Agents V2 Py V2

Azure AI Hosted Agents (Python)

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/agents-v2-py from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses metadata.json plus ORIGIN.md as the provenance anchor for review.

Azure AI Hosted Agents (Python) Build container-based hosted agents using ImageBasedHostedAgentDefinition from the Azure AI Projects SDK.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Environment Variables, Prerequisites, Authentication, ImageBasedHostedAgentDefinition Parameters, Protocol Versions, Tools Configuration.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• This skill is applicable to execute the workflow or actions described in the overview.
• Use when the request clearly matches the imported source intent: Build container-based Foundry Agents with Azure AI Projects SDK (ImageBasedHostedAgentDefinition). Use when creating hosted agents with custom container images in Azure AI Foundry.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. `bash pip install azure-ai-projects>=2.0.0b3 azure-identity Minimum SDK Version: 2.0.0b3 or later required for hosted agent support.
2. 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.
3. Create Hosted Agent python client = AIProjectClient( endpoint=os.environ["AZUREAIPROJECTENDPOINT"], credential=DefaultAzureCredential() ) agent = client.agents.createversion( agentname="my-hosted-agent", definition=ImageBasedHostedAgentDefinition( containerprotocolversions=[ ProtocolVersionRecord(protocol=AgentProtocol.RESPONSES, version="v1") ], cpu="1", memory="2Gi", image="myregistry.azurecr.io/my-agent:latest", tools=[{"type": "codeinterpreter"}], environmentvariables={ "AZUREAIPROJECTENDPOINT": os.environ["AZUREAIPROJECTENDPOINT"], "MODELNAME": "gpt-4o-mini" } ) ) print(f"Created agent: {agent.name} (version: {agent.version})") ### 3.
4. List Agent Versions python versions = client.agents.listversions(agentname="my-hosted-agent") for version in versions: print(f"Version: {version.version}, State: {version.state}") ### 4.
5. Delete Agent Version python client.agents.deleteversion( agentname="my-hosted-agent", version=agent.version ) `
6. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
7. Read the overview and provenance files before loading any copied upstream support files.

Imported Workflow Notes

Imported: Installation

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

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

Imported: 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
)

Imported: Environment Variables

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

Examples

Example 1: Ask for the upstream workflow directly

Use @agents-v2-py-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @agents-v2-py-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @agents-v2-py-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @agents-v2-py-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: 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()

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• 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
• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.

Imported Operating Notes

Imported: 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

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/agents-v2-py, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Load only the files that materially change the answer, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @20-andruia-niche-intelligence-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @2d-games - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: 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:

Imported: Reference Links

• Azure AI Projects SDK
• Hosted Agents Documentation
• Azure Container Registry

Imported: 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

Imported: 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
)

Imported: ImageBasedHostedAgentDefinition Parameters

Imported: 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:

Imported: 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"
    }
]

Imported: 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.

Imported: 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

Imported: Common Errors

Imported: 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.