Skip to content
agents-v2-py logo

Azure AI Hosted Agents (Python)

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.

SKILL.md

Full skill instructions

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

ParameterTypeRequiredDescription
container_protocol_versionslist[ProtocolVersionRecord]YesProtocol versions the agent supports
imagestrYesFull container image path (registry/​image:tag)
cpustrNoCPU allocation (e.g., "1", "2")
memorystrNoMemory allocation (e.g., "2Gi", "4Gi")
toolslist[dict]NoTools available to the agent
environment_variablesdict[str, str]NoEnvironment 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:

ProtocolDescription
AgentProtocol.RESPONSESStandard 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:

ResourceMinMaxDefault
CPU0.541
Memory1Gi8Gi2Gi

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

ErrorCauseSolution
ImagePullBackOffACR pull permission deniedGrant AcrPull role to project's managed identity
InvalidContainerImageImage not foundVerify image path and tag exist in ACR
CapabilityHostNotFoundNo capability host configuredCreate account-level capability host
ProtocolVersionNotSupportedInvalid protocol versionUse 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

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.

More skills from boisenoise

database-cloud-optimization-cost-optimize logo
boisenoise/skills-collections

database-cloud-optimization-cost-optimize

You are a cloud cost optimization expert specializing in reducing infrastructure expenses while maintaining performance and reliability. Analyze cloud spending, identify savings opportunities, and implement cost-effective architectures across AWS, Azure, and GCP.

10 0
View
postmark-automation logo
boisenoise/skills-collections

postmark-automation

Automate Postmark email delivery tasks via Rube MCP (Composio): send templated emails, manage templates, monitor delivery stats and bounces. Always search tools first for current schemas.

10 0
View
game-development logo
boisenoise/skills-collections

game-development

Game development orchestrator. Routes to platform-specific skills based on project needs.

10 0
View
test-fixing logo
boisenoise/skills-collections

test-fixing

Systematically identify and fix all failing tests using smart grouping strategies. Use when explicitly asks to fix tests (\"fix these tests\", \"make tests pass\"), reports test failures (\"tests are failing\", \"test suite is broken\"), or completes implementation and wants tests passing.

10 0
View
notebooklm logo
boisenoise/skills-collections

notebooklm

Interact with Google NotebookLM to query documentation with Gemini's source-grounded answers. Each question opens a fresh browser session, retrieves the answer exclusively from your uploaded documents, and closes.

10 0
View
agent-evaluation logo
boisenoise/skills-collections

agent-evaluation

Testing and benchmarking LLM agents including behavioral testing,

10 0
View
clickup-automation logo
boisenoise/skills-collections

clickup-automation

Automate ClickUp project management including tasks, spaces, folders, lists, comments, and team operations via Rube MCP (Composio). Always search tools first for current schemas.

10 0
View
wordpress-penetration-testing logo
boisenoise/skills-collections

wordpress-penetration-testing

Assess WordPress installations for common vulnerabilities and WordPress 7.0 attack surfaces.

10 0
View
ln-761-secret-scanner logo
boisenoise/skills-collections

ln-761-secret-scanner

Scans codebase for hardcoded secrets with severity classification and remediation guidance. Use when auditing a project for leaked credentials.

10 0
View
azure-monitor-ingestion-java logo
boisenoise/skills-collections

azure-monitor-ingestion-java

Azure Monitor Ingestion SDK for Java. Send custom logs to Azure Monitor via Data Collection Rules (DCR) and Data Collection Endpoints (DCE).

10 0
View
ln-513-agent-reviewer logo
boisenoise/skills-collections

ln-513-agent-reviewer

Runs parallel external agent reviews (Codex + Gemini) on code changes. Process-as-arrive, critical verification with debate. Returns filtered suggestions.

10 0
View
drip-jobs-automation logo
boisenoise/skills-collections

drip-jobs-automation

Automate Drip Jobs tasks via Rube MCP (Composio). Always search tools first for current schemas.

10 0
View

Popular AI tools

Kaiber logo
Video

Kaiber

Generate, edit, and beat-sync AI video with leading models in one workspace.

Paid
View
Vimcal logo
Productivity

Vimcal

The world's fastest calendar for remote work

Free
View

Transform Your Design with AI Designer by ImgCreator.ai

Freemium
View
Akool AI logo
Content & writing

Akool AI

Revolutionizing Video Production with AI-Powered Creativity

Paid
View

Extend an image past the frame and let AI fill the new aspect ratio.

Freemium
View
StarByFace logo
Security

StarByFace

Discover your celebrity doppelgänger with StarByFace!

Free
View
C

ChainClarity explains 700+ crypto whitepapers in plain English, with layered summaries, comparisons, research tools, alerts, and a $4.99 Pro plan.

Freemium
View
Opus Clip logo
Coding & apps

Opus Clip

Opus.ai: Revolutionize Your Web Experience

Free
View