Hosted Agents V2 Py V2

---
name: hosted-agents-v2-py-v2
description: "Azure AI Hosted Agents (Python) workflow skill. Use this skill when the user needs Build hosted agents using Azure AI Projects SDK with ImageBasedHostedAgentDefinition. Use when creating container-based agents in Azure AI Foundry and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off."
version: "0.0.1"
category: ai-agents
tags: ["hosted-agents-v2-py-v2", "hosted-agents-v2-py", "build", "hosted", "using", "azure", "projects", "sdk"]
complexity: intermediate
risk: caution
tools: ["codex-cli", "claude-code", "cursor", "gemini-cli", "opencode"]
source: community
author: "sickn33"
date_added: "2026-04-16"
date_updated: "2026-05-17"
---

# Azure AI Hosted Agents (Python)

## Overview

This public intake copy packages `plugins/antigravity-awesome-skills/skills/hosted-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 the `external_source` block in `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 hosted agents using Azure AI Projects SDK with ImageBasedHostedAgentDefinition. Use when creating container-based agents 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

| Situation | Start here | Why it matters |
| --- | --- | --- |
| First-time use | `metadata.json` | Confirms repository, branch, commit, and imported path through the `external_source` block before touching the copied workflow |
| Provenance review | `ORIGIN.md` | Gives reviewers a plain-language audit trail for the imported source |
| Workflow execution | `SKILL.md` | Starts with the smallest copied file that materially changes execution |
| Supporting context | `SKILL.md` | Adds the next most relevant copied source file without loading the entire package |
| Handoff decision | `## Related Skills` | Helps the operator switch to a stronger native skill when the task drifts |

## 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

```bash
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

```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
)
```

#### Imported: Environment Variables

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

## Examples

### Example 1: Ask for the upstream workflow directly

```text
Use @hosted-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

```text
Review @hosted-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

```text
Use @hosted-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

```text
Review @hosted-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

```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()
```

## 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/hosted-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. Check the `external_source` block first, 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

- `@cred-omega-v2` - Use when the work is better handled by that native specialization after this imported skill establishes context.
- `@customs-trade-compliance-v2` - Use when the work is better handled by that native specialization after this imported skill establishes context.
- `@docker-expert-v2` - Use when the work is better handled by that native specialization after this imported skill establishes context.
- `@ejentum-reasoning-harness-v2` - 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.

| Resource family | What it gives the reviewer | Example path |
| --- | --- | --- |
| `references` | copied reference notes, guides, or background material from upstream | `references/n/a` |
| `examples` | worked examples or reusable prompts copied from upstream | `examples/n/a` |
| `scripts` | upstream helper scripts that change execution or validation | `scripts/n/a` |
| `agents` | routing or delegation notes that are genuinely part of the imported package | `agents/n/a` |
| `assets` | supporting assets or schemas copied from the source package | `assets/n/a` |



### Imported Reference Notes

#### Imported: 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 |

#### Imported: Reference Links

- [Azure AI Projects SDK](https://pypi.org/project/azure-ai-projects/)
- [Hosted Agents Documentation](https://learn.microsoft.com/azure/ai-services/agents/how-to/hosted-agents)
- [Azure Container Registry](https://learn.microsoft.com/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`:

```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
)
```

#### Imported: 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 |

#### Imported: 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 |

#### Imported: 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.

#### Imported: 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
```

#### Imported: 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"` |

#### 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.