Agent skill

azure-ai-projects-py

Build AI applications using the Azure AI Projects Python SDK (azure-ai-projects). Use when working with Foundry project clients, creating versioned agents with PromptAgentDefinition, running evaluations, managing connections/deployments/datasets/indexes, or using OpenAI-compatible clients. This is the high-level Foundry SDK - for low-level agent operations, use azure-ai-agents-python skill.

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SKILL.md

Azure AI Projects Python SDK (Foundry SDK)

Build AI applications on Microsoft Foundry using the azure-ai-projects SDK.

Installation

bash
pip install azure-ai-projects azure-identity

Environment Variables

bash
AZURE_AI_PROJECT_ENDPOINT="https://<resource>.services.ai.azure.com/api/projects/<project>"
AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"

Authentication

python
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient

credential = DefaultAzureCredential()
client = AIProjectClient(
    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    credential=credential,
)

Client Operations Overview

Operation Access Purpose
client.agents .agents.* Agent CRUD, versions, threads, runs
client.connections .connections.* List/get project connections
client.deployments .deployments.* List model deployments
client.datasets .datasets.* Dataset management
client.indexes .indexes.* Index management
client.evaluations .evaluations.* Run evaluations
client.red_teams .red_teams.* Red team operations

Two Client Approaches

1. AIProjectClient (Native Foundry)

python
from azure.ai.projects import AIProjectClient

client = AIProjectClient(
    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
)

# Use Foundry-native operations
agent = client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are helpful.",
)

2. OpenAI-Compatible Client

python
# Get OpenAI-compatible client from project
openai_client = client.get_openai_client()

# Use standard OpenAI API
response = openai_client.chat.completions.create(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    messages=[{"role": "user", "content": "Hello!"}],
)

Agent Operations

Create Agent (Basic)

python
agent = client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are a helpful assistant.",
)

Create Agent with Tools

python
from azure.ai.agents import CodeInterpreterTool, FileSearchTool

agent = client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="tool-agent",
    instructions="You can execute code and search files.",
    tools=[CodeInterpreterTool(), FileSearchTool()],
)

Versioned Agents with PromptAgentDefinition

python
from azure.ai.projects.models import PromptAgentDefinition

# Create a versioned agent
agent_version = client.agents.create_version(
    agent_name="customer-support-agent",
    definition=PromptAgentDefinition(
        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
        instructions="You are a customer support specialist.",
        tools=[],  # Add tools as needed
    ),
    version_label="v1.0",
)

See references/agents.md for detailed agent patterns.

Tools Overview

Tool Class Use Case
Code Interpreter CodeInterpreterTool Execute Python, generate files
File Search FileSearchTool RAG over uploaded documents
Bing Grounding BingGroundingTool Web search (requires connection)
Azure AI Search AzureAISearchTool Search your indexes
Function Calling FunctionTool Call your Python functions
OpenAPI OpenApiTool Call REST APIs
MCP McpTool Model Context Protocol servers
Memory Search MemorySearchTool Search agent memory stores
SharePoint SharepointGroundingTool Search SharePoint content

See references/tools.md for all tool patterns.

Thread and Message Flow

python
# 1. Create thread
thread = client.agents.threads.create()

# 2. Add message
client.agents.messages.create(
    thread_id=thread.id,
    role="user",
    content="What's the weather like?",
)

# 3. Create and process run
run = client.agents.runs.create_and_process(
    thread_id=thread.id,
    agent_id=agent.id,
)

# 4. Get response
if run.status == "completed":
    messages = client.agents.messages.list(thread_id=thread.id)
    for msg in messages:
        if msg.role == "assistant":
            print(msg.content[0].text.value)

Connections

python
# List all connections
connections = client.connections.list()
for conn in connections:
    print(f"{conn.name}: {conn.connection_type}")

# Get specific connection
connection = client.connections.get(connection_name="my-search-connection")

See references/connections.md for connection patterns.

Deployments

python
# List available model deployments
deployments = client.deployments.list()
for deployment in deployments:
    print(f"{deployment.name}: {deployment.model}")

See references/deployments.md for deployment patterns.

Datasets and Indexes

python
# List datasets
datasets = client.datasets.list()

# List indexes
indexes = client.indexes.list()

See references/datasets-indexes.md for data operations.

Evaluation

python
# Using OpenAI client for evals
openai_client = client.get_openai_client()

# Create evaluation with built-in evaluators
eval_run = openai_client.evals.runs.create(
    eval_id="my-eval",
    name="quality-check",
    data_source={
        "type": "custom",
        "item_references": [{"item_id": "test-1"}],
    },
    testing_criteria=[
        {"type": "fluency"},
        {"type": "task_adherence"},
    ],
)

See references/evaluation.md for evaluation patterns.

Async Client

python
from azure.ai.projects.aio import AIProjectClient

async with AIProjectClient(
    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
) as client:
    agent = await client.agents.create_agent(...)
    # ... async operations

See references/async-patterns.md for async patterns.

Memory Stores

python
# Create memory store for agent
memory_store = client.agents.create_memory_store(
    name="conversation-memory",
)

# Attach to agent for persistent memory
agent = client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="memory-agent",
    tools=[MemorySearchTool()],
    tool_resources={"memory": {"store_ids": [memory_store.id]}},
)

Best Practices

  1. Use context managers for async client: async with AIProjectClient(...) as client:
  2. Clean up agents when done: client.agents.delete_agent(agent.id)
  3. Use create_and_process for simple runs, streaming for real-time UX
  4. Use versioned agents for production deployments
  5. Prefer connections for external service integration (AI Search, Bing, etc.)

SDK Comparison

Feature azure-ai-projects azure-ai-agents
Level High-level (Foundry) Low-level (Agents)
Client AIProjectClient AgentsClient
Versioning create_version() Not available
Connections Yes No
Deployments Yes No
Datasets/Indexes Yes No
Evaluation Via OpenAI client No
When to use Full Foundry integration Standalone agent apps

Reference Files

  • references/agents.md: Agent operations with PromptAgentDefinition
  • references/tools.md: All agent tools with examples
  • references/evaluation.md: Evaluation operations overview
  • references/built-in-evaluators.md: Complete built-in evaluator reference
  • references/custom-evaluators.md: Code and prompt-based evaluator patterns
  • references/connections.md: Connection operations
  • references/deployments.md: Deployment enumeration
  • references/datasets-indexes.md: Dataset and index operations
  • references/async-patterns.md: Async client usage
  • references/api-reference.md: Complete API reference for all 373 SDK exports (v2.0.0b4)
  • scripts/run_batch_evaluation.py: CLI tool for batch evaluations

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