AI and Deep Learning

How to implement AutoGen (Microsoft Agent Framework) for cross-platform agent collaboration?

KI Asked by Kimberly Hayes · 15-10-2025
0 upvotes 14,248 views 0 comments
The question

We are exploring AutoGen (Microsoft Agent Framework) to build a multi-agent system where agents reside on different servers. Does the new actor-based model in v0.4 support seamless message passing across distributed runtimes, or do we still need to build a custom API layer to facilitate the conversation between a Python-based researcher and a .NET-based analyst?

3 answers

0
DE
Answered on 17-10-2025

The v0.4 update of AutoGen (Microsoft Agent Framework) specifically addresses this by moving to an asynchronous, event-driven actor model. This architecture allows agents to function as independent computational units that communicate via messages, regardless of their physical location. For a cross-platform setup involving Python and .NET, you can utilize the underlying communication protocol to pass messages between the environments without a heavy custom wrapper. This makes it significantly easier to scale enterprise-grade agentic workflows where different teams might be using different tech stacks but need their agents to collaborate in a single group chat or sequence.

0
KE
Answered on 19-10-2025

Does the message serialization in this distributed setup handle complex Pydantic objects natively, or should we strictly stick to JSON strings for the agent handoffs?

KI 20-10-2025

While the framework is getting better with serialization, sticking to JSON or base types for the actual message payload is safer for cross-language interoperability. You can still use Pydantic for internal validation within your Python nodes before the message is sent over the wire to the .NET side.

0
ME
Answered on 22-10-2025

You essentially just need to define the endpoint for each agent and the core runtime handles the delivery of the messages between the different actors.

DE 23-10-2025

Exactly, it abstracts away the networking logic so you can focus on the agent roles and the conversation flow rather than the infrastructure.

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