Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Agent Framework Azure Ai Py
Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
skill openclawclawhub Free
Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
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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/advanced.md, references/mcp.md, references/threads.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
Agent Framework Azure Hosted Agents
Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.
Architecture
User Query โ AzureAIAgentsProvider โ Azure AI Agent Service (Persistent) โ Agent.run() / Agent.run_stream() โ Tools: Functions | Hosted (Code/Search/Web) | MCP โ AgentThread (conversation persistence)
Installation
# Full framework (recommended) pip install agent-framework --pre # Or Azure-specific package only pip install agent-framework-azure-ai --pre
Environment Variables
export AZURE_AI_PROJECT_ENDPOINT="https://<project>.services.ai.azure.com/api/projects/<project-id>" export AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini" export BING_CONNECTION_ID="your-bing-connection-id" # For web search
Authentication
from azure.identity.aio import AzureCliCredential, DefaultAzureCredential # Development credential = AzureCliCredential() # Production credential = DefaultAzureCredential()
Basic Agent
import asyncio from agent_framework.azure import AzureAIAgentsProvider from azure.identity.aio import AzureCliCredential async def main(): async with ( AzureCliCredential() as credential, AzureAIAgentsProvider(credential=credential) as provider, ): agent = await provider.create_agent( name="MyAgent", instructions="You are a helpful assistant.", ) result = await agent.run("Hello!") print(result.text) asyncio.run(main())
Agent with Function Tools
from typing import Annotated from pydantic import Field from agent_framework.azure import AzureAIAgentsProvider from azure.identity.aio import AzureCliCredential def get_weather( location: Annotated[str, Field(description="City name to get weather for")], ) -> str: """Get the current weather for a location.""" return f"Weather in {location}: 72ยฐF, sunny" def get_current_time() -> str: """Get the current UTC time.""" from datetime import datetime, timezone return datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S UTC") async def main(): async with ( AzureCliCredential() as credential, AzureAIAgentsProvider(credential=credential) as provider, ): agent = await provider.create_agent( name="WeatherAgent", instructions="You help with weather and time queries.", tools=[get_weather, get_current_time], # Pass functions directly ) result = await agent.run("What's the weather in Seattle?") print(result.text)
Agent with Hosted Tools
from agent_framework import ( HostedCodeInterpreterTool, HostedFileSearchTool, HostedWebSearchTool, ) from agent_framework.azure import AzureAIAgentsProvider from azure.identity.aio import AzureCliCredential async def main(): async with ( AzureCliCredential() as credential, AzureAIAgentsProvider(credential=credential) as provider, ): agent = await provider.create_agent( name="MultiToolAgent", instructions="You can execute code, search files, and search the web.", tools=[ HostedCodeInterpreterTool(), HostedWebSearchTool(name="Bing"), ], ) result = await agent.run("Calculate the factorial of 20 in Python") print(result.text)
Streaming Responses
async def main(): async with ( AzureCliCredential() as credential, AzureAIAgentsProvider(credential=credential) as provider, ): agent = await provider.create_agent( name="StreamingAgent", instructions="You are a helpful assistant.", ) print("Agent: ", end="", flush=True) async for chunk in agent.run_stream("Tell me a short story"): if chunk.text: print(chunk.text, end="", flush=True) print()
Conversation Threads
from agent_framework.azure import AzureAIAgentsProvider from azure.identity.aio import AzureCliCredential async def main(): async with ( AzureCliCredential() as credential, AzureAIAgentsProvider(credential=credential) as provider, ): agent = await provider.create_agent( name="ChatAgent", instructions="You are a helpful assistant.", tools=[get_weather], ) # Create thread for conversation persistence thread = agent.get_new_thread() # First turn result1 = await agent.run("What's the weather in Seattle?", thread=thread) print(f"Agent: {result1.text}") # Second turn - context is maintained result2 = await agent.run("What about Portland?", thread=thread) print(f"Agent: {result2.text}") # Save thread ID for later resumption print(f"Conversation ID: {thread.conversation_id}")
Structured Outputs
from pydantic import BaseModel, ConfigDict from agent_framework.azure import AzureAIAgentsProvider from azure.identity.aio import AzureCliCredential class WeatherResponse(BaseModel): model_config = ConfigDict(extra="forbid") location: str temperature: float unit: str conditions: str async def main(): async with ( AzureCliCredential() as credential, AzureAIAgentsProvider(credential=credential) as provider, ): agent = await provider.create_agent( name="StructuredAgent", instructions="Provide weather information in structured format.", response_format=WeatherResponse, ) result = await agent.run("Weather in Seattle?") weather = WeatherResponse.model_validate_json(result.text) print(f"{weather.location}: {weather.temperature}ยฐ{weather.unit}")
Provider Methods
MethodDescriptioncreate_agent()Create new agent on Azure AI serviceget_agent(agent_id)Retrieve existing agent by IDas_agent(sdk_agent)Wrap SDK Agent object (no HTTP call)
Hosted Tools Quick Reference
ToolImportPurposeHostedCodeInterpreterToolfrom agent_framework import HostedCodeInterpreterToolExecute Python codeHostedFileSearchToolfrom agent_framework import HostedFileSearchToolSearch vector storesHostedWebSearchToolfrom agent_framework import HostedWebSearchToolBing web searchHostedMCPToolfrom agent_framework import HostedMCPToolService-managed MCPMCPStreamableHTTPToolfrom agent_framework import MCPStreamableHTTPToolClient-managed MCP
Complete Example
import asyncio from typing import Annotated from pydantic import BaseModel, Field from agent_framework import ( HostedCodeInterpreterTool, HostedWebSearchTool, MCPStreamableHTTPTool, ) from agent_framework.azure import AzureAIAgentsProvider from azure.identity.aio import AzureCliCredential def get_weather( location: Annotated[str, Field(description="City name")], ) -> str: """Get weather for a location.""" return f"Weather in {location}: 72ยฐF, sunny" class AnalysisResult(BaseModel): summary: str key_findings: list[str] confidence: float async def main(): async with ( AzureCliCredential() as credential, MCPStreamableHTTPTool( name="Docs MCP", url="https://learn.microsoft.com/api/mcp", ) as mcp_tool, AzureAIAgentsProvider(credential=credential) as provider, ): agent = await provider.create_agent( name="ResearchAssistant", instructions="You are a research assistant with multiple capabilities.", tools=[ get_weather, HostedCodeInterpreterTool(), HostedWebSearchTool(name="Bing"), mcp_tool, ], ) thread = agent.get_new_thread() # Non-streaming result = await agent.run( "Search for Python best practices and summarize", thread=thread, ) print(f"Response: {result.text}") # Streaming print("\nStreaming: ", end="") async for chunk in agent.run_stream("Continue with examples", thread=thread): if chunk.text: print(chunk.text, end="", flush=True) print() # Structured output result = await agent.run( "Analyze findings", thread=thread, response_format=AnalysisResult, ) analysis = AnalysisResult.model_validate_json(result.text) print(f"\nConfidence: {analysis.confidence}") if __name__ == "__main__": asyncio.run(main())
Conventions
Always use async context managers: async with provider: Pass functions directly to tools= parameter (auto-converted to AIFunction) Use Annotated[type, Field(description=...)] for function parameters Use get_new_thread() for multi-turn conversations Prefer HostedMCPTool for service-managed MCP, MCPStreamableHTTPTool for client-managed
Reference Files
references/tools.md: Detailed hosted tool patterns references/mcp.md: MCP integration (hosted + local) references/threads.md: Thread and conversation management references/advanced.md: OpenAPI, citations, structured outputs
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
6 Docs
- SKILL.md
- references/acceptance-criteria.md
- references/advanced.md
- references/mcp.md
- references/threads.md
- references/tools.md