Agents V2 Py

lingxling/agents-v2-py

Build container-based Foundry Agents with Azure AI Projects SDK (ImageBasedHostedAgentDefinition). Use when creating hosted agents with custom container images in Azure AI Foundry.

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npx skills add https://github.com/lingxling/awesome-skills-cn --skill agents-v2-py

The instruction itself

25 sections, as written by the author

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:

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:

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

|-----------|------|----------|-------------|

| 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") |

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=[
                ProtocolVersionRecord(
                    protocol=AgentProtocol.RESPONSES,
                    version="v1"
                )
            ],
            image="myregistry.azurecr.io/data-processor:v1.0",
            cpu="2",
            memory="4Gi",
            tools=[
                {"type": "code_interpreter"},
                {"type": "file_search"}
            ],
            environment_variables={
                "AZURE_AI_PROJECT_ENDPOINT": os.environ["AZURE_AI_PROJECT_ENDPOINT"],
                "MODEL_NAME": "gpt-4o-mini",
                "MAX_RETRIES": "3"
            }
        )
    )
    
    print(f"Created hosted agent: {agent.name}")
    print(f"Version: {agent.version}")
    print(f"State: {agent.state}")
    
    return agent

if __name__ == "__main__":
    create_hosted_agent()

Async Pattern

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

async def create_hosted_agent_async():
    """Create a hosted agent asynchronously."""
    
    async with DefaultAzureCredential() as credential:
        async with AIProjectClient(
            endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
            credential=credential
        ) as client:
            agent = await client.agents.create_version(
                agent_name="async-agent",
                definition=ImageBasedHostedAgentDefinition(
                    container_protocol_versions=[
                        ProtocolVersionRecord(
                            protocol=AgentProtocol.RESPONSES,
                            version="v1"
                        )
                    ],
                    image="myregistry.azurecr.io/async-agent:latest",
                    cpu="1",
                    memory="2Gi"
                )
            )
            return agent

Common Errors

| Error | Cause | Solution |

|-------|-------|----------|

| ImagePullBackOff | ACR pull permission denied | Grant AcrPull role to project's managed identity |

| InvalidContainerImage | Image not found | Verify image path and tag exist in ACR |

| CapabilityHostNotFound | No capability host configured | Create account-level capability host |

| ProtocolVersionNotSupported | Invalid protocol version | Use AgentProtocol.RESPONSES with version "v1" |

Best Practices

Version Your Images - Use specific tags, not latest in production
Minimal Resources - Start with minimum CPU/memory, scale up as needed
Environment Variables - Use for all configuration, never hardcode
Error Handling - Wrap agent creation in try/except blocks
Cleanup - Delete unused agent versions to free resources

Reference Links

When to Use

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

Limitations

Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Repackaged in 1 other repositories

same content, different owner

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lingxling/awesome-skills-cn open on GitHub →

How to use it

Copy the folder

Take lingxling/agents-v2-py from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

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Install what it needs

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