardcode
5. **Filter deployments** - Use `modelPublisher` filter to find specific models