Readit News> If you do use a framework, ensure you understand the underlying code. Incorrect assumptions about what's under the hood are a common source of customer error.
I was actually arguing *for* MCP in this blog
But I don't think its good for all use cases! hence the quote
Would be interesting to see a complex agent implementation in both Flow and regular LangGraph to compare maintainability.
The “chaining” part is a huge problem space where the proper solution looks different in every context. It’s all the problems of templating engines, ETL scripts and workflow orchestration. (Actually I’ve had a pet idea for a while, of implementing a custom react renderer for “JSX for LLMs”). Stay away from that.
My other advice would be to build a lot of these small libraries… take advantage of your resources to iterate quickly on different ideas and see which sticks. Then go deep on those. What you’re doing now is doubling down on your first success, even though it might not be the best solution to the problem (or that it might be a solution looking for a problem).
a lot of our effort recently has been going into standardizing model wrappers, including for tool calling, images etc. this will continue to be a huge focus
> My other advice would be to build a lot of these small libraries… take advantage of your resources to iterate quickly on different ideas and see which sticks. Then go deep on those. What you’re doing now is doubling down on your first success, even though it might not be the best solution to the problem (or that it might be a solution looking for a problem).
I would actually argue we have done this (to some extent). we've invested a lot in LangSmith (about half our team), making it usable with or without langchain. Likewise, we're investing more and more in langgraph, also usable with or without langchain (that is in the orchestration space, which youre separately not bullish on, but for us that was a separate bet than LangChain orchestration)
Main takeaways (which I'd love feedback on) are:
There are series of agents recently (claude code, manus, deep research) which execute tasks over longer time horizons particular well
At the core of it, it's just an LLM running in a loop calling tools... but when you try to do this naively (or at least, when I try to do it) the LLM struggles with doing long/complex tasks
So how do these other agents accomplish it?
These agents all do similar things, namely:
1. They use a planning tool
2. They use sub agents
3. They use a file system like thing to offload context
4. They have a detailed system prompt (prompting isn't dead!)
I don't think any of these things individually is novel... but I also think that they are not super common place to do when building agents. And the combination of them is (I think) an interesting insight!
Would love any feedback :)