Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Azure Ai Projects - Microsoft Foundry SDKs
Build AI applications using the Azure AI Projects Python SDK (azure-ai-projects). Use when working with Foundry project clients, creating versioned agents with PromptAgentDefinition, running evaluations, managing connections/deployments/datasets/indexes, or using OpenAI-compatible clients. This is the high-level Foundry SDK - for low-level agent operations, use azure-ai-agents-python skill.
skill openclawclawhub Free
Build AI applications using the Azure AI Projects Python SDK (azure-ai-projects). Use when working with Foundry project clients, creating versioned agents with PromptAgentDefinition, running evaluations, managing connections/deployments/datasets/indexes, or using OpenAI-compatible clients. This is the high-level Foundry SDK - for low-level agent operations, use azure-ai-agents-python skill.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/acceptance-criteria.md, references/agents.md, references/async-patterns.md, references/connections.md, references/datasets-indexes.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- 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.
New install
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
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.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- thegovind
- Source page
- View original listing
- Canonical URL
- Open canonical page
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
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
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Package contents
Included in package
6 Docs
- SKILL.md
- references/acceptance-criteria.md
- references/agents.md
- references/async-patterns.md
- references/connections.md
- references/datasets-indexes.md