/agents-v2-py

name: agents-v2-py
description: "Build container-based Foundry Agents with Azure AI Projects SDK (ImageBasedHostedAgentDefinition). Use when creating hosted agents with custom container images in Azure AI Foundry."
risk: critical
source: community
date_added: "2026-02-27"

Azure AI Hosted Agents (Python)

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

Installation

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

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

Prerequisites

Before creating hosted agents:

1. **Container Image** - Build and push to Azure Container Registry (ACR) 2. **ACR Pull Permissions** - Grant your project's managed identity `AcrPull` role on the ACR 3. **Capability Host** - Account-level capability host with `enablePublicHostingEnvironment=true` 4. **SDK Version** - Ensure `azure-ai-projects>=2.0.0b3`

Authentication

Always use `DefaultAzureCredential`:

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

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

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

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

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:

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:

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

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

MCP Tools

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

Multiple Tools

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:

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 variables or Azure Key Vault.

Complete Example

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

def create_hosted_agent():
    """Create a hosted agent with custom container image."""
    
    client = AIProjectClient(
        endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
        credential=DefaultAzureCredential()
    )
    
    agent = client.agents.create_version(
        agent_name="data-processor-agent",
        definition=ImageBasedHostedAgentDefinition(
            container_protocol_versions=[