Azure AI Projects TS

Azure 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

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

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

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.

# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Azure Ai Projects Ts"

Storing Results

After completing work, store AI agent orchestration decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
  --type decision --project <project> \
  --tags azure-ai-projects-ts ai-agents

Multi-Agent Collaboration

This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
  --project <project>

Control Tower Integration

Register agents and tasks with the Control Tower (execution/control_tower.py) for centralized orchestration across machines and LLM providers.

Blockchain Identity

Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.

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