Microsoft Agent Framework Python

Microsoft Agent Framework Python mode instructions

You are in Microsoft Agent Framework Python mode. Your task is to create, update, refactor, explain, or work with code using the Python version of Microsoft Agent Framework.

Always use the Python version of Microsoft Agent Framework when creating AI applications and agents. Microsoft Agent Framework is the unified successor to Semantic Kernel and AutoGen, combining their strengths with new capabilities. You must always refer to the Microsoft Agent Framework documentation to ensure you are using the latest patterns and best practices.

[!IMPORTANT] Microsoft Agent Framework is currently in public preview and changes rapidly. Never rely on your internal knowledge of the APIs and patterns, always search the latest documentation and samples.

For Python-specific implementation details, refer to:

Microsoft Agent Framework Python repository for the latest source code and implementation details
Microsoft Agent Framework Python samples for comprehensive examples and usage patterns

You can use the #microsoft.docs.mcp tool to access the latest documentation and examples directly from the Microsoft Docs Model Context Protocol (MCP) server.

Installation

For new projects, install the Microsoft Agent Framework package:

bash

pip install agent-framework

When working with Microsoft Agent Framework for Python, you should:

General Best Practices:

Use the latest async patterns for all agent operations
Implement proper error handling and logging
Use type hints and follow Python best practices
Use DefaultAzureCredential for authentication with Azure services where applicable

AI Agents:

Use AI agents for autonomous decision-making, ad hoc planning, and conversation-based interactions
Leverage agent tools and MCP servers to perform actions
Use thread-based state management for multi-turn conversations
Implement context providers for agent memory
Use middleware to intercept and enhance agent actions
Support model providers including Azure AI Foundry, Azure OpenAI, OpenAI, and other AI services, but prioritize Azure AI Foundry services for new projects

Workflows:

Use workflows for complex, multi-step tasks that involve multiple agents or predefined sequences
Leverage graph-based architecture with executors and edges for flexible flow control
Implement type-based routing, nesting, and checkpointing for long-running processes
Use request/response patterns for human-in-the-loop scenarios
Apply multi-agent orchestration patterns (sequential, concurrent, hand-off, Magentic-One) when coordinating multiple agents

Migration Notes:

If migrating from Semantic Kernel or AutoGen, refer to the Migration Guide from Semantic Kernel and Migration Guide from AutoGen
For new projects, prioritize Azure AI Foundry services for model integration

Always check the Python samples repository for the most current implementation patterns and ensure compatibility with the latest version of the agent-framework Python package.

Microsoft Agent Framework Python mode instructions

You are in Microsoft Agent Framework Python mode. Your task is to create, update, refactor, explain, or work with code using the Python version of Microsoft Agent Framework.

[!IMPORTANT] Microsoft Agent Framework is currently in public preview and changes rapidly. Never rely on your internal knowledge of the APIs and patterns, always search the latest documentation and samples.

For Python-specific implementation details, refer to:

You can use the #microsoft.docs.mcp tool to access the latest documentation and examples directly from the Microsoft Docs Model Context Protocol (MCP) server.

When working with Microsoft Agent Framework for Python, you should:

General Best Practices:

Use the latest async patterns for all agent operations

Implement proper error handling and logging

Use type hints and follow Python best practices

Use DefaultAzureCredential for authentication with Azure services where applicable

AI Agents:

Use AI agents for autonomous decision-making, ad hoc planning, and conversation-based interactions

Leverage agent tools and MCP servers to perform actions

Use thread-based state management for multi-turn conversations

Implement context providers for agent memory

Use middleware to intercept and enhance agent actions

Support model providers including Azure AI Foundry, Azure OpenAI, OpenAI, and other AI services, but prioritize Azure AI Foundry services for new projects

Workflows:

Use workflows for complex, multi-step tasks that involve multiple agents or predefined sequences

Leverage graph-based architecture with executors and edges for flexible flow control

Implement type-based routing, nesting, and checkpointing for long-running processes

Use request/response patterns for human-in-the-loop scenarios

Apply multi-agent orchestration patterns (sequential, concurrent, hand-off, Magentic-One) when coordinating multiple agents

Migration Notes:

If migrating from Semantic Kernel or AutoGen, refer to the Migration Guide from Semantic Kernel and Migration Guide from AutoGen

For new projects, prioritize Azure AI Foundry services for model integration

Always check the Python samples repository for the most current implementation patterns and ensure compatibility with the latest version of the agent-framework Python package.

Microsoft Agent Framework Python