A high-level Python SDK for building, managing, and evaluating enterprise AI applications within the Azure AI Foundry ecosystem.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install azure-ai-projects-py
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install azure-ai-projects-py using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Azure AI Projects Python SDK?
The Azure AI Projects Python SDK, frequently referred to as the Foundry SDK, provides a comprehensive framework for developers to architect and deploy sophisticated AI applications. Unlike lower-level libraries, this SDK offers high-level abstractions for managing the entire application lifecycle, from resource connections to agent versioning. By integrating this tool into your Openclaw Skills library, you gain the ability to manage complex Foundry project clients, run detailed evaluations, and maintain model-agnostic workflows using OpenAI-compatible interfaces.
This skill is particularly valuable for developers who need more than just a simple chatbot. It provides the plumbing for enterprise features like red-teaming, dataset management, and infrastructure-as-code for AI agents. Whether you are building internal productivity tools or customer-facing assistants, this SDK ensures your application is grounded in the robust security and scalability of the Azure cloud environment.
Azure AI Projects Python SDK Use Cases
- Creating production-ready AI agents with immutable versioning via PromptAgentDefinition.
- Implementing Retrieval-Augmented Generation (RAG) using Azure AI Search or SharePoint connections.
- Automating model evaluation and red-teaming to ensure safety and performance standards.
- Scaling AI operations with asynchronous client support for high-concurrency environments.
How Azure AI Projects Python SDK Works
- Authentication is established using the Azure Identity library and a project-specific endpoint to initialize the AIProjectClient.
- Developers define agent behavior by specifying instructions, selecting a model deployment, and attaching specialized tools like the Code Interpreter.
- The SDK manages stateful interactions through Threads, where user messages are stored and processed sequentially.
- Runs are initiated to execute agent logic, which can include calling external functions or searching private indexes.
- The client provides built-in methods to monitor run status and retrieve assistant responses once processing is complete.
Azure AI Projects Python SDK Setup
To utilize this skill within your Openclaw Skills environment, install the core SDK and identity packages:
pip install azure-ai-projects azure-identity
Configure your environment variables to point to your Azure AI Foundry resource:
export AZURE_AI_PROJECT_ENDPOINT="https://<resource>.services.ai.azure.com/api/projects/<project>"
export AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
Azure AI Projects Python SDK Data Schema & Taxonomy
The skill organizes Azure Foundry resources into a logical hierarchy accessible through the primary client. The following table describes how data and metadata are structured:
| Component | Organization Method | Purpose |
|---|---|---|
| Agents | client.agents |
Manages CRUD operations and versioning for AI personas. |
| Connections | client.connections |
Stores metadata for external integrations like Bing or Search. |
| Datasets | client.datasets |
Tracks files and data sources used for training or RAG. |
| Threads | client.agents.threads |
Organizes conversation history and message metadata. |
| Evaluations | client.evaluations |
Stores results from quality and adherence testing. |
Azure AI Projects Python SDK Advanced Features
- Versioned Agents: Use PromptAgentDefinition to create stable, labeled versions of your agent logic for safe deployment.
- Multi-Tool Integration: Combine Code Interpreter, File Search, and Bing Grounding within a single agent instance.
- MCP Support: Connect to Model Context Protocol servers to extend agent capabilities with custom external tools.
- OpenAI Compatibility: Use
client.get_openai_client()to leverage existing OpenAI-based code within the Azure ecosystem. - Persistent Memory: Create and attach dedicated memory stores to agents for long-term context retention across different user sessions.
SKILL.md
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