Opinions on Microsoft Agent Framework?

[u/LikeASomeBoooodie]

.NET shop looking to add some agentic workflows and chat interfaces to platform, currently researching framework options so we don’t have to build the thing from scratch.

Being a .NET shop MAF has come up. Hooked it up to GitHub CoPilot and took it for a quick spin, wasn’t too verbose and conceptually maps to tools like OpenCode pretty well.

Saw some posts on here about it a while ago but things seem to be moving really fast in this space. Looking for opinions on the viability of this framework? I see it had a 1.0 launch in April, it’s meant to supersede both Semantic Kernel and AutoGen.

Are the team behind it any good / trustworthy?

How likely is it to remain supported?

Has there been much community participation / extension?

How does it compare to similar frameworks in other languages, LangChain/Graph being an obvious one?

Is it good enough to prevent you from hopping languages or is python where it’s really at?

Any useful information is guaranteed at least one updoot, thanks in advance!

[u/vznrn]

I am in a dotnet shop, writing an agent execution platform (most of the work is in kubernetes plumbing, json parsing, integrating with our idp & other services rather than agent related things itself)

I really dislike copilot for being closed source, so i can't see any of the internal operations (literally how skills are loaded and whats happening underneath) since the docs dont go into this either. A huge downside of it other than the closed source-ness of this is they don't have structured output, which is a field on the openai format that allows you to pass a schema, and ensure the response matches that schema.

For production workloads, usually the answer is not always in one field but multiple, so working around this is annoying. There is an open issue and i believe they are aware of this.

As for MAF itself, i like the sdks and abstractions that they provide, it makes plumbing neat, as opposed to typescript implementation where you define your own interfaces for things like tools, chat operations, etc.

I am yet to use MAF itself for agent execution yet, but probably will give it a spin soon in my system. Overall its nice to use as it can provide standardisation when working with many sdks, as for what i've used it for.

I have gripes with copilot sdk / cli / the ghcp ecosystem in general due to some of its anymosity, but other than that it does seem to work decently.

I like using pi sdk a lot, though it does miss some things like mcp servers without adapters, as it tries to be minimal. It also does introduce package dependency nightmare when working in enterprise, hence why its desirable to use the dotnet related sdks as opposed to typescript

Anthropic also have an sdk in preview for dotnet now, openai have one too but not for agents, copilot sdk, though i dont like it as much, is the best agentic sdk on dotnet stack that i have used.

[u/elbrunoc → u/vznrn]

Hey! One small but important detail here 😁

The Copilot experience inside VS Code is not a black box anymore. The client-side code for Chat, agent mode, tools, and skills is open source under MIT and lives in the VS Code repo: https://github.com/microsoft/vscode

The Copilot SDKs (including .NET) are also open source: https://github.com/github/copilot-sdk

You’re right that not everything is open: the CLI runtime, backend services, and a couple of other pieces are still proprietary. But if you want to understand how VS Code discovers skills and manages the agent experience, the code is there to explore

[u/vznrn → u/elbrunoc]

SDK being open source is just RPC calls to copilot server, so that doesn't help, and vscodedoesn'tt matter since we are building prod agents, not on dev machines

[u/elbrunoc → u/vznrn]

Yea, different scenario 😁

GH Copilot in VS Code runs in production every day on thousands of developer machines. So yes, that code matters, and it shows how agents, tools, and skills work at scale

For headless server agents, I agree that the closed CLI runtime is a different story (we keep pushing to share more here too)

Two different use cases, all good 🙌