This framework provides a comprehensive, multi-language solution for building, orchestrating, and deploying AI agents and complex multi-agent workflows. Targeting developers familiar with Python or .NET, it offers a unified approach from simple chat bots to sophisticated, graph-based systems, aiming to streamline AI agent development and integration.

How It Works

The core of the framework revolves around graph-based orchestration, enabling the connection of agents and deterministic functions through data flows. This design supports advanced features like streaming, checkpointing, human-in-the-loop interactions, and time-travel debugging. It offers consistent APIs across both Python and C#/.NET implementations, enhancing developer productivity and code reusability.

Quick Start & Requirements

• Python Installation: pip install agent-framework --prerelease=allow
• .NET Installation: dotnet add package Microsoft.Agents.AI
• Prerequisites: Examples often require Azure CLI (az login) for authentication. Specific LLM provider credentials may also be necessary.
• Documentation: Overview, Quick Start, Tutorials, User Guide.
• Migration Guides: Semantic Kernel, AutoGen.

Highlighted Details

• Graph-based Workflows: Enables complex agent interactions with streaming, checkpointing, and human-in-the-loop capabilities.
• DevUI: An interactive developer UI for testing and debugging agent workflows.
• Multi-Language Support: Consistent APIs for both Python and C#/.NET.
• Observability: Integrated OpenTelemetry support for tracing and monitoring.
• Extensible Agent Providers: Supports multiple LLM providers with ongoing additions.
• Middleware System: Provides a flexible pipeline for request/response processing and exception handling.

Maintenance & Community

The project encourages feedback via GitHub issues for bugs. Contributor resources, including a contributing guide and development guides for Python, are available. Specific community channels (e.g., Discord, Slack) or details on major contributors/sponsorships are not explicitly mentioned in the README.

Licensing & Compatibility

The specific open-source license for this project is not stated in the provided README. This omission requires further investigation for compatibility with commercial or closed-source projects.

Limitations & Caveats

Installation via --prerelease suggests the framework may be in an unstable or rapidly evolving state. The "AF Labs" packages are explicitly experimental. Initial examples are heavily tied to Azure services, potentially indicating a strong ecosystem dependency for setup and operation.