Agent skill

hosted-agents-v2-py

Build hosted agents using Azure AI Projects SDK with ImageBasedHostedAgentDefinition. Use when creating container-based agents that run custom code in Azure AI Foundry. Triggers: "ImageBasedHostedAgentDefinition", "hosted agent", "container agent", "create_version", "ProtocolVersionRecord", "AgentProtocol.RESPONSES".

View SKILL.md on GitHub Repository
Stars 232
Forks 15

Install this agent skill to your Project

npx add-skill https://github.com/aiskillstore/marketplace/tree/main/skills/sickn33/hosted-agents-v2-py

SKILL.md

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:

  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:

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 variables or Azure Key Vault.

Complete Example

python
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

python
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

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

Reference Links

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

aiskillstore/marketplace

perigon-backend

Perigon ASP.NET Core + EF Core + Aspire conventions

232 15
Explore
aiskillstore/marketplace

perigon-agent

Pointers for Copilot/agents to apply Perigon conventions

232 15
Explore
aiskillstore/marketplace

perigon-angular

Angular 21+ standalone/Material/signal conventions for Perigon WebApp

232 15
Explore
aiskillstore/marketplace

fastapi-mastery

Comprehensive FastAPI development skill covering REST API creation, routing, request/response handling, validation, authentication, database integration, middleware, and deployment. Use when working with FastAPI projects, building APIs, implementing CRUD operations, setting up authentication/authorization, integrating databases (SQL/NoSQL), adding middleware, handling WebSockets, or deploying FastAPI applications. Triggered by requests involving .py files with FastAPI code, API endpoint creation, Pydantic models, or FastAPI-specific features.

232 15
Explore
aiskillstore/marketplace

context7-efficient

Token-efficient library documentation fetcher using Context7 MCP with 86.8% token savings through intelligent shell pipeline filtering. Fetches code examples, API references, and best practices for JavaScript, Python, Go, Rust, and other libraries. Use when users ask about library documentation, need code examples, want API usage patterns, are learning a new framework, need syntax reference, or troubleshooting with library-specific information. Triggers include questions like "Show me React hooks", "How do I use Prisma", "What's the Next.js routing syntax", or any request for library/framework documentation.

232 15
Explore
aiskillstore/marketplace

browser-use

Browser automation using Playwright MCP. Navigate websites, fill forms, click elements, take screenshots, and extract data. Use when tasks require web browsing, form submission, web scraping, UI testing, or any browser interaction.

232 15
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results