Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Azure Ai Agents Py - Microsoft Foundry
Build AI agents using the Azure AI Agents Python SDK (azure-ai-agents). Use when creating agents hosted on Azure AI Foundry with tools (File Search, Code Interpreter, Bing Grounding, Azure AI Search, Function Calling, OpenAPI, MCP), managing threads and messages, implementing streaming responses, or working with vector stores. This is the low-level SDK - for higher-level abstractions, use the agent-framework skill instead.
skill openclawclawhub Free
Build AI agents using the Azure AI Agents Python SDK (azure-ai-agents). Use when creating agents hosted on Azure AI Foundry with tools (File Search, Code Interpreter, Bing Grounding, Azure AI Search, Function Calling, OpenAPI, MCP), managing threads and messages, implementing streaming responses, or working with vector stores. This is the low-level SDK - for higher-level abstractions, use the agent-framework skill instead.
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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/async-patterns.md, references/streaming.md, references/tools.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 Agents Python SDK
Build agents hosted on Azure AI Foundry using the azure-ai-agents SDK.
Installation
pip install azure-ai-agents azure-identity # Or with azure-ai-projects for additional features pip install azure-ai-projects azure-identity
Environment Variables
PROJECT_ENDPOINT="https://<resource>.services.ai.azure.com/api/projects/<project>" MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
Authentication
from azure.identity import DefaultAzureCredential from azure.ai.agents import AgentsClient credential = DefaultAzureCredential() client = AgentsClient( endpoint=os.environ["PROJECT_ENDPOINT"], credential=credential, )
Core Workflow
The basic agent lifecycle: create agent โ create thread โ create message โ create run โ get response
Minimal Example
import os from azure.identity import DefaultAzureCredential from azure.ai.agents import AgentsClient client = AgentsClient( endpoint=os.environ["PROJECT_ENDPOINT"], credential=DefaultAzureCredential(), ) # 1. Create agent agent = client.create_agent( model=os.environ["MODEL_DEPLOYMENT_NAME"], name="my-agent", instructions="You are a helpful assistant.", ) # 2. Create thread thread = client.threads.create() # 3. Add message client.messages.create( thread_id=thread.id, role="user", content="Hello!", ) # 4. Create and process run run = client.runs.create_and_process(thread_id=thread.id, agent_id=agent.id) # 5. Get response if run.status == "completed": messages = client.messages.list(thread_id=thread.id) for msg in messages: if msg.role == "assistant": print(msg.content[0].text.value) # Cleanup client.delete_agent(agent.id)
Tools Overview
ToolClassUse CaseCode InterpreterCodeInterpreterToolExecute Python, generate filesFile SearchFileSearchToolRAG over uploaded documentsBing GroundingBingGroundingToolWeb searchAzure AI SearchAzureAISearchToolSearch your indexesFunction CallingFunctionToolCall your Python functionsOpenAPIOpenApiToolCall REST APIsMCPMcpToolModel Context Protocol servers See references/tools.md for detailed patterns.
Adding Tools
from azure.ai.agents import CodeInterpreterTool, FileSearchTool agent = client.create_agent( model=os.environ["MODEL_DEPLOYMENT_NAME"], name="tool-agent", instructions="You can execute code and search files.", tools=[CodeInterpreterTool()], tool_resources={"code_interpreter": {"file_ids": [file.id]}}, )
Function Calling
from azure.ai.agents import FunctionTool, ToolSet def get_weather(location: str) -> str: """Get weather for a location.""" return f"Weather in {location}: 72F, sunny" functions = FunctionTool(functions=[get_weather]) toolset = ToolSet() toolset.add(functions) agent = client.create_agent( model=os.environ["MODEL_DEPLOYMENT_NAME"], name="function-agent", instructions="Help with weather queries.", toolset=toolset, ) # Process run - toolset auto-executes functions run = client.runs.create_and_process( thread_id=thread.id, agent_id=agent.id, toolset=toolset, # Pass toolset for auto-execution )
Streaming
from azure.ai.agents import AgentEventHandler class MyHandler(AgentEventHandler): def on_message_delta(self, delta): if delta.text: print(delta.text.value, end="", flush=True) def on_error(self, data): print(f"Error: {data}") with client.runs.stream( thread_id=thread.id, agent_id=agent.id, event_handler=MyHandler(), ) as stream: stream.until_done() See references/streaming.md for advanced patterns.
Upload File
file = client.files.upload_and_poll( file_path="data.csv", purpose="assistants", )
Create Vector Store
vector_store = client.vector_stores.create_and_poll( file_ids=[file.id], name="my-store", ) agent = client.create_agent( model=os.environ["MODEL_DEPLOYMENT_NAME"], tools=[FileSearchTool()], tool_resources={"file_search": {"vector_store_ids": [vector_store.id]}}, )
Async Client
from azure.ai.agents.aio import AgentsClient async with AgentsClient( endpoint=os.environ["PROJECT_ENDPOINT"], credential=DefaultAzureCredential(), ) as client: agent = await client.create_agent(...) # ... async operations See references/async-patterns.md for async patterns.
JSON Mode
agent = client.create_agent( model=os.environ["MODEL_DEPLOYMENT_NAME"], response_format={"type": "json_object"}, )
JSON Schema
agent = client.create_agent( model=os.environ["MODEL_DEPLOYMENT_NAME"], response_format={ "type": "json_schema", "json_schema": { "name": "weather_response", "schema": { "type": "object", "properties": { "temperature": {"type": "number"}, "conditions": {"type": "string"}, }, "required": ["temperature", "conditions"], }, }, }, )
Continue Conversation
# Save thread_id for later thread_id = thread.id # Resume later client.messages.create( thread_id=thread_id, role="user", content="Follow-up question", ) run = client.runs.create_and_process(thread_id=thread_id, agent_id=agent.id)
List Messages
messages = client.messages.list(thread_id=thread.id, order="asc") for msg in messages: role = msg.role content = msg.content[0].text.value print(f"{role}: {content}")
Best Practices
Use context managers for async client Clean up agents when done: client.delete_agent(agent.id) Use create_and_process for simple cases, streaming for real-time UX Pass toolset to run for automatic function execution Poll operations use *_and_poll methods for long operations
Reference Files
references/tools.md: All tool types with detailed examples references/streaming.md: Event handlers and streaming patterns references/async-patterns.md: Async client usage
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
5 Docs
- SKILL.md
- references/acceptance-criteria.md
- references/async-patterns.md
- references/streaming.md
- references/tools.md