# Send Azure Ai Projects - Microsoft Foundry SDKs to your agent
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
## Fast path
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
## Suggested prompts
### New install

```text
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
```
### Upgrade existing

```text
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
```
## Machine-readable fields
```json
{
  "schemaVersion": "1.0",
  "item": {
    "slug": "azure-ai-projects-py",
    "name": "Azure Ai Projects - Microsoft Foundry SDKs",
    "source": "tencent",
    "type": "skill",
    "category": "开发工具",
    "sourceUrl": "https://clawhub.ai/thegovind/azure-ai-projects-py",
    "canonicalUrl": "https://clawhub.ai/thegovind/azure-ai-projects-py",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadUrl": "/downloads/azure-ai-projects-py",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=azure-ai-projects-py",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "packageFormat": "ZIP package",
    "primaryDoc": "SKILL.md",
    "includedAssets": [
      "SKILL.md",
      "references/acceptance-criteria.md",
      "references/agents.md",
      "references/async-patterns.md",
      "references/connections.md",
      "references/datasets-indexes.md"
    ],
    "downloadMode": "redirect",
    "sourceHealth": {
      "source": "tencent",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-04-23T16:43:11.935Z",
      "expiresAt": "2026-04-30T16:43:11.935Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
        "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"",
        "redirectLocation": null,
        "bodySnippet": null
      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/azure-ai-projects-py"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    }
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/azure-ai-projects-py",
    "downloadUrl": "https://openagent3.xyz/downloads/azure-ai-projects-py",
    "agentUrl": "https://openagent3.xyz/skills/azure-ai-projects-py/agent",
    "manifestUrl": "https://openagent3.xyz/skills/azure-ai-projects-py/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/azure-ai-projects-py/agent.md"
  }
}
```
## Documentation

### Azure AI Projects Python SDK (Foundry SDK)

Build AI applications on Azure AI Foundry using the azure-ai-projects SDK.

### Installation

pip install azure-ai-projects azure-identity

### Environment Variables

AZURE_AI_PROJECT_ENDPOINT="https://<resource>.services.ai.azure.com/api/projects/<project>"
AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"

### Authentication

import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient

credential = DefaultAzureCredential()
client = AIProjectClient(
    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    credential=credential,
)

### Client Operations Overview

OperationAccessPurposeclient.agents.agents.*Agent CRUD, versions, threads, runsclient.connections.connections.*List/get project connectionsclient.deployments.deployments.*List model deploymentsclient.datasets.datasets.*Dataset managementclient.indexes.indexes.*Index managementclient.evaluations.evaluations.*Run evaluationsclient.red_teams.red_teams.*Red team operations

### 1. AIProjectClient (Native Foundry)

from azure.ai.projects import AIProjectClient

client = AIProjectClient(
    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
)

# Use Foundry-native operations
agent = client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are helpful.",
)

### 2. OpenAI-Compatible Client

# Get OpenAI-compatible client from project
openai_client = client.get_openai_client()

# Use standard OpenAI API
response = openai_client.chat.completions.create(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    messages=[{"role": "user", "content": "Hello!"}],
)

### Create Agent (Basic)

agent = client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are a helpful assistant.",
)

### Create Agent with Tools

from azure.ai.agents import CodeInterpreterTool, FileSearchTool

agent = client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="tool-agent",
    instructions="You can execute code and search files.",
    tools=[CodeInterpreterTool(), FileSearchTool()],
)

### Versioned Agents with PromptAgentDefinition

from azure.ai.projects.models import PromptAgentDefinition

# Create a versioned agent
agent_version = client.agents.create_version(
    agent_name="customer-support-agent",
    definition=PromptAgentDefinition(
        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
        instructions="You are a customer support specialist.",
        tools=[],  # Add tools as needed
    ),
    version_label="v1.0",
)

See references/agents.md for detailed agent patterns.

### Tools Overview

ToolClassUse CaseCode InterpreterCodeInterpreterToolExecute Python, generate filesFile SearchFileSearchToolRAG over uploaded documentsBing GroundingBingGroundingToolWeb search (requires connection)Azure AI SearchAzureAISearchToolSearch your indexesFunction CallingFunctionToolCall your Python functionsOpenAPIOpenApiToolCall REST APIsMCPMcpToolModel Context Protocol serversMemory SearchMemorySearchToolSearch agent memory storesSharePointSharepointGroundingToolSearch SharePoint content

See references/tools.md for all tool patterns.

### Thread and Message Flow

# 1. Create thread
thread = client.agents.threads.create()

# 2. Add message
client.agents.messages.create(
    thread_id=thread.id,
    role="user",
    content="What's the weather like?",
)

# 3. Create and process run
run = client.agents.runs.create_and_process(
    thread_id=thread.id,
    agent_id=agent.id,
)

# 4. Get response
if run.status == "completed":
    messages = client.agents.messages.list(thread_id=thread.id)
    for msg in messages:
        if msg.role == "assistant":
            print(msg.content[0].text.value)

### Connections

# List all connections
connections = client.connections.list()
for conn in connections:
    print(f"{conn.name}: {conn.connection_type}")

# Get specific connection
connection = client.connections.get(connection_name="my-search-connection")

See references/connections.md for connection patterns.

### Deployments

# List available model deployments
deployments = client.deployments.list()
for deployment in deployments:
    print(f"{deployment.name}: {deployment.model}")

See references/deployments.md for deployment patterns.

### Datasets and Indexes

# List datasets
datasets = client.datasets.list()

# List indexes
indexes = client.indexes.list()

See references/datasets-indexes.md for data operations.

### Evaluation

# Using OpenAI client for evals
openai_client = client.get_openai_client()

# Create evaluation with built-in evaluators
eval_run = openai_client.evals.runs.create(
    eval_id="my-eval",
    name="quality-check",
    data_source={
        "type": "custom",
        "item_references": [{"item_id": "test-1"}],
    },
    testing_criteria=[
        {"type": "fluency"},
        {"type": "task_adherence"},
    ],
)

See references/evaluation.md for evaluation patterns.

### Async Client

from azure.ai.projects.aio import AIProjectClient

async with AIProjectClient(
    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
) as client:
    agent = await client.agents.create_agent(...)
    # ... async operations

See references/async-patterns.md for async patterns.

### Memory Stores

# Create memory store for agent
memory_store = client.agents.create_memory_store(
    name="conversation-memory",
)

# Attach to agent for persistent memory
agent = client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="memory-agent",
    tools=[MemorySearchTool()],
    tool_resources={"memory": {"store_ids": [memory_store.id]}},
)

### Best Practices

Use context managers for async client: async with AIProjectClient(...) as client:
Clean up agents when done: client.agents.delete_agent(agent.id)
Use create_and_process for simple runs, streaming for real-time UX
Use versioned agents for production deployments
Prefer connections for external service integration (AI Search, Bing, etc.)

### SDK Comparison

Featureazure-ai-projectsazure-ai-agentsLevelHigh-level (Foundry)Low-level (Agents)ClientAIProjectClientAgentsClientVersioningcreate_version()Not availableConnectionsYesNoDeploymentsYesNoDatasets/IndexesYesNoEvaluationVia OpenAI clientNoWhen to useFull Foundry integrationStandalone agent apps

### Reference Files

references/agents.md: Agent operations with PromptAgentDefinition
references/tools.md: All agent tools with examples
references/evaluation.md: Evaluation operations and built-in evaluators
references/connections.md: Connection operations
references/deployments.md: Deployment enumeration
references/datasets-indexes.md: Dataset and index operations
references/async-patterns.md: Async client usage
## Trust
- Source: tencent
- Verification: Indexed source record
- Publisher: thegovind
- Version: 0.1.0
## Source health
- Status: healthy
- Source download looks usable.
- Yavira can redirect you to the upstream package for this source.
- Health scope: source
- Reason: direct_download_ok
- Checked at: 2026-04-23T16:43:11.935Z
- Expires at: 2026-04-30T16:43:11.935Z
- Recommended action: Download for OpenClaw
## Links
- [Detail page](https://openagent3.xyz/skills/azure-ai-projects-py)
- [Send to Agent page](https://openagent3.xyz/skills/azure-ai-projects-py/agent)
- [JSON manifest](https://openagent3.xyz/skills/azure-ai-projects-py/agent.json)
- [Markdown brief](https://openagent3.xyz/skills/azure-ai-projects-py/agent.md)
- [Download page](https://openagent3.xyz/downloads/azure-ai-projects-py)