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  • README.md
  • libraries
    • microsoft-agents-a365-notifications
    • microsoft-agents-a365-observability-core
    • microsoft-agents-a365-observability-extensions-agentframework
    • microsoft-agents-a365-observability-extensions-langchain
    • microsoft-agents-a365-observability-extensions-openai
    • microsoft-agents-a365-observability-extensions-semantickernel
    • microsoft-agents-a365-runtime
    • microsoft-agents-a365-tooling-extensions-agentframework
    • microsoft-agents-a365-tooling-extensions-azureaifoundry
    • microsoft-agents-a365-tooling-extensions-openai
    • microsoft-agents-a365-tooling-extensions-semantickernel
    • microsoft-agents-a365-tooling

‎README.md‎

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# Microsoft Agents 365 SDK - Python
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# Microsoft Agent 365 SDK - Python
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[![PyPI](https://img.shields.io/pypi/v/microsoft-agents-a365-observability-core?label=PyPI&logo=pypi)](https://pypi.org/search/?q=microsoft-agents-a365)
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[![PyPI Downloads](https://img.shields.io/pypi/dm/microsoft-agents-a365-observability-core?label=Downloads&logo=pypi)](https://pypi.org/search/?q=microsoft-agents-a365)
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[![Contributors](https://img.shields.io/github/contributors/microsoft/Agent365-python?label=Contributors&logo=github)](https://github.com/microsoft/Agent365-python/graphs/contributors)
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> #### Note:
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> Use the information in this README to contribute to this open-source project. To learn about using this SDK in your projects, refer to the [Microsoft Agents 365 Developer documentation](https://learn.microsoft.com/microsoft-agent-365/developer/).
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> Use the information in this README to contribute to this open-source project. To learn about using this SDK in your projects, refer to the [Microsoft Agent 365 Developer documentation](https://learn.microsoft.com/microsoft-agent-365/developer/).
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The Microsoft Agents 365 SDK extends the Microsoft 365 Agents SDK with enterprise-grade capabilities for building sophisticated agents. This SDK provides comprehensive tooling for observability, notifications, runtime utilities, and development tools that help developers create production-ready agents for platforms including M365, Teams, Copilot Studio, and Webchat.
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The Microsoft Agent 365 SDK extends the Microsoft 365 Agents SDK with enterprise-grade capabilities for building sophisticated agents. This SDK provides comprehensive tooling for observability, notifications, runtime utilities, and development tools that help developers create production-ready agents for platforms including M365, Teams, Copilot Studio, and Webchat.
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The Microsoft Agents 365 SDK focuses on four core areas:
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The Microsoft Agent 365 SDK focuses on four core areas:
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- **Observability**: Comprehensive tracing, caching, and monitoring capabilities for agent applications
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- **Notifications**: Agent notification services and models for handling user notifications
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## Project Structure
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- **libraries/microsoft-agents-a365-notifications**: Microsoft Agents 365 Notifications - Agent notification services and models
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- **libraries/microsoft-agents-a365-observability-core**: Microsoft Agents 365 Observability Core - Core observability functionality
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- **libraries/microsoft-agents-a365-notifications**: Microsoft Agent 365 Notifications SDK - Agent notification services and models
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- **libraries/microsoft-agents-a365-observability-core**: Microsoft Agent 365 Observability Core - Core observability functionality
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- **libraries/microsoft-agents-a365-observability-extensions-agentframework**: Agent Framework observability extensions
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- **libraries/microsoft-agents-a365-observability-extensions-langchain**: LangChain observability extensions
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- **libraries/microsoft-agents-a365-observability-extensions-openai**: OpenAI observability extensions
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- **libraries/microsoft-agents-a365-observability-extensions-semantickernel**: Semantic Kernel observability extensions
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- **libraries/microsoft-agents-a365-runtime**: Microsoft Agents 365 Runtime - Core runtime utilities and extensions
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- **libraries/microsoft-agents-a365-tooling**: Microsoft Agents 365 Tooling - Agent tooling and MCP integration
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- **libraries/microsoft-agents-a365-runtime**: Microsoft Agent 365 Runtime - Core runtime utilities and extensions
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- **libraries/microsoft-agents-a365-tooling**: Microsoft Agent 365 Tooling SDK - Agent tooling and MCP integration
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- **libraries/microsoft-agents-a365-tooling-extensions-agentframework**: Agent Framework tooling extensions
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- **libraries/microsoft-agents-a365-tooling-extensions-azureaifoundry**: Azure AI Foundry tooling extensions
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- **libraries/microsoft-agents-a365-tooling-extensions-openai**: OpenAI tooling extensions
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## Useful Links
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### Microsoft 365 Agents SDK
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### Microsoft 365 Agent SDK
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The core SDK for building conversational AI agents for Microsoft 365 platforms.
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- [Agent365-python repository](https://github.com/microsoft/Agent365-python) - You are here
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- [Agent365-nodejs repository](https://github.com/microsoft/Agent365-nodejs)
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- [Agent365-Samples repository](https://github.com/microsoft/Agent365-Samples)
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- [Microsoft Agents 365 developer documentation](https://learn.microsoft.com/microsoft-agent-365/developer/)
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- [Microsoft Agent 365 developer documentation](https://learn.microsoft.com/microsoft-agent-365/developer/)
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### Additional Resources
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‎libraries/microsoft-agents-a365-notifications/README.md‎

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## Usage
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### Basic Notification Handler
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```python
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from microsoft_agents_a365.notifications import AgentNotification, AgentNotificationActivity
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from microsoft_agents.activity import ChannelId
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from microsoft_agents.hosting.core import TurnContext, TurnState
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# Initialize notification handler
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agent_notification = AgentNotification(
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app=app,
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known_subchannels=["email", "word", "excel"],
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)
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# Register notification handler for specific channel
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@agent_notification.on_agent_notification(
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channel_id=ChannelId(channel="msteams", sub_channel="email")
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)
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async def handle_email_notification(
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context: TurnContext,
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state: TurnState,
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notification: AgentNotificationActivity
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):
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# Process email notification
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print(f"Received notification: {notification.notification_type}")
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await context.send_activity("Processing your email notification")
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```
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### Handle Multiple Subchannels
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```python
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# Handle all notifications for a channel (wildcard)
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@agent_notification.on_agent_notification(
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channel_id=ChannelId(channel="msteams", sub_channel="*")
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)
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async def handle_all_notifications(
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context: TurnContext,
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state: TurnState,
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notification: AgentNotificationActivity
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):
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# Route based on notification type
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if notification.notification_type == "email":
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await handle_email(context, notification)
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elif notification.notification_type == "document":
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await handle_document(context, notification)
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```
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For usage examples and detailed documentation, see the [Notification documentation](https://learn.microsoft.com/microsoft-agent-365/developer/notification?tabs=python) on Microsoft Learn.
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## Support
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‎libraries/microsoft-agents-a365-observability-core/README.md‎

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## Usage
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### Basic Configuration
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```python
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from microsoft_agents_a365.observability.core import configure, get_tracer
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# Configure observability with service details
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configure(
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service_name="my-agent-service",
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service_namespace="my.namespace"
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)
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# Get tracer instance
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tracer = get_tracer()
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```
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### Using Tracing Scopes
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```python
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from microsoft_agents_a365.observability.core import (
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InvokeAgentScope,
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ExecuteToolScope,
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InferenceScope,
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InvokeAgentDetails,
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ToolCallDetails,
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InferenceCallDetails,
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AgentDetails,
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TenantDetails,
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Request
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)
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# Trace agent invocation
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agent_details = AgentDetails(agent_id="my-agent", agent_name="My Agent")
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tenant_details = TenantDetails(tenant_id="tenant-123")
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request = Request(content="User query")
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invoke_details = InvokeAgentDetails(
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details=agent_details,
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session_id="session-42"
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)
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with InvokeAgentScope.start(invoke_details, tenant_details, request):
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# Agent execution code here
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result = await process_request()
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# Trace tool execution
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tool_details = ToolCallDetails(
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tool_name="search_tool",
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tool_type="function"
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)
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with ExecuteToolScope.start(tool_details, agent_details, tenant_details):
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# Tool execution code
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search_result = await execute_search()
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# Trace LLM inference
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inference_details = InferenceCallDetails(
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operationName="chat",
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model="gpt-4"
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)
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with InferenceScope.start(inference_details, agent_details, tenant_details, request):
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# LLM call
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response = await llm.complete(prompt)
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```
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For usage examples and detailed documentation, see the [Observability documentation](https://learn.microsoft.com/microsoft-agent-365/developer/observability?tabs=python) on Microsoft Learn.
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## Support
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‎libraries/microsoft-agents-a365-observability-extensions-agentframework/README.md‎

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## Usage
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### Basic Configuration
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```python
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from microsoft_agents_a365.observability.extensions.agentframework import (
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AgentFrameworkTraceInstrumentor
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)
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# Initialize instrumentor
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instrumentor = AgentFrameworkTraceInstrumentor()
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# Instrument your agent framework application
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instrumentor.instrument()
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# Your agent code runs with automatic tracing
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# ...
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# Optional: Uninstrument when done
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instrumentor.uninstrument()
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```
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For usage examples and detailed documentation, see the [Observability documentation](https://learn.microsoft.com/microsoft-agent-365/developer/observability?tabs=python) on Microsoft Learn.
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## Support
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‎libraries/microsoft-agents-a365-observability-extensions-langchain/README.md‎

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## Usage
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### Basic Configuration
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from microsoft_agents_a365.observability.core import configure
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from microsoft_agents_a365.observability.extensions.langchain import (
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CustomLangChainInstrumentor
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)
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# Configure observability
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configure(
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service_name="my-langchain-agent",
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service_namespace="ai.agents"
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)
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# Enable automatic instrumentation
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instrumentor = CustomLangChainInstrumentor()
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instrumentor.instrument()
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# Your LangChain code is now automatically traced
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from langchain.chains import LLMChain
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from langchain.prompts import PromptTemplate
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# All LangChain operations will be automatically instrumented
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chain = LLMChain(llm=llm, prompt=prompt_template)
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result = chain.run(input_text)
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```
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For usage examples and detailed documentation, see the [Observability documentation](https://learn.microsoft.com/microsoft-agent-365/developer/observability?tabs=python) on Microsoft Learn.
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## Support
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‎libraries/microsoft-agents-a365-observability-extensions-openai/README.md‎

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## Usage
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### Basic Configuration
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from microsoft_agents_a365.observability.extensions.openai import (
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OpenAIAgentsTraceInstrumentor
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)
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# Configure observability
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configure(
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service_name="my-openai-agent",
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service_namespace="ai.agents"
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)
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# Enable automatic instrumentation
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instrumentor = OpenAIAgentsTraceInstrumentor()
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instrumentor.instrument()
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# Your OpenAI Agents code is now automatically traced
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from openai import OpenAI
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client = OpenAI()
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# All agent operations will be automatically instrumented
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```
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For usage examples and detailed documentation, see the [Observability documentation](https://learn.microsoft.com/microsoft-agent-365/developer/observability?tabs=python) on Microsoft Learn.
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## Support
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‎libraries/microsoft-agents-a365-observability-extensions-semantickernel/README.md‎

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## Usage
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### Basic Configuration
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from microsoft_agents_a365.observability.extensions.semantickernel import (
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)
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# Configure observability
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configure(
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service_name="my-semantic-kernel-agent",
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service_namespace="ai.agents"
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)
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# Enable automatic instrumentation
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instrumentor = SemanticKernelInstrumentor()
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instrumentor.instrument()
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# Your Semantic Kernel code is now automatically traced
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from semantic_kernel import Kernel
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kernel = Kernel()
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# All kernel operations will be automatically instrumented
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```
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For usage examples and detailed documentation, see the [Observability documentation](https://learn.microsoft.com/microsoft-agent-365/developer/observability?tabs=python) on Microsoft Learn.
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## Support
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‎libraries/microsoft-agents-a365-runtime/README.md‎

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## Usage
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### Power Platform API Discovery
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```python
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from microsoft_agents_a365.runtime import (
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PowerPlatformApiDiscovery,
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ClusterCategory
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)
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# Initialize API discovery for preprod environment
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api_discovery = PowerPlatformApiDiscovery(ClusterCategory.PREPROD)
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# Discover API endpoint for an environment
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endpoint = api_discovery.discover_api(environment_id="env-123")
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print(f"API Endpoint: {endpoint}")
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```
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### Get Authentication Scope
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```python
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from microsoft_agents_a365.runtime import get_observability_authentication_scope
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# Get authentication scope for observability services
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scope = get_observability_authentication_scope(environment_id="env-123")
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print(f"Auth Scope: {scope}")
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```
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For usage examples and detailed documentation, see the [Microsoft Agent 365 Developer documentation](https://learn.microsoft.com/microsoft-agent-365/developer/?tabs=python) on Microsoft Learn.
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## Support
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‎libraries/microsoft-agents-a365-tooling-extensions-agentframework/README.md‎

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## Usage
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### Basic MCP Tool Registration
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```python
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from microsoft_agents_a365.tooling.extensions.agentframework import (
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McpToolRegistrationService
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)
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from agent_framework.azure import AzureOpenAIChatClient
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# Initialize the tool registration service
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mcp_service = McpToolRegistrationService()
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# Create chat client
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chat_client = AzureOpenAIChatClient(
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endpoint="https://your-endpoint.openai.azure.com",
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api_key="your-api-key",
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model="gpt-4"
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)
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# Add MCP tool servers to agent
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agent = await mcp_service.add_tool_servers_to_agent(
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chat_client=chat_client,
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agent_instructions="You are a helpful assistant",
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initial_tools=[], # Any initial tools
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agentic_app_id="your-agent-id",
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environment_id="your-environment-id",
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auth=authorization,
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turn_context=turn_context
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)
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# Use the agent with registered MCP tools
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response = await agent.run("Help me with my task")
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```
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For usage examples and detailed documentation, see the [Tooling documentation](https://learn.microsoft.com/microsoft-agent-365/developer/tooling?tabs=python) on Microsoft Learn.
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## Support
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