---
name: azure-ai-projects-py
description: "Build AI applications on Microsoft Foundry using the azure-ai-projects SDK."
risk: critical
source: community
date_added: "2026-02-27"
---
## Bundled upstream support

Supporting files are included at skill-relative paths. See [UPSTREAM-NOTICE.md](UPSTREAM-NOTICE.md) for their pinned sources, licenses, hashes, and import changes.

## Restored helper safety

`scripts/run_batch_evaluation.py` uploads the entire selected JSONL file to the configured Azure AI project, creates a remote evaluation and run, and may incur model or cloud charges. Before invocation, review the data classification and confirm the exact file, endpoint, deployment, evaluators, and expected cost with the user. Do not run it during documentation-only work.

# Azure AI Projects Python SDK (Foundry SDK)

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

## Installation

```bash
pip install azure-ai-projects azure-identity
```

## Environment Variables

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

## Authentication

```python
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

| Operation | Access | Purpose |
|-----------|--------|---------|
| `client.agents` | `.agents.*` | Agent CRUD, versions, threads, runs |
| `client.connections` | `.connections.*` | List/get project connections |
| `client.deployments` | `.deployments.*` | List model deployments |
| `client.datasets` | `.datasets.*` | Dataset management |
| `client.indexes` | `.indexes.*` | Index management |
| `client.evaluations` | `.evaluations.*` | Run evaluations |
| `client.red_teams` | `.red_teams.*` | Red team operations |

## Two Client Approaches

### 1. AIProjectClient (Native Foundry)

```python
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

```python
# 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!"}],
)
```

## Agent Operations

### Create Agent (Basic)

```python
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

```python
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

```python
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

| Tool | Class | Use Case |
|------|-------|----------|
| Code Interpreter | `CodeInterpreterTool` | Execute Python, generate files |
| File Search | `FileSearchTool` | RAG over uploaded documents |
| Bing Grounding | `BingGroundingTool` | Web search (requires connection) |
| Azure AI Search | `AzureAISearchTool` | Search your indexes |
| Function Calling | `FunctionTool` | Call your Python functions |
| OpenAPI | `OpenApiTool` | Call REST APIs |
| MCP | `McpTool` | Model Context Protocol servers |
| Memory Search | `MemorySearchTool` | Search agent memory stores |
| SharePoint | `SharepointGroundingTool` | Search SharePoint content |

See references/tools.md for all tool patterns.

## Thread and Message Flow

```python
# 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

```python
# 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

```python
# 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

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

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

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

## Evaluation

```python
# 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

```python
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

```python
# 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

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

## SDK Comparison

| Feature | `azure-ai-projects` | `azure-ai-agents` |
|---------|---------------------|-------------------|
| Level | High-level (Foundry) | Low-level (Agents) |
| Client | `AIProjectClient` | `AgentsClient` |
| Versioning | `create_version()` | Not available |
| Connections | Yes | No |
| Deployments | Yes | No |
| Datasets/Indexes | Yes | No |
| Evaluation | Via OpenAI client | No |
| When to use | Full Foundry integration | Standalone agent apps |

## Reference Files

- references/agents.md: Agent operations with PromptAgentDefinition
- references/tools.md: All agent tools with examples
- references/evaluation.md: Evaluation operations overview
- references/built-in-evaluators.md: Complete built-in evaluator reference
- references/custom-evaluators.md: Code and prompt-based evaluator patterns
- 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
- references/api-reference.md: Complete API reference for all 373 SDK exports (v2.0.0b4)
- scripts/run_batch_evaluation.py: CLI tool for batch evaluations

## 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.