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There's a really good thread on Twitter from the designer of the new APIs going into the background behind many of the design decisions: https://twitter.com/athyuttamre/status/1899541471532867821 Here's the alternative link for people who aren't signed in to Twitter: https://nitter.net/athyuttamre/status/1899541471532867821
Notably not mentioned: Model Context Protocol https://www.anthropic.com/news/model-context-protocol
> [Q] Does the Agents SDK support MCP connections? So can we easily give certain agents tools via MCP client server connections?
> [A] You're able to define any tools you want, so you could implement MCP tools via function calling
in short, we need to do some plumbing work.
relevant issue in the repo: https://github.com/openai/openai-agents-python/issues/23
Notably not mentioned: Model Context Protocol https://www.anthropic.com/news/model-context-protocol
Do you have experience with MCP? If yes, what do you think of it?
Earlier quoted context omitted.
MCP is a protocol, and Anthropic has provided SDKs for implementing that protocol. In practice, I find the MCP protocol to be pretty great, but it leaves basically everything except the model parts out. I.e. MCP really only addresses how "agentic" systems interact with one another, nothing else. This SDK is trying to provide a bunch of code for implementing specific agent codebases. There are a bunch of open source o…
Thank you. Which open source ones do you recommend?
https://github.com/slavakurilyak/awesome-ai-agents
CrewAI is a popular VC-backed one, but two that I think are kind of interesting in the open source space are:
https://github.com/i-am-bee/beeai-framework
https://github.com/lastmile-ai/mcp-agent
... However I think the vast majority of "AI Agent" use-cases in practice right now are actually just workflows, and imo dify is great for those:
https://github.com/langgenius/dify
[edit] worth mentioning [langfuse](https://github.com/langfuse/langfuse), which is more like a platform that addresses the observability/evals/prompt management piece of the puzzle as opposed to a full-on "agent framework". In practice I have not yet run into a case where I needed something like what OpenAI just released, nor crewAI etc (despite it feeling like those cases may be coming)
At the end of the day, all I ever seem to use is the chat completion API with structured outputs turned on. Despite my "basic" usage, I am employing tool use, recursive conversations, RAG, etc. I don't see the value in outsourcing state management of my "agent" to a 3rd party. I have way more autonomy if I keep things like this local.
The entire premise of these products is that you are feeding a string literal into some black box and it gives you a new string. Hopefully, as JSON or whatever you requested. If you focus just on the idea of composing the appropriate string each time, everything else melts away. This is the only grain that really matters. Think about other ways in which we compose highly-structured strings based upon business state stored in a database. It's literally the exact same thing you do when you SSR a webpage with PHP. The only real difference is how it is served.