Microsoft Agent Framework
Overview
Open-source Microsoft framework for building, orchestrating, and deploying multi-agent workflows in Python and .NET.
Microsoft Agent Framework is an open-source, multi-language framework for developers building production-oriented AI agents and multi-agent workflows. It supports Python and C#/.NET, includes Go ecosystem support through a related SDK, and is designed for orchestration patterns such as sequential, concurrent, handoff, and group collaboration workflows. The framework is positioned for teams that need provider flexibility, durability, observability, governance, and human-in-the-loop control when moving agent systems from prototype to production.
AI Agent Store research
What the evidence says about Microsoft Agent Framework
Microsoft Agent Framework is distinctive as an open, pro-code Microsoft framework that brings agent construction, orchestration, and deployment into one multi-language foundation for Python, .NET, and Go-oriented ecosystems. Its positioning is especially useful for developer teams that need multi-agent workflow patterns, provider flexibility, and production concerns such as durability, observability, governance, and human-in-the-loop control.
Verified September 12, 2026
Verified capabilities
- Multi-agent workflow development
Provides a framework for building, orchestrating, and deploying AI agents and multi-agent workflows.[1]
- Python and C#/.NET support
The official repository describes full framework support for Python and C#/.NET implementations with consistent APIs.[1]
- Go ecosystem support
The official page notes Go support and points Go SDK users to the related Microsoft Agent Framework Go project for documentation, samples, contribution guidance, and issue tracking.[1]
- Graph-based orchestration patterns
The framework is positioned for teams that need graph-based patterns including sequential, concurrent, handoff, and group collaboration workflows.[1]
- Production-oriented controls
The official guidance highlights production concerns such as durability, restartability, observability, governance, and human-in-the-loop control.[1]
- Provider and ecosystem flexibility
The framework supports a broad ecosystem including Microsoft Foundry, Azure OpenAI, OpenAI, and the GitHub Copilot SDK.[1]
Where it fits best
- Developer teams building AI agents and multi-agent workflows that are expected to move from prototype to production.[1]
- Projects that need orchestration beyond a single prompt or stateless chat loop, including graph-based patterns such as sequential, concurrent, handoff, and group collaboration.[1]
- Teams that want provider flexibility across ecosystems including Microsoft Foundry, Azure OpenAI, OpenAI, and the GitHub Copilot SDK.[1]
Buying and deployment notes
The supplied official context identifies Microsoft Agent Framework as a public, MIT-licensed open-source project. No separate hosted pricing page or paid plan is shown in the supplied context.[1]
Platforms: Python, C#/.NET, Go[1]
Deployment: Open-source GitHub repository, Local development, Cloud deployment patterns[1]
Important considerations
- This is a pro-code framework, so adoption requires developers comfortable working with SDKs and implementation code rather than a turnkey no-code agent product.[1]
- The Go SDK documentation, samples, contribution guidance, and issue tracker are handled through a separate Microsoft Agent Framework Go repository rather than solely inside the main repository.[1]
- The supplied official context identifies the framework and repository but does not provide hosted service pricing, support tiers, or a managed dashboard offering.[1]
Sources and research method (1)
We record only claims tied to public sources checked by our team or listing workflow. Counts above are derived directly from this profile, not a subjective rating.
Autonomy level
50%
Comparisons
Custom Comparisons
Some of the use cases of Microsoft Agent Framework:
- Building production-grade agent applications
- Orchestrating multi-agent workflows
- Creating Python and .NET agent systems
- Experimenting with graph-based agent patterns
- Developing agent systems that can run locally or in cloud deployments
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