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)
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
# 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)
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
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
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
# 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
# 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
# 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
# List datasets
datasets = client.datasets.list()
# List indexes
indexes = client.indexes.list()
See references/datasets-indexes.md for data operations.
