Azure AI Hosted Agents (Python)

Build container-based hosted agents using ImageBasedHostedAgentDefinition from the Azure AI Projects SDK.

Installation

bash

pip install azure-ai-projects>=2.0.0b3 azure-identity

Minimum SDK Version: 2.0.0b3 or later required for hosted agent support.

Environment Variables

bash

AZURE_AI_PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>

Prerequisites

Before creating hosted agents:

•Container Image - Build and push to Azure Container Registry (ACR)
•ACR Pull Permissions - Grant your project's managed identity AcrPull role on the ACR
•Capability Host - Account-level capability host with enablePublicHostingEnvironment=true
•SDK Version - Ensure azure-ai-projects>=2.0.0b3

Authentication

Always use DefaultAzureCredential:

python

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

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

Core Workflow

1. Imports

python

import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import (
    ImageBasedHostedAgentDefinition,
    ProtocolVersionRecord,
    AgentProtocol,
)

2. Create Hosted Agent

python

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

agent = client.agents.create_version(
    agent_name="my-hosted-agent",
    definition=ImageBasedHostedAgentDefinition(
        container_protocol_versions=[
            ProtocolVersionRecord(protocol=AgentProtocol.RESPONSES, version="v1")
        ],
        cpu="1",
        memory="2Gi",
        image="myregistry.azurecr.io/my-agent:latest",
        tools=[{"type": "code_interpreter"}],
        environment_variables={
            "AZURE_AI_PROJECT_ENDPOINT": os.environ["AZURE_AI_PROJECT_ENDPOINT"],
            "MODEL_NAME": "gpt-4o-mini"
        }
    )
)

print(f"Created agent: {agent.name} (version: {agent.version})")

3. List Agent Versions

python

versions = client.agents.list_versions(agent_name="my-hosted-agent")
for version in versions:
    print(f"Version: {version.version}, State: {version.state}")

4. Delete Agent Version

python

client.agents.delete_version(
    agent_name="my-hosted-agent",
    version=agent.version
)

ImageBasedHostedAgentDefinition Parameters

Parameter	Type	Required	Description
`container_protocol_versions`	`list[ProtocolVersionRecord]`	Yes	Protocol versions the agent supports
`image`	`str`	Yes	Full container image path (registry/image:tag)
`cpu`	`str`	No	CPU allocation (e.g., "1", "2")
`memory`	`str`	No	Memory allocation (e.g., "2Gi", "4Gi")
`tools`	`list[dict]`	No	Tools available to the agent
`environment_variables`	`dict[str, str]`	No	Environment variables for the container

Protocol Versions

The container_protocol_versions parameter specifies which protocols your agent supports:

python

from azure.ai.projects.models import ProtocolVersionRecord, AgentProtocol

# RESPONSES protocol - standard agent responses
container_protocol_versions=[
    ProtocolVersionRecord(protocol=AgentProtocol.RESPONSES, version="v1")
]

Available Protocols:

Protocol	Description
`AgentProtocol.RESPONSES`	Standard response protocol for agent interactions

Resource Allocation

Specify CPU and memory for your container:

python

definition=ImageBasedHostedAgentDefinition(
    container_protocol_versions=[...],
    image="myregistry.azurecr.io/my-agent:latest",
    cpu="2",      # 2 CPU cores
    memory="4Gi"  # 4 GiB memory
)

Resource Limits:

Resource	Min	Max	Default
CPU	0.5	4	1
Memory	1Gi	8Gi	2Gi

Tools Configuration

Add tools to your hosted agent:

Code Interpreter

python

tools=[{"type": "code_interpreter"}]

MCP Tools

python

tools=[
    {"type": "code_interpreter"},
    {
        "type": "mcp",
        "server_label": "my-mcp-server",
        "server_url": "https://my-mcp-server.example.com"
    }
]

Multiple Tools

python

tools=[
    {"type": "code_interpreter"},
    {"type": "file_search"},
    {
        "type": "mcp",
        "server_label": "custom-tool",
        "server_url": "https://custom-tool.example.com"
    }
]

Environment Variables

Pass configuration to your container:

python

environment_variables={
    "AZURE_AI_PROJECT_ENDPOINT": os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    "MODEL_NAME": "gpt-4o-mini",
    "LOG_LEVEL": "INFO",
    "CUSTOM_CONFIG": "value"
}

Best Practice: Never hardcode secrets. Use environment vari